A multipath routing protocol based on clustering and ant colony optimization for wireless sensor networks.

PubMed

Yang, Jing; Xu, Mai; Zhao, Wei; Xu, Baoguo

2010-01-01

For monitoring burst events in a kind of reactive wireless sensor networks (WSNs), a multipath routing protocol (MRP) based on dynamic clustering and ant colony optimization (ACO) is proposed. Such an approach can maximize the network lifetime and reduce the energy consumption. An important attribute of WSNs is their limited power supply, and therefore some metrics (such as energy consumption of communication among nodes, residual energy, path length) were considered as very important criteria while designing routing in the MRP. Firstly, a cluster head (CH) is selected among nodes located in the event area according to some parameters, such as residual energy. Secondly, an improved ACO algorithm is applied in the search for multiple paths between the CH and sink node. Finally, the CH dynamically chooses a route to transmit data with a probability that depends on many path metrics, such as energy consumption. The simulation results show that MRP can prolong the network lifetime, as well as balance of energy consumption among nodes and reduce the average energy consumption effectively.

Fuel consumption optimization for smart hybrid electric vehicle during a car-following process

NASA Astrophysics Data System (ADS)

Li, Liang; Wang, Xiangyu; Song, Jian

2017-03-01

Hybrid electric vehicles (HEVs) provide large potential to save energy and reduce emission, and smart vehicles bring out great convenience and safety for drivers. By combining these two technologies, vehicles may achieve excellent performances in terms of dynamic, economy, environmental friendliness, safety, and comfort. Hence, a smart hybrid electric vehicle (s-HEV) is selected as a platform in this paper to study a car-following process with optimizing the fuel consumption. The whole process is a multi-objective optimal problem, whose optimal solution is not just adding an energy management strategy (EMS) to an adaptive cruise control (ACC), but a deep fusion of these two methods. The problem has more restricted conditions, optimal objectives, and system states, which may result in larger computing burden. Therefore, a novel fuel consumption optimization algorithm based on model predictive control (MPC) is proposed and some search skills are adopted in receding horizon optimization to reduce computing burden. Simulations are carried out and the results indicate that the fuel consumption of proposed method is lower than that of the ACC+EMS method on the condition of ensuring car-following performances.

Optimizing Cellular Networks Enabled with Renewal Energy via Strategic Learning.

PubMed

Sohn, Insoo; Liu, Huaping; Ansari, Nirwan

2015-01-01

An important issue in the cellular industry is the rising energy cost and carbon footprint due to the rapid expansion of the cellular infrastructure. Greening cellular networks has thus attracted attention. Among the promising green cellular network techniques, the renewable energy-powered cellular network has drawn increasing attention as a critical element towards reducing carbon emissions due to massive energy consumption in the base stations deployed in cellular networks. Game theory is a branch of mathematics that is used to evaluate and optimize systems with multiple players with conflicting objectives and has been successfully used to solve various problems in cellular networks. In this paper, we model the green energy utilization and power consumption optimization problem of a green cellular network as a pilot power selection strategic game and propose a novel distributed algorithm based on a strategic learning method. The simulation results indicate that the proposed algorithm achieves correlated equilibrium of the pilot power selection game, resulting in optimum green energy utilization and power consumption reduction.

Energy Consumption Information Services for Smart Home Inhabitants

NASA Astrophysics Data System (ADS)

Schwanzer, Michael; Fensel, Anna

We investigate services giving users an adequate insight on his or her energy consumption habits in order to optimize it in the long run. The explored energy awareness services are addressed to inhabitants of smart homes, equipped with smart meters, advanced communication facilities, sensors and actuators. To analyze the potential of such services, a game at a social network Facebook has been designed and implemented, and the information about players' responses and interactions within the game environment has been collected and analyzed. The players have had their virtual home energy usage visualized in different ways, and had to optimize the energy consumption basing on their own perceptions of the consumption information. Evaluations reveal, in particular, that users are specifically responsive to information shown as a real-time graph and as costs in Euro, and are able to produce and share with each other policies for managing their smart home environments.

An Efficient Offloading Scheme For MEC System Considering Delay and Energy Consumption

NASA Astrophysics Data System (ADS)

Sun, Yanhua; Hao, Zhe; Zhang, Yanhua

2018-01-01

With the increasing numbers of mobile devices, mobile edge computing (MEC) which provides cloud computing capabilities proximate to mobile devices in 5G networks has been envisioned as a promising paradigm to enhance users experience. In this paper, we investigate a joint consideration of delay and energy consumption offloading scheme (JCDE) for MEC system in 5G heterogeneous networks. An optimization is formulated to minimize the delay as well as energy consumption of the offloading system, which the delay and energy consumption of transmitting and calculating tasks are taken into account. We adopt an iterative greedy algorithm to solve the optimization problem. Furthermore, simulations were carried out to validate the utility and effectiveness of our proposed scheme. The effect of parameter variations on the system is analysed as well. Numerical results demonstrate delay and energy efficiency promotion of our proposed scheme compared with another paper’s scheme.

[Optimization of Energy Saving Measures with ABR-MBR Integrated Process].

PubMed

Wu, Peng; Lu, Shuang-jun; Xu, Yue-zhong; Liu, Jie; Shen, Yao-liang

2015-08-01

High energy consumption and membrane fouling are important factors that limit the wide use of membrane bioreactor (MBR). In order to reduce energy consumption and delay the process of membrane fouling, the process of anaerobic baffled reactor (ABR)-MBR was used to treat domestic sewage. The structure of the process and conditions of nitrogen and phosphorus removal were optimized in this study. The results showed that energy consumption was reduced by 43% through optimizing the structure of ABR-MBR process. Meanwhile, the process achieved a high level of COD, NH: -N, TN and TP removal, with the average removal efficiencies of 91%, 85%, 76% and 86%, respectively. In addition, the added particulate media could effectively delay membrane fouling, while the formation process of membrane fouling was changed. The extracted amount of carbohydrates increased while the amount of proteins decreased. Finally, the potential was enhanced for the practical application of MBR.

An Energy-Efficient Spectrum-Aware Reinforcement Learning-Based Clustering Algorithm for Cognitive Radio Sensor Networks

PubMed Central

Mustapha, Ibrahim; Ali, Borhanuddin Mohd; Rasid, Mohd Fadlee A.; Sali, Aduwati; Mohamad, Hafizal

2015-01-01

It is well-known that clustering partitions network into logical groups of nodes in order to achieve energy efficiency and to enhance dynamic channel access in cognitive radio through cooperative sensing. While the topic of energy efficiency has been well investigated in conventional wireless sensor networks, the latter has not been extensively explored. In this paper, we propose a reinforcement learning-based spectrum-aware clustering algorithm that allows a member node to learn the energy and cooperative sensing costs for neighboring clusters to achieve an optimal solution. Each member node selects an optimal cluster that satisfies pairwise constraints, minimizes network energy consumption and enhances channel sensing performance through an exploration technique. We first model the network energy consumption and then determine the optimal number of clusters for the network. The problem of selecting an optimal cluster is formulated as a Markov Decision Process (MDP) in the algorithm and the obtained simulation results show convergence, learning and adaptability of the algorithm to dynamic environment towards achieving an optimal solution. Performance comparisons of our algorithm with the Groupwise Spectrum Aware (GWSA)-based algorithm in terms of Sum of Square Error (SSE), complexity, network energy consumption and probability of detection indicate improved performance from the proposed approach. The results further reveal that an energy savings of 9% and a significant Primary User (PU) detection improvement can be achieved with the proposed approach. PMID:26287191

An Energy-Efficient Spectrum-Aware Reinforcement Learning-Based Clustering Algorithm for Cognitive Radio Sensor Networks.

PubMed

Mustapha, Ibrahim; Mohd Ali, Borhanuddin; Rasid, Mohd Fadlee A; Sali, Aduwati; Mohamad, Hafizal

2015-08-13

It is well-known that clustering partitions network into logical groups of nodes in order to achieve energy efficiency and to enhance dynamic channel access in cognitive radio through cooperative sensing. While the topic of energy efficiency has been well investigated in conventional wireless sensor networks, the latter has not been extensively explored. In this paper, we propose a reinforcement learning-based spectrum-aware clustering algorithm that allows a member node to learn the energy and cooperative sensing costs for neighboring clusters to achieve an optimal solution. Each member node selects an optimal cluster that satisfies pairwise constraints, minimizes network energy consumption and enhances channel sensing performance through an exploration technique. We first model the network energy consumption and then determine the optimal number of clusters for the network. The problem of selecting an optimal cluster is formulated as a Markov Decision Process (MDP) in the algorithm and the obtained simulation results show convergence, learning and adaptability of the algorithm to dynamic environment towards achieving an optimal solution. Performance comparisons of our algorithm with the Groupwise Spectrum Aware (GWSA)-based algorithm in terms of Sum of Square Error (SSE), complexity, network energy consumption and probability of detection indicate improved performance from the proposed approach. The results further reveal that an energy savings of 9% and a significant Primary User (PU) detection improvement can be achieved with the proposed approach.

Energy-Efficient Next-Generation Passive Optical Networks Based on Sleep Mode and Heuristic Optimization

NASA Astrophysics Data System (ADS)

Zulai, Luis G. T.; Durand, Fábio R.; Abrão, Taufik

2015-05-01

In this article, an energy-efficiency mechanism for next-generation passive optical networks is investigated through heuristic particle swarm optimization. Ten-gigabit Ethernet-wavelength division multiplexing optical code division multiplexing-passive optical network next-generation passive optical networks are based on the use of a legacy 10-gigabit Ethernet-passive optical network with the advantage of using only an en/decoder pair of optical code division multiplexing technology, thus eliminating the en/decoder at each optical network unit. The proposed joint mechanism is based on the sleep-mode power-saving scheme for a 10-gigabit Ethernet-passive optical network, combined with a power control procedure aiming to adjust the transmitted power of the active optical network units while maximizing the overall energy-efficiency network. The particle swarm optimization based power control algorithm establishes the optimal transmitted power in each optical network unit according to the network pre-defined quality of service requirements. The objective is controlling the power consumption of the optical network unit according to the traffic demand by adjusting its transmitter power in an attempt to maximize the number of transmitted bits with minimum energy consumption, achieving maximal system energy efficiency. Numerical results have revealed that it is possible to save 75% of energy consumption with the proposed particle swarm optimization based sleep-mode energy-efficiency mechanism compared to 55% energy savings when just a sleeping-mode-based mechanism is deployed.

Optimal design of a magnetorheological damper used in smart prosthetic knees

NASA Astrophysics Data System (ADS)

Gao, Fei; Liu, Yan-Nan; Liao, Wei-Hsin

2017-03-01

In this paper, a magnetorheological (MR) damper is optimally designed for use in smart prosthetic knees. The objective of optimization is to minimize the total energy consumption during one gait cycle and weight of the MR damper. Firstly, a smart prosthetic knee employing a DC motor, MR damper and springs is developed based on the kinetics characteristics of human knee during walking. Then the function of the MR damper is analyzed. In the initial stance phase and swing phase, the MR damper is powered off (off-state). While during the late stance phase, the MR damper is powered on to work as a clutch (on-state). Based on the MR damper model as well as the prosthetic knee model, the instantaneous energy consumption of the MR damper is derived in the two working states. Then by integrating in one gait cycle, the total energy consumption is obtained. Particle swarm optimization algorithm is used to optimize the geometric dimensions of MR damper. Finally, a prototype of the optimized MR damper is fabricated and tested with comparison to simulation.

[Research on carbon reduction potential of electric vehicles for low-carbon transportation and its influencing factors].

PubMed

Shi, Xiao-Qing; Li, Xiao-Nuo; Yang, Jian-Xin

2013-01-01

Transportation is the key industry of urban energy consumption and carbon emissions. The transformation of conventional gasoline vehicles to new energy vehicles is an important initiative to realize the goal of developing low-carbon city through energy saving and emissions reduction, while electric vehicles (EV) will play an important role in this transition due to their advantage in energy saving and lower carbon emissions. After reviewing the existing researches on energy saving and emissions reduction of electric vehicles, this paper analyzed the factors affecting carbon emissions reduction. Combining with electric vehicles promotion program in Beijing, the paper analyzed carbon emissions and reduction potential of electric vehicles in six scenarios using the optimized energy consumption related carbon emissions model from the perspective of fuel life cycle. The scenarios included power energy structure, fuel type (energy consumption per 100 km), car type (CO2 emission factor of fuel), urban traffic conditions (speed), coal-power technologies and battery type (weight, energy efficiency). The results showed that the optimized model was able to estimate carbon emissions caused by fuel consumption more reasonably; electric vehicles had an obvious restrictive carbon reduction potential with the fluctuation of 57%-81.2% in the analysis of six influencing factors, while power energy structure and coal-power technologies play decisive roles in life-cycle carbon emissions of electric vehicles with the reduction potential of 78.1% and 81.2%, respectively. Finally, some optimized measures were proposed to reduce transport energy consumption and carbon emissions during electric vehicles promotion including improving energy structure and coal technology, popularizing energy saving technologies and electric vehicles, accelerating the battery R&D and so on. The research provides scientific basis and methods for the policy development for the transition of new energy vehicles in low-carbon transport.

Solving Energy-Aware Real-Time Tasks Scheduling Problem with Shuffled Frog Leaping Algorithm on Heterogeneous Platforms

PubMed Central

Zhang, Weizhe; Bai, Enci; He, Hui; Cheng, Albert M.K.

2015-01-01

Reducing energy consumption is becoming very important in order to keep battery life and lower overall operational costs for heterogeneous real-time multiprocessor systems. In this paper, we first formulate this as a combinatorial optimization problem. Then, a successful meta-heuristic, called Shuffled Frog Leaping Algorithm (SFLA) is proposed to reduce the energy consumption. Precocity remission and local optimal avoidance techniques are proposed to avoid the precocity and improve the solution quality. Convergence acceleration significantly reduces the search time. Experimental results show that the SFLA-based energy-aware meta-heuristic uses 30% less energy than the Ant Colony Optimization (ACO) algorithm, and 60% less energy than the Genetic Algorithm (GA) algorithm. Remarkably, the running time of the SFLA-based meta-heuristic is 20 and 200 times less than ACO and GA, respectively, for finding the optimal solution. PMID:26110406

Prediction of Industrial Electric Energy Consumption in Anhui Province Based on GA-BP Neural Network

NASA Astrophysics Data System (ADS)

Zhang, Jiajing; Yin, Guodong; Ni, Youcong; Chen, Jinlan

2018-01-01

In order to improve the prediction accuracy of industrial electrical energy consumption, a prediction model of industrial electrical energy consumption was proposed based on genetic algorithm and neural network. The model use genetic algorithm to optimize the weights and thresholds of BP neural network, and the model is used to predict the energy consumption of industrial power in Anhui Province, to improve the prediction accuracy of industrial electric energy consumption in Anhui province. By comparing experiment of GA-BP prediction model and BP neural network model, the GA-BP model is more accurate with smaller number of neurons in the hidden layer.

Kinetics of ultrasound-assisted extraction of antioxidant polyphenols from food by-products: Extraction and energy consumption optimization.

PubMed

Pradal, Delphine; Vauchel, Peggy; Decossin, Stéphane; Dhulster, Pascal; Dimitrov, Krasimir

2016-09-01

Ultrasound-assisted extraction (UAE) of antioxidant polyphenols from chicory grounds was studied in order to propose a suitable valorization of this food industry by-product. The main parameters influencing the extraction process were identified. A new mathematical model for multi-criteria optimization of UAE was proposed. This kinetic model permitted the following and the prediction of the yield of extracted polyphenols, the antioxidant activity of the obtained extracts and the energy consumption during the extraction process in wide ranges of temperature (20-60°C), ethanol content in the solvent (0-60% (vol.) in ethanol-water mixtures) and ultrasound power (0-100W). After experimental validation of the model, several simulations at different technological restrictions were performed to illustrate the potentiality of the model to find the optimal conditions for obtaining a given yield within minimal process duration or with minimal energy consumption. The advantage of ultrasound assistance was clearly demonstrated both for the reduction of extraction duration and for the reduction of energy consumption. Copyright © 2016 Elsevier B.V. All rights reserved.

Sustainable Mobility Initiative | Transportation Research | NREL

Science.gov Websites

optimize mobility and significantly reduce related energy consumption. This concept of an intelligent measures to explore these technologies' effects on transportation energy use, emissions, and overall system . Efficient driving with smoother starts, stops, and accelerations to reduce energy consumption and

Nonlinear Multiobjective MPC-Based Optimal Operation of a High Consistency Refining System in Papermaking

DOE PAGES

Li, Mingjie; Zhou, Ping; Wang, Hong; ...

2017-09-19

As one of the most important unit in the papermaking industry, the high consistency (HC) refining system is confronted with challenges such as improving pulp quality, energy saving, and emissions reduction in its operation processes. Here in this correspondence, an optimal operation of HC refining system is presented using nonlinear multiobjective model predictive control strategies that aim at set-point tracking objective of pulp quality, economic objective, and specific energy (SE) consumption objective, respectively. First, a set of input and output data at different times are employed to construct the subprocess model of the state process model for the HC refiningmore » system, and then the Wiener-type model can be obtained through combining the mechanism model of Canadian Standard Freeness and the state process model that determines their structures based on Akaike information criterion. Second, the multiobjective optimization strategy that optimizes both the set-point tracking objective of pulp quality and SE consumption is proposed simultaneously, which uses NSGA-II approach to obtain the Pareto optimal set. Furthermore, targeting at the set-point tracking objective of pulp quality, economic objective, and SE consumption objective, the sequential quadratic programming method is utilized to produce the optimal predictive controllers. In conclusion, the simulation results demonstrate that the proposed methods can make the HC refining system provide a better performance of set-point tracking of pulp quality when these predictive controllers are employed. In addition, while the optimal predictive controllers orienting with comprehensive economic objective and SE consumption objective, it has been shown that they have significantly reduced the energy consumption.« less

Nonlinear Multiobjective MPC-Based Optimal Operation of a High Consistency Refining System in Papermaking

DOE Office of Scientific and Technical Information (OSTI.GOV)

Li, Mingjie; Zhou, Ping; Wang, Hong

As one of the most important unit in the papermaking industry, the high consistency (HC) refining system is confronted with challenges such as improving pulp quality, energy saving, and emissions reduction in its operation processes. Here in this correspondence, an optimal operation of HC refining system is presented using nonlinear multiobjective model predictive control strategies that aim at set-point tracking objective of pulp quality, economic objective, and specific energy (SE) consumption objective, respectively. First, a set of input and output data at different times are employed to construct the subprocess model of the state process model for the HC refiningmore » system, and then the Wiener-type model can be obtained through combining the mechanism model of Canadian Standard Freeness and the state process model that determines their structures based on Akaike information criterion. Second, the multiobjective optimization strategy that optimizes both the set-point tracking objective of pulp quality and SE consumption is proposed simultaneously, which uses NSGA-II approach to obtain the Pareto optimal set. Furthermore, targeting at the set-point tracking objective of pulp quality, economic objective, and SE consumption objective, the sequential quadratic programming method is utilized to produce the optimal predictive controllers. In conclusion, the simulation results demonstrate that the proposed methods can make the HC refining system provide a better performance of set-point tracking of pulp quality when these predictive controllers are employed. In addition, while the optimal predictive controllers orienting with comprehensive economic objective and SE consumption objective, it has been shown that they have significantly reduced the energy consumption.« less

Energy-Filtered Tunnel Transistor: A New Device Concept Toward Extremely-Low Energy Consumption Electronics

DTIC Science & Technology

2015-12-17

temperature . New device architecture that utilizes cold-electron transport for ultra-low energy consumption electronics has been designed in a configuration...the oxygen has also been found important for the SiC>2 sputter deposition. The sputter was carried out at room temperature . Our optimized process...have been pursued for two electronic devices, 1) room- temperature single-electron transistors, and 2) ultralow energy consumption transistors. For

Deployment-based lifetime optimization for linear wireless sensor networks considering both retransmission and discrete power control.

PubMed

Li, Ruiying; Ma, Wenting; Huang, Ning; Kang, Rui

2017-01-01

A sophisticated method for node deployment can efficiently reduce the energy consumption of a Wireless Sensor Network (WSN) and prolong the corresponding network lifetime. Pioneers have proposed many node deployment based lifetime optimization methods for WSNs, however, the retransmission mechanism and the discrete power control strategy, which are widely used in practice and have large effect on the network energy consumption, are often neglected and assumed as a continuous one, respectively, in the previous studies. In this paper, both retransmission and discrete power control are considered together, and a more realistic energy-consumption-based network lifetime model for linear WSNs is provided. Using this model, we then propose a generic deployment-based optimization model that maximizes network lifetime under coverage, connectivity and transmission rate success constraints. The more accurate lifetime evaluation conduces to a longer optimal network lifetime in the realistic situation. To illustrate the effectiveness of our method, both one-tiered and two-tiered uniformly and non-uniformly distributed linear WSNs are optimized in our case studies, and the comparisons between our optimal results and those based on relatively inaccurate lifetime evaluation show the advantage of our method when investigating WSN lifetime optimization problems.

Energy Center Structure Optimization by using Smart Technologies in Process Control System

NASA Astrophysics Data System (ADS)

Shilkina, Svetlana V.

2018-03-01

The article deals with practical application of fuzzy logic methods in process control systems. A control object - agroindustrial greenhouse complex, which includes its own energy center - is considered. The paper analyzes object power supply options taking into account connection to external power grids and/or installation of own power generating equipment with various layouts. The main problem of a greenhouse facility basic process is extremely uneven power consumption, which forces to purchase redundant generating equipment idling most of the time, which quite negatively affects project profitability. Energy center structure optimization is largely based on solving the object process control system construction issue. To cut investor’s costs it was proposed to optimize power consumption by building an energy-saving production control system based on a fuzzy logic controller. The developed algorithm of automated process control system functioning ensured more even electric and thermal energy consumption, allowed to propose construction of the object energy center with a smaller number of units due to their more even utilization. As a result, it is shown how practical use of microclimate parameters fuzzy control system during object functioning leads to optimization of agroindustrial complex energy facility structure, which contributes to a significant reduction in object construction and operation costs.

Energy Optimization Using a Case-Based Reasoning Strategy

PubMed Central

Herrera-Viedma, Enrique

2018-01-01

At present, the domotization of homes and public buildings is becoming increasingly popular. Domotization is most commonly applied to the field of energy management, since it gives the possibility of managing the consumption of the devices connected to the electric network, the way in which the users interact with these devices, as well as other external factors that influence consumption. In buildings, Heating, Ventilation and Air Conditioning (HVAC) systems have the highest consumption rates. The systems proposed so far have not succeeded in optimizing the energy consumption associated with a HVAC system because they do not monitor all the variables involved in electricity consumption. For this reason, this article presents an agent approach that benefits from the advantages provided by a Multi-Agent architecture (MAS) deployed in a Cloud environment with a wireless sensor network (WSN) in order to achieve energy savings. The agents of the MAS learn social behavior thanks to the collection of data and the use of an artificial neural network (ANN). The proposed system has been assessed in an office building achieving an average energy savings of 41% in the experimental group offices. PMID:29543729

Energy Optimization Using a Case-Based Reasoning Strategy.

PubMed

González-Briones, Alfonso; Prieto, Javier; De La Prieta, Fernando; Herrera-Viedma, Enrique; Corchado, Juan M

2018-03-15

At present, the domotization of homes and public buildings is becoming increasingly popular. Domotization is most commonly applied to the field of energy management, since it gives the possibility of managing the consumption of the devices connected to the electric network, the way in which the users interact with these devices, as well as other external factors that influence consumption. In buildings, Heating, Ventilation and Air Conditioning (HVAC) systems have the highest consumption rates. The systems proposed so far have not succeeded in optimizing the energy consumption associated with a HVAC system because they do not monitor all the variables involved in electricity consumption. For this reason, this article presents an agent approach that benefits from the advantages provided by a Multi-Agent architecture (MAS) deployed in a Cloud environment with a wireless sensor network (WSN) in order to achieve energy savings. The agents of the MAS learn social behavior thanks to the collection of data and the use of an artificial neural network (ANN). The proposed system has been assessed in an office building achieving an average energy savings of 41% in the experimental group offices.

Imaging Tasks Scheduling for High-Altitude Airship in Emergency Condition Based on Energy-Aware Strategy

PubMed Central

Zhimeng, Li; Chuan, He; Dishan, Qiu; Jin, Liu; Manhao, Ma

2013-01-01

Aiming to the imaging tasks scheduling problem on high-altitude airship in emergency condition, the programming models are constructed by analyzing the main constraints, which take the maximum task benefit and the minimum energy consumption as two optimization objectives. Firstly, the hierarchy architecture is adopted to convert this scheduling problem into three subproblems, that is, the task ranking, value task detecting, and energy conservation optimization. Then, the algorithms are designed for the sub-problems, and the solving results are corresponding to feasible solution, efficient solution, and optimization solution of original problem, respectively. This paper makes detailed introduction to the energy-aware optimization strategy, which can rationally adjust airship's cruising speed based on the distribution of task's deadline, so as to decrease the total energy consumption caused by cruising activities. Finally, the application results and comparison analysis show that the proposed strategy and algorithm are effective and feasible. PMID:23864822

Green ultrasound-assisted extraction of anthocyanin and phenolic compounds from purple sweet potato using response surface methodology

NASA Astrophysics Data System (ADS)

Zhu, Zhenzhou; Guan, Qingyan; Guo, Ying; He, Jingren; Liu, Gang; Li, Shuyi; Barba, Francisco J.; Jaffrin, Michel Y.

2016-01-01

Response surface methodology was used to optimize experimental conditions for ultrasound-assisted extraction of valuable components (anthocyanins and phenolics) from purple sweet potatoes using water as a solvent. The Box-Behnken design was used for optimizing extraction responses of anthocyanin extraction yield, phenolic extraction yield, and specific energy consumption. Conditions to obtain maximal anthocyanin extraction yield, maximal phenolic extraction yield, and minimal specific energy consumption were different; an overall desirability function was used to search for overall optimal conditions: extraction temperature of 68ºC, ultrasonic treatment time of 52 min, and a liquid/solid ratio of 20. The optimized anthocyanin extraction yield, phenolic extraction yield, and specific energy consumption were 4.91 mg 100 g-1 fresh weight, 3.24 mg g-1 fresh weight, and 2.07 kWh g-1, respectively, with a desirability of 0.99. This study indicates that ultrasound-assisted extraction should contribute to a green process for valorization of purple sweet potatoes.

Improving energy sustainability for public buildings in Italian mountain communities.

PubMed

Mutani, Guglielmina; Cornaglia, Mauro; Berto, Massimo

2018-05-01

The objective of this work is to analyze and then optimize thermal energy consumptions of public buildings located within the mountain community of Lanzo, Ceronda and Casternone Valleys. Some measures have been proposed to reduce energy consumption and consequently the economic cost for energy production, as well as the harmful GHG emissions in the atmosphere. Initially, a study of the mountain territory has been carried out, because of its vast extension and climatic differences. Defined the communities and the buildings under investigation, energy dependant data were collected for the analysis of energy consumption monitoring: consumption data of three heating seasons, geometric buildings characteristics, type of opaque and transparent envelope, heating systems information with boiler performance and climatic data. Afterward, five buildings with critical energy performances were selected; for each of these buildings, different retrofit interventions have been hypothesized to reduce the energy consumption, with thermal insulation of vertical or horizontal structures, new windows or boiler substitution. The cost-optimal technique was used to choose the interventions that offered higher energy performance at lower costs; then a retrofit scenario has been planned with a specific financial investment. Finally, results showed possible future developments and scenarios related to buildings energy efficiency with regard to the topic of biomass exploitation and its local availability in this area. In this context, the biomass energy resource could to create a virtuous environmental, economic and social process, favouring also local development.

A New Distributed Optimization for Community Microgrids Scheduling

DOE Office of Scientific and Technical Information (OSTI.GOV)

Starke, Michael R; Tomsovic, Kevin

This paper proposes a distributed optimization model for community microgrids considering the building thermal dynamics and customer comfort preference. The microgrid central controller (MCC) minimizes the total cost of operating the community microgrid, including fuel cost, purchasing cost, battery degradation cost and voluntary load shedding cost based on the customers' consumption, while the building energy management systems (BEMS) minimize their electricity bills as well as the cost associated with customer discomfort due to room temperature deviation from the set point. The BEMSs and the MCC exchange information on energy consumption and prices. When the optimization converges, the distributed generation scheduling,more » energy storage charging/discharging and customers' consumption as well as the energy prices are determined. In particular, we integrate the detailed thermal dynamic characteristics of buildings into the proposed model. The heating, ventilation and air-conditioning (HVAC) systems can be scheduled intelligently to reduce the electricity cost while maintaining the indoor temperature in the comfort range set by customers. Numerical simulation results show the effectiveness of proposed model.« less

Prediction-based manufacturing center self-adaptive demand side energy optimization in cyber physical systems

NASA Astrophysics Data System (ADS)

Sun, Xinyao; Wang, Xue; Wu, Jiangwei; Liu, Youda

2014-05-01

Cyber physical systems(CPS) recently emerge as a new technology which can provide promising approaches to demand side management(DSM), an important capability in industrial power systems. Meanwhile, the manufacturing center is a typical industrial power subsystem with dozens of high energy consumption devices which have complex physical dynamics. DSM, integrated with CPS, is an effective methodology for solving energy optimization problems in manufacturing center. This paper presents a prediction-based manufacturing center self-adaptive energy optimization method for demand side management in cyber physical systems. To gain prior knowledge of DSM operating results, a sparse Bayesian learning based componential forecasting method is introduced to predict 24-hour electric load levels for specific industrial areas in China. From this data, a pricing strategy is designed based on short-term load forecasting results. To minimize total energy costs while guaranteeing manufacturing center service quality, an adaptive demand side energy optimization algorithm is presented. The proposed scheme is tested in a machining center energy optimization experiment. An AMI sensing system is then used to measure the demand side energy consumption of the manufacturing center. Based on the data collected from the sensing system, the load prediction-based energy optimization scheme is implemented. By employing both the PSO and the CPSO method, the problem of DSM in the manufacturing center is solved. The results of the experiment show the self-adaptive CPSO energy optimization method enhances optimization by 5% compared with the traditional PSO optimization method.

Machine learning techniques for energy optimization in mobile embedded systems

NASA Astrophysics Data System (ADS)

Donohoo, Brad Kyoshi

Mobile smartphones and other portable battery operated embedded systems (PDAs, tablets) are pervasive computing devices that have emerged in recent years as essential instruments for communication, business, and social interactions. While performance, capabilities, and design are all important considerations when purchasing a mobile device, a long battery lifetime is one of the most desirable attributes. Battery technology and capacity has improved over the years, but it still cannot keep pace with the power consumption demands of today's mobile devices. This key limiter has led to a strong research emphasis on extending battery lifetime by minimizing energy consumption, primarily using software optimizations. This thesis presents two strategies that attempt to optimize mobile device energy consumption with negligible impact on user perception and quality of service (QoS). The first strategy proposes an application and user interaction aware middleware framework that takes advantage of user idle time between interaction events of the foreground application to optimize CPU and screen backlight energy consumption. The framework dynamically classifies mobile device applications based on their received interaction patterns, then invokes a number of different power management algorithms to adjust processor frequency and screen backlight levels accordingly. The second strategy proposes the usage of machine learning techniques to learn a user's mobile device usage pattern pertaining to spatiotemporal and device contexts, and then predict energy-optimal data and location interface configurations. By learning where and when a mobile device user uses certain power-hungry interfaces (3G, WiFi, and GPS), the techniques, which include variants of linear discriminant analysis, linear logistic regression, non-linear logistic regression, and k-nearest neighbor, are able to dynamically turn off unnecessary interfaces at runtime in order to save energy.

Network Modeling and Energy-Efficiency Optimization for Advanced Machine-to-Machine Sensor Networks

PubMed Central

Jung, Sungmo; Kim, Jong Hyun; Kim, Seoksoo

2012-01-01

Wireless machine-to-machine sensor networks with multiple radio interfaces are expected to have several advantages, including high spatial scalability, low event detection latency, and low energy consumption. Here, we propose a network model design method involving network approximation and an optimized multi-tiered clustering algorithm that maximizes node lifespan by minimizing energy consumption in a non-uniformly distributed network. Simulation results show that the cluster scales and network parameters determined with the proposed method facilitate a more efficient performance compared to existing methods. PMID:23202190

Energy consumption model on WiMAX subscriber station

NASA Astrophysics Data System (ADS)

Mubarakah, N.; Suherman; Al-Hakim, M. Y.; Warman, E.

2018-02-01

Mobile communication technologies move toward miniaturization. Mobile device’s energy source relies on its battery endurance. The smaller the mobile device, it is expected the slower the battery drains. Energy consumption reduction in mobile devices has been of interest of researcher. In order to optimize energy consumption, its usage should be predictable. This paper proposes a model of predicted energy amount consumed by the WiMAX subscriber station by using regression analysis of active WiMAX states and their durations. The proposed model was assessed by using NS-2 simulation for more than a hundred thousand of recorded energy consumptions data in every WiMAX states. The assessment show a small average deviation between predicted and measured energy consumptions, about 0.18% for training data and 0.187% and 0.191% for test data.

Reliable Adaptive Data Aggregation Route Strategy for a Trade-off between Energy and Lifetime in WSNs

PubMed Central

Guo, Wenzhong; Hong, Wei; Zhang, Bin; Chen, Yuzhong; Xiong, Naixue

2014-01-01

Mobile security is one of the most fundamental problems in Wireless Sensor Networks (WSNs). The data transmission path will be compromised for some disabled nodes. To construct a secure and reliable network, designing an adaptive route strategy which optimizes energy consumption and network lifetime of the aggregation cost is of great importance. In this paper, we address the reliable data aggregation route problem for WSNs. Firstly, to ensure nodes work properly, we propose a data aggregation route algorithm which improves the energy efficiency in the WSN. The construction process achieved through discrete particle swarm optimization (DPSO) saves node energy costs. Then, to balance the network load and establish a reliable network, an adaptive route algorithm with the minimal energy and the maximum lifetime is proposed. Since it is a non-linear constrained multi-objective optimization problem, in this paper we propose a DPSO with the multi-objective fitness function combined with the phenotype sharing function and penalty function to find available routes. Experimental results show that compared with other tree routing algorithms our algorithm can effectively reduce energy consumption and trade off energy consumption and network lifetime. PMID:25215944

Cross-layer cluster-based energy-efficient protocol for wireless sensor networks.

PubMed

Mammu, Aboobeker Sidhik Koyamparambil; Hernandez-Jayo, Unai; Sainz, Nekane; de la Iglesia, Idoia

2015-04-09

Recent developments in electronics and wireless communications have enabled the improvement of low-power and low-cost wireless sensors networks (WSNs). One of the most important challenges in WSNs is to increase the network lifetime due to the limited energy capacity of the network nodes. Another major challenge in WSNs is the hot spots that emerge as locations under heavy traffic load. Nodes in such areas quickly drain energy resources, leading to disconnection in network services. In such an environment, cross-layer cluster-based energy-efficient algorithms (CCBE) can prolong the network lifetime and energy efficiency. CCBE is based on clustering the nodes to different hexagonal structures. A hexagonal cluster consists of cluster members (CMs) and a cluster head (CH). The CHs are selected from the CMs based on nodes near the optimal CH distance and the residual energy of the nodes. Additionally, the optimal CH distance that links to optimal energy consumption is derived. To balance the energy consumption and the traffic load in the network, the CHs are rotated among all CMs. In WSNs, energy is mostly consumed during transmission and reception. Transmission collisions can further decrease the energy efficiency. These collisions can be avoided by using a contention-free protocol during the transmission period. Additionally, the CH allocates slots to the CMs based on their residual energy to increase sleep time. Furthermore, the energy consumption of CH can be further reduced by data aggregation. In this paper, we propose a data aggregation level based on the residual energy of CH and a cost-aware decision scheme for the fusion of data. Performance results show that the CCBE scheme performs better in terms of network lifetime, energy consumption and throughput compared to low-energy adaptive clustering hierarchy (LEACH) and hybrid energy-efficient distributed clustering (HEED).

Simulation of value stream mapping and discrete optimization of energy consumption in modular construction

NASA Astrophysics Data System (ADS)

Chowdhury, Md Mukul

With the increased practice of modularization and prefabrication, the construction industry gained the benefits of quality management, improved completion time, reduced site disruption and vehicular traffic, and improved overall safety and security. Whereas industrialized construction methods, such as modular and manufactured buildings, have evolved over decades, core techniques used in prefabrication plants vary only slightly from those employed in traditional site-built construction. With a focus on energy and cost efficient modular construction, this research presents the development of a simulation, measurement and optimization system for energy consumption in the manufacturing process of modular construction. The system is based on Lean Six Sigma principles and loosely coupled system operation to identify the non-value adding tasks and possible causes of low energy efficiency. The proposed system will also include visualization functions for demonstration of energy consumption in modular construction. The benefits of implementing this system include a reduction in the energy consumption in production cost, decrease of energy cost in the production of lean-modular construction, and increase profit. In addition, the visualization functions will provide detailed information about energy efficiency and operation flexibility in modular construction. A case study is presented to validate the reliability of the system.

Multi Dimensional Honey Bee Foraging Algorithm Based on Optimal Energy Consumption

NASA Astrophysics Data System (ADS)

Saritha, R.; Vinod Chandra, S. S.

2017-10-01

In this paper a new nature inspired algorithm is proposed based on natural foraging behavior of multi-dimensional honey bee colonies. This method handles issues that arise when food is shared from multiple sources by multiple swarms at multiple destinations. The self organizing nature of natural honey bee swarms in multiple colonies is based on the principle of energy consumption. Swarms of multiple colonies select a food source to optimally fulfill the requirements of its colonies. This is based on the energy requirement for transporting food between a source and destination. Minimum use of energy leads to maximizing profit in each colony. The mathematical model proposed here is based on this principle. This has been successfully evaluated by applying it on multi-objective transportation problem for optimizing cost and time. The algorithm optimizes the needs at each destination in linear time.

[Analysis of grey correlation between energy consumption and economic growth in Liaoning Province, China.

PubMed

Wang, Li; Xi, Feng Ming; Wang, Jiao Yue

2016-03-01

The contradiction between energy consumption and economic growth is increasingly prominent in China. Liaoning Province as one of Chinese heavy industrial bases, consumes a large amount of energy. Its economic development has a strong dependence on energy consumption, but the energy in short supply become more apparent. In order to further understand the relationship between energy consumption and economic growth and put forward scientific suggestions on low carbon development, we used the grey correlation analysis method to separately examine the relevance of economic growth with energy consumption industries and energy consumption varieties through analy sis of energy consumption and economic growth data in Liaoning Province from 2000 to 2012. The results showed that the wholesale and retail sector and hotel and restaurant sector were in the minimum energy consumption in all kinds of sectors, but they presented the closest connection with the economic growth. Although industry energy consumption was the maximum, the degree of connection between industry energy consumption and economic growth was weak. In all types of energy consumption, oil and hydro-power consumption had a significant connection with economic growth. However, the degree of connection of coal consumption with economic growth was not significant, which meant that coal utilization efficiency was low. In order to achieve low carbon and sustainable development, Liaoning Province should transform the economic growth mode, adjust industry structure, optimize energy structure, and improve energy utilization efficiency, especially promote producer services and develop clean and renewable energy.

Energy simulation and optimization for a small commercial building through Modelica

NASA Astrophysics Data System (ADS)

Rivas, Bryan

Small commercial buildings make up the majority of buildings in the United States. Energy consumed by these buildings is expected to drastically increase in the next few decades, with a large percentage of the energy consumed attributed to cooling systems. This work presents the simulation and optimization of a thermostat schedule to minimize energy consumption in a small commercial building test bed during the cooling season. The simulation occurs through the use of the multi-engineering domain Dymola environment based on the Modelica open source programming language and is optimized with the Java based optimization program GenOpt. The simulation uses both physically based modeling utilizing heat transfer principles for the building and regression analysis for energy consumption. GenOpt is dynamically coupled to Dymola through various interface files. There are very few studies that have coupled GenOpt to a building simulation program and even fewer studies have used Dymola for building simulation as extensively as the work presented here. The work presented proves Dymola as a viable alternative to other building simulation programs such as EnergyPlus and MatLab. The model developed is used to simulate the energy consumption of a test bed, a commissioned real world small commercial building, while maintaining indoor thermal comfort. Potential applications include smart or intelligent building systems, predictive simulation of small commercial buildings, and building diagnostics.

A self-optimizing scheme for energy balanced routing in Wireless Sensor Networks using SensorAnt.

PubMed

Shamsan Saleh, Ahmed M; Ali, Borhanuddin Mohd; Rasid, Mohd Fadlee A; Ismail, Alyani

2012-01-01

Planning of energy-efficient protocols is critical for Wireless Sensor Networks (WSNs) because of the constraints on the sensor nodes' energy. The routing protocol should be able to provide uniform power dissipation during transmission to the sink node. In this paper, we present a self-optimization scheme for WSNs which is able to utilize and optimize the sensor nodes' resources, especially the batteries, to achieve balanced energy consumption across all sensor nodes. This method is based on the Ant Colony Optimization (ACO) metaheuristic which is adopted to enhance the paths with the best quality function. The assessment of this function depends on multi-criteria metrics such as the minimum residual battery power, hop count and average energy of both route and network. This method also distributes the traffic load of sensor nodes throughout the WSN leading to reduced energy usage, extended network life time and reduced packet loss. Simulation results show that our scheme performs much better than the Energy Efficient Ant-Based Routing (EEABR) in terms of energy consumption, balancing and efficiency.

Autonomous Hybrid Priority Queueing for Scheduling Residential Energy Demands

NASA Astrophysics Data System (ADS)

Kalimullah, I. Q.; Shamroukh, M.; Sahar, N.; Shetty, S.

2017-05-01

The advent of smart grid technologies has opened up opportunities to manage the energy consumption of the users within a residential smart grid system. Demand response management is particularly being employed to reduce the overall load on an electricity network which could in turn reduce outages and electricity costs. The objective of this paper is to develop an intelligible scheduler to optimize the energy available to a micro grid through hybrid queueing algorithm centered around the consumers’ energy demands. This is achieved by shifting certain schedulable load appliances to light load hours. Various factors such as the type of demand, grid load, consumers’ energy usage patterns and preferences are considered while formulating the logical constraints required for the algorithm. The algorithm thus obtained is then implemented in MATLAB workspace to simulate its execution by an Energy Consumption Scheduler (ECS) found within smart meters, which automatically finds the optimal energy consumption schedule tailor made to fit each consumer within the micro grid network.

Minimizing water consumption when producing hydropower

NASA Astrophysics Data System (ADS)

Leon, A. S.

2015-12-01

In 2007, hydropower accounted for only 16% of the world electricity production, with other renewable sources totaling 3%. Thus, it is not surprising that when alternatives are evaluated for new energy developments, there is strong impulse for fossil fuel or nuclear energy as opposed to renewable sources. However, as hydropower schemes are often part of a multipurpose water resources development project, they can often help to finance other components of the project. In addition, hydropower systems and their associated dams and reservoirs provide human well-being benefits, such as flood control and irrigation, and societal benefits such as increased recreational activities and improved navigation. Furthermore, hydropower due to its associated reservoir storage, can provide flexibility and reliability for energy production in integrated energy systems. The storage capability of hydropower systems act as a regulating mechanism by which other intermittent and variable renewable energy sources (wind, wave, solar) can play a larger role in providing electricity of commercial quality. Minimizing water consumption for producing hydropower is critical given that overuse of water for energy production may result in a shortage of water for other purposes such as irrigation, navigation or fish passage. This paper presents a dimensional analysis for finding optimal flow discharge and optimal penstock diameter when designing impulse and reaction water turbines for hydropower systems. The objective of this analysis is to provide general insights for minimizing water consumption when producing hydropower. This analysis is based on the geometric and hydraulic characteristics of the penstock, the total hydraulic head and the desired power production. As part of this analysis, various dimensionless relationships between power production, flow discharge and head losses were derived. These relationships were used to withdraw general insights on determining optimal flow discharge and optimal penstock diameter. For instance, it was found that for minimizing water consumption, the ratio of head loss to gross head should not exceed about 15%. Two examples of application are presented to illustrate the procedure for determining optimal flow discharge and optimal penstock diameter for impulse and reaction turbines.

Modeling an impact of road geometric design on vehicle energy consumption

NASA Astrophysics Data System (ADS)

Luin, Blaž; Petelin, Stojan; Al-Mansour, Fouad

2017-11-01

Some roads connect traffic origins and destinations directly, some use winding, indirect routes. Indirect connections result in longer distances driven and increased fuel consumption. A similar effect is observed on congested roads and mountain roads with many changes in altitude. Therefore a framework to assess road networks based on energy consumption is proposed. It has been shown that road geometry has significant impact on overall traffic energy consumption and emissions. The methodology presented in the paper analyzes impact of traffic volume, shares of vehicle classes, road network configuration on the energy used by the vehicles. It can be used to optimize energy consumption with efficient traffic management and to choose optimum new road in the design phase. This is especially important as the energy consumed by the vehicles shortly after construction supersedes the energy spent for the road construction.

Optimization of fuel-cell tram operation based on two dimension dynamic programming

NASA Astrophysics Data System (ADS)

Zhang, Wenbin; Lu, Xuecheng; Zhao, Jingsong; Li, Jianqiu

2018-02-01

This paper proposes an optimal control strategy based on the two-dimension dynamic programming (2DDP) algorithm targeting at minimizing operation energy consumption for a fuel-cell tram. The energy consumption model with the tram dynamics is firstly deduced. Optimal control problem are analyzed and the 2DDP strategy is applied to solve the problem. The optimal tram speed profiles are obtained for each interstation which consist of three stages: accelerate to the set speed with the maximum traction power, dynamically adjust to maintain a uniform speed and decelerate to zero speed with the maximum braking power at a suitable timing. The optimal control curves of all the interstations are connected with the parking time to form the optimal control method of the whole line. The optimized speed profiles are also simplified for drivers to follow.

Simulation based energy-resource efficient manufacturing integrated with in-process virtual management

NASA Astrophysics Data System (ADS)

Katchasuwanmanee, Kanet; Cheng, Kai; Bateman, Richard

2016-09-01

As energy efficiency is one of the key essentials towards sustainability, the development of an energy-resource efficient manufacturing system is among the great challenges facing the current industry. Meanwhile, the availability of advanced technological innovation has created more complex manufacturing systems that involve a large variety of processes and machines serving different functions. To extend the limited knowledge on energy-efficient scheduling, the research presented in this paper attempts to model the production schedule at an operation process by considering the balance of energy consumption reduction in production, production work flow (productivity) and quality. An innovative systematic approach to manufacturing energy-resource efficiency is proposed with the virtual simulation as a predictive modelling enabler, which provides real-time manufacturing monitoring, virtual displays and decision-makings and consequentially an analytical and multidimensional correlation analysis on interdependent relationships among energy consumption, work flow and quality errors. The regression analysis results demonstrate positive relationships between the work flow and quality errors and the work flow and energy consumption. When production scheduling is controlled through optimization of work flow, quality errors and overall energy consumption, the energy-resource efficiency can be achieved in the production. Together, this proposed multidimensional modelling and analysis approach provides optimal conditions for the production scheduling at the manufacturing system by taking account of production quality, energy consumption and resource efficiency, which can lead to the key competitive advantages and sustainability of the system operations in the industry.

Prediction-based Dynamic Energy Management in Wireless Sensor Networks

PubMed Central

Wang, Xue; Ma, Jun-Jie; Wang, Sheng; Bi, Dao-Wei

2007-01-01

Energy consumption is a critical constraint in wireless sensor networks. Focusing on the energy efficiency problem of wireless sensor networks, this paper proposes a method of prediction-based dynamic energy management. A particle filter was introduced to predict a target state, which was adopted to awaken wireless sensor nodes so that their sleep time was prolonged. With the distributed computing capability of nodes, an optimization approach of distributed genetic algorithm and simulated annealing was proposed to minimize the energy consumption of measurement. Considering the application of target tracking, we implemented target position prediction, node sleep scheduling and optimal sensing node selection. Moreover, a routing scheme of forwarding nodes was presented to achieve extra energy conservation. Experimental results of target tracking verified that energy-efficiency is enhanced by prediction-based dynamic energy management.

Energy accounting and optimization for mobile systems

NASA Astrophysics Data System (ADS)

Dong, Mian

Energy accounting determines how much a software process contributes to the total system energy consumption. It is the foundation for evaluating software and has been widely used by operating system based energy management. While various energy accounting policies have been tried, there is no known way to evaluate them directly simply because it is hard to track every hardware use by software in a heterogeneous multi-core system like modern smartphones and tablets. In this thesis, we provide the ground truth for energy accounting based on multi-player game theory and offer the first evaluation of existing energy accounting policies, revealing their important flaws. The proposed ground truth is based on Shapley value, a single value solution to multi-player games of which four axiomatic properties are natural and self-evident to energy accounting. To obtain the Shapley value-based ground truth, one only needs to know if a process is active during the time under question and the system energy consumption during the same time. We further provide a utility optimization formulation of energy management and show, surprisingly, that energy accounting does not matter for existing energy management solutions that control the energy use of a process by giving it an energy budget, or budget based energy management (BEM). We show an optimal energy management (OEM) framework can always outperform BEM. While OEM does not require any form of energy accounting, it is related to Shapley value in that both require the system energy consumption for all possible combination of processes under question. We provide a novel system solution that meet this requirement by acquiring system energy consumption in situ for an OS scheduler period, i.e.,10 ms. We report a prototype implementation of both Shapley value-based energy accounting and OEM based scheduling. Using this prototype and smartphone workload, we experimentally demonstrate how erroneous existing energy accounting policies can be, show that existing BEM solutions are unnecessarily complicated yet underperforming by 20% compared to OEM.

Comparative study of air-conditioning energy use of four office buildings in China and USA

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhou, Xin; Yan, Da; An, Jingjing

Energy use in buildings has great variability. In order to design and operate low energy buildings as well as to establish building energy codes and standards and effective energy policy, it is crucial to understand and quantify key factors influencing building energy performance. Here, this study investigates air-conditioning (AC) energy use of four office buildings in four locations: Beijing, Taiwan, Hong Kong, and Berkeley. Building simulation was employed to quantify the influences of key factors, including climate, building envelope and occupant behavior. Through simulation of various combinations of the three influencing elements, it is found that climate can lead tomore » AC cooling consumption differences by almost two times, while occupant behavior resulted in the greatest differences (of up to three times) in AC cooling consumption. The influence of occupant behavior on AC energy consumption is not homogeneous. Under similar climates, when the occupant behavior in the building differed, the optimized building envelope design also differed. In conclusion, the optimal building envelope should be determined according to the climate as well as the occupants who use the building.« less

Comparative study of air-conditioning energy use of four office buildings in China and USA

DOE PAGES

Zhou, Xin; Yan, Da; An, Jingjing; ...

2018-04-05

Energy use in buildings has great variability. In order to design and operate low energy buildings as well as to establish building energy codes and standards and effective energy policy, it is crucial to understand and quantify key factors influencing building energy performance. Here, this study investigates air-conditioning (AC) energy use of four office buildings in four locations: Beijing, Taiwan, Hong Kong, and Berkeley. Building simulation was employed to quantify the influences of key factors, including climate, building envelope and occupant behavior. Through simulation of various combinations of the three influencing elements, it is found that climate can lead tomore » AC cooling consumption differences by almost two times, while occupant behavior resulted in the greatest differences (of up to three times) in AC cooling consumption. The influence of occupant behavior on AC energy consumption is not homogeneous. Under similar climates, when the occupant behavior in the building differed, the optimized building envelope design also differed. In conclusion, the optimal building envelope should be determined according to the climate as well as the occupants who use the building.« less

DOE Office of Scientific and Technical Information (OSTI.GOV)

Simpkins, Travis; Cutler, Dylan; Hirsch, Brian

There are thousands of isolated, diesel-powered microgrids that deliver energy to remote communities around the world at very high energy costs. The Remote Communities Renewable Energy program aims to help these communities reduce their fuel consumption and lower their energy costs through the use of high penetration renewable energy. As part of this program, the REopt modeling platform for energy system integration and optimization was used to analyze cost-optimal pathways toward achieving a combined 75% reduction in diesel fuel and fuel oil consumption in a select Alaskan village. In addition to the existing diesel generator and fuel oil heating technologies,more » the model was able to select from among wind, battery storage, and dispatchable electric heaters to meet the electrical and thermal loads. The model results indicate that while 75% fuel reduction appears to be technically feasible it may not be economically viable at this time. When the fuel reduction target was relaxed, the results indicate that by installing high-penetration renewable energy, the community could lower their energy costs by 21% while still reducing their fuel consumption by 54%.« less

Optimizing Energy Consumption in Building Designs Using Building Information Model (BIM)

NASA Astrophysics Data System (ADS)

Egwunatum, Samuel; Joseph-Akwara, Esther; Akaigwe, Richard

2016-09-01

Given the ability of a Building Information Model (BIM) to serve as a multi-disciplinary data repository, this paper seeks to explore and exploit the sustainability value of Building Information Modelling/models in delivering buildings that require less energy for their operation, emit less CO2 and at the same time provide a comfortable living environment for their occupants. This objective was achieved by a critical and extensive review of the literature covering: (1) building energy consumption, (2) building energy performance and analysis, and (3) building information modeling and energy assessment. The literature cited in this paper showed that linking an energy analysis tool with a BIM model helped project design teams to predict and create optimized energy consumption. To validate this finding, an in-depth analysis was carried out on a completed BIM integrated construction project using the Arboleda Project in the Dominican Republic. The findings showed that the BIM-based energy analysis helped the design team achieve the world's first 103% positive energy building. From the research findings, the paper concludes that linking an energy analysis tool with a BIM model helps to expedite the energy analysis process, provide more detailed and accurate results as well as deliver energy-efficient buildings. The study further recommends that the adoption of a level 2 BIM and the integration of BIM in energy optimization analyse should be made compulsory for all projects irrespective of the method of procurement (government-funded or otherwise) or its size.

Energy Harvesting Based Body Area Networks for Smart Health.

PubMed

Hao, Yixue; Peng, Limei; Lu, Huimin; Hassan, Mohammad Mehedi; Alamri, Atif

2017-07-10

Body area networks (BANs) are configured with a great number of ultra-low power consumption wearable devices, which constantly monitor physiological signals of the human body and thus realize intelligent monitoring. However, the collection and transfer of human body signals consume energy, and considering the comfort demand of wearable devices, both the size and the capacity of a wearable device's battery are limited. Thus, minimizing the energy consumption of wearable devices and optimizing the BAN energy efficiency is still a challenging problem. Therefore, in this paper, we propose an energy harvesting-based BAN for smart health and discuss an optimal resource allocation scheme to improve BAN energy efficiency. Specifically, firstly, considering energy harvesting in a BAN and the time limits of human body signal transfer, we formulate the energy efficiency optimization problem of time division for wireless energy transfer and wireless information transfer. Secondly, we convert the optimization problem into a convex optimization problem under a linear constraint and propose a closed-form solution to the problem. Finally, simulation results proved that when the size of data acquired by the wearable devices is small, the proportion of energy consumed by the circuit and signal acquisition of the wearable devices is big, and when the size of data acquired by the wearable devices is big, the energy consumed by the signal transfer of the wearable device is decisive.

Energy Harvesting Based Body Area Networks for Smart Health

PubMed Central

Hao, Yixue; Peng, Limei; Alamri, Atif

2017-01-01

Body area networks (BANs) are configured with a great number of ultra-low power consumption wearable devices, which constantly monitor physiological signals of the human body and thus realize intelligent monitoring. However, the collection and transfer of human body signals consume energy, and considering the comfort demand of wearable devices, both the size and the capacity of a wearable device’s battery are limited. Thus, minimizing the energy consumption of wearable devices and optimizing the BAN energy efficiency is still a challenging problem. Therefore, in this paper, we propose an energy harvesting-based BAN for smart health and discuss an optimal resource allocation scheme to improve BAN energy efficiency. Specifically, firstly, considering energy harvesting in a BAN and the time limits of human body signal transfer, we formulate the energy efficiency optimization problem of time division for wireless energy transfer and wireless information transfer. Secondly, we convert the optimization problem into a convex optimization problem under a linear constraint and propose a closed-form solution to the problem. Finally, simulation results proved that when the size of data acquired by the wearable devices is small, the proportion of energy consumed by the circuit and signal acquisition of the wearable devices is big, and when the size of data acquired by the wearable devices is big, the energy consumed by the signal transfer of the wearable device is decisive. PMID:28698501

Smart City Energy Interconnection Technology Framework Preliminary Research

NASA Astrophysics Data System (ADS)

Zheng, Guotai; Zhao, Baoguo; Zhao, Xin; Li, Hao; Huo, Xianxu; Li, Wen; Xia, Yu

2018-01-01

to improve urban energy efficiency, improve the absorptive ratio of new energy resources and renewable energy sources, and reduce environmental pollution and other energy supply and consumption technology framework matched with future energy restriction conditions and applied technology level are required to be studied. Relative to traditional energy supply system, advanced information technology-based “Energy Internet” technical framework may give play to energy integrated application and load side interactive technology advantages, as a whole optimize energy supply and consumption and improve the overall utilization efficiency of energy.

Energy efficiency analysis of the manipulation process by the industrial objects with the use of Bernoulli gripping devices

NASA Astrophysics Data System (ADS)

Savkiv, Volodymyr; Mykhailyshyn, Roman; Duchon, Frantisek; Mikhalishin, Mykhailo

2017-11-01

The article deals with the topical issue of reducing energy consumption for transportation of industrial objects. The energy efficiency of the process of objects manipulation with the use of the orientation optimization method while gripping with the help of different methods has been studied. The analysis of the influence of the constituent parts of inertial forces, that affect the object of manipulation, on the necessary force characteristics and energy consumption of Bernoulli gripping device has been proposed. The economic efficiency of the use of the optimal orientation of Bernoulli gripping device while transporting the object of manipulation in comparison to the transportation without re-orientation has been proved.

Neural network based optimal control of HVAC&R systems

NASA Astrophysics Data System (ADS)

Ning, Min

Heating, Ventilation, Air-Conditioning and Refrigeration (HVAC&R) systems have wide applications in providing a desired indoor environment for different types of buildings. It is well acknowledged that 30%-40% of the total energy generated is consumed by buildings and HVAC&R systems alone account for more than 50% of the building energy consumption. Low operational efficiency especially under partial load conditions and poor control are part of reasons for such high energy consumption. To improve energy efficiency, HVAC&R systems should be properly operated to maintain a comfortable and healthy indoor environment under dynamic ambient and indoor conditions with the least energy consumption. This research focuses on the optimal operation of HVAC&R systems. The optimization problem is formulated and solved to find the optimal set points for the chilled water supply temperature, discharge air temperature and AHU (air handling unit) fan static pressure such that the indoor environment is maintained with the least chiller and fan energy consumption. To achieve this objective, a dynamic system model is developed first to simulate the system behavior under different control schemes and operating conditions. The system model is modular in structure, which includes a water-cooled vapor compression chiller model and a two-zone VAV system model. A fuzzy-set based extended transformation approach is then applied to investigate the uncertainties of this model caused by uncertain parameters and the sensitivities of the control inputs with respect to the interested model outputs. A multi-layer feed forward neural network is constructed and trained in unsupervised mode to minimize the cost function which is comprised of overall energy cost and penalty cost when one or more constraints are violated. After training, the network is implemented as a supervisory controller to compute the optimal settings for the system. In order to implement the optimal set points predicted by the supervisory controller, a set of five adaptive PI (proportional-integral) controllers are designed for each of the five local control loops of the HVAC&R system. The five controllers are used to track optimal set points and zone air temperature set points. Parameters of these PI controllers are tuned online to reduce tracking errors. The updating rules are derived from Lyapunov stability analysis. Simulation results show that compared to the conventional night reset operation scheme, the optimal operation scheme saves around 10% energy under full load condition and 19% energy under partial load conditions.

Torque-based optimal acceleration control for electric vehicle

NASA Astrophysics Data System (ADS)

Lu, Dongbin; Ouyang, Minggao

2014-03-01

The existing research of the acceleration control mainly focuses on an optimization of the velocity trajectory with respect to a criterion formulation that weights acceleration time and fuel consumption. The minimum-fuel acceleration problem in conventional vehicle has been solved by Pontryagin's maximum principle and dynamic programming algorithm, respectively. The acceleration control with minimum energy consumption for battery electric vehicle(EV) has not been reported. In this paper, the permanent magnet synchronous motor(PMSM) is controlled by the field oriented control(FOC) method and the electric drive system for the EV(including the PMSM, the inverter and the battery) is modeled to favor over a detailed consumption map. The analytical algorithm is proposed to analyze the optimal acceleration control and the optimal torque versus speed curve in the acceleration process is obtained. Considering the acceleration time, a penalty function is introduced to realize a fast vehicle speed tracking. The optimal acceleration control is also addressed with dynamic programming(DP). This method can solve the optimal acceleration problem with precise time constraint, but it consumes a large amount of computation time. The EV used in simulation and experiment is a four-wheel hub motor drive electric vehicle. The simulation and experimental results show that the required battery energy has little difference between the acceleration control solved by analytical algorithm and that solved by DP, and is greatly reduced comparing with the constant pedal opening acceleration. The proposed analytical and DP algorithms can minimize the energy consumption in EV's acceleration process and the analytical algorithm is easy to be implemented in real-time control.

A new procedure to analyze the effect of air changes in building energy consumption

PubMed Central

2014-01-01

Background Today, the International Energy Agency is working under good practice guides that integrate appropriate and cost effective technologies. In this paper a new procedure to define building energy consumption in accordance with the ISO 13790 standard was performed and tested based on real data from a Spanish region. Results Results showed that the effect of air changes on building energy consumption can be defined using the Weibull peak function model. Furthermore, the effect of climate change on building energy consumption under several different air changes was nearly nil during the summer season. Conclusions The procedure obtained could be the much sought-after solution to the problem stated by researchers in the past and future research works relating to this new methodology could help us define the optimal improvement in real buildings to reduce energy consumption, and its related carbon dioxide emissions, at minimal economical cost. PMID:24456655

Building Performance Optimization while Empowering Occupants Toward Environmentally Sustainable Behavior through Continuous Monitoring and Diagnostics

DTIC Science & Technology

2016-12-01

investment at the PaANG installation. In compliance with the NIST handbook’s guidelines, the team used the actual energy prices of the buildings...Costing Manual for the Federal Energy Management Program. For example, the team used the actual energy price and the measured energy consumption at the...least 30% of a DOD building’s HVAC and plug load annual energy consumption can be saved through continuous diagnostics and controls, while empowering

Thermal environment analysis and energy conservation research of rural residence in cold regions of China based on BIM platform

NASA Astrophysics Data System (ADS)

Dong, J. Y.; Cheng, W.; Ma, C. P.; Xin, L. S.; Tan, Y. T.

2017-06-01

In order to study the issue of rural residential energy consumption in cold regions of China, modeled an architecture prototype based on BIM platform according to the affecting factors of rural residential thermal environment, and imported the virtual model which contains building information into energy analysis tools and chose the appropriate building orientation. By analyzing the energy consumption of the residential buildings with different enclosure structure forms, we designed the optimal energy-saving residence form. There is a certain application value of this method for researching the energy consumption and energy-saving design for the rural residence in cold regions of China.

Agreement Technologies for Energy Optimization at Home.

PubMed

González-Briones, Alfonso; Chamoso, Pablo; De La Prieta, Fernando; Demazeau, Yves; Corchado, Juan M

2018-05-19

Nowadays, it is becoming increasingly common to deploy sensors in public buildings or homes with the aim of obtaining data from the environment and taking decisions that help to save energy. Many of the current state-of-the-art systems make decisions considering solely the environmental factors that cause the consumption of energy. These systems are successful at optimizing energy consumption; however, they do not adapt to the preferences of users and their comfort. Any system that is to be used by end-users should consider factors that affect their wellbeing. Thus, this article proposes an energy-saving system, which apart from considering the environmental conditions also adapts to the preferences of inhabitants. The architecture is based on a Multi-Agent System (MAS), its agents use Agreement Technologies (AT) to perform a negotiation process between the comfort preferences of the users and the degree of optimization that the system can achieve according to these preferences. A case study was conducted in an office building, showing that the proposed system achieved average energy savings of 17.15%.

Pneumafil casing blower through moving reference frame (MRF) - A CFD simulation

NASA Astrophysics Data System (ADS)

Manivel, R.; Vijayanandh, R.; Babin, T.; Sriram, G.

2018-05-01

In this analysis work, the ring frame of Pneumafil casing blower of the textile mills with a power rating of 5 kW have been simulated using Computational Fluid Dynamics (CFD) code. The CFD analysis of the blower is carried out in Ansys Workbench 16.2 with Fluent using MRF solver settings. The simulation settings and boundary conditions are based on literature study and field data acquired. The main objective of this work is to reduce the energy consumption of the blower. The flow analysis indicated that the power consumption is influenced by the deflector plate orientation and deflector plate strip situated at the outlet casing of the blower. The energy losses occurred in the blower is due to the recirculation zones formed around the deflector plate strip. The deflector plate orientation is changed and optimized to reduce the energy consumption. The proposed optimized model is based on the simulation results which had relatively lesser power consumption than the existing and other cases. The energy losses in the Pneumafil casing blower are reduced through CFD analysis.

Optimization of Power Generation Rights Under the Requirements of Energy Conservation and Emission Reduction

NASA Astrophysics Data System (ADS)

Hu-ping, YANY; Chong-wei, ZHONG; Fei-fei, YAN; Cheng-yi, TANG

2018-03-01

In recent years, the energy crisis and greenhouse effect problem have caused wide public concern, if these issues cannot be resolved quickly, they will bring troubles to people’s lives.In response, many countries around the world have implemented policies to reduce energy consumption and greenhouse gas emissions. In our country, the electric power industry has made great contribution to the daily life of people and the development of industry, but it is also an industry of high consumption and high emission.In order to realize the sustainable development of society, it is necessary to make energy conservation and emission reduction in the power industry as an important part of the realization of this goal.In this context, power generation trade has become a hot topic in energy conservation and emission reduction.Through the electricity consumption of the units with different power efficiency and coal consumption rate,it can achieve the target of reducing coal consumption, reducing network loss, reducing greenhouse gas emission, and increasing social benefit,and so on. This article put forward a optimal energy model on the basis of guaranteeing safety and environmental protection.In this paper, they used the IEEE30, IEEE39, IEEE57 and IEEE118 node system as an example, and set up the control groups to prove the practicality of the presented model.The solving method of this model was interior-point method.

The method of planning the energy consumption for electricity market

NASA Astrophysics Data System (ADS)

Russkov, O. V.; Saradgishvili, S. E.

2017-10-01

The limitations of existing forecast models are defined. The offered method is based on game theory, probabilities theory and forecasting the energy prices relations. New method is the basis for planning the uneven energy consumption of industrial enterprise. Ecological side of the offered method is disclosed. The program module performed the algorithm of the method is described. Positive method tests at the industrial enterprise are shown. The offered method allows optimizing the difference between planned and factual consumption of energy every hour of a day. The conclusion about applicability of the method for addressing economic and ecological challenges is made.

An Enhanced PSO-Based Clustering Energy Optimization Algorithm for Wireless Sensor Network.

PubMed

Vimalarani, C; Subramanian, R; Sivanandam, S N

2016-01-01

Wireless Sensor Network (WSN) is a network which formed with a maximum number of sensor nodes which are positioned in an application environment to monitor the physical entities in a target area, for example, temperature monitoring environment, water level, monitoring pressure, and health care, and various military applications. Mostly sensor nodes are equipped with self-supported battery power through which they can perform adequate operations and communication among neighboring nodes. Maximizing the lifetime of the Wireless Sensor networks, energy conservation measures are essential for improving the performance of WSNs. This paper proposes an Enhanced PSO-Based Clustering Energy Optimization (EPSO-CEO) algorithm for Wireless Sensor Network in which clustering and clustering head selection are done by using Particle Swarm Optimization (PSO) algorithm with respect to minimizing the power consumption in WSN. The performance metrics are evaluated and results are compared with competitive clustering algorithm to validate the reduction in energy consumption.

To Eat or Not to Eat: An Easy Simulation of Optimal Diet Selection in the Classroom

ERIC Educational Resources Information Center

Ray, Darrell L.

2010-01-01

Optimal diet selection, a component of optimal foraging theory, suggests that animals should select a diet that either maximizes energy or nutrient consumption per unit time or minimizes the foraging time needed to attain required energy or nutrients. In this exercise, students simulate the behavior of foragers that either show no foraging…

Optimization of design parameters of low-energy buildings

NASA Astrophysics Data System (ADS)

Vala, Jiří; Jarošová, Petra

2017-07-01

Evaluation of temperature development and related consumption of energy required for heating, air-conditioning, etc. in low-energy buildings requires the proper physical analysis, covering heat conduction, convection and radiation, including beam and diffusive components of solar radiation, on all building parts and interfaces. The system approach and the Fourier multiplicative decomposition together with the finite element technique offers the possibility of inexpensive and robust numerical and computational analysis of corresponding direct problems, as well as of the optimization ones with several design variables, using the Nelder-Mead simplex method. The practical example demonstrates the correlation between such numerical simulations and the time series of measurements of energy consumption on a small family house in Ostrov u Macochy (35 km northern from Brno).

Power Consumption Optimization in Tooth Gears Processing

NASA Astrophysics Data System (ADS)

Kanatnikov, N.; Harlamov, G.; Kanatnikova, P.; Pashmentova, A.

2018-01-01

The paper reviews the issue of optimization of technological process of tooth gears production of the power consumption criteria. The authors dwell on the indices used for cutting process estimation by the consumed energy criteria and their applicability in the analysis of the toothed wheel production process. The inventors proposed a method for optimization of power consumptions based on the spatial modeling of cutting pattern. The article is aimed at solving the problem of effective source management in order to achieve economical and ecological effect during the mechanical processing of toothed gears. The research was supported by Russian Science Foundation (project No. 17-79-10316).

An energy-optimal solution for transportation control of cranes with double pendulum dynamics: Design and experiments

NASA Astrophysics Data System (ADS)

Sun, Ning; Wu, Yiming; Chen, He; Fang, Yongchun

2018-03-01

Underactuated cranes play an important role in modern industry. Specifically, in most situations of practical applications, crane systems exhibit significant double pendulum characteristics, which makes the control problem quite challenging. Moreover, most existing planners/controllers obtained with standard methods/techniques for double pendulum cranes cannot minimize the energy consumption when fulfilling the transportation tasks. Therefore, from a practical perspective, this paper proposes an energy-optimal solution for transportation control of double pendulum cranes. By applying the presented approach, the transportation objective, including fast trolley positioning and swing elimination, is achieved with minimized energy consumption, and the residual oscillations are suppressed effectively with all the state constrains being satisfied during the entire transportation process. As far as we know, this is the first energy-optimal solution for transportation control of underactuated double pendulum cranes with various state and control constraints. Hardware experimental results are included to verify the effectiveness of the proposed approach, whose superior performance is reflected by being experimentally compared with some comparative controllers.

Optimal policies for simultaneous energy consumption and ancillary service provision for flexible loads under stochastic prices and no capacity reservation constraint

NASA Astrophysics Data System (ADS)

Kefayati, Mahdi; Baldick, Ross

2015-07-01

Flexible loads, i.e. the loads whose power trajectory is not bound to a specific one, constitute a sizable portion of current and future electric demand. This flexibility can be used to improve the performance of the grid, should the right incentives be in place. In this paper, we consider the optimal decision making problem faced by a flexible load, demanding a certain amount of energy over its availability period, subject to rate constraints. The load is also capable of providing ancillary services (AS) by decreasing or increasing its consumption in response to signals from the independent system operator (ISO). Under arbitrarily distributed and correlated Markovian energy and AS prices, we obtain the optimal policy for minimising expected total cost, which includes cost of energy and benefits from AS provision, assuming no capacity reservation requirement for AS provision. We also prove that the optimal policy has a multi-threshold form and can be computed, stored and operated efficiently. We further study the effectiveness of our proposed optimal policy and its impact on the grid. We show that, while optimal simultaneous consumption and AS provision under real-time stochastic prices are achievable with acceptable computational burden, the impact of adopting such real-time pricing schemes on the network might not be as good as suggested by the majority of the existing literature. In fact, we show that such price responsive loads are likely to induce peak-to-average ratios much more than what is observed in the current distribution networks and adversely affect the grid.

Parametric and energy consumption optimization of Basic Red 2 removal by electrocoagulation/egg shell adsorption coupling using response surface methodology in a batch system.

PubMed

de Carvalho, Helder Pereira; Huang, Jiguo; Zhao, Meixia; Liu, Gang; Yang, Xinyu; Dong, Lili; Liu, Xingjuan

2016-01-01

In this study, response surface methodology (RSM) model was applied for optimization of Basic Red 2 (BR2) removal using electrocoagulation/eggshell (ES) coupling process in a batch system. Central composite design was used to evaluate the effects and interactions of process parameters including current density, reaction time, initial pH and ES dosage on the BR2 removal efficiency and energy consumption. The analysis of variance revealed high R(2) values (≥85%) indicating that the predictions of RSM models are adequately applicable for both responses. The optimum conditions when the dye removal efficiency of 93.18% and energy consumption of 0.840 kWh/kg were observed were 11.40 mA/cm(2) current density, 5 min and 3 s reaction time, 6.5 initial pH and 10.91 g/L ES dosage.

Joint Optimal Placement and Energy Allocation of Underwater Sensors in a Tree Topology

DTIC Science & Technology

2014-03-10

underwater acoustic sensor nodes with respect to the capacity of the wireless links between the... underwater acoustic sensor nodes with respect to the capacity of the wireless links between the nodes. We assumed that the energy consumption of...nodes’ optimal placements. We achieve the optimal placement of the underwater acoustic sensor nodes with respect to the capacity of the wireless

A new approach on the upgrade of energetic system based on green energy. A complex comparative analysis of the EEDI and EEOI

NASA Astrophysics Data System (ADS)

Faitar, C.; Novac, I.

2016-08-01

In recent years, many environmental organizations was interested to optimize the energy consumption which has become, today, one of the main concerns to the whole world. From this point of view, the maritime industry, has strove to optimize the fuel consumption of ship through the development of engines and propulsion system, improve the hull design, or using alternative energies, this way making a reduction in the amount of CO2 released to the atmosphere. The main idea of this paper is to realize a complex comparative analysis of Energy Efficiency Design Index and Energy Efficiency Operational Indicator which are calculated in two cases: first, in a classical approach for a crude oil super tanker ship and second, after the energy performance of this ship has been improved by introducing alternative energy sources on board.

An extended continuum model considering optimal velocity change with memory and numerical tests

NASA Astrophysics Data System (ADS)

Qingtao, Zhai; Hongxia, Ge; Rongjun, Cheng

2018-01-01

In this paper, an extended continuum model of traffic flow is proposed with the consideration of optimal velocity changes with memory. The new model's stability condition and KdV-Burgers equation considering the optimal velocities change with memory are deduced through linear stability theory and nonlinear analysis, respectively. Numerical simulation is carried out to study the extended continuum model, which explores how optimal velocity changes with memory affected velocity, density and energy consumption. Numerical results show that when considering the effects of optimal velocity changes with memory, the traffic jams can be suppressed efficiently. Both the memory step and sensitivity parameters of optimal velocity changes with memory will enhance the stability of traffic flow efficiently. Furthermore, numerical results demonstrates that the effect of optimal velocity changes with memory can avoid the disadvantage of historical information, which increases the stability of traffic flow on road, and so it improve the traffic flow stability and minimize cars' energy consumptions.

Optimization of wireless sensor networks based on chicken swarm optimization algorithm

NASA Astrophysics Data System (ADS)

Wang, Qingxi; Zhu, Lihua

2017-05-01

In order to reduce the energy consumption of wireless sensor network and improve the survival time of network, the clustering routing protocol of wireless sensor networks based on chicken swarm optimization algorithm was proposed. On the basis of LEACH agreement, it was improved and perfected that the points on the cluster and the selection of cluster head using the chicken group optimization algorithm, and update the location of chicken which fall into the local optimum by Levy flight, enhance population diversity, ensure the global search capability of the algorithm. The new protocol avoided the die of partial node of intensive using by making balanced use of the network nodes, improved the survival time of wireless sensor network. The simulation experiments proved that the protocol is better than LEACH protocol on energy consumption, also is better than that of clustering routing protocol based on particle swarm optimization algorithm.

Cross Layer Design for Optimizing Transmission Reliability, Energy Efficiency, and Lifetime in Body Sensor Networks.

PubMed

Chen, Xi; Xu, Yixuan; Liu, Anfeng

2017-04-19

High transmission reliability, energy efficiency, and long lifetime are pivotal issues for wireless body area networks (WBANs. However, these performance metrics are not independent of each other, making it hard to obtain overall improvements through optimizing one single aspect. Therefore, a Cross Layer Design Optimal (CLDO) scheme is proposed to simultaneously optimize transmission reliability, energy efficiency, and lifetime of WBANs from several layers. Firstly, due to the fact that the transmission power of nodes directly influences the reliability of links, the optimized transmission power of different nodes is deduced, which is able to maximize energy efficiency in theory under the premise that requirements on delay and jitter are fulfilled. Secondly, a relay decision algorithm is proposed to choose optimized relay nodes. Using this algorithm, nodes will choose relay nodes that ensure a balance of network energy consumption, provided that all nodes transmit with optimized transmission power and the same packet size. Thirdly, the energy consumption of nodes is still unbalanced even with optimized transmission power because of their different locations in the topology of the network. In addition, packet size also has an impact on final performance metrics. Therefore, a synthesized cross layer method for optimization is proposed. With this method, the transmission power of nodes with more residual energy will be enhanced while suitable packet size is determined for different links in the network, leading to further improvements in the WBAN system. Both our comprehensive theoretical analysis and experimental results indicate that the performance of our proposed scheme is better than reported in previous studies. Relative to the relay selection and power control game (RSPCG) scheme, the CLDO scheme can enhance transmission reliability by more than 44.6% and prolong the lifetime by as much as 33.2%.

Cross Layer Design for Optimizing Transmission Reliability, Energy Efficiency, and Lifetime in Body Sensor Networks

PubMed Central

Chen, Xi; Xu, Yixuan; Liu, Anfeng

2017-01-01

High transmission reliability, energy efficiency, and long lifetime are pivotal issues for wireless body area networks (WBANs). However, these performance metrics are not independent of each other, making it hard to obtain overall improvements through optimizing one single aspect. Therefore, a Cross Layer Design Optimal (CLDO) scheme is proposed to simultaneously optimize transmission reliability, energy efficiency, and lifetime of WBANs from several layers. Firstly, due to the fact that the transmission power of nodes directly influences the reliability of links, the optimized transmission power of different nodes is deduced, which is able to maximize energy efficiency in theory under the premise that requirements on delay and jitter are fulfilled. Secondly, a relay decision algorithm is proposed to choose optimized relay nodes. Using this algorithm, nodes will choose relay nodes that ensure a balance of network energy consumption, provided that all nodes transmit with optimized transmission power and the same packet size. Thirdly, the energy consumption of nodes is still unbalanced even with optimized transmission power because of their different locations in the topology of the network. In addition, packet size also has an impact on final performance metrics. Therefore, a synthesized cross layer method for optimization is proposed. With this method, the transmission power of nodes with more residual energy will be enhanced while suitable packet size is determined for different links in the network, leading to further improvements in the WBAN system. Both our comprehensive theoretical analysis and experimental results indicate that the performance of our proposed scheme is better than reported in previous studies. Relative to the relay selection and power control game (RSPCG) scheme, the CLDO scheme can enhance transmission reliability by more than 44.6% and prolong the lifetime by as much as 33.2%. PMID:28422062

Central Plant Optimization for Waste Energy Reduction (CPOWER)

DTIC Science & Technology

2016-12-01

data such as windspeed and solar radiation is recorded in CPOWER. For these periods, the following data fields from the CPOWER database and the weather...The solar radiation data did not appear reliable in the weather dataset for the location, and hence we did not use this. The energy consumption...that several factors affect the total energy consumption of the chiller plant and additional data and additional factors (e.g., solar insolation) may be

Optimized efficiency of all-electric ships by dc hybrid power systems

NASA Astrophysics Data System (ADS)

Zahedi, Bijan; Norum, Lars E.; Ludvigsen, Kristine B.

2014-06-01

Hybrid power systems with dc distribution are being considered for commercial marine vessels to comply with new stringent environmental regulations, and to achieve higher fuel economy. In this paper, detailed efficiency analysis of a shipboard dc hybrid power system is carried out. An optimization algorithm is proposed to minimize fuel consumption under various loading conditions. The studied system includes diesel engines, synchronous generator-rectifier units, a full-bridge bidirectional converter, and a Li-Ion battery bank as energy storage. In order to evaluate potential fuel saving provided by such a system, an online optimization strategy for fuel consumption is implemented. An Offshore Support Vessel (OSV) is simulated over different operating modes using the online control strategy. The resulted consumed fuel in the simulation is compared to that of a conventional ac power system, and also a dc power system without energy storage. The results show that while the dc system without energy storage provides noticeable fuel saving compared to the conventional ac system, optimal utilization of the energy storage in the dc system results in twice as much fuel saving.

Consumption Behavior Analytics-Aided Energy Forecasting and Dispatch

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhang, Yingchen; Yang, Rui; Jiang, Huaiguang

For decades, electricity customers have been treated as mere recipients of electricity in vertically integrated power systems. However, as customers have widely adopted distributed energy resources and other forms of customer participation in active dispatch (such as demand response) have taken shape, the value of mining knowledge from customer behavior patterns and using it for power system operation is increasing. Further, the variability of renewable energy resources has been considered a liability to the grid. However, electricity consumption has shown the same level of variability and uncertainty, and this is sometimes overlooked. This article investigates data analytics and forecasting methodsmore » to identify correlations between electricity consumption behavior and distributed photovoltaic (PV) output. The forecasting results feed into a predictive energy management system that optimizes energy consumption in the near future to balance customer demand and power system needs.« less

Agreement Technologies for Energy Optimization at Home

PubMed Central

2018-01-01

Nowadays, it is becoming increasingly common to deploy sensors in public buildings or homes with the aim of obtaining data from the environment and taking decisions that help to save energy. Many of the current state-of-the-art systems make decisions considering solely the environmental factors that cause the consumption of energy. These systems are successful at optimizing energy consumption; however, they do not adapt to the preferences of users and their comfort. Any system that is to be used by end-users should consider factors that affect their wellbeing. Thus, this article proposes an energy-saving system, which apart from considering the environmental conditions also adapts to the preferences of inhabitants. The architecture is based on a Multi-Agent System (MAS), its agents use Agreement Technologies (AT) to perform a negotiation process between the comfort preferences of the users and the degree of optimization that the system can achieve according to these preferences. A case study was conducted in an office building, showing that the proposed system achieved average energy savings of 17.15%. PMID:29783768

Analysis on energy consumption index system of thermal power plant

NASA Astrophysics Data System (ADS)

Qian, J. B.; Zhang, N.; Li, H. F.

2017-05-01

Currently, the increasingly tense situation in the context of resources, energy conservation is a realistic choice to ease the energy constraint contradictions, reduce energy consumption thermal power plants has become an inevitable development direction. And combined with computer network technology to build thermal power “small index” to monitor and optimize the management system, the power plant is the application of information technology and to meet the power requirements of the product market competition. This paper, first described the research status of thermal power saving theory, then attempted to establish the small index system and build “small index” monitoring and optimization management system in thermal power plant. Finally elaborated key issues in the field of small thermal power plant technical and economic indicators to be further studied and resolved.

An Effective Hybrid Routing Algorithm in WSN: Ant Colony Optimization in combination with Hop Count Minimization.

PubMed

Jiang, Ailian; Zheng, Lihong

2018-03-29

Low cost, high reliability and easy maintenance are key criteria in the design of routing protocols for wireless sensor networks (WSNs). This paper investigates the existing ant colony optimization (ACO)-based WSN routing algorithms and the minimum hop count WSN routing algorithms by reviewing their strengths and weaknesses. We also consider the critical factors of WSNs, such as energy constraint of sensor nodes, network load balancing and dynamic network topology. Then we propose a hybrid routing algorithm that integrates ACO and a minimum hop count scheme. The proposed algorithm is able to find the optimal routing path with minimal total energy consumption and balanced energy consumption on each node. The algorithm has unique superiority in terms of searching for the optimal path, balancing the network load and the network topology maintenance. The WSN model and the proposed algorithm have been implemented using C++. Extensive simulation experimental results have shown that our algorithm outperforms several other WSN routing algorithms on such aspects that include the rate of convergence, the success rate in searching for global optimal solution, and the network lifetime.

An Effective Hybrid Routing Algorithm in WSN: Ant Colony Optimization in combination with Hop Count Minimization

PubMed Central

2018-01-01

Low cost, high reliability and easy maintenance are key criteria in the design of routing protocols for wireless sensor networks (WSNs). This paper investigates the existing ant colony optimization (ACO)-based WSN routing algorithms and the minimum hop count WSN routing algorithms by reviewing their strengths and weaknesses. We also consider the critical factors of WSNs, such as energy constraint of sensor nodes, network load balancing and dynamic network topology. Then we propose a hybrid routing algorithm that integrates ACO and a minimum hop count scheme. The proposed algorithm is able to find the optimal routing path with minimal total energy consumption and balanced energy consumption on each node. The algorithm has unique superiority in terms of searching for the optimal path, balancing the network load and the network topology maintenance. The WSN model and the proposed algorithm have been implemented using C++. Extensive simulation experimental results have shown that our algorithm outperforms several other WSN routing algorithms on such aspects that include the rate of convergence, the success rate in searching for global optimal solution, and the network lifetime. PMID:29596336

Multi-Objective Approach for Energy-Aware Workflow Scheduling in Cloud Computing Environments

PubMed Central

Kadima, Hubert; Granado, Bertrand

2013-01-01

We address the problem of scheduling workflow applications on heterogeneous computing systems like cloud computing infrastructures. In general, the cloud workflow scheduling is a complex optimization problem which requires considering different criteria so as to meet a large number of QoS (Quality of Service) requirements. Traditional research in workflow scheduling mainly focuses on the optimization constrained by time or cost without paying attention to energy consumption. The main contribution of this study is to propose a new approach for multi-objective workflow scheduling in clouds, and present the hybrid PSO algorithm to optimize the scheduling performance. Our method is based on the Dynamic Voltage and Frequency Scaling (DVFS) technique to minimize energy consumption. This technique allows processors to operate in different voltage supply levels by sacrificing clock frequencies. This multiple voltage involves a compromise between the quality of schedules and energy. Simulation results on synthetic and real-world scientific applications highlight the robust performance of the proposed approach. PMID:24319361

Multi-objective approach for energy-aware workflow scheduling in cloud computing environments.

PubMed

Yassa, Sonia; Chelouah, Rachid; Kadima, Hubert; Granado, Bertrand

2013-01-01

We address the problem of scheduling workflow applications on heterogeneous computing systems like cloud computing infrastructures. In general, the cloud workflow scheduling is a complex optimization problem which requires considering different criteria so as to meet a large number of QoS (Quality of Service) requirements. Traditional research in workflow scheduling mainly focuses on the optimization constrained by time or cost without paying attention to energy consumption. The main contribution of this study is to propose a new approach for multi-objective workflow scheduling in clouds, and present the hybrid PSO algorithm to optimize the scheduling performance. Our method is based on the Dynamic Voltage and Frequency Scaling (DVFS) technique to minimize energy consumption. This technique allows processors to operate in different voltage supply levels by sacrificing clock frequencies. This multiple voltage involves a compromise between the quality of schedules and energy. Simulation results on synthetic and real-world scientific applications highlight the robust performance of the proposed approach.

Reduced energy consumption by massive thermoelectric waste heat recovery in light duty trucks

NASA Astrophysics Data System (ADS)

Magnetto, D.; Vidiella, G.

2012-06-01

The main objective of the EC funded HEATRECAR project is to reduce the energy consumption and curb CO2 emissions of vehicles by massively harvesting electrical energy from the exhaust system and re-use this energy to supply electrical components within the vehicle or to feed the power train of hybrid electrical vehicles. HEATRECAR is targeting light duty trucks and focuses on the development and the optimization of a Thermo Electric Generator (TEG) including heat exchanger, thermoelectric modules and DC/DC converter. The main objective of the project is to design, optimize and produce a prototype system to be tested on a 2.3l diesel truck. The base case is a Thermo Electric Generator (TEG) producing 1 KWel at 130 km/h. We present the system design and estimated output power from benchmark Bi2Te3 modules. We discuss key drivers for the optimization of the thermal-to-electric efficiency, such as materials, thermo-mechanical aspects and integration.

Analysis and optimization of indicators of energy and resource consumption of gas turbine and electric drives for transportation of hydrocarbons

NASA Astrophysics Data System (ADS)

Golik, V. V.; Zemenkova, M. Yu; Seroshtanov, I. V.; Begalko, Z. V.

2018-05-01

The paper presents the results of the analysis of statistical indicators of energy and resource consumption in oil and gas transportation by the example of one of the regions of Russia. The article analyzes engineering characteristics of compressor station drives. Official statistical bulletins on the fuel and energy resources of the region in the pipeline oil and gas transportation system were used as the initial data.

Discrete Particle Swarm Optimization Routing Protocol for Wireless Sensor Networks with Multiple Mobile Sinks.

PubMed

Yang, Jin; Liu, Fagui; Cao, Jianneng; Wang, Liangming

2016-07-14

Mobile sinks can achieve load-balancing and energy-consumption balancing across the wireless sensor networks (WSNs). However, the frequent change of the paths between source nodes and the sinks caused by sink mobility introduces significant overhead in terms of energy and packet delays. To enhance network performance of WSNs with mobile sinks (MWSNs), we present an efficient routing strategy, which is formulated as an optimization problem and employs the particle swarm optimization algorithm (PSO) to build the optimal routing paths. However, the conventional PSO is insufficient to solve discrete routing optimization problems. Therefore, a novel greedy discrete particle swarm optimization with memory (GMDPSO) is put forward to address this problem. In the GMDPSO, particle's position and velocity of traditional PSO are redefined under discrete MWSNs scenario. Particle updating rule is also reconsidered based on the subnetwork topology of MWSNs. Besides, by improving the greedy forwarding routing, a greedy search strategy is designed to drive particles to find a better position quickly. Furthermore, searching history is memorized to accelerate convergence. Simulation results demonstrate that our new protocol significantly improves the robustness and adapts to rapid topological changes with multiple mobile sinks, while efficiently reducing the communication overhead and the energy consumption.

A Review of Industrial Heat Exchange Optimization

NASA Astrophysics Data System (ADS)

Yao, Junjie

2018-01-01

Heat exchanger is an energy exchange equipment, it transfers the heat from a working medium to another working medium, which has been wildly used in petrochemical industry, HVAC refrigeration, aerospace and so many other fields. The optimal design and efficient operation of the heat exchanger and heat transfer network are of great significance to the process industry to realize energy conservation, production cost reduction and energy consumption reduction. In this paper, the optimization of heat exchanger, optimal algorithm and heat exchanger optimization with different objective functions are discussed. Then, optimization of the heat exchanger and the heat exchanger network considering different conditions are compared and analysed. Finally, all the problems discussed are summarized and foresights are proposed.

Energy consumption optimization of the total-FETI solver by changing the CPU frequency

NASA Astrophysics Data System (ADS)

Horak, David; Riha, Lubomir; Sojka, Radim; Kruzik, Jakub; Beseda, Martin; Cermak, Martin; Schuchart, Joseph

2017-07-01

The energy consumption of supercomputers is one of the critical problems for the upcoming Exascale supercomputing era. The awareness of power and energy consumption is required on both software and hardware side. This paper deals with the energy consumption evaluation of the Finite Element Tearing and Interconnect (FETI) based solvers of linear systems, which is an established method for solving real-world engineering problems. We have evaluated the effect of the CPU frequency on the energy consumption of the FETI solver using a linear elasticity 3D cube synthetic benchmark. In this problem, we have evaluated the effect of frequency tuning on the energy consumption of the essential processing kernels of the FETI method. The paper provides results for two types of frequency tuning: (1) static tuning and (2) dynamic tuning. For static tuning experiments, the frequency is set before execution and kept constant during the runtime. For dynamic tuning, the frequency is changed during the program execution to adapt the system to the actual needs of the application. The paper shows that static tuning brings up 12% energy savings when compared to default CPU settings (the highest clock rate). The dynamic tuning improves this further by up to 3%.

Essays on environmental regulations in electricity markets

NASA Astrophysics Data System (ADS)

Sun, Yanming

Reducing the Greenhouse Gas pollution and promoting energy efficiency among consumers' energy use have been major public policy issues recently. Currently, both the United States and the European Union have set up explicit percentage requirements that require energy generators or consumers to undertake a certain percentage of their energy production or consumption from renewable sources. To achieve their renewable targets, the Tradable Green Certificates (TGC) system has been introduced in their electricity markets. Moreover, in order to promote energy conservation and achieve energy efficiency targets, price policies and price changes derived from environmental regulations have played a more important role in reducing electricity consumption. My research studies problems associated with these policy implementations. In Chapter 1, I analyze a competitive electricity market with two countries operated under a common TGC system. By using geometric illustrations, I compare the two countries' welfare when the renewable quota is chosen optimally under the common certificate market with three different situations. The policy recommendation is that when the value of damage parameter is sufficiently small, full integration with a TGC market is welfare superior to full integration of an all fossil-fuel based market with an optimal emissions standard. In Chapter 2, by analyzing a stylized theoretical model and numerical examples, I investigate the performance of the optimal renewables policy under full separation and full integration scenarios for two countries' electricity markets operated under TGC systems. In my third chapter, I look at residential electricity consumption responsiveness to increases of electricity price in the U.S. and the different effect of a price increase on electricity use for states of different income levels. My analysis reveals that raising the energy price in the short run will not give consumers much incentive to adjust their appliances and make energy conservation investments to reduce electricity use, while in the long run, consumers are more likely to lower their electricity consumption, facing the higher electricity price induced from regulation policies. In addition, for states of higher per capita GDP, raising the electricity price may be more effective to ensure a cut in electricity consumption.

Optimal pitching axis location of flapping wings for efficient hovering flight.

PubMed

Wang, Q; Goosen, J F L; van Keulen, F

2017-09-01

Flapping wings can pitch passively about their pitching axes due to their flexibility, inertia, and aerodynamic loads. A shift in the pitching axis location can dynamically alter the aerodynamic loads, which in turn changes the passive pitching motion and the flight efficiency. Therefore, it is of great interest to investigate the optimal pitching axis for flapping wings to maximize the power efficiency during hovering flight. In this study, flapping wings are modeled as rigid plates with non-uniform mass distribution. The wing flexibility is represented by a linearly torsional spring at the wing root. A predictive quasi-steady aerodynamic model is used to evaluate the lift generated by such wings. Two extreme power consumption scenarios are modeled for hovering flight, i.e. the power consumed by a drive system with and without the capacity of kinetic energy recovery. For wings with different shapes, the optimal pitching axis location is found such that the cycle-averaged power consumption during hovering flight is minimized. Optimization results show that the optimal pitching axis is located between the leading edge and the mid-chord line, which shows close resemblance to insect wings. An optimal pitching axis can save up to 33% of power during hovering flight when compared to traditional wings used by most of flapping wing micro air vehicles (FWMAVs). Traditional wings typically use the straight leading edge as the pitching axis. With the optimized pitching axis, flapping wings show higher pitching amplitudes and start the pitching reversals in advance of the sweeping reversals. These phenomena lead to higher lift-to-drag ratios and, thus, explain the lower power consumption. In addition, the optimized pitching axis provides the drive system higher potential to recycle energy during the deceleration phases as compared to their counterparts. This observation underlines the particular importance of the wing pitching axis location for energy-efficient FWMAVs when using kinetic energy recovery drive systems.

Evaluation of energy consumption during aerobic sewage sludge treatment in dairy wastewater treatment plant.

PubMed

Dąbrowski, Wojciech; Żyłka, Radosław; Malinowski, Paweł

2017-02-01

The subject of the research conducted in an operating dairy wastewater treatment plant (WWTP) was to examine electric energy consumption during sewage sludge treatment. The excess sewage sludge was aerobically stabilized and dewatered with a screw press. Organic matter varied from 48% to 56% in sludge after stabilization and dewatering. It proves that sludge was properly stabilized and it was possible to apply it as a fertilizer. Measurement factors for electric energy consumption for mechanically dewatered sewage sludge were determined, which ranged between 0.94 and 1.5 kWhm -3 with the average value at 1.17 kWhm -3 . The shares of devices used for sludge dewatering and aerobic stabilization in the total energy consumption of the plant were also established, which were 3% and 25% respectively. A model of energy consumption during sewage sludge treatment was estimated according to experimental data. Two models were applied: linear regression for dewatering process and segmented linear regression for aerobic stabilization. The segmented linear regression model was also applied to total energy consumption during sewage sludge treatment in the examined dairy WWTP. The research constitutes an introduction for further studies on defining a mathematical model used to optimize electric energy consumption by dairy WWTPs. Copyright © 2016 Elsevier Inc. All rights reserved.

Study on the optimization allocation of wind-solar in power system based on multi-region production simulation

NASA Astrophysics Data System (ADS)

Xu, Zhicheng; Yuan, Bo; Zhang, Fuqiang

2018-06-01

In this paper, a power supply optimization model is proposed. The model takes the minimum fossil energy consumption as the target, considering the output characteristics of the conventional power supply and the renewable power supply. The optimal capacity ratio of wind-solar in the power supply under various constraints is calculated, and the interrelation between conventional power supply and renewable energy is analyzed in the system of high proportion renewable energy integration. Using the model, we can provide scientific guidance for the coordinated and orderly development of renewable energy and conventional power sources.

Energy: It is life

DOE Office of Scientific and Technical Information (OSTI.GOV)

Arques, P.

1998-07-01

The relationships that seem to exist between energy and man are presented in this paper. Habitually, social coefficients are connected to the gross domestic product; some parameters with correlations are: birth rate, infant mortality rate, death rate, literacy, etc. Along with energy these define the optimal energy consumption per capita; the author presents the correlation between these parameters and energy consumed per capita. There exists a high correlation between energy consumption per capita and gross domestic product per capita. The set of parameters considered are correlated with similar values relative to these two parameters. Using data collected on a groupmore » of the different countries of the world, a table of 165 countries and 22 variables has been drawn up. From the [Country x variable] matrix, a correlation table is calculated and a factorial analysis is applied to this matrix. The first factorial plan comprises 57% of the information contained in this table. Results from this first factorial plan are presented. These parameters are analyzed: influence of a country's latitude on its inhabitants' consumption; relationship between consumed energy and gross domestic product; women's fertility rate; birth rate per 1000 population; sex ratio; life expectancy at birth; rate of literacy; death rate; population growth rate. Finally, it is difficult to define precise criteria for: an optimal distribution of population according to age, but with a power consumed of above 300 W per capita, the population becomes younger; the birth rate per 1000 population; the total fertility rate per woman; the population growth rate. The authors determine that optimal energy is approximately between 200 W and 677 W inclusive.« less

Action Potential Energy Efficiency Varies Among Neuron Types in Vertebrates and Invertebrates

PubMed Central

Sengupta, Biswa; Stemmler, Martin; Laughlin, Simon B.; Niven, Jeremy E.

2010-01-01

The initiation and propagation of action potentials (APs) places high demands on the energetic resources of neural tissue. Each AP forces ATP-driven ion pumps to work harder to restore the ionic concentration gradients, thus consuming more energy. Here, we ask whether the ionic currents underlying the AP can be predicted theoretically from the principle of minimum energy consumption. A long-held supposition that APs are energetically wasteful, based on theoretical analysis of the squid giant axon AP, has recently been overturned by studies that measured the currents contributing to the AP in several mammalian neurons. In the single compartment models studied here, AP energy consumption varies greatly among vertebrate and invertebrate neurons, with several mammalian neuron models using close to the capacitive minimum of energy needed. Strikingly, energy consumption can increase by more than ten-fold simply by changing the overlap of the Na+ and K+ currents during the AP without changing the APs shape. As a consequence, the height and width of the AP are poor predictors of energy consumption. In the Hodgkin–Huxley model of the squid axon, optimizing the kinetics or number of Na+ and K+ channels can whittle down the number of ATP molecules needed for each AP by a factor of four. In contrast to the squid AP, the temporal profile of the currents underlying APs of some mammalian neurons are nearly perfectly matched to the optimized properties of ionic conductances so as to minimize the ATP cost. PMID:20617202

Action potential energy efficiency varies among neuron types in vertebrates and invertebrates.

PubMed

Sengupta, Biswa; Stemmler, Martin; Laughlin, Simon B; Niven, Jeremy E

2010-07-01

The initiation and propagation of action potentials (APs) places high demands on the energetic resources of neural tissue. Each AP forces ATP-driven ion pumps to work harder to restore the ionic concentration gradients, thus consuming more energy. Here, we ask whether the ionic currents underlying the AP can be predicted theoretically from the principle of minimum energy consumption. A long-held supposition that APs are energetically wasteful, based on theoretical analysis of the squid giant axon AP, has recently been overturned by studies that measured the currents contributing to the AP in several mammalian neurons. In the single compartment models studied here, AP energy consumption varies greatly among vertebrate and invertebrate neurons, with several mammalian neuron models using close to the capacitive minimum of energy needed. Strikingly, energy consumption can increase by more than ten-fold simply by changing the overlap of the Na(+) and K(+) currents during the AP without changing the APs shape. As a consequence, the height and width of the AP are poor predictors of energy consumption. In the Hodgkin-Huxley model of the squid axon, optimizing the kinetics or number of Na(+) and K(+) channels can whittle down the number of ATP molecules needed for each AP by a factor of four. In contrast to the squid AP, the temporal profile of the currents underlying APs of some mammalian neurons are nearly perfectly matched to the optimized properties of ionic conductances so as to minimize the ATP cost.

A Novel Energy Saving Algorithm with Frame Response Delay Constraint in IEEE 802.16e

NASA Astrophysics Data System (ADS)

Nga, Dinh Thi Thuy; Kim, Mingon; Kang, Minho

Sleep-mode operation of a Mobile Subscriber Station (MSS) in IEEE 802.16e effectively saves energy consumption; however, it induces frame response delay. In this letter, we propose an algorithm to quickly find the optimal value of the final sleep interval in sleep-mode in order to minimize energy consumption with respect to a given frame response delay constraint. The validations of our proposed algorithm through analytical results and simulation results suggest that our algorithm provide a potential guidance to energy saving.

Wireless network interface energy consumption implications of popular streaming formats

NASA Astrophysics Data System (ADS)

Chandra, Surendar

2001-12-01

With the proliferation of mobile streaming multimedia, available battery capacity constrains the end-user experience. Since streaming applications tend to be long running, wireless network interface card's (WNIC) energy consumption is particularly an acute problem. In this work, we explore the WNIC energy consumption implications of popular multimedia streaming formats from Microsoft (Windows media), Real (Real media) and Apple (Quick Time). We investigate the energy consumption under varying stream bandwidth and network loss rates. We also explore history-based client-side strategies to reduce the energy consumed by transitioning the WNICs to a lower power consuming sleep state. We show that Microsoft media tends to transmit packets at regular intervals; streams optimized for 28.8 Kbps can save over 80% in energy consumption with 2% data loss. A high bandwidth stream (768 Kbps) can still save 57% in energy consumption with less than 0.3% data loss. For high bandwidth streams, Microsoft media exploits network-level packet fragmentation, which can lead to excessive packet loss (and wasted energy) in a lossy network. Real stream packets tend to be sent closer to each other, especially at higher bandwidths. Quicktime packets sometimes arrive in quick succession; most likely an application level fragmentation mechanism. Such packets are harder to predict at the network level without understanding the packet semantics.

Opportunities for Improving the Energy Efficiency of Multi-Modal Intra-City Freight Movement

DOE Office of Scientific and Technical Information (OSTI.GOV)

Walkowicz, Kevin; Duran, Adam

This poster focuses on the National Renewable Energy Laboratory's analysis of opportunities for freight movement energy savings via optimization and integration of existing/emerging intra-city goods delivery modes as well as an assessment of the efficacy and energy consumption impact of new technologies.

Deployment-based lifetime optimization model for homogeneous Wireless Sensor Network under retransmission.

PubMed

Li, Ruiying; Liu, Xiaoxi; Xie, Wei; Huang, Ning

2014-12-10

Sensor-deployment-based lifetime optimization is one of the most effective methods used to prolong the lifetime of Wireless Sensor Network (WSN) by reducing the distance-sensitive energy consumption. In this paper, data retransmission, a major consumption factor that is usually neglected in the previous work, is considered. For a homogeneous WSN, monitoring a circular target area with a centered base station, a sensor deployment model based on regular hexagonal grids is analyzed. To maximize the WSN lifetime, optimization models for both uniform and non-uniform deployment schemes are proposed by constraining on coverage, connectivity and success transmission rate. Based on the data transmission analysis in a data gathering cycle, the WSN lifetime in the model can be obtained through quantifying the energy consumption at each sensor location. The results of case studies show that it is meaningful to consider data retransmission in the lifetime optimization. In particular, our investigations indicate that, with the same lifetime requirement, the number of sensors needed in a non-uniform topology is much less than that in a uniform one. Finally, compared with a random scheme, simulation results further verify the advantage of our deployment model.

Methane mitigation timelines to inform energy technology evaluation

NASA Astrophysics Data System (ADS)

Roy, Mandira; Edwards, Morgan R.; Trancik, Jessika E.

2015-11-01

Energy technologies emitting differing proportions of methane (CH4) and carbon dioxide (CO2) vary significantly in their relative climate impacts over time, due to the distinct atmospheric lifetimes and radiative efficiencies of the two gases. Standard technology comparisons using the global warming potential (GWP) with a fixed time horizon do not account for the timing of emissions in relation to climate policy goals. Here we develop a portfolio optimization model that incorporates changes in technology impacts based on the temporal proximity of emissions to a radiative forcing (RF) stabilization target. An optimal portfolio, maximizing allowed energy consumption while meeting the RF target, is obtained by year-wise minimization of the marginal RF impact in an intended stabilization year. The optimal portfolio calls for using certain higher-CH4-emitting technologies prior to an optimal switching year, followed by CH4-light technologies as the stabilization year approaches. We apply the model to evaluate transportation technology pairs and find that accounting for dynamic emissions impacts, in place of using the static GWP, can result in CH4 mitigation timelines and technology transitions that allow for significantly greater energy consumption while meeting a climate policy target. The results can inform the forward-looking evaluation of energy technologies by engineers, private investors, and policy makers.

The pressure cold wind system on the impact of industrial boiler economy and security

NASA Astrophysics Data System (ADS)

Li, Henan; Qian, Hongli; Jiang, Lei; Yu, Dekai; Li, Guannan; Yuan, Hong

2017-05-01

Industrial boiler is one of the most energy-consuming equipment in china, the annual consumption of energy accounts for about one-third of the national energy consumption.Industrial boiler in service at present have several severe problems such as small capacity, low efficiency, high energy consumption and causing severe pollution on environment, the average industrial boiler operation efficiency is only 65%. If the efficiency increased by 15% ∼ 20%, which meet the international advanced level, each year 70 million tons of coal saving and reduce environmental pollution[1]. As energy conservation and emissions reduction becomes the basic national policy of our country, improving the efficiency of industrial boiler energy is facing opportunities and challenges, optimizing the operation mode of the existing units, it is necessary to increase the flexibility of the boiler control.

Ascent trajectory optimization for stratospheric airship with thermal effects

NASA Astrophysics Data System (ADS)

Guo, Xiao; Zhu, Ming

2013-09-01

Ascent trajectory optimization with thermal effects is addressed for a stratospheric airship. Basic thermal characteristics of the stratospheric airship are introduced. Besides, the airship’s equations of motion are constructed by including the factors about aerodynamic force, added mass and wind profiles which are developed based on horizontal-wind model. For both minimum-time and minimum-energy flights during ascent, the trajectory optimization problem is described with the path and terminal constraints in different scenarios and then, is converted into a parameter optimization problem by a direct collocation method. Sparse Nonlinear OPTimizer(SNOPT) is employed as a nonlinear programming solver and two scenarios are adopted. The solutions obtained illustrate that the trajectories are greatly affected by the thermal behaviors which prolong the daytime minimum-time flights of about 20.8% compared with that of nighttime in scenario 1 and of about 10.5% in scenario 2. And there is the same trend for minimum-energy flights. For the energy consumption of minimum-time flights, 6% decrease is abstained in scenario 1 and 5% decrease in scenario 2. However, a few energy consumption reduction is achieved for minimum-energy flights. Solar radiation is the principal component and the natural wind also affects the thermal behaviors of stratospheric airship during ascent. The relationship between take-off time and performance of airship during ascent is discussed. it is found that the take-off time at dusk is best choice for stratospheric airship. And in addition, for saving energy, airship prefers to fly downwind.

Energy efficiency analysis and optimization for mobile platforms

NASA Astrophysics Data System (ADS)

Metri, Grace Camille

The introduction of mobile devices changed the landscape of computing. Gradually, these devices are replacing traditional personal computer (PCs) to become the devices of choice for entertainment, connectivity, and productivity. There are currently at least 45.5 million people in the United States who own a mobile device, and that number is expected to increase to 1.5 billion by 2015. Users of mobile devices expect and mandate that their mobile devices have maximized performance while consuming minimal possible power. However, due to the battery size constraints, the amount of energy stored in these devices is limited and is only growing by 5% annually. As a result, we focused in this dissertation on energy efficiency analysis and optimization for mobile platforms. We specifically developed SoftPowerMon, a tool that can power profile Android platforms in order to expose the power consumption behavior of the CPU. We also performed an extensive set of case studies in order to determine energy inefficiencies of mobile applications. Through our case studies, we were able to propose optimization techniques in order to increase the energy efficiency of mobile devices and proposed guidelines for energy-efficient application development. In addition, we developed BatteryExtender, an adaptive user-guided tool for power management of mobile devices. The tool enables users to extend battery life on demand for a specific duration until a particular task is completed. Moreover, we examined the power consumption of System-on-Chips (SoCs) and observed the impact on the energy efficiency in the event of offloading tasks from the CPU to the specialized custom engines. Based on our case studies, we were able to demonstrate that current software-based power profiling techniques for SoCs can have an error rate close to 12%, which needs to be addressed in order to be able to optimize the energy consumption of the SoC. Finally, we summarize our contributions and outline possible direction for future research in this field.

Assessment of the Charging Policy in Energy Efficiency of the Enterprise

NASA Astrophysics Data System (ADS)

Shutov, E. A.; E Turukina, T.; Anisimov, T. S.

2017-04-01

The forecasting problem for energy facilities with a power exceeding 670 kW is currently one of the main. In connection with rules of the retail electricity market such customers also pay for actual energy consumption deviations from plan value. In compliance with the hierarchical stages of the electricity market a guaranteeing supplier is to respect the interests of distribution and generation companies that require load leveling. The answer to this question for industrial enterprise is possible only within technological process through implementation of energy-efficient processing chains with the adaptive function and forecasting tool. In such a circumstance the primary objective of a forecasting is reduce the energy consumption costs by taking account of the energy cost correlation for 24 hours for forming of pumping unit work schedule. The pumping unit virtual model with the variable frequency drive is considered. The forecasting tool and the optimizer are integrated into typical control circuit. Economic assessment of the optimization method was estimated.

Energy consumption estimation of an OMAP-based Android operating system

NASA Astrophysics Data System (ADS)

González, Gabriel; Juárez, Eduardo; Castro, Juan José; Sanz, César

2011-05-01

System-level energy optimization of battery-powered multimedia embedded systems has recently become a design goal. The poor operational time of multimedia terminals makes computationally demanding applications impractical in real scenarios. For instance, the so-called smart-phones are currently unable to remain in operation longer than several hours. The OMAP3530 processor basically consists of two processing cores, a General Purpose Processor (GPP) and a Digital Signal Processor (DSP). The former, an ARM Cortex-A8 processor, is aimed to run a generic Operating System (OS) while the latter, a DSP core based on the C64x+, has architecture optimized for video processing. The BeagleBoard, a commercial prototyping board based on the OMAP processor, has been used to test the Android Operating System and measure its performance. The board has 128 MB of SDRAM external memory, 256 MB of Flash external memory and several interfaces. Note that the clock frequency of the ARM and DSP OMAP cores is 600 MHz and 430 MHz, respectively. This paper describes the energy consumption estimation of the processes and multimedia applications of an Android v1.6 (Donut) OS on the OMAP3530-Based BeagleBoard. In addition, tools to communicate the two processing cores have been employed. A test-bench to profile the OS resource usage has been developed. As far as the energy estimates concern, the OMAP processor energy consumption model provided by the manufacturer has been used. The model is basically divided in two energy components. The former, the baseline core energy, describes the energy consumption that is independent of any chip activity. The latter, the module active energy, describes the energy consumed by the active modules depending on resource usage.

Advances in Energy Conservation of China Steel Industry

PubMed Central

Sun, Wenqiang; Cai, Jiuju; Ye, Zhu

2013-01-01

The course, technical progresses, and achievements of energy conservation of China steel industry (CSI) during 1980–2010 were summarized. Then, the paper adopted e-p method to analyze the variation law and influencing factors of energy consumptions of large- and medium-scale steel plants within different stages. It is pointed out that energy consumption per ton of crude steel has been almost one half lower in these thirty years, with 60% as direct energy conservation owing to the change of process energy consumption and 40% as indirect energy conservation attributed to the adjustment of production structure. Next, the latest research progress of some key common technologies in CSI was introduced. Also, the downtrend of energy consumption per ton of crude steel and the potential energy conservation for CSI during 2011–2025 were forecasted. Finally, it is indicated that the key topic of the next 15 years' research on the energy conservation of CSI is the synergistic operation of material flow and energy flow. It could be achieved by the comprehensive study on energy flow network optimization, such as production, allocation, utilization, recovery, reuse, and resource, according to the energy quantity, quality, and user demand following the first and second laws of thermodynamics. PMID:23533344

Advances in energy conservation of China steel industry.

PubMed

Sun, Wenqiang; Cai, Jiuju; Ye, Zhu

2013-01-01

The course, technical progresses, and achievements of energy conservation of China steel industry (CSI) during 1980-2010 were summarized. Then, the paper adopted e-p method to analyze the variation law and influencing factors of energy consumptions of large- and medium-scale steel plants within different stages. It is pointed out that energy consumption per ton of crude steel has been almost one half lower in these thirty years, with 60% as direct energy conservation owing to the change of process energy consumption and 40% as indirect energy conservation attributed to the adjustment of production structure. Next, the latest research progress of some key common technologies in CSI was introduced. Also, the downtrend of energy consumption per ton of crude steel and the potential energy conservation for CSI during 2011-2025 were forecasted. Finally, it is indicated that the key topic of the next 15 years' research on the energy conservation of CSI is the synergistic operation of material flow and energy flow. It could be achieved by the comprehensive study on energy flow network optimization, such as production, allocation, utilization, recovery, reuse, and resource, according to the energy quantity, quality, and user demand following the first and second laws of thermodynamics.

REopt: A Platform for Energy System Integration and Optimization: Preprint

DOE Office of Scientific and Technical Information (OSTI.GOV)

Simpkins, T.; Cutler, D.; Anderson, K.

2014-08-01

REopt is NREL's energy planning platform offering concurrent, multi-technology integration and optimization capabilities to help clients meet their cost savings and energy performance goals. The REopt platform provides techno-economic decision-support analysis throughout the energy planning process, from agency-level screening and macro planning to project development to energy asset operation. REopt employs an integrated approach to optimizing a site?s energy costs by considering electricity and thermal consumption, resource availability, complex tariff structures including time-of-use, demand and sell-back rates, incentives, net-metering, and interconnection limits. Formulated as a mixed integer linear program, REopt recommends an optimally-sized mix of conventional and renewable energy, andmore » energy storage technologies; estimates the net present value associated with implementing those technologies; and provides the cost-optimal dispatch strategy for operating them at maximum economic efficiency. The REopt platform can be customized to address a variety of energy optimization scenarios including policy, microgrid, and operational energy applications. This paper presents the REopt techno-economic model along with two examples of recently completed analysis projects.« less

Commande optimale minimisant la consommation d'energie d'un drone utilise comme relai de communication

NASA Astrophysics Data System (ADS)

Mechirgui, Monia

The purpose of this project is to implement an optimal control regulator, particularly the linear quadratic regulator in order to control the position of an unmanned aerial vehicle known as a quadrotor. This type of UAV has a symmetrical and simple structure. Thus, its control is relatively easy compared to conventional helicopters. Optimal control can be proven to be an ideal controller to reconcile between the tracking performance and energy consumption. In practice, the linearity requirements are not met, but some elaborations of the linear quadratic regulator have been used in many nonlinear applications with good results. The linear quadratic controller used in this thesis is presented in two forms: simple and adapted to the state of charge of the battery. Based on the traditional structure of the linear quadratic regulator, we introduced a new criterion which relies on the state of charge of the battery, in order to optimize energy consumption. This command is intended to be used to monitor and maintain the desired trajectory during several maneuvers while minimizing energy consumption. Both simple and adapted, linear quadratic controller are implemented in Simulink in discrete time. The model simulates the dynamics and control of a quadrotor. Performance and stability of the system are analyzed with several tests, from the simply hover to the complex trajectories in closed loop.

Multiple response optimization for high efficiency energy saving treatment of rhodamine B wastewater in a three-dimensional electrochemical reactor.

PubMed

Ji, Jing; Liu, Yang; Yang, Xue-Yuan; Xu, Juan; Li, Xiu-Yan

2018-07-15

The removal of high-concentration rhodamine B (RhB) wastewater was investigated in a three-dimensional electrochemical reactor (3DER) packed with granular activated carbon (GAC) particle electrodes. Response surface methodology (RSM) coupled with grey relational analysis (GRA) was used to evaluate the effects of voltage, initial pH, aeration rate and NaCl dosage on RhB removal and energy consumption of the 3DER. The optimal conditions were determined as voltage 7.25 V, pH 5.99, aeration rate 151.13 mL/min, and NaCl concentration 0.11 mol/L. After 30 min electrolysis, COD removal rate could arrive at 60.13% with an extremely low energy consumption of 6.22 kWh/kg COD. The voltage and NaCl were demonstrated to be the most significant factors affecting the COD removal and energy consumption of 3DER. The intermediates generated during the treatment process were identified and the possible degradation pathway of RhB was proposed. It is worth noting that 3DER also showed an excellent performance in total nitrogen (TN) removal under the optimal condition. The activated chlorine generated from chloride had great contributions to eliminate carbon and nitrogen of RhB wastewater. The treatment effluent had a good biodegradability, which was suitable for subsequent biological treatment. Copyright © 2018 Elsevier Ltd. All rights reserved.

Fragmentation Energy-Saving Theory of Full Face Rock Tunnel Boring Machine Disc Cutters

NASA Astrophysics Data System (ADS)

Zhang, Zhao-Huang; Gong, Guo-Fang; Gao, Qing-Feng; Sun, Fei

2017-07-01

Attempts to minimize energy consumption of a tunnel boring machine disc cutter during the process of fragmentation have largely focused on optimizing disc-cutter spacing, as determined by the minimum specific energy required for fragmentation; however, indentation tests showed that rock deforms plastically beneath the cutters. Equations for thrust were developed for both the traditional, popularly employed disc cutter and anew design based on three-dimensional theory. The respective energy consumption for penetration, rolling, and side-slip fragmentations were obtained. A change in disc-cutter fragmentation angles resulted in a change in the nature of the interaction between the cutter and rock, which lowered the specific energy of fragmentation. During actual field excavations to the same penetration length, the combined energy consumption for fragmentation using the newly designed cutters was 15% lower than that when using the traditional design. This paper presents a theory for energy saving in tunnel boring machines. Investigation results showed that the disc cutters designed using this theory were more durable than traditional designs, and effectively lowered the energy consumption.

Discrete Particle Swarm Optimization Routing Protocol for Wireless Sensor Networks with Multiple Mobile Sinks

PubMed Central

Yang, Jin; Liu, Fagui; Cao, Jianneng; Wang, Liangming

2016-01-01

Mobile sinks can achieve load-balancing and energy-consumption balancing across the wireless sensor networks (WSNs). However, the frequent change of the paths between source nodes and the sinks caused by sink mobility introduces significant overhead in terms of energy and packet delays. To enhance network performance of WSNs with mobile sinks (MWSNs), we present an efficient routing strategy, which is formulated as an optimization problem and employs the particle swarm optimization algorithm (PSO) to build the optimal routing paths. However, the conventional PSO is insufficient to solve discrete routing optimization problems. Therefore, a novel greedy discrete particle swarm optimization with memory (GMDPSO) is put forward to address this problem. In the GMDPSO, particle’s position and velocity of traditional PSO are redefined under discrete MWSNs scenario. Particle updating rule is also reconsidered based on the subnetwork topology of MWSNs. Besides, by improving the greedy forwarding routing, a greedy search strategy is designed to drive particles to find a better position quickly. Furthermore, searching history is memorized to accelerate convergence. Simulation results demonstrate that our new protocol significantly improves the robustness and adapts to rapid topological changes with multiple mobile sinks, while efficiently reducing the communication overhead and the energy consumption. PMID:27428971

Behavior-aware cache hierarchy optimization for low-power multi-core embedded systems

NASA Astrophysics Data System (ADS)

Zhao, Huatao; Luo, Xiao; Zhu, Chen; Watanabe, Takahiro; Zhu, Tianbo

2017-07-01

In modern embedded systems, the increasing number of cores requires efficient cache hierarchies to ensure data throughput, but such cache hierarchies are restricted by their tumid size and interference accesses which leads to both performance degradation and wasted energy. In this paper, we firstly propose a behavior-aware cache hierarchy (BACH) which can optimally allocate the multi-level cache resources to many cores and highly improved the efficiency of cache hierarchy, resulting in low energy consumption. The BACH takes full advantage of the explored application behaviors and runtime cache resource demands as the cache allocation bases, so that we can optimally configure the cache hierarchy to meet the runtime demand. The BACH was implemented on the GEM5 simulator. The experimental results show that energy consumption of a three-level cache hierarchy can be saved from 5.29% up to 27.94% compared with other key approaches while the performance of the multi-core system even has a slight improvement counting in hardware overhead.

Energy optimization for upstream data transfer in 802.15.4 beacon-enabled star formulation

NASA Astrophysics Data System (ADS)

Liu, Hua; Krishnamachari, Bhaskar

2008-08-01

Energy saving is one of the major concerns for low rate personal area networks. This paper models energy consumption for beacon-enabled time-slotted media accessing control cooperated with sleeping scheduling in a star network formulation for IEEE 802.15.4 standard. We investigate two different upstream (data transfer from devices to a network coordinator) strategies: a) tracking strategy: the devices wake up and check status (track the beacon) in each time slot; b) non-tracking strategy: nodes only wake-up upon data arriving and stay awake till data transmitted to the coordinator. We consider the tradeoff between energy cost and average data transmission delay for both strategies. Both scenarios are formulated as optimization problems and the optimal solutions are discussed. Our results show that different data arrival rate and system parameters (such as contention access period interval, upstream speed etc.) result in different strategies in terms of energy optimization with maximum delay constraints. Hence, according to different applications and system settings, different strategies might be chosen by each node to achieve energy optimization for both self-interested view and system view. We give the relation among the tunable parameters by formulas and plots to illustrate which strategy is better under corresponding parameters. There are two main points emphasized in our results with delay constraints: on one hand, when the system setting is fixed by coordinator, nodes in the network can intelligently change their strategies according to corresponding application data arrival rate; on the other hand, when the nodes' applications are known by the coordinator, the coordinator can tune the system parameters to achieve optimal system energy consumption.

Configuration of Wireless Cooperative/Sensor Networks

DTIC Science & Technology

2008-05-25

WSN), the advantages of cooperation can be further exploited by optimally allocating the energy and bandwidth resources among users based on the... consumption and extend system lifetime [Sin98]. The implementation of a minimum energy routing protocol is discussed in [Dos02a, Dos02b]. An online...power consumption in the network given the required SER at the destination. For example, with source power Ps=20dB, the EP algorithm requires one relay

Energy Performance Monitoring and Optimization System for DoD Campuses

DTIC Science & Technology

2014-02-01

estimated that, on average, the EPMO system exceeded the energy consumption reduction target of 20% and improved occupant thermal comfort by reducing the...dynamic models, operational and thermal comfort constraints, and plant efficiency in the same framework (Borrelli and Keviczky, 2008; Borrelli, Pekar...optimization modeling language uses the models described above in conjunction with information such as: thermal comfort constraints, equipment constraints, and

ACMV Energy Analysis for Academic Building: A Case Study

NASA Astrophysics Data System (ADS)

Hywel, R.; Tee, B. T.; Arifin, M. Y.; Tan, C. F.; Gan, C. K.; Chong, CT

2015-09-01

Building energy audit examines the ways actual energy consumption is currently used in the facility, in the case of a completed and occupied building and identifies some alternatives to reduce current energy usage. Implementation of energy audit are practically used to analyze energy consumption pattern, monitoring on how the energy used varies with time in the building, how the system element interrelate, and study the effect of external environment towards building. In this case study, a preliminary energy audit is focusing on Air-Conditioning & Mechanical Ventilation (ACMV) system which reportedly consumed 40% of the total energy consumption in typical building. It is also the main system that provides comfortable and healthy environment for the occupants. The main purpose of this study is to evaluate the current ACMV system performance, energy optimization and identifying the energy waste on UTeM's academic building. To attain this, the preliminary data is collected and then analyzed. Based on the data, economic analysis will be determined before cost-saving methods are being proposed.

Self-balancing dynamic scheduling of electrical energy for energy-intensive enterprises

NASA Astrophysics Data System (ADS)

Gao, Yunlong; Gao, Feng; Zhai, Qiaozhu; Guan, Xiaohong

2013-06-01

Balancing production and consumption with self-generation capacity in energy-intensive enterprises has huge economic and environmental benefits. However, balancing production and consumption with self-generation capacity is a challenging task since the energy production and consumption must be balanced in real time with the criteria specified by power grid. In this article, a mathematical model for minimising the production cost with exactly realisable energy delivery schedule is formulated. And a dynamic programming (DP)-based self-balancing dynamic scheduling algorithm is developed to obtain the complete solution set for such a multiple optimal solutions problem. For each stage, a set of conditions are established to determine whether a feasible control trajectory exists. The state space under these conditions is partitioned into subsets and each subset is viewed as an aggregate state, the cost-to-go function is then expressed as a function of initial and terminal generation levels of each stage and is proved to be a staircase function with finite steps. This avoids the calculation of the cost-to-go of every state to resolve the issue of dimensionality in DP algorithm. In the backward sweep process of the algorithm, an optimal policy is determined to maximise the realisability of energy delivery schedule across the entire time horizon. And then in the forward sweep process, the feasible region of the optimal policy with the initial and terminal state at each stage is identified. Different feasible control trajectories can be identified based on the region; therefore, optimising for the feasible control trajectory is performed based on the region with economic and reliability objectives taken into account.

Multi-Scale Low-Entropy Method for Optimizing the Processing Parameters during Automated Fiber Placement

PubMed Central

Han, Zhenyu; Sun, Shouzheng; Fu, Hongya; Fu, Yunzhong

2017-01-01

Automated fiber placement (AFP) process includes a variety of energy forms and multi-scale effects. This contribution proposes a novel multi-scale low-entropy method aiming at optimizing processing parameters in an AFP process, where multi-scale effect, energy consumption, energy utilization efficiency and mechanical properties of micro-system could be taken into account synthetically. Taking a carbon fiber/epoxy prepreg as an example, mechanical properties of macro–meso–scale are obtained by Finite Element Method (FEM). A multi-scale energy transfer model is then established to input the macroscopic results into the microscopic system as its boundary condition, which can communicate with different scales. Furthermore, microscopic characteristics, mainly micro-scale adsorption energy, diffusion coefficient entropy–enthalpy values, are calculated under different processing parameters based on molecular dynamics method. Low-entropy region is then obtained in terms of the interrelation among entropy–enthalpy values, microscopic mechanical properties (interface adsorbability and matrix fluidity) and processing parameters to guarantee better fluidity, stronger adsorption, lower energy consumption and higher energy quality collaboratively. Finally, nine groups of experiments are carried out to verify the validity of the simulation results. The results show that the low-entropy optimization method can reduce void content effectively, and further improve the mechanical properties of laminates. PMID:28869520

Multi-Scale Low-Entropy Method for Optimizing the Processing Parameters during Automated Fiber Placement.

PubMed

Han, Zhenyu; Sun, Shouzheng; Fu, Hongya; Fu, Yunzhong

2017-09-03

Automated fiber placement (AFP) process includes a variety of energy forms and multi-scale effects. This contribution proposes a novel multi-scale low-entropy method aiming at optimizing processing parameters in an AFP process, where multi-scale effect, energy consumption, energy utilization efficiency and mechanical properties of micro-system could be taken into account synthetically. Taking a carbon fiber/epoxy prepreg as an example, mechanical properties of macro-meso-scale are obtained by Finite Element Method (FEM). A multi-scale energy transfer model is then established to input the macroscopic results into the microscopic system as its boundary condition, which can communicate with different scales. Furthermore, microscopic characteristics, mainly micro-scale adsorption energy, diffusion coefficient entropy-enthalpy values, are calculated under different processing parameters based on molecular dynamics method. Low-entropy region is then obtained in terms of the interrelation among entropy-enthalpy values, microscopic mechanical properties (interface adsorbability and matrix fluidity) and processing parameters to guarantee better fluidity, stronger adsorption, lower energy consumption and higher energy quality collaboratively. Finally, nine groups of experiments are carried out to verify the validity of the simulation results. The results show that the low-entropy optimization method can reduce void content effectively, and further improve the mechanical properties of laminates.

CPAC: Energy-Efficient Data Collection through Adaptive Selection of Compression Algorithms for Sensor Networks

PubMed Central

Lee, HyungJune; Kim, HyunSeok; Chang, Ik Joon

2014-01-01

We propose a technique to optimize the energy efficiency of data collection in sensor networks by exploiting a selective data compression. To achieve such an aim, we need to make optimal decisions regarding two aspects: (1) which sensor nodes should execute compression; and (2) which compression algorithm should be used by the selected sensor nodes. We formulate this problem into binary integer programs, which provide an energy-optimal solution under the given latency constraint. Our simulation results show that the optimization algorithm significantly reduces the overall network-wide energy consumption for data collection. In the environment having a stationary sink from stationary sensor nodes, the optimized data collection shows 47% energy savings compared to the state-of-the-art collection protocol (CTP). More importantly, we demonstrate that our optimized data collection provides the best performance in an intermittent network under high interference. In such networks, we found that the selective compression for frequent packet retransmissions saves up to 55% energy compared to the best known protocol. PMID:24721763

On Reliable and Efficient Data Gathering Based Routing in Underwater Wireless Sensor Networks.

PubMed

Liaqat, Tayyaba; Akbar, Mariam; Javaid, Nadeem; Qasim, Umar; Khan, Zahoor Ali; Javaid, Qaisar; Alghamdi, Turki Ali; Niaz, Iftikhar Azim

2016-08-30

This paper presents cooperative routing scheme to improve data reliability. The proposed protocol achieves its objective, however, at the cost of surplus energy consumption. Thus sink mobility is introduced to minimize the energy consumption cost of nodes as it directly collects data from the network nodes at minimized communication distance. We also present delay and energy optimized versions of our proposed RE-AEDG to further enhance its performance. Simulation results prove the effectiveness of our proposed RE-AEDG in terms of the selected performance matrics.

Feature-based Approach in Product Design with Energy Efficiency Consideration

NASA Astrophysics Data System (ADS)

Li, D. D.; Zhang, Y. J.

2017-10-01

In this paper, a method to measure the energy efficiency and ecological footprint metrics of features is proposed for product design. First the energy consumption models of various manufacturing features, like cutting feature, welding feature, etc. are studied. Then, the total energy consumption of a product is modeled and estimated according to its features. Finally, feature chains that combined by several sequence features based on the producing operation orders are defined and analyzed to calculate global optimal solution. The corresponding assessment model is also proposed to estimate their energy efficiency and ecological footprint. Finally, an example is given to validate the proposed approach in the improvement of sustainability.

Combining gait optimization with passive system to increase the energy efficiency of a humanoid robot walking movement

DOE Office of Scientific and Technical Information (OSTI.GOV)

Pereira, Ana I.; ALGORITMI,University of Minho; Lima, José

There are several approaches to create the Humanoid robot gait planning. This problem presents a large number of unknown parameters that should be found to make the humanoid robot to walk. Optimization in simulation models can be used to find the gait based on several criteria such as energy minimization, acceleration, step length among the others. The energy consumption can also be reduced with elastic elements coupled to each joint. The presented paper addresses an optimization method, the Stretched Simulated Annealing, that runs in an accurate and stable simulation model to find the optimal gait combined with elastic elements. Finalmore » results demonstrate that optimization is a valid gait planning technique.« less

Study on Influencing Factors of Carbon Emissions from Energy Consumption of Shandong Province of China from 1995 to 2012

PubMed Central

Song, Jiekun; Song, Qing; Zhang, Dong; Lu, Youyou; Luan, Long

2014-01-01

Carbon emissions from energy consumption of Shandong province from 1995 to 2012 are calculated. Three zero-residual decomposition models (LMDI, MRCI and Shapley value models) are introduced for decomposing carbon emissions. Based on the results, Kendall coordination coefficient method is employed for testing their compatibility, and an optimal weighted combination decomposition model is constructed for improving the objectivity of decomposition. STIRPAT model is applied to evaluate the impact of each factor on carbon emissions. The results show that, using 1995 as the base year, the cumulative effects of population, per capita GDP, energy consumption intensity, and energy consumption structure of Shandong province in 2012 are positive, while the cumulative effect of industrial structure is negative. Per capita GDP is the largest driver of the increasing carbon emissions and has a great impact on carbon emissions; energy consumption intensity is a weak driver and has certain impact on carbon emissions; population plays a weak driving role, but it has the most significant impact on carbon emissions; energy consumption structure is a weak driver of the increasing carbon emissions and has a weak impact on carbon emissions; industrial structure has played a weak inhibitory role, and its impact on carbon emissions is great. PMID:24977216

Optimization Based Data Mining Approah for Forecasting Real-Time Energy Demand

DOE Office of Scientific and Technical Information (OSTI.GOV)

Omitaomu, Olufemi A; Li, Xueping; Zhou, Shengchao

The worldwide concern over environmental degradation, increasing pressure on electric utility companies to meet peak energy demand, and the requirement to avoid purchasing power from the real-time energy market are motivating the utility companies to explore new approaches for forecasting energy demand. Until now, most approaches for forecasting energy demand rely on monthly electrical consumption data. The emergence of smart meters data is changing the data space for electric utility companies, and creating opportunities for utility companies to collect and analyze energy consumption data at a much finer temporal resolution of at least 15-minutes interval. While the data granularity providedmore » by smart meters is important, there are still other challenges in forecasting energy demand; these challenges include lack of information about appliances usage and occupants behavior. Consequently, in this paper, we develop an optimization based data mining approach for forecasting real-time energy demand using smart meters data. The objective of our approach is to develop a robust estimation of energy demand without access to these other building and behavior data. Specifically, the forecasting problem is formulated as a quadratic programming problem and solved using the so-called support vector machine (SVM) technique in an online setting. The parameters of the SVM technique are optimized using simulated annealing approach. The proposed approach is applied to hourly smart meters data for several residential customers over several days.« less

Power allocation strategies to minimize energy consumption in wireless body area networks.

PubMed

Kailas, Aravind

2011-01-01

The wide scale deployment of wireless body area networks (WBANs) hinges on designing energy efficient communication protocols to support the reliable communication as well as to prolong the network lifetime. Cooperative communications, a relatively new idea in wireless communications, offers the benefits of multi-antenna systems, thereby improving the link reliability and boosting energy efficiency. In this short paper, the advantages of resorting to cooperative communications for WBANs in terms of minimized energy consumption are investigated. Adopting an energy model that encompasses energy consumptions in the transmitter and receiver circuits, and transmitting energy per bit, it is seen that cooperative transmission can improve energy efficiency of the wireless network. In particular, the problem of optimal power allocation is studied with the constraint of targeted outage probability. Two strategies of power allocation are considered: power allocation with and without posture state information. Using analysis and simulation-based results, two key points are demonstrated: (i) allocating power to the on-body sensors making use of the posture information can reduce the total energy consumption of the WBAN; and (ii) when the channel condition is good, it is better to recruit less relays for cooperation to enhance energy efficiency.

Varietal Variability for Cotton Ginning Efficiency

USDA-ARS?s Scientific Manuscript database

Energy consumption is one of the largest expenses of a cotton gin. In light of the rising cost of energy, all avenues should be exploited to optimize energy use in modern cotton gins. One option is to study genetic variability within the available germplasm to look for varieties that gin faster and ...

Premium cost optimization of operational and maintenance of green building in Indonesia using life cycle assessment method

NASA Astrophysics Data System (ADS)

Latief, Yusuf; Berawi, Mohammed Ali; Basten, Van; Budiman, Rachmat; Riswanto

2017-06-01

Building has a big impact on the environmental developments. There are three general motives in building, namely the economy, society, and environment. Total completed building construction in Indonesia increased by 116% during 2009 to 2011. It made the energy consumption increased by 11% within the last three years. In fact, 70% of energy consumption is used for electricity needs on commercial buildings which leads to an increase of greenhouse gas emissions by 25%. Green Building cycle costs is known as highly building upfront cost in Indonesia. The purpose of optimization in this research improves building performance with some of green concept alternatives. Research methodology is mixed method of qualitative and quantitative approaches through questionnaire surveys and case study. Assessing the successful of optimization functions in the existing green building is based on the operational and maintenance phase with the Life Cycle Assessment Method. Choosing optimization results were based on the largest efficiency of building life cycle and the most effective cost to refund.

Optimal weight based on energy imbalance and utility maximization

NASA Astrophysics Data System (ADS)

Sun, Ruoyan

2016-01-01

This paper investigates the optimal weight for both male and female using energy imbalance and utility maximization. Based on the difference of energy intake and expenditure, we develop a state equation that reveals the weight gain from this energy gap. We ​construct an objective function considering food consumption, eating habits and survival rate to measure utility. Through applying mathematical tools from optimal control methods and qualitative theory of differential equations, we obtain some results. For both male and female, the optimal weight is larger than the physiologically optimal weight calculated by the Body Mass Index (BMI). We also study the corresponding trajectories to steady state weight respectively. Depending on the value of a few parameters, the steady state can either be a saddle point with a monotonic trajectory or a focus with dampened oscillations.

Energy benchmarking in wastewater treatment plants: the importance of site operation and layout.

PubMed

Belloir, C; Stanford, C; Soares, A

2015-01-01

Energy benchmarking is a powerful tool in the optimization of wastewater treatment plants (WWTPs) in helping to reduce costs and greenhouse gas emissions. Traditionally, energy benchmarking methods focused solely on reporting electricity consumption, however, recent developments in this area have led to the inclusion of other types of energy, including electrical, manual, chemical and mechanical consumptions that can be expressed in kWh/m3. In this study, two full-scale WWTPs were benchmarked, both incorporated preliminary, secondary (oxidation ditch) and tertiary treatment processes, Site 1 also had an additional primary treatment step. The results indicated that Site 1 required 2.32 kWh/m3 against 0.98 kWh/m3 for Site 2. Aeration presented the highest energy consumption for both sites with 2.08 kWh/m3 required for Site 1 and 0.91 kWh/m3 in Site 2. The mechanical energy represented the second biggest consumption for Site 1 (9%, 0.212 kWh/m3) and chemical input was significant in Site 2 (4.1%, 0.026 kWh/m3). The analysis of the results indicated that Site 2 could be optimized by constructing a primary settling tank that would reduce the biochemical oxygen demand, total suspended solids and NH4 loads to the oxidation ditch by 55%, 75% and 12%, respectively, and at the same time reduce the aeration requirements by 49%. This study demonstrated that the effectiveness of the energy benchmarking exercise in identifying the highest energy-consuming assets, nevertheless it points out the need to develop a holistic overview of the WWTP and the need to include parameters such as effluent quality, site operation and plant layout to allow adequate benchmarking.

Investigation of Cost and Energy Optimization of Drinking Water Distribution Systems.

PubMed

Cherchi, Carla; Badruzzaman, Mohammad; Gordon, Matthew; Bunn, Simon; Jacangelo, Joseph G

2015-11-17

Holistic management of water and energy resources through energy and water quality management systems (EWQMSs) have traditionally aimed at energy cost reduction with limited or no emphasis on energy efficiency or greenhouse gas minimization. This study expanded the existing EWQMS framework and determined the impact of different management strategies for energy cost and energy consumption (e.g., carbon footprint) reduction on system performance at two drinking water utilities in California (United States). The results showed that optimizing for cost led to cost reductions of 4% (Utility B, summer) to 48% (Utility A, winter). The energy optimization strategy was successfully able to find the lowest energy use operation and achieved energy usage reductions of 3% (Utility B, summer) to 10% (Utility A, winter). The findings of this study revealed that there may be a trade-off between cost optimization (dollars) and energy use (kilowatt-hours), particularly in the summer, when optimizing the system for the reduction of energy use to a minimum incurred cost increases of 64% and 184% compared with the cost optimization scenario. Water age simulations through hydraulic modeling did not reveal any adverse effects on the water quality in the distribution system or in tanks from pump schedule optimization targeting either cost or energy minimization.

Global optimization framework for solar building design

NASA Astrophysics Data System (ADS)

Silva, N.; Alves, N.; Pascoal-Faria, P.

2017-07-01

The generative modeling paradigm is a shift from static models to flexible models. It describes a modeling process using functions, methods and operators. The result is an algorithmic description of the construction process. Each evaluation of such an algorithm creates a model instance, which depends on its input parameters (width, height, volume, roof angle, orientation, location). These values are normally chosen according to aesthetic aspects and style. In this study, the model's parameters are automatically generated according to an objective function. A generative model can be optimized according to its parameters, in this way, the best solution for a constrained problem is determined. Besides the establishment of an overall framework design, this work consists on the identification of different building shapes and their main parameters, the creation of an algorithmic description for these main shapes and the formulation of the objective function, respecting a building's energy consumption (solar energy, heating and insulation). Additionally, the conception of an optimization pipeline, combining an energy calculation tool with a geometric scripting engine is presented. The methods developed leads to an automated and optimized 3D shape generation for the projected building (based on the desired conditions and according to specific constrains). The approach proposed will help in the construction of real buildings that account for less energy consumption and for a more sustainable world.

Robust planning of dynamic wireless charging infrastructure for battery electric buses

DOE Office of Scientific and Technical Information (OSTI.GOV)

Liu, Zhaocai; Song, Ziqi

Battery electric buses with zero tailpipe emissions have great potential in improving environmental sustainability and livability of urban areas. However, the problems of high cost and limited range associated with on-board batteries have substantially limited the popularity of battery electric buses. The technology of dynamic wireless power transfer (DWPT), which provides bus operators with the ability to charge buses while in motion, may be able to effectively alleviate the drawbacks of electric buses. In this paper, we address the problem of simultaneously selecting the optimal location of the DWPT facilities and designing the optimal battery sizes of electric buses formore » a DWPT electric bus system. The problem is first constructed as a deterministic model in which the uncertainty of energy consumption and travel time of electric buses is neglected. The methodology of robust optimization (RO) is then adopted to address the uncertainty of energy consumption and travel time. The affinely adjustable robust counterpart (AARC) of the deterministic model is developed, and its equivalent tractable mathematical programming is derived. Both the deterministic model and the robust model are demonstrated with a real-world bus system. The results of our study demonstrate that the proposed deterministic model can effectively determine the allocation of DWPT facilities and the battery sizes of electric buses for a DWPT electric bus system; and the robust model can further provide optimal designs that are robust against the uncertainty of energy consumption and travel time for electric buses.« less

Robust planning of dynamic wireless charging infrastructure for battery electric buses

DOE PAGES

Liu, Zhaocai; Song, Ziqi

2017-10-01

Battery electric buses with zero tailpipe emissions have great potential in improving environmental sustainability and livability of urban areas. However, the problems of high cost and limited range associated with on-board batteries have substantially limited the popularity of battery electric buses. The technology of dynamic wireless power transfer (DWPT), which provides bus operators with the ability to charge buses while in motion, may be able to effectively alleviate the drawbacks of electric buses. In this paper, we address the problem of simultaneously selecting the optimal location of the DWPT facilities and designing the optimal battery sizes of electric buses formore » a DWPT electric bus system. The problem is first constructed as a deterministic model in which the uncertainty of energy consumption and travel time of electric buses is neglected. The methodology of robust optimization (RO) is then adopted to address the uncertainty of energy consumption and travel time. The affinely adjustable robust counterpart (AARC) of the deterministic model is developed, and its equivalent tractable mathematical programming is derived. Both the deterministic model and the robust model are demonstrated with a real-world bus system. The results of our study demonstrate that the proposed deterministic model can effectively determine the allocation of DWPT facilities and the battery sizes of electric buses for a DWPT electric bus system; and the robust model can further provide optimal designs that are robust against the uncertainty of energy consumption and travel time for electric buses.« less

Stochastic DG Placement for Conservation Voltage Reduction Based on Multiple Replications Procedure

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wang, Zhaoyu; Chen, Bokan; Wang, Jianhui

2015-06-01

Conservation voltage reduction (CVR) and distributed-generation (DG) integration are popular strategies implemented by utilities to improve energy efficiency. This paper investigates the interactions between CVR and DG placement to minimize load consumption in distribution networks, while keeping the lowest voltage level within the predefined range. The optimal placement of DG units is formulated as a stochastic optimization problem considering the uncertainty of DG outputs and load consumptions. A sample average approximation algorithm-based technique is developed to solve the formulated problem effectively. A multiple replications procedure is developed to test the stability of the solution and calculate the confidence interval ofmore » the gap between the candidate solution and optimal solution. The proposed method has been applied to the IEEE 37-bus distribution test system with different scenarios. The numerical results indicate that the implementations of CVR and DG, if combined, can achieve significant energy savings.« less

Modeling of policies for reduction of GHG emissions in energy sector using ANN: case study-Croatia (EU).

PubMed

Bolanča, Tomislav; Strahovnik, Tomislav; Ukić, Šime; Stankov, Mirjana Novak; Rogošić, Marko

2017-07-01

This study describes the development of tool for testing different policies for reduction of greenhouse gas (GHG) emissions in energy sector using artificial neural networks (ANNs). The case study of Croatia was elaborated. Two different energy consumption scenarios were used as a base for calculations and predictions of GHG emissions: the business as usual (BAU) scenario and sustainable scenario. Both of them are based on predicted energy consumption using different growth rates; the growth rates within the second scenario resulted from the implementation of corresponding energy efficiency measures in final energy consumption and increasing share of renewable energy sources. Both ANN architecture and training methodology were optimized to produce network that was able to successfully describe the existing data and to achieve reliable prediction of emissions in a forward time sense. The BAU scenario was found to produce continuously increasing emissions of all GHGs. The sustainable scenario was found to decrease the GHG emission levels of all gases with respect to BAU. The observed decrease was attributed to the group of measures termed the reduction of final energy consumption through energy efficiency measures.

Portfolio Optimization of Nanomaterial Use in Clean Energy Technologies.

PubMed

Moore, Elizabeth A; Babbitt, Callie W; Gaustad, Gabrielle; Moore, Sean T

2018-04-03

While engineered nanomaterials (ENMs) are increasingly incorporated in diverse applications, risks of ENM adoption remain difficult to predict and mitigate proactively. Current decision-making tools do not adequately account for ENM uncertainties including varying functional forms, unique environmental behavior, economic costs, unknown supply and demand, and upstream emissions. The complexity of the ENM system necessitates a novel approach: in this study, the adaptation of an investment portfolio optimization model is demonstrated for optimization of ENM use in renewable energy technologies. Where a traditional investment portfolio optimization model maximizes return on investment through optimal selection of stock, ENM portfolio optimization maximizes the performance of energy technology systems by optimizing selective use of ENMs. Cumulative impacts of multiple ENM material portfolios are evaluated in two case studies: organic photovoltaic cells (OPVs) for renewable energy and lithium-ion batteries (LIBs) for electric vehicles. Results indicate ENM adoption is dependent on overall performance and variance of the material, resource use, environmental impact, and economic trade-offs. From a sustainability perspective, improved clean energy applications can help extend product lifespans, reduce fossil energy consumption, and substitute ENMs for scarce incumbent materials.

Control strategy optimization of HVAC plants

DOE Office of Scientific and Technical Information (OSTI.GOV)

Facci, Andrea Luigi; Zanfardino, Antonella; Martini, Fabrizio

In this paper we present a methodology to optimize the operating conditions of heating, ventilation and air conditioning (HVAC) plants to achieve a higher energy efficiency in use. Semi-empiric numerical models of the plant components are used to predict their performances as a function of their set-point and the environmental and occupied space conditions. The optimization is performed through a graph-based algorithm that finds the set-points of the system components that minimize energy consumption and/or energy costs, while matching the user energy demands. The resulting model can be used with systems of almost any complexity, featuring both HVAC components andmore » energy systems, and is sufficiently fast to make it applicable to real-time setting.« less

Residential Consumer-Centric Demand-Side Management Based on Energy Disaggregation-Piloting Constrained Swarm Intelligence: Towards Edge Computing

PubMed Central

Hu, Yu-Chen

2018-01-01

The emergence of smart Internet of Things (IoT) devices has highly favored the realization of smart homes in a down-stream sector of a smart grid. The underlying objective of Demand Response (DR) schemes is to actively engage customers to modify their energy consumption on domestic appliances in response to pricing signals. Domestic appliance scheduling is widely accepted as an effective mechanism to manage domestic energy consumption intelligently. Besides, to residential customers for DR implementation, maintaining a balance between energy consumption cost and users’ comfort satisfaction is a challenge. Hence, in this paper, a constrained Particle Swarm Optimization (PSO)-based residential consumer-centric load-scheduling method is proposed. The method can be further featured with edge computing. In contrast with cloud computing, edge computing—a method of optimizing cloud computing technologies by driving computing capabilities at the IoT edge of the Internet as one of the emerging trends in engineering technology—addresses bandwidth-intensive contents and latency-sensitive applications required among sensors and central data centers through data analytics at or near the source of data. A non-intrusive load-monitoring technique proposed previously is utilized to automatic determination of physical characteristics of power-intensive home appliances from users’ life patterns. The swarm intelligence, constrained PSO, is used to minimize the energy consumption cost while considering users’ comfort satisfaction for DR implementation. The residential consumer-centric load-scheduling method proposed in this paper is evaluated under real-time pricing with inclining block rates and is demonstrated in a case study. The experimentation reported in this paper shows the proposed residential consumer-centric load-scheduling method can re-shape loads by home appliances in response to DR signals. Moreover, a phenomenal reduction in peak power consumption is achieved by 13.97%. PMID:29702607

Equipment and Energy Usage in a Large Teaching Hospital in Norway.

PubMed

Rohde, Tarald; Martinez, Robert

2015-01-01

This article presents a study of how equipment is used in a Norwegian University hospital and suggests ways to reduce hospital energy consumption. Analysis of energy data from Norway's newest teaching hospital showed that electricity consumption was up to 50% of the whole-building energy consumption. Much of this is due to the increasing energy intensity of hospital-specific equipment. Measured power and reported usage patterns for equipment in the studied departments show daytime energy intensity of equipment at about 28.5 kBTU/ft2 per year (90 kWh/m2 per year), compared to building code standard value of only 14.9 kBTU/ft2 (47 kWh/m2 per year) for hospitals. This article intends to fill gaps in our understanding of how users and their equipment affect the energy balance in hospitals and suggests ways in which designers and equipment suppliers can help optimize energy performance while maintaining quality in the delivery of health services.

Life cycle optimization model for integrated cogeneration and energy systems applications in buildings

NASA Astrophysics Data System (ADS)

Osman, Ayat E.

Energy use in commercial buildings constitutes a major proportion of the energy consumption and anthropogenic emissions in the USA. Cogeneration systems offer an opportunity to meet a building's electrical and thermal demands from a single energy source. To answer the question of what is the most beneficial and cost effective energy source(s) that can be used to meet the energy demands of the building, optimizations techniques have been implemented in some studies to find the optimum energy system based on reducing cost and maximizing revenues. Due to the significant environmental impacts that can result from meeting the energy demands in buildings, building design should incorporate environmental criteria in the decision making criteria. The objective of this research is to develop a framework and model to optimize a building's operation by integrating congregation systems and utility systems in order to meet the electrical, heating, and cooling demand by considering the potential life cycle environmental impact that might result from meeting those demands as well as the economical implications. Two LCA Optimization models have been developed within a framework that uses hourly building energy data, life cycle assessment (LCA), and mixed-integer linear programming (MILP). The objective functions that are used in the formulation of the problems include: (1) Minimizing life cycle primary energy consumption, (2) Minimizing global warming potential, (3) Minimizing tropospheric ozone precursor potential, (4) Minimizing acidification potential, (5) Minimizing NOx, SO 2 and CO2, and (6) Minimizing life cycle costs, considering a study period of ten years and the lifetime of equipment. The two LCA optimization models can be used for: (a) long term planning and operational analysis in buildings by analyzing the hourly energy use of a building during a day and (b) design and quick analysis of building operation based on periodic analysis of energy use of a building in a year. A Pareto-optimal frontier is also derived, which defines the minimum cost required to achieve any level of environmental emission or primary energy usage value or inversely the minimum environmental indicator and primary energy usage value that can be achieved and the cost required to achieve that value.

Optimization of vibratory energy harvesters with stochastic parametric uncertainty: a new perspective

NASA Astrophysics Data System (ADS)

Haji Hosseinloo, Ashkan; Turitsyn, Konstantin

2016-04-01

Vibration energy harvesting has been shown as a promising power source for many small-scale applications mainly because of the considerable reduction in the energy consumption of the electronics and scalability issues of the conventional batteries. However, energy harvesters may not be as robust as the conventional batteries and their performance could drastically deteriorate in the presence of uncertainty in their parameters. Hence, study of uncertainty propagation and optimization under uncertainty is essential for proper and robust performance of harvesters in practice. While all studies have focused on expectation optimization, we propose a new and more practical optimization perspective; optimization for the worst-case (minimum) power. We formulate the problem in a generic fashion and as a simple example apply it to a linear piezoelectric energy harvester. We study the effect of parametric uncertainty in its natural frequency, load resistance, and electromechanical coupling coefficient on its worst-case power and then optimize for it under different confidence levels. The results show that there is a significant improvement in the worst-case power of thus designed harvester compared to that of a naively-optimized (deterministically-optimized) harvester.

Energy consumption analysis of graphene based all spin logic device with voltage controlled magnetic anisotropy

NASA Astrophysics Data System (ADS)

Zhang, Zhizhong; Zhang, Yue; Zheng, Zhenyi; Wang, Guanda; Su, Li; Zhang, Youguang; Zhao, Weisheng

2017-05-01

All spin logic device (ASLD) is a promising option to realize the ultra-low power computing systems. However, the low spin transport efficiency and the non-local switching of the detector have become two key challenges of the ASLD. In this paper, we analyze the energy consumption of a graphene based ASLD with the ferromagnetic layer switching assistance by voltage control magnetic anisotropy (VCMA) effect. This structure has significant potential towards ultra-low power consumption: the applied voltage can not only shorten switching time of the ferromagnetic layer, but also decreases the critical injection current; the graphene channel enhances greatly the spin transport efficiency. By applying the approximate circuit model, the impact of material configurations, interfaces and geometry can be synthetically studied. An accurate physic model was also developed, based on which, we carry out the micro-magnetic simulations to analyze the magnetization dynamics. Combining these electrical and magnetic investigations, the energy consumption of the proposed ASLD can be estimated. With the optimizing parameters, the energy consumption can be reduced to 2.5 pJ for a logic operation.

Energy efficient LED layout optimization for near-uniform illumination

NASA Astrophysics Data System (ADS)

Ali, Ramy E.; Elgala, Hany

2016-09-01

In this paper, we consider the problem of designing energy efficient light emitting diodes (LEDs) layout while satisfying the illumination constraints. Towards this objective, we present a simple approach to the illumination design problem based on the concept of the virtual LED. We formulate a constrained optimization problem for minimizing the power consumption while maintaining a near-uniform illumination throughout the room. By solving the resulting constrained linear program, we obtain the number of required LEDs and the optimal output luminous intensities that achieve the desired illumination constraints.

Bi-Objective Flexible Job-Shop Scheduling Problem Considering Energy Consumption under Stochastic Processing Times.

PubMed

Yang, Xin; Zeng, Zhenxiang; Wang, Ruidong; Sun, Xueshan

2016-01-01

This paper presents a novel method on the optimization of bi-objective Flexible Job-shop Scheduling Problem (FJSP) under stochastic processing times. The robust counterpart model and the Non-dominated Sorting Genetic Algorithm II (NSGA-II) are used to solve the bi-objective FJSP with consideration of the completion time and the total energy consumption under stochastic processing times. The case study on GM Corporation verifies that the NSGA-II used in this paper is effective and has advantages to solve the proposed model comparing with HPSO and PSO+SA. The idea and method of the paper can be generalized widely in the manufacturing industry, because it can reduce the energy consumption of the energy-intensive manufacturing enterprise with less investment when the new approach is applied in existing systems.

Bi-Objective Flexible Job-Shop Scheduling Problem Considering Energy Consumption under Stochastic Processing Times

PubMed Central

Zeng, Zhenxiang; Wang, Ruidong; Sun, Xueshan

2016-01-01

This paper presents a novel method on the optimization of bi-objective Flexible Job-shop Scheduling Problem (FJSP) under stochastic processing times. The robust counterpart model and the Non-dominated Sorting Genetic Algorithm II (NSGA-II) are used to solve the bi-objective FJSP with consideration of the completion time and the total energy consumption under stochastic processing times. The case study on GM Corporation verifies that the NSGA-II used in this paper is effective and has advantages to solve the proposed model comparing with HPSO and PSO+SA. The idea and method of the paper can be generalized widely in the manufacturing industry, because it can reduce the energy consumption of the energy-intensive manufacturing enterprise with less investment when the new approach is applied in existing systems. PMID:27907163

Washing when the sun is shining! How users interact with a household energy management system.

PubMed

Kobus, Charlotte B A; Mugge, Ruth; Schoormans, Jan P L

2013-01-01

To make optimal use of sustainable energy, domestic electricity consumption should shift to match local supply conditions. Energy management systems (EMS) are a new sustainable technology that can help to disrupt consumers' habits concerning electricity consumption, whilst reinforcing desired behaviours. This research examined the factors that influence the likelihood that people will shift their electricity consumption to match sustainable supply. Twenty-one interviews were conducted with households who had used the EMS 'Smart Wash' for several months. The findings showed that the likelihood of behaviour change is influenced by a combination of the user's motivation, specific contextual factors and the design of the EMS. Based on these results, several recommendations are given for the future design of EMSs. Energy management systems (EMS) are a new technology that encourages people to shift electricity consumption to match local solar supply. Interviews among users of an EMS showed that the likelihood of behaviour change is influenced by the combination of the user's motivation, contextual factors and the EMS design.

Modelling energy costs for different operational strategies of a large water resource recovery facility.

PubMed

Póvoa, P; Oehmen, A; Inocêncio, P; Matos, J S; Frazão, A

2017-05-01

The main objective of this paper is to demonstrate the importance of applying dynamic modelling and real energy prices on a full scale water resource recovery facility (WRRF) for the evaluation of control strategies in terms of energy costs with aeration. The Activated Sludge Model No. 1 (ASM1) was coupled with real energy pricing and a power consumption model and applied as a dynamic simulation case study. The model calibration is based on the STOWA protocol. The case study investigates the importance of providing real energy pricing comparing (i) real energy pricing, (ii) weighted arithmetic mean energy pricing and (iii) arithmetic mean energy pricing. The operational strategies evaluated were (i) old versus new air diffusers, (ii) different DO set-points and (iii) implementation of a carbon removal controller based on nitrate sensor readings. The application in a full scale WRRF of the ASM1 model coupled with real energy costs was successful. Dynamic modelling with real energy pricing instead of constant energy pricing enables the wastewater utility to optimize energy consumption according to the real energy price structure. Specific energy cost allows the identification of time periods with potential for linking WRRF with the electric grid to optimize the treatment costs, satisfying operational goals.

Basic aspects and contributions to the optimization of energy systems exploitation of a super tanker ship

NASA Astrophysics Data System (ADS)

Faitar, C.; Novac, I.

2017-08-01

Today, the concept of energy efficiency or energy optimization in ships has become one of the main problems of engineers in the whole world. To increase the fiability of a crude oil super tanker ship it means, among other things, to improve the energy performance and optimize the fuel consumption of ship through the development of engines and propulsion system or using alternative energies. Also, the importance of having an effective and reliable Power Management System (PMS) in a vessel operating system means to reduce operational costs and maintain power system of machine parts working in minimum stress in all operating conditions. Studying the Energy Efficiency Design Index and Energy Efficiency Operational Indicator for a crude oil super tanker ship, it allows us to study the reconfiguration of ship power system introducing new generation systems.

Optimizing Irregular Applications for Energy and Performance on the Tilera Many-core Architecture

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chavarría-Miranda, Daniel; Panyala, Ajay R.; Halappanavar, Mahantesh

Optimizing applications simultaneously for energy and performance is a complex problem. High performance, parallel, irregular applications are notoriously hard to optimize due to their data-dependent memory accesses, lack of structured locality and complex data structures and code patterns. Irregular kernels are growing in importance in applications such as machine learning, graph analytics and combinatorial scientific computing. Performance- and energy-efficient implementation of these kernels on modern, energy efficient, multicore and many-core platforms is therefore an important and challenging problem. We present results from optimizing two irregular applications { the Louvain method for community detection (Grappolo), and high-performance conjugate gradient (HPCCG) {more » on the Tilera many-core system. We have significantly extended MIT's OpenTuner auto-tuning framework to conduct a detailed study of platform-independent and platform-specific optimizations to improve performance as well as reduce total energy consumption. We explore the optimization design space along three dimensions: memory layout schemes, compiler-based code transformations, and optimization of parallel loop schedules. Using auto-tuning, we demonstrate whole node energy savings of up to 41% relative to a baseline instantiation, and up to 31% relative to manually optimized variants.« less

'My lips are sealed' - The impact of package resealability on the consumption of tempting foods.

PubMed

De Bondt, Caroline; Van Kerckhove, Anneleen; Geuens, Maggie

2017-10-01

Resealable packages are nowadays omnipresent on store shelves. While the main advantage of the resealability feature is its ability to reclose the package in order to extend the shelf life of the food product inside, the present research's aim is to assess whether this advantage also has implications for palatable, energy-dense food consumption. Two studies provide intentional as well as behavioral evidence for the claim that consumers are better able to self-regulate their consumption and thus eat less in one occasion when a palatable, energy-dense food product is offered in a resealable (vs. unresealable) package. A third study investigates the effect of package resealability across multiple consumption occasions and reveals that the resealability feature limits the volume consumed on each occasion (conditional on consumption incidence) while it does not accelerate consumption frequency, resulting in a lower total consumed volume of palatable, energy-dense snacks over a six-day period. This research offers actionable insights for consumer welfare and public health care and aids manufacturers in delineating optimal food packaging strategies. Copyright © 2017 Elsevier Ltd. All rights reserved.

An Open Source "Smart Lamp" for the Optimization of Plant Systems and Thermal Comfort of Offices.

PubMed

Salamone, Francesco; Belussi, Lorenzo; Danza, Ludovico; Ghellere, Matteo; Meroni, Italo

2016-03-07

The article describes the design phase, development and practical application of a smart object integrated in a desk lamp and called "Smart Lamp", useful to optimize the indoor thermal comfort and energy savings that are two important workplace issues where the comfort of the workers and the consumption of the building strongly affect the economic balance of a company. The Smart Lamp was built using a microcontroller, an integrated temperature and relative humidity sensor, some other modules and a 3D printer. This smart device is similar to the desk lamps that are usually found in offices but it allows one to adjust the indoor thermal comfort, by interacting directly with the air conditioner. After the construction phase, the Smart Lamp was installed in an office normally occupied by four workers to evaluate the indoor thermal comfort and the cooling consumption in summer. The results showed how the application of the Smart Lamp effectively reduced the energy consumption, optimizing the thermal comfort. The use of DIY approach combined with read-write functionality of websites, blog and social platforms, also allowed to customize, improve, share, reproduce and interconnect technologies so that anybody could use them in any occupied environment.

Optimizing Energy Consumption in Vehicular Sensor Networks by Clustering Using Fuzzy C-Means and Fuzzy Subtractive Algorithms

NASA Astrophysics Data System (ADS)

Ebrahimi, A.; Pahlavani, P.; Masoumi, Z.

2017-09-01

Traffic monitoring and managing in urban intelligent transportation systems (ITS) can be carried out based on vehicular sensor networks. In a vehicular sensor network, vehicles equipped with sensors such as GPS, can act as mobile sensors for sensing the urban traffic and sending the reports to a traffic monitoring center (TMC) for traffic estimation. The energy consumption by the sensor nodes is a main problem in the wireless sensor networks (WSNs); moreover, it is the most important feature in designing these networks. Clustering the sensor nodes is considered as an effective solution to reduce the energy consumption of WSNs. Each cluster should have a Cluster Head (CH), and a number of nodes located within its supervision area. The cluster heads are responsible for gathering and aggregating the information of clusters. Then, it transmits the information to the data collection center. Hence, the use of clustering decreases the volume of transmitting information, and, consequently, reduces the energy consumption of network. In this paper, Fuzzy C-Means (FCM) and Fuzzy Subtractive algorithms are employed to cluster sensors and investigate their performance on the energy consumption of sensors. It can be seen that the FCM algorithm and Fuzzy Subtractive have been reduced energy consumption of vehicle sensors up to 90.68% and 92.18%, respectively. Comparing the performance of the algorithms implies the 1.5 percent improvement in Fuzzy Subtractive algorithm in comparison.

A decision support model for improving a multi-family housing complex based on CO2 emission from electricity consumption.

PubMed

Hong, Taehoon; Koo, Choongwan; Kim, Hyunjoong

2012-12-15

The number of deteriorated multi-family housing complexes in South Korea continues to rise, and consequently their electricity consumption is also increasing. This needs to be addressed as part of the nation's efforts to reduce energy consumption. The objective of this research was to develop a decision support model for determining the need to improve multi-family housing complexes. In this research, 1664 cases located in Seoul were selected for model development. The research team collected the characteristics and electricity energy consumption data of these projects in 2009-2010. The following were carried out in this research: (i) using the Decision Tree, multi-family housing complexes were clustered based on their electricity energy consumption; (ii) using Case-Based Reasoning, similar cases were retrieved from the same cluster; and (iii) using a combination of Multiple Regression Analysis, Artificial Neural Network, and Genetic Algorithm, the prediction performance of the developed model was improved. The results of this research can be used as follows: (i) as basic research data for continuously managing several energy consumption data of multi-family housing complexes; (ii) as advanced research data for predicting energy consumption based on the project characteristics; (iii) as practical research data for selecting the most optimal multi-family housing complex with the most potential in terms of energy savings; and (iv) as consistent and objective criteria for incentives and penalties. Copyright © 2012 Elsevier Ltd. All rights reserved.

Study on feasible technical potential of coal to electricity in china

NASA Astrophysics Data System (ADS)

Jia, Dexiang; Tan, Xiandong

2017-01-01

The control of bulk coal is one of the important work of air pollution control in China’s future. Existing research mainly focuses on the adaptability, economy, construction and renovation plan, and operation optimization of specific energy substitution utilization, and lacks the strategy research of long-term layout of energy substitution utilization in large area. This paper puts forward a technical potential prediction method of coal to electricity based on the thermal equivalent method, which is based on the characteristics of regional coal consumption, and combined with the trend of adaptability and economy of energy substitution utilization. Also, the paper calculates the comprehensive benefit of coal to electricity according to the varieties of energy consumption and pollutant emission level of unit energy consumption in China’s future. The research result shows that the development technical potential of coal to electricity in China is huge, about 1.8 trillion kWh, including distributed electric heating, heat pump and electric heating boiler, mainly located in North China, East China, and Northeast China. The implementation of coal to electricity has remarkable comprehensive benefits in energy conservation and emission reduction, and improvement of energy consumption safety level. Case study shows the rationality of the proposed method.

Push the flash floating gate memories toward the future low energy application

NASA Astrophysics Data System (ADS)

Della Marca, V.; Just, G.; Regnier, A.; Ogier, J.-L.; Simola, R.; Niel, S.; Postel-Pellerin, J.; Lalande, F.; Masoero, L.; Molas, G.

2013-01-01

In this paper the energy consumption of flash floating gate cell, during a channel hot electron operation, is investigated. We characterize the device using different ramp and box pulses on control gate, to find the best solution to have low energy consumption and good cell performances. We use a new dynamic method to measure the drain current absorption in order to evaluate the impact of different bias conditions, and to study the cell behavior. The programming window and the energy consumption are considered as fundamental parameters. Using this dynamic technique, three zones of work are found; it is possible to optimize the drain voltage during the programming operation to minimize the energy consumption. Moreover, the cell's performances are improved using the CHISEL effect, with a reverse body bias. After the study concerning the programming pulses adjusting, we show the results obtained by increasing the channel doping dose parameter. Considering a channel hot electron programming operation, it is important to focus our attention on the bitline leakage consumption contribution. We measured it for the unselected bitline cells, and we show the effects of the lightly doped drain implantation energy on the leakage current. In this way the impact of gate induced drain leakage in band-to-band tunneling regime decreases, improving the cell's performances in a memory array.

Energy aware path planning in complex four dimensional environments

NASA Astrophysics Data System (ADS)

Chakrabarty, Anjan

This dissertation addresses the problem of energy-aware path planning for small autonomous vehicles. While small autonomous vehicles can perform missions that are too risky (or infeasible) for larger vehicles, the missions are limited by the amount of energy that can be carried on board the vehicle. Path planning techniques that either minimize energy consumption or exploit energy available in the environment can thus increase range and endurance. Path planning is complicated by significant spatial (and potentially temporal) variations in the environment. While the main focus is on autonomous aircraft, this research also addresses autonomous ground vehicles. Range and endurance of small unmanned aerial vehicles (UAVs) can be greatly improved by utilizing energy from the atmosphere. Wind can be exploited to minimize energy consumption of a small UAV. But wind, like any other atmospheric component , is a space and time varying phenomenon. To effectively use wind for long range missions, both exploration and exploitation of wind is critical. This research presents a kinematics based tree algorithm which efficiently handles the four dimensional (three spatial and time) path planning problem. The Kinematic Tree algorithm provides a sequence of waypoints, airspeeds, heading and bank angle commands for each segment of the path. The planner is shown to be resolution complete and computationally efficient. Global optimality of the cost function cannot be claimed, as energy is gained from the atmosphere, making the cost function inadmissible. However the Kinematic Tree is shown to be optimal up to resolution if the cost function is admissible. Simulation results show the efficacy of this planning method for a glider in complex real wind data. Simulation results verify that the planner is able to extract energy from the atmosphere enabling long range missions. The Kinematic Tree planning framework, developed to minimize energy consumption of UAVs, is applied for path planning in ground robots. In traditional path planning problem the focus is on obstacle avoidance and navigation. The optimal Kinematic Tree algorithm named Kinematic Tree* is shown to find optimal paths to reach the destination while avoiding obstacles. A more challenging path planning scenario arises for planning in complex terrain. This research shows how the Kinematic Tree* algorithm can be extended to find minimum energy paths for a ground vehicle in difficult mountainous terrain.

Central Plant Optimization for Waste Energy Reduction (CPOWER). ESTCP Cost and Performance Report

DTIC Science & Technology

2016-12-01

in the regression models. The solar radiation data did not appear reliable in the weather dataset for the location, and hence it was not used. The...and additional factors (e.g., solar insolation) may be needed to obtain a better model. 2. Inputs to optimizer: During several periods of...Location: North Carolina Energy Consumption Cost Savings $ 443,698.00 Analysis Type: FEMP PV of total savings 215,698.00$ Base Date: April 1

Production of Low Cost Carbon-Fiber through Energy Optimization of Stabilization Process.

PubMed

Golkarnarenji, Gelayol; Naebe, Minoo; Badii, Khashayar; Milani, Abbas S; Jazar, Reza N; Khayyam, Hamid

2018-03-05

To produce high quality and low cost carbon fiber-based composites, the optimization of the production process of carbon fiber and its properties is one of the main keys. The stabilization process is the most important step in carbon fiber production that consumes a large amount of energy and its optimization can reduce the cost to a large extent. In this study, two intelligent optimization techniques, namely Support Vector Regression (SVR) and Artificial Neural Network (ANN), were studied and compared, with a limited dataset obtained to predict physical property (density) of oxidative stabilized PAN fiber (OPF) in the second zone of a stabilization oven within a carbon fiber production line. The results were then used to optimize the energy consumption in the process. The case study can be beneficial to chemical industries involving carbon fiber manufacturing, for assessing and optimizing different stabilization process conditions at large.

Production of Low Cost Carbon-Fiber through Energy Optimization of Stabilization Process

PubMed Central

Golkarnarenji, Gelayol; Naebe, Minoo; Badii, Khashayar; Milani, Abbas S.; Jazar, Reza N.; Khayyam, Hamid

2018-01-01

To produce high quality and low cost carbon fiber-based composites, the optimization of the production process of carbon fiber and its properties is one of the main keys. The stabilization process is the most important step in carbon fiber production that consumes a large amount of energy and its optimization can reduce the cost to a large extent. In this study, two intelligent optimization techniques, namely Support Vector Regression (SVR) and Artificial Neural Network (ANN), were studied and compared, with a limited dataset obtained to predict physical property (density) of oxidative stabilized PAN fiber (OPF) in the second zone of a stabilization oven within a carbon fiber production line. The results were then used to optimize the energy consumption in the process. The case study can be beneficial to chemical industries involving carbon fiber manufacturing, for assessing and optimizing different stabilization process conditions at large. PMID:29510592

Cost minimization in a full-scale conventional wastewater treatment plant: associated costs of biological energy consumption versus sludge production.

PubMed

Sid, S; Volant, A; Lesage, G; Heran, M

2017-11-01

Energy consumption and sludge production minimization represent rising challenges for wastewater treatment plants (WWTPs). The goal of this study is to investigate how energy is consumed throughout the whole plant and how operating conditions affect this energy demand. A WWTP based on the activated sludge process was selected as a case study. Simulations were performed using a pre-compiled model implemented in GPS-X simulation software. Model validation was carried out by comparing experimental and modeling data of the dynamic behavior of the mixed liquor suspended solids (MLSS) concentration and nitrogen compounds concentration, energy consumption for aeration, mixing and sludge treatment and annual sludge production over a three year exercise. In this plant, the energy required for bioreactor aeration was calculated at approximately 44% of the total energy demand. A cost optimization strategy was applied by varying the MLSS concentrations (from 1 to 8 gTSS/L) while recording energy consumption, sludge production and effluent quality. An increase of MLSS led to an increase of the oxygen requirement for biomass aeration, but it also reduced total sludge production. Results permit identification of a key MLSS concentration allowing identification of the best compromise between levels of treatment required, biological energy demand and sludge production while minimizing the overall costs.

Distributed Channel Allocation and Time Slot Optimization for Green Internet of Things.

PubMed

Ding, Kaiqi; Zhao, Haitao; Hu, Xiping; Wei, Jibo

2017-10-28

In sustainable smart cities, power saving is a severe challenge in the energy-constrained Internet of Things (IoT). Efficient utilization of limited multiple non-overlap channels and time resources is a promising solution to reduce the network interference and save energy consumption. In this paper, we propose a joint channel allocation and time slot optimization solution for IoT. First, we propose a channel ranking algorithm which enables each node to rank its available channels based on the channel properties. Then, we propose a distributed channel allocation algorithm so that each node can choose a proper channel based on the channel ranking and its own residual energy. Finally, the sleeping duration and spectrum sensing duration are jointly optimized to maximize the normalized throughput and satisfy energy consumption constraints simultaneously. Different from the former approaches, our proposed solution requires no central coordination or any global information that each node can operate based on its own local information in a total distributed manner. Also, theoretical analysis and extensive simulations have validated that when applying our solution in the network of IoT: (i) each node can be allocated to a proper channel based on the residual energy to balance the lifetime; (ii) the network can rapidly converge to a collision-free transmission through each node's learning ability in the process of the distributed channel allocation; and (iii) the network throughput is further improved via the dynamic time slot optimization.

Cooperative and Integrated Vehicle and Intersection Control for Energy Efficiency (CIVIC-E²)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hou, Yunfei; Seliman, Salaheldeen M. S.; Wang, Enshu

Recent advances in connected vehicle technologies enable vehicles and signal controllers to cooperate and improve the traffic management at intersections. This paper explores the opportunity for cooperative and integrated vehicle and intersection control for energy efficiency (CIVIC-E 2) to contribute to a more sustainable transportation system. We propose a two-level approach that jointly optimizes the traffic signal timing and vehicles' approach speed, with the objective being to minimize total energy consumption for all vehicles passing through an isolated intersection. More specifically, at the intersection level, a dynamic programming algorithm is designed to find the optimal signal timing by explicitly consideringmore » the arrival time and energy profile of each vehicle. At the vehicle level, a model predictive control strategy is adopted to ensure that vehicles pass through the intersection in a timely fashion. Our simulation study has shown that the proposed CIVIC-E 2 system can significantly improve intersection performance under various traffic conditions. Compared with conventional fixed-time and actuated signal control strategies, the proposed algorithm can reduce energy consumption and queue length by up to 31% and 95%, respectively.« less

Cooperative and Integrated Vehicle and Intersection Control for Energy Efficiency (CIVIC-E²)

DOE PAGES

Hou, Yunfei; Seliman, Salaheldeen M. S.; Wang, Enshu; ...

2018-02-15

Recent advances in connected vehicle technologies enable vehicles and signal controllers to cooperate and improve the traffic management at intersections. This paper explores the opportunity for cooperative and integrated vehicle and intersection control for energy efficiency (CIVIC-E 2) to contribute to a more sustainable transportation system. We propose a two-level approach that jointly optimizes the traffic signal timing and vehicles' approach speed, with the objective being to minimize total energy consumption for all vehicles passing through an isolated intersection. More specifically, at the intersection level, a dynamic programming algorithm is designed to find the optimal signal timing by explicitly consideringmore » the arrival time and energy profile of each vehicle. At the vehicle level, a model predictive control strategy is adopted to ensure that vehicles pass through the intersection in a timely fashion. Our simulation study has shown that the proposed CIVIC-E 2 system can significantly improve intersection performance under various traffic conditions. Compared with conventional fixed-time and actuated signal control strategies, the proposed algorithm can reduce energy consumption and queue length by up to 31% and 95%, respectively.« less

Research on energy-saving optimal control of trains in a following operation under a fixed four-aspect autoblock system based on multi-dimension parallel GA

NASA Astrophysics Data System (ADS)

Lu, Qiheng; Feng, Xiaoyun

2013-03-01

After analyzing the working principle of the four-aspect fixed autoblock system, an energy-saving control model was created based on the dynamics equations of the trains in order to study the energy-saving optimal control strategy of trains in a following operation. Besides the safety and punctuality, the main aims of the model were the energy consumption and the time error. Based on this model, the static and dynamic speed restraints under a four-aspect fixed autoblock system were put forward. The multi-dimension parallel genetic algorithm (GA) and the external punishment function were adopted to solve this problem. By using the real number coding and the strategy of ramps divided into three parts, the convergence of GA was speeded up and the length of chromosomes was shortened. A vector of Gaussian random disturbance with zero mean was superposed to the mutation operator. The simulation result showed that the method could reduce the energy consumption effectively based on safety and punctuality.

Exploiting node mobility for energy optimization in wireless sensor networks

NASA Astrophysics Data System (ADS)

El-Moukaddem, Fatme Mohammad

Wireless Sensor Networks (WSNs) have become increasingly available for data-intensive applications such as micro-climate monitoring, precision agriculture, and audio/video surveillance. A key challenge faced by data-intensive WSNs is to transmit the sheer amount of data generated within an application's lifetime to the base station despite the fact that sensor nodes have limited power supplies such as batteries or small solar panels. The availability of numerous low-cost robotic units (e.g. Robomote and Khepera) has made it possible to construct sensor networks consisting of mobile sensor nodes. It has been shown that the controlled mobility offered by mobile sensors can be exploited to improve the energy efficiency of a network. In this thesis, we propose schemes that use mobile sensor nodes to reduce the energy consumption of data-intensive WSNs. Our approaches differ from previous work in two main aspects. First, our approaches do not require complex motion planning of mobile nodes, and hence can be implemented on a number of low-cost mobile sensor platforms. Second, we integrate the energy consumption due to both mobility and wireless communications into a holistic optimization framework. We consider three problems arising from the limited energy in the sensor nodes. In the first problem, the network consists of mostly static nodes and contains only a few mobile nodes. In the second and third problems, we assume essentially that all nodes in the WSN are mobile. We first study a new problem called max-data mobile relay configuration (MMRC ) that finds the positions of a set of mobile sensors, referred to as relays, that maximize the total amount of data gathered by the network during its lifetime. We show that the MMRC problem is surprisingly complex even for a trivial network topology due to the joint consideration of the energy consumption of both wireless communication and mechanical locomotion. We present optimal MMRC algorithms and practical distributed implementations for several important network topologies and applications. Second, we consider the problem of minimizing the total energy consumption of a network. We design an iterative algorithm that improves a given configuration by relocating nodes to new positions. We show that this algorithm converges to the optimal configuration for the given transmission routes. Moreover, we propose an efficient distributed implementation that does not require explicit synchronization. Finally, we consider the problem of maximizing the lifetime of the network. We propose an approach that exploits the mobility of the nodes to balance the energy consumption throughout the network. We develop efficient algorithms for single and multiple round approaches. For all three problems, we evaluate the efficiency of our algorithms through simulations. Our simulation results based on realistic energy models obtained from existing mobile and static sensor platforms show that our approaches significantly improve the network's performance and outperform existing approaches.

Energy saving achieved by limited filamentous bulking sludge under low dissolved oxygen.

PubMed

Guo, Jian-Hua; Peng, Yong-Zhen; Peng, Cheng-Yao; Wang, Shu-Ying; Chen, Ying; Huang, Hui-Jun; Sun, Zhi-Rong

2010-02-01

Limited filamentous bulking caused by low dissolved oxygen (DO) was proposed to establish a low energy consumption wastewater treatment system. This method for energy saving was derived from two full-scale field observations, which showed pollutants removal would be enhanced and energy consumption could be reduced by at least 10% using limited filamentous bulking. Furthermore, preliminary investigation including the abundance evaluation and the identification of filamentous bacteria demonstrated that the limited filamentous bulking could be repeated steadily in a lab-scale anoxic-oxic reactor fed with domestic wastewater. The sludge loss did not occur in the secondary clarifier, while COD and total nitrogen removal efficiencies were improved by controlling DO for optimal filamentous bacterial population. Suspended solids in effluent were negligible and turbidity was lower than 2 NTU, which were distinctly lower than those under no bulking. Theoretical and experimental results indicated the aeration consumption could be saved by the application of limited filamentous bulking.

Evaluating Student Assessments: The Use of Optimal Foraging Theory

ERIC Educational Resources Information Center

Whalley, W. Brian

2016-01-01

The concepts of optimal foraging theory and the marginal value theorem are used to investigate possible student behaviour in accruing marks in various forms of assessment. The ideas of predator energy consumption, handling and search times can be evaluated in terms of student behaviour and gaining marks or "attainment". These ideas can…

Optimization of intermittent microwave–convective drying using response surface methodology

PubMed Central

Aghilinategh, Nahid; Rafiee, Shahin; Hosseinpur, Soleiman; Omid, Mahmoud; Mohtasebi, Seyed Saeid

2015-01-01

In this study, response surface methodology was used for optimization of intermittent microwave–convective air drying (IMWC) parameters with employing desirability function. Optimization factors were air temperature (40–80°C), air velocity (1–2 m/sec), pulse ratio) PR ((2–6), and microwave power (200–600 W) while responses were rehydration ratio, bulk density, total phenol content (TPC), color change, and energy consumption. Minimum color change, bulk density, energy consumption, maximum rehydration ratio, and TPC were assumed as criteria for optimizing drying conditions of apple slices in IMWC. The optimum values of process variables were 1.78 m/sec air velocity, 40°C air temperature, PR 4.48, and 600 W microwave power that characterized by maximum desirability function (0.792) using Design expert 8.0. The air temperature and microwave power had significant effect on total responses, but the role of air velocity can be ignored. Generally, the results indicated that it was possible to obtain a higher desirability value if the microwave power and temperature, respectively, increase and decrease. PMID:26286706

Electrochemical oxidation of ampicillin antibiotic at boron-doped diamond electrodes and process optimization using response surface methodology.

PubMed

Körbahti, Bahadır K; Taşyürek, Selin

2015-03-01

Electrochemical oxidation and process optimization of ampicillin antibiotic at boron-doped diamond electrodes (BDD) were investigated in a batch electrochemical reactor. The influence of operating parameters, such as ampicillin concentration, electrolyte concentration, current density, and reaction temperature, on ampicillin removal, COD removal, and energy consumption was analyzed in order to optimize the electrochemical oxidation process under specified cost-driven constraints using response surface methodology. Quadratic models for the responses satisfied the assumptions of the analysis of variance well according to normal probability, studentized residuals, and outlier t residual plots. Residual plots followed a normal distribution, and outlier t values indicated that the approximations of the fitted models to the quadratic response surfaces were very good. Optimum operating conditions were determined at 618 mg/L ampicillin concentration, 3.6 g/L electrolyte concentration, 13.4 mA/cm(2) current density, and 36 °C reaction temperature. Under response surface optimized conditions, ampicillin removal, COD removal, and energy consumption were obtained as 97.1 %, 92.5 %, and 71.7 kWh/kg CODr, respectively.

An Energy-Efficient Target-Tracking Strategy for Mobile Sensor Networks.

PubMed

Mahboubi, Hamid; Masoudimansour, Walid; Aghdam, Amir G; Sayrafian-Pour, Kamran

2017-02-01

In this paper, an energy-efficient strategy is proposed for tracking a moving target in an environment with obstacles, using a network of mobile sensors. Typically, the most dominant sources of energy consumption in a mobile sensor network are sensing, communication, and movement. The proposed algorithm first divides the field into a grid of sufficiently small cells. The grid is then represented by a graph whose edges are properly weighted to reflect the energy consumption of sensors. The proposed technique searches for near-optimal locations for the sensors in different time instants to route information from the target to destination, using a shortest path algorithm. Simulations confirm the efficacy of the proposed algorithm.

An energy management for series hybrid electric vehicle using improved dynamic programming

NASA Astrophysics Data System (ADS)

Peng, Hao; Yang, Yaoquan; Liu, Chunyu

2018-02-01

With the increasing numbers of hybrid electric vehicle (HEV), management for two energy sources, engine and battery, is more and more important to achieve the minimum fuel consumption. This paper introduces several working modes of series hybrid electric vehicle (SHEV) firstly and then describes the mathematical model of main relative components in SHEV. On the foundation of this model, dynamic programming is applied to distribute energy of engine and battery on the platform of matlab and acquires less fuel consumption compared with traditional control strategy. Besides, control rule recovering energy in brake profiles is added into dynamic programming, so shorter computing time is realized by improved dynamic programming and optimization on algorithm.

Research on charging and discharging control strategy for electric vehicles as distributed energy storage devices

NASA Astrophysics Data System (ADS)

Zhang, Min; Yang, Feng; Zhang, Dongqing; Tang, Pengcheng

2018-02-01

A large number of electric vehicles are connected to the family micro grid will affect the operation safety of the power grid and the quality of power. Considering the factors of family micro grid price and electric vehicle as a distributed energy storage device, a two stage optimization model is established, and the improved discrete binary particle swarm optimization algorithm is used to optimize the parameters in the model. The proposed control strategy of electric vehicle charging and discharging is of practical significance for the rational control of electric vehicle as a distributed energy storage device and electric vehicle participating in the peak load regulation of power consumption.

Numerical simulation study on the optimization design of the crown shape of PDC drill bit.

PubMed

Ju, Pei; Wang, Zhenquan; Zhai, Yinghu; Su, Dongyu; Zhang, Yunchi; Cao, Zhaohui

The design of bit crown is an important part of polycrystalline diamond compact (PDC) bit design, although predecessors have done a lot of researches on the design principles of PDC bit crown, the study of the law about rock-breaking energy consumption according to different bit crown shape is not very systematic, and the mathematical model of design is over-simplified. In order to analyze the relation between rock-breaking energy consumption and bit crown shape quantificationally, the paper puts forward an idea to take "per revolution-specific rock-breaking work" as objective function, and analyzes the relationship between rock properties, inner cone angle, outer cone arc radius, and per revolution-specific rock-breaking work by means of explicit dynamic finite element method. Results show that the change law between per revolution-specific rock-breaking work and the radius of gyration is similar for rocks with different properties, it is beneficial to decrease rock-breaking energy consumption by decreasing inner cone angle or outer cone arc radius. Of course, we should also consider hydraulic structure and processing technology in the optimization design of PDC bit crown.

Energy Consumption Forecasting Using Semantic-Based Genetic Programming with Local Search Optimizer.

PubMed

Castelli, Mauro; Trujillo, Leonardo; Vanneschi, Leonardo

2015-01-01

Energy consumption forecasting (ECF) is an important policy issue in today's economies. An accurate ECF has great benefits for electric utilities and both negative and positive errors lead to increased operating costs. The paper proposes a semantic based genetic programming framework to address the ECF problem. In particular, we propose a system that finds (quasi-)perfect solutions with high probability and that generates models able to produce near optimal predictions also on unseen data. The framework blends a recently developed version of genetic programming that integrates semantic genetic operators with a local search method. The main idea in combining semantic genetic programming and a local searcher is to couple the exploration ability of the former with the exploitation ability of the latter. Experimental results confirm the suitability of the proposed method in predicting the energy consumption. In particular, the system produces a lower error with respect to the existing state-of-the art techniques used on the same dataset. More importantly, this case study has shown that including a local searcher in the geometric semantic genetic programming system can speed up the search process and can result in fitter models that are able to produce an accurate forecasting also on unseen data.

Optimization of Wireless Transceivers under Processing Energy Constraints

NASA Astrophysics Data System (ADS)

Wang, Gaojian; Ascheid, Gerd; Wang, Yanlu; Hanay, Oner; Negra, Renato; Herrmann, Matthias; Wehn, Norbert

2017-09-01

Focus of the article is on achieving maximum data rates under a processing energy constraint. For a given amount of processing energy per information bit, the overall power consumption increases with the data rate. When targeting data rates beyond 100 Gb/s, the system's overall power consumption soon exceeds the power which can be dissipated without forced cooling. To achieve a maximum data rate under this power constraint, the processing energy per information bit must be minimized. Therefore, in this article, suitable processing efficient transmission schemes together with energy efficient architectures and their implementations are investigated in a true cross-layer approach. Target use cases are short range wireless transmitters working at carrier frequencies around 60 GHz and bandwidths between 1 GHz and 10 GHz.

A Fast Evaluation Method for Energy Building Consumption Based on the Design of Experiments

NASA Astrophysics Data System (ADS)

Belahya, Hocine; Boubekri, Abdelghani; Kriker, Abdelouahed

2017-08-01

Building sector is one of the effective consumer energy by 42% in Algeria. The need for energy has continued to grow, in inordinate way, due to lack of legislation on energy performance in this large consumer sector. Another reason is the simultaneous change of users’ requirements to maintain their comfort, especially summer in dry lands and parts of southern Algeria, where the town of Ouargla presents a typical example which leads to a large amount of electricity consumption through the use of air conditioning. In order to achieve a high performance envelope of the building, an optimization of major parameters building envelope is required, using design of experiments (DOE), can determine the most effective parameters and eliminate the less importance. The study building is often complex and time consuming due to the large number of parameters to consider. This study focuses on reducing the computing time and determines the major parameters of building energy consumption, such as area of building, factor shape, orientation, ration walls to windows …etc to make some proposal models in order to minimize the seasonal energy consumption due to air conditioning needs.

Electrochemical treatment of rice grain-based distillery effluent: chemical oxygen demand and colour removal.

PubMed

Prajapati, Abhinesh Kumar; Chaudhari, Parmesh Kumar

2014-01-01

The electrochemical (EC) treatment of rice grain-based distillery wastewater was carried out in a 1.5 dm3 electrolytic batch reactor using aluminium plate electrodes. With the four-plate configurations, a current density (j) of 89.3 A/m2 and pH 8 was found to be optimal, obtaining a maximum chemical oxygen demand (COD) and colour removal of 93% and 87%, respectively. The chemical dissolution of aluminium was strongly influenced by initial pH (pHi). At higher pHi (pH 9.5) anode consumption decreased while energy consumption increased. At the optimal current density 89.3 A/m2, the aluminium electrode consumption was 16.855 g/dm3 wastewater and energy consumption was 31.4 Wh/dm3 achieving a maximum COD removal of 87%. The settling and filterability characteristics ofelectrochemically treated sludge were also analysed at different pH. It was noted that treated slurry at pHi 9.5 gave best settling characteristic, which decreased with increase in pH. EC-treated effluent at pHi 8 had provided best filterability. Characteristics of scum and residues are also analysed at different pH.

Danny Studer | NREL

Science.gov Websites

Daniel.Studer@nrel.gov | 303-275-4368 Daniel joined NREL in 2009. As a member of the Commercial Buildings using EnergyPlus to identify large-scale areas for reducing and optimizing commercial building energy consumption. Recently, Daniel led NREL's commercial building workforce development efforts and he is leading

Validating Savings Claims of Cold Climate Zero Energy Ready Homes

DOE Office of Scientific and Technical Information (OSTI.GOV)

Williamson, J.; Puttagunta, S.

This study was intended to validate actual performance of three ZERHs in the Northeast to energy models created in REM/Rate v14.5 (one of the certified software programs used to generate a HERS Index) and the National Renewable Energy Laboratory’s Building Energy Optimization (BEopt™) v2.3 E+ (a more sophisticated hourly energy simulation software). This report details the validation methods used to analyze energy consumption at each home.

Passive designs and renewable energy systems optimization of a net zero energy building in Embrun/France

NASA Astrophysics Data System (ADS)

Harkouss, F.; Biwole, P. H.; Fardoun, F.

2018-05-01

Buildings’ optimization is a smart method to inspect the available design choices starting from passive strategies, to energy efficient systems and finally towards the adequate renewable energy system to be implemented. This paper outlines the methodology and the cost-effectiveness potential for optimizing the design of net-zero energy building in a French city; Embrun. The non-dominated sorting genetic algorithm is chosen in order to minimize thermal, electrical demands and life cycle cost while reaching the net zero energy balance; and thus getting the Pareto-front. Elimination and Choice Expressing the Reality decision making method is applied to the Pareto-front so as to obtain one optimal solution. A wide range of energy efficiency measures are investigated, besides solar energy systems are employed to produce required electricity and hot water for domestic purposes. The results indicate that the appropriate selection of the passive parameters is very important and critical in reducing the building energy consumption. The optimum design parameters yield to a decrease of building’s thermal loads and life cycle cost by 32.96% and 14.47% respectively.

Systems modeling and analysis for Saudi Arabian electric power requirements

DOE Office of Scientific and Technical Information (OSTI.GOV)

Al-Mohawes, N.A.

This thesis addresses the long-range generation planning problem in Saudi Arabia up to the year 2000. The first part presents various models for electric energy consumption in the residential and industrial sectors. These models can be used by the decision makers for the purposes of policy analysis, evaluation, and forecasting. Forecasts of energy in each sector are obtained from two different models for each sector. These models are based on two forecasting techniques: (1) Hybrid econometric/time series model. The idea of adaptive smoothing was utilized to produce forecasts under several scenarios. (2) Box-Jenkins time series technique. Box-Jenkins models and forecastsmore » are developed for the monthly number of electric consumers and the monthly energy consumption per consumer. The results obtained indicate that high energy consumption is expected during the coming two decades which necessitate serious energy assessment and optimization. Optimization of a mix of energy sources was considered using the group multiattribute utility (MAU) function. The results of MAU for three classes of decision makers (managerial, technical, and consumers) are developed through personal interactions. The computer package WASP was also used to develop a tentative optimum plan. According to this plan, four heavy-water nuclear power plants (800 MW) and four light-water nuclear power plants (1200 MW) have to be introduced by the year 2000 in addition to sixteen oil-fired power plants (400 MW) and nine gas turbines (100 MW).« less

Investigating the water consumption for electricity generation at Turkish power plants

NASA Astrophysics Data System (ADS)

El-Khozondar, Balkess; Aydinalp Koksal, Merih

2017-11-01

The water-energy intertwined relationship has recently gained more importance due to the high water consumption in the energy sector and to the limited availability of the water resources. The energy and electricity demand of Turkey is increasing rapidly in the last two decades. More thermal power plants are expected to be built in the near future to supply the rapidly increasing demand in Turkey which will put pressure on water availability. In this study, the water consumption for electricity generation at Turkish power plants is investigated. The main objectives of this study are to identify the amount of water consumed to generate 1 kWh of electricity for each generation technology currently used in Turkey and to investigate ways to reduce the water consumption at power plants expected to be built in the near future to supply the increasing demand. The various electricity generation technology mixture scenarios are analyzed to determine the future total and per generation water consumption, and water savings based on changes of cooling systems used for each technology. The Long-range Energy Alternatives Planning (LEAP) program is used to determine the minimum water consuming electricity generation technology mixtures using optimization approaches between 2017 and 2035.

Optimal conditions for preparation of banana peels, sugarcane bagasse and watermelon rind in removing copper from water.

PubMed

Liu, Cong; Ngo, Huu Hao; Guo, Wenshan; Tung, Kuo-Lun

2012-09-01

In this study, three agro-waste materials were used as biosorbents for removal of copper (Cu) from water. This work aims to optimise conditions for preparation of these materials to obtain maximum Cu adsorption capacity. The optimal conditions were determined in terms of Cu removal efficiency and/or energy consumption. The results indicate that banana peels dried at 120°C for 2h and ground into powder form led to a better performance in terms of both copper removal efficiency and energy consumption. For sugarcane bagasse and watermelon rind, 120°C was the suitable drying temperature. However, the best drying time was 1h for sugarcane bagasse and 3h for watermelon rind. The powder form with size of <150 μm was optimal for all biosorbents in terms of removal efficiency and equilibration time. The findings are beneficial to the application of these agro-waste materials for Cu removal from water and wastewater treatment. Copyright © 2012. Published by Elsevier Ltd.

Platform-dependent optimization considerations for mHealth applications

NASA Astrophysics Data System (ADS)

Kaghyan, Sahak; Akopian, David; Sarukhanyan, Hakob

2015-03-01

Modern mobile devices contain integrated sensors that enable multitude of applications in such fields as mobile health (mHealth), entertainment, sports, etc. Human physical activity monitoring is one of such the emerging applications. There exists a range of challenges that relate to activity monitoring tasks, and, particularly, exploiting optimal solutions and architectures for respective mobile software application development. This work addresses mobile computations related to integrated inertial sensors for activity monitoring, such as accelerometers, gyroscopes, integrated global positioning system (GPS) and WLAN-based positioning, that can be used for activity monitoring. Some of the aspects will be discussed in this paper. Each of the sensing data sources has its own characteristics such as specific data formats, data rates, signal acquisition durations etc., and these specifications affect energy consumption. Energy consumption significantly varies as sensor data acquisition is followed by data analysis including various transformations and signal processing algorithms. This paper will address several aspects of more optimal activity monitoring implementations exploiting state-of-the-art capabilities of modern platforms.

Regional energy planning: Some suggestions to public administration

NASA Astrophysics Data System (ADS)

Sozzi, R.

A methodology is proposed to estimate the relevant data and to improve the energy efficiency in regional energy planning. The quantification of the regional energy system is subdivided in three independent parameters which are separetely estimated: energy demand, energy consumption, and transformation capacity. Definitions and estimating procedures are given. The optimization of the regional planning includes the application, wherever possible, of the technologies which centralize the space-heating energy production or combine the production of electric energy with space-heating energy distribution.

Real time monitoring system used in route planning for the electric vehicle

NASA Astrophysics Data System (ADS)

Ionescu, LM; Mazare, A.; Serban, G.; Ionita, S.

2017-10-01

The electric vehicle is a new consumer of electricity that is becoming more and more widespread. Under these circumstances, new strategies for optimizing power consumption and increasing vehicle autonomy must be designed. These must include route planning along with consumption, fuelling points and points of interest. The hardware and software solution proposed by us allows: non-invasive monitoring of power consumption, energy autonomy - it does not add any extra consumption, data transmission to a server and data fusion with the route, the points of interest of the route and the power supply points. As a result: an optimal route planning service will be provided to the driver, considering the route, the requirements of the electric vehicle and the consumer profile. The solution can be easily installed on any type of electric car - it does not involve any intervention on the equipment.

Improving Energy Efficiency in CNC Machining

NASA Astrophysics Data System (ADS)

Pavanaskar, Sushrut S.

We present our work on analyzing and improving the energy efficiency of multi-axis CNC milling process. Due to the differences in energy consumption behavior, we treat 3- and 5-axis CNC machines separately in our work. For 3-axis CNC machines, we first propose an energy model that estimates the energy requirement for machining a component on a specified 3-axis CNC milling machine. Our model makes machine-specific predictions of energy requirements while also considering the geometric aspects of the machining toolpath. Our model - and the associated software tool - facilitate direct comparison of various alternative toolpath strategies based on their energy-consumption performance. Further, we identify key factors in toolpath planning that affect energy consumption in CNC machining. We then use this knowledge to propose and demonstrate a novel toolpath planning strategy that may be used to generate new toolpaths that are inherently energy-efficient, inspired by research on digital micrography -- a form of computational art. For 5-axis CNC machines, the process planning problem consists of several sub-problems that researchers have traditionally solved separately to obtain an approximate solution. After illustrating the need to solve all sub-problems simultaneously for a truly optimal solution, we propose a unified formulation based on configuration space theory. We apply our formulation to solve a problem variant that retains key characteristics of the full problem but has lower dimensionality, allowing visualization in 2D. Given the complexity of the full 5-axis toolpath planning problem, our unified formulation represents an important step towards obtaining a truly optimal solution. With this work on the two types of CNC machines, we demonstrate that without changing the current infrastructure or business practices, machine-specific, geometry-based, customized toolpath planning can save energy in CNC machining.

Optimal charge control strategies for stationary photovoltaic battery systems

NASA Astrophysics Data System (ADS)

Li, Jiahao; Danzer, Michael A.

2014-07-01

Battery systems coupled to photovoltaic (PV) modules for example fulfill one major function: they locally decouple PV generation and consumption of electrical power leading to two major effects. First, they reduce the grid load, especially at peak times and therewith reduce the necessity of a network expansion. And second, they increase the self-consumption in households and therewith help to reduce energy expenses. For the management of PV batteries charge control strategies need to be developed to reach the goals of both the distribution system operators and the local power producer. In this work optimal control strategies regarding various optimization goals are developed on the basis of the predicted household loads and PV generation profiles using the method of dynamic programming. The resulting charge curves are compared and essential differences discussed. Finally, a multi-objective optimization shows that charge control strategies can be derived that take all optimization goals into account.

Modeling Battery Behavior on Sensory Operations for Context-Aware Smartphone Sensing

PubMed Central

Yurur, Ozgur; Liu, Chi Harold; Moreno, Wilfrido

2015-01-01

Energy consumption is a major concern in context-aware smartphone sensing. This paper first studies mobile device-based battery modeling, which adopts the kinetic battery model (KiBaM), under the scope of battery non-linearities with respect to variant loads. Second, this paper models the energy consumption behavior of accelerometers analytically and then provides extensive simulation results and a smartphone application to examine the proposed sensor model. Third, a Markov reward process is integrated to create energy consumption profiles, linking with sensory operations and their effects on battery non-linearity. Energy consumption profiles consist of different pairs of duty cycles and sampling frequencies during sensory operations. Furthermore, the total energy cost by each profile is represented by an accumulated reward in this process. Finally, three different methods are proposed on the evolution of the reward process, to present the linkage between different usage patterns on the accelerometer sensor through a smartphone application and the battery behavior. By doing this, this paper aims at achieving a fine efficiency in power consumption caused by sensory operations, while maintaining the accuracy of smartphone applications based on sensor usages. More importantly, this study intends that modeling the battery non-linearities together with investigating the effects of different usage patterns in sensory operations in terms of the power consumption and the battery discharge may lead to discovering optimal energy reduction strategies to extend the battery lifetime and help a continual improvement in context-aware mobile services. PMID:26016916

Modeling battery behavior on sensory operations for context-aware smartphone sensing.

PubMed

Yurur, Ozgur; Liu, Chi Harold; Moreno, Wilfrido

2015-05-26

Energy consumption is a major concern in context-aware smartphone sensing. This paper first studies mobile device-based battery modeling, which adopts the kinetic battery model (KiBaM), under the scope of battery non-linearities with respect to variant loads. Second, this paper models the energy consumption behavior of accelerometers analytically and then provides extensive simulation results and a smartphone application to examine the proposed sensor model. Third, a Markov reward process is integrated to create energy consumption profiles, linking with sensory operations and their effects on battery non-linearity. Energy consumption profiles consist of different pairs of duty cycles and sampling frequencies during sensory operations. Furthermore, the total energy cost by each profile is represented by an accumulated reward in this process. Finally, three different methods are proposed on the evolution of the reward process, to present the linkage between different usage patterns on the accelerometer sensor through a smartphone application and the battery behavior. By doing this, this paper aims at achieving a fine efficiency in power consumption caused by sensory operations, while maintaining the accuracy of smartphone applications based on sensor usages. More importantly, this study intends that modeling the battery non-linearities together with investigating the effects of different usage patterns in sensory operations in terms of the power consumption and the battery discharge may lead to discovering optimal energy reduction strategies to extend the battery lifetime and help a continual improvement in context-aware mobile services.

Energy Performance and Optimal Control of Air-conditioned Buildings Integrated with Phase Change Materials

NASA Astrophysics Data System (ADS)

Zhu, Na

This thesis presents an overview of the previous research work on dynamic characteristics and energy performance of buildings due to the integration of PCMs. The research work on dynamic characteristics and energy performance of buildings using PCMs both with and without air-conditioning is reviewed. Since the particular interest in using PCMs for free cooling and peak load shifting, specific research efforts on both subjects are reviewed separately. A simplified physical dynamic model of building structures integrated with SSPCM (shaped-stabilized phase change material) is developed and validated in this study. The simplified physical model represents the wall by 3 resistances and 2 capacitances and the PCM layer by 4 resistances and 2 capacitances respectively while the key issue is the parameter identification of the model. This thesis also presents the studies on the thermodynamic characteristics of buildings enhanced by PCM and on the investigation of the impacts of PCM on the building cooling load and peak cooling demand at different climates and seasons as well as the optimal operation and control strategies to reduce the energy consumption and energy cost by reducing the air-conditioning energy consumption and peak load. An office building floor with typical variable air volume (VAV) air-conditioning system is used and simulated as the reference building in the comparison study. The envelopes of the studied building are further enhanced by integrating the PCM layers. The building system is tested in two selected cities of typical climates in China including Hong Kong and Beijing. The cold charge and discharge processes, the operation and control strategies of night ventilation and the air temperature set-point reset strategy for minimizing the energy consumption and electricity cost are studied. This thesis presents the simulation test platform, the test results on the cold storage and discharge processes, the air-conditioning energy consumption and demand reduction potentials in typical air-conditioning seasons in typical China cites as well as the impacts of operation and control strategies.

Energy-signal quality trade-offs in a WiMAX mobile station with a booster amplifier

NASA Astrophysics Data System (ADS)

Suherman; Mubarakah, N.; Wiranata, O.; Kasim, S. T.

2018-02-01

Worldwide Interoperability for Microwave Access (WiMAX) is a broadband wireless access technology that is able to provide high bit rate mobile internet services. Battery endurance remains a problem in current mobile communication. On the other hand, signal quality determines the successful run of the mobile applications. Energy consumption optimization cannot sacrifice the signal level required by the application to run smoothly. On the contrary, the application should consider battery life time. This paper examines the tradeoffs between energy and signal quality in WiMAX subscriber station by adjusting signal level using a booster amplifier. Simulation evaluations show that an increment of 0.00000104% energy consumption on using amplifier adaptively produces 16.411% signal to noise ratio (SNR) increment and 10.7% bit error rate (BER) decrement. By keeping the amplifier turned on, energy consumption increases up to 0.00000136%, causing the SNR rises to 17.2638% and BER drops to 11.13%. The evaluated application is video streaming, other application may behave differently.

Effect of default menus on food selection and consumption in a college dining hall simulation study.

PubMed

Radnitz, Cynthia; Loeb, Katharine L; Keller, Kathleen L; Boutelle, Kerri; Schwartz, Marlene B; Todd, Lauren; Marcus, Sue

2018-05-01

To test an obesity prevention strategy derived from behavioural economics (optimal defaults plus delay), focused on changing the college dining hall service method. After a uniform pre-load, participants attended an experimental lunch in groups randomized to one of three conditions: a nutrient-dense, lower-fat/energy lunch as an optimal default (OD); a less-nutrient-dense, higher-fat/energy lunch as a suboptimal default (SD); or a free array (FA) lunch. In the OD condition, students were presented a menu depicting healthier vegetarian and omnivore foods as default, with opt-out alternatives (SD menu) available on request with a 15 min wait. In the SD condition, the same menu format was used with the positioning of food items switched. In the FA condition, all choices were presented in uniform fonts and were available immediately. Private rooms designed to provide a small version of a college dining hall, on two campuses of a Northeastern US university. First-year college students (n 129). There was a significant main effect for condition on percentage of optimal choices selected, with 94 % of food choices in the OD condition optimal, 47 % in the FA condition optimal and none in the SD condition optimal. Similarly, energy intake for those in the SD condition significantly exceeded that in the FA condition, which exceeded that in the OD condition. Presenting menu items as optimal defaults with a delay had a significant impact on choice and consumption, suggesting that further research into its long-term applicability is warranted.

Hierarchical fuzzy control of low-energy building systems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yu, Zhen; Dexter, Arthur

2010-04-15

A hierarchical fuzzy supervisory controller is described that is capable of optimizing the operation of a low-energy building, which uses solar energy to heat and cool its interior spaces. The highest level fuzzy rules choose the most appropriate set of lower level rules according to the weather and occupancy information; the second level fuzzy rules determine an optimal energy profile and the overall modes of operation of the heating, ventilating and air-conditioning system (HVAC); the third level fuzzy rules select the mode of operation of specific equipment, and assign schedules to the local controllers so that the optimal energy profilemore » can be achieved in the most efficient way. Computer simulation is used to compare the hierarchical fuzzy control scheme with a supervisory control scheme based on expert rules. The performance is evaluated by comparing the energy consumption and thermal comfort. (author)« less

Optimal energy-utilization ratio for long-distance cruising of a model fish

NASA Astrophysics Data System (ADS)

Liu, Geng; Yu, Yong-Liang; Tong, Bing-Gang

2012-07-01

The efficiency of total energy utilization and its optimization for long-distance migration of fish have attracted much attention in the past. This paper presents theoretical and computational research, clarifying the above well-known classic questions. Here, we specify the energy-utilization ratio (fη) as a scale of cruising efficiency, which consists of the swimming speed over the sum of the standard metabolic rate and the energy consumption rate of muscle activities per unit mass. Theoretical formulation of the function fη is made and it is shown that based on a basic dimensional analysis, the main dimensionless parameters for our simplified model are the Reynolds number (Re) and the dimensionless quantity of the standard metabolic rate per unit mass (Rpm). The swimming speed and the hydrodynamic power output in various conditions can be computed by solving the coupled Navier-Stokes equations and the fish locomotion dynamic equations. Again, the energy consumption rate of muscle activities can be estimated by the quotient of dividing the hydrodynamic power by the muscle efficiency studied by previous researchers. The present results show the following: (1) When the value of fη attains a maximum, the dimensionless parameter Rpm keeps almost constant for the same fish species in different sizes. (2) In the above cases, the tail beat period is an exponential function of the fish body length when cruising is optimal, e.g., the optimal tail beat period of Sockeye salmon is approximately proportional to the body length to the power of 0.78. Again, the larger fish's ability of long-distance cruising is more excellent than that of smaller fish. (3) The optimal swimming speed we obtained is consistent with previous researchers’ estimations.

Modified method to improve the design of Petlyuk distillation columns.

PubMed

Zapiain-Salinas, Javier G; Barajas-Fernández, Juan; González-García, Raúl

2014-01-01

A response surface analysis was performed to study the effect of the composition and feeding thermal conditions of ternary mixtures on the number of theoretical stages and the energy consumption of Petlyuk columns. A modification of the pre-design algorithm was necessary for this purpose. The modified algorithm provided feasible results in 100% of the studied cases, compared with only 8.89% for the current algorithm. The proposed algorithm allowed us to attain the desired separations, despite the type of mixture and the operating conditions in the feed stream, something that was not possible with the traditional pre-design method. The results showed that the type of mixture had great influence on the number of stages and on energy consumption. A higher number of stages and a lower consumption of energy were attained with mixtures rich in the light component, while higher energy consumption occurred when the mixture was rich in the heavy component. The proposed strategy expands the search of an optimal design of Petlyuk columns within a feasible region, which allow us to find a feasible design that meets output specifications and low thermal loads.

Electric train energy consumption modeling

DOE PAGES

Wang, Jinghui; Rakha, Hesham A.

2017-05-01

For this paper we develop an electric train energy consumption modeling framework considering instantaneous regenerative braking efficiency in support of a rail simulation system. The model is calibrated with data from Portland, Oregon using an unconstrained non-linear optimization procedure, and validated using data from Chicago, Illinois by comparing model predictions against the National Transit Database (NTD) estimates. The results demonstrate that regenerative braking efficiency varies as an exponential function of the deceleration level, rather than an average constant as assumed in previous studies. The model predictions are demonstrated to be consistent with the NTD estimates, producing a predicted error ofmore » 1.87% and -2.31%. The paper demonstrates that energy recovery reduces the overall power consumption by 20% for the tested Chicago route. Furthermore, the paper demonstrates that the proposed modeling approach is able to capture energy consumption differences associated with train, route and operational parameters, and thus is applicable for project-level analysis. The model can be easily implemented in traffic simulation software, used in smartphone applications and eco-transit programs given its fast execution time and easy integration in complex frameworks.« less

Electric train energy consumption modeling

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wang, Jinghui; Rakha, Hesham A.

For this paper we develop an electric train energy consumption modeling framework considering instantaneous regenerative braking efficiency in support of a rail simulation system. The model is calibrated with data from Portland, Oregon using an unconstrained non-linear optimization procedure, and validated using data from Chicago, Illinois by comparing model predictions against the National Transit Database (NTD) estimates. The results demonstrate that regenerative braking efficiency varies as an exponential function of the deceleration level, rather than an average constant as assumed in previous studies. The model predictions are demonstrated to be consistent with the NTD estimates, producing a predicted error ofmore » 1.87% and -2.31%. The paper demonstrates that energy recovery reduces the overall power consumption by 20% for the tested Chicago route. Furthermore, the paper demonstrates that the proposed modeling approach is able to capture energy consumption differences associated with train, route and operational parameters, and thus is applicable for project-level analysis. The model can be easily implemented in traffic simulation software, used in smartphone applications and eco-transit programs given its fast execution time and easy integration in complex frameworks.« less

System and method for optimal load and source scheduling in context aware homes

DOE Office of Scientific and Technical Information (OSTI.GOV)

Shetty, Pradeep; Foslien Graber, Wendy; Mangsuli, Purnaprajna R.

A controller for controlling energy consumption in a home includes a constraints engine to define variables for multiple appliances in the home corresponding to various home modes and persona of an occupant of the home. A modeling engine models multiple paths of energy utilization of the multiple appliances to place the home into a desired state from a current context. An optimal scheduler receives the multiple paths of energy utilization and generates a schedule as a function of the multiple paths and a selected persona to place the home in a desired state.

The Study of Cross-layer Optimization for Wireless Rechargeable Sensor Networks Implemented in Coal Mines

PubMed Central

Ding, Xu; Shi, Lei; Han, Jianghong; Lu, Jingting

2016-01-01

Wireless sensor networks deployed in coal mines could help companies provide workers working in coal mines with more qualified working conditions. With the underground information collected by sensor nodes at hand, the underground working conditions could be evaluated more precisely. However, sensor nodes may tend to malfunction due to their limited energy supply. In this paper, we study the cross-layer optimization problem for wireless rechargeable sensor networks implemented in coal mines, of which the energy could be replenished through the newly-brewed wireless energy transfer technique. The main results of this article are two-fold: firstly, we obtain the optimal relay nodes’ placement according to the minimum overall energy consumption criterion through the Lagrange dual problem and KKT conditions; secondly, the optimal strategies for recharging locomotives and wireless sensor networks are acquired by solving a cross-layer optimization problem. The cyclic nature of these strategies is also manifested through simulations in this paper. PMID:26828500

The Study of Cross-layer Optimization for Wireless Rechargeable Sensor Networks Implemented in Coal Mines.

PubMed

Ding, Xu; Shi, Lei; Han, Jianghong; Lu, Jingting

2016-01-28

Wireless sensor networks deployed in coal mines could help companies provide workers working in coal mines with more qualified working conditions. With the underground information collected by sensor nodes at hand, the underground working conditions could be evaluated more precisely. However, sensor nodes may tend to malfunction due to their limited energy supply. In this paper, we study the cross-layer optimization problem for wireless rechargeable sensor networks implemented in coal mines, of which the energy could be replenished through the newly-brewed wireless energy transfer technique. The main results of this article are two-fold: firstly, we obtain the optimal relay nodes' placement according to the minimum overall energy consumption criterion through the Lagrange dual problem and KKT conditions; secondly, the optimal strategies for recharging locomotives and wireless sensor networks are acquired by solving a cross-layer optimization problem. The cyclic nature of these strategies is also manifested through simulations in this paper.

Residential Consumer-Centric Demand-Side Management Based on Energy Disaggregation-Piloting Constrained Swarm Intelligence: Towards Edge Computing.

PubMed

Lin, Yu-Hsiu; Hu, Yu-Chen

2018-04-27

The emergence of smart Internet of Things (IoT) devices has highly favored the realization of smart homes in a down-stream sector of a smart grid. The underlying objective of Demand Response (DR) schemes is to actively engage customers to modify their energy consumption on domestic appliances in response to pricing signals. Domestic appliance scheduling is widely accepted as an effective mechanism to manage domestic energy consumption intelligently. Besides, to residential customers for DR implementation, maintaining a balance between energy consumption cost and users’ comfort satisfaction is a challenge. Hence, in this paper, a constrained Particle Swarm Optimization (PSO)-based residential consumer-centric load-scheduling method is proposed. The method can be further featured with edge computing. In contrast with cloud computing, edge computing—a method of optimizing cloud computing technologies by driving computing capabilities at the IoT edge of the Internet as one of the emerging trends in engineering technology—addresses bandwidth-intensive contents and latency-sensitive applications required among sensors and central data centers through data analytics at or near the source of data. A non-intrusive load-monitoring technique proposed previously is utilized to automatic determination of physical characteristics of power-intensive home appliances from users’ life patterns. The swarm intelligence, constrained PSO, is used to minimize the energy consumption cost while considering users’ comfort satisfaction for DR implementation. The residential consumer-centric load-scheduling method proposed in this paper is evaluated under real-time pricing with inclining block rates and is demonstrated in a case study. The experimentation reported in this paper shows the proposed residential consumer-centric load-scheduling method can re-shape loads by home appliances in response to DR signals. Moreover, a phenomenal reduction in peak power consumption is achieved by 13.97%.

An Open Source “Smart Lamp” for the Optimization of Plant Systems and Thermal Comfort of Offices

PubMed Central

Salamone, Francesco; Belussi, Lorenzo; Danza, Ludovico; Ghellere, Matteo; Meroni, Italo

2016-01-01

The article describes the design phase, development and practical application of a smart object integrated in a desk lamp and called “Smart Lamp”, useful to optimize the indoor thermal comfort and energy savings that are two important workplace issues where the comfort of the workers and the consumption of the building strongly affect the economic balance of a company. The Smart Lamp was built using a microcontroller, an integrated temperature and relative humidity sensor, some other modules and a 3D printer. This smart device is similar to the desk lamps that are usually found in offices but it allows one to adjust the indoor thermal comfort, by interacting directly with the air conditioner. After the construction phase, the Smart Lamp was installed in an office normally occupied by four workers to evaluate the indoor thermal comfort and the cooling consumption in summer. The results showed how the application of the Smart Lamp effectively reduced the energy consumption, optimizing the thermal comfort. The use of DIY approach combined with read-write functionality of websites, blog and social platforms, also allowed to customize, improve, share, reproduce and interconnect technologies so that anybody could use them in any occupied environment. PMID:26959035

Theoretical potential for low energy consumption phase change memory utilizing electrostatically-induced structural phase transitions in 2D materials

NASA Astrophysics Data System (ADS)

Rehn, Daniel A.; Li, Yao; Pop, Eric; Reed, Evan J.

2018-01-01

Structural phase-change materials are of great importance for applications in information storage devices. Thermally driven structural phase transitions are employed in phase-change memory to achieve lower programming voltages and potentially lower energy consumption than mainstream nonvolatile memory technologies. However, the waste heat generated by such thermal mechanisms is often not optimized, and could present a limiting factor to widespread use. The potential for electrostatically driven structural phase transitions has recently been predicted and subsequently reported in some two-dimensional materials, providing an athermal mechanism to dynamically control properties of these materials in a nonvolatile fashion while achieving potentially lower energy consumption. In this work, we employ DFT-based calculations to make theoretical comparisons of the energy required to drive electrostatically-induced and thermally-induced phase transitions. Determining theoretical limits in monolayer MoTe2 and thin films of Ge2Sb2Te5, we find that the energy consumption per unit volume of the electrostatically driven phase transition in monolayer MoTe2 at room temperature is 9% of the adiabatic lower limit of the thermally driven phase transition in Ge2Sb2Te5. Furthermore, experimentally reported phase change energy consumption of Ge2Sb2Te5 is 100-10,000 times larger than the adiabatic lower limit due to waste heat flow out of the material, leaving the possibility for energy consumption in monolayer MoTe2-based devices to be orders of magnitude smaller than Ge2Sb2Te5-based devices.

Self-adaptive multimethod optimization applied to a tailored heating forging process

NASA Astrophysics Data System (ADS)

Baldan, M.; Steinberg, T.; Baake, E.

2018-05-01

The presented paper describes an innovative self-adaptive multi-objective optimization code. Investigation goals concern proving the superiority of this code compared to NGSA-II and applying it to an inductor’s design case study addressed to a “tailored” heating forging application. The choice of the frequency and the heating time are followed by the determination of the turns number and their positions. Finally, a straightforward optimization is performed in order to minimize energy consumption using “optimal control”.

Joint optimization of green vehicle scheduling and routing problem with time-varying speeds

PubMed Central

Zhang, Dezhi; Wang, Xin; Ni, Nan; Zhang, Zhuo

2018-01-01

Based on an analysis of the congestion effect and changes in the speed of vehicle flow during morning and evening peaks in a large- or medium-sized city, the piecewise function is used to capture the rules of the time-varying speed of vehicles, which are very important in modelling their fuel consumption and CO2 emission. A joint optimization model of the green vehicle scheduling and routing problem with time-varying speeds is presented in this study. Extra wages during nonworking periods and soft time-window constraints are considered. A heuristic algorithm based on the adaptive large neighborhood search algorithm is also presented. Finally, a numerical simulation example is provided to illustrate the optimization model and its algorithm. Results show that, (1) the shortest route is not necessarily the route that consumes the least energy, (2) the departure time influences the vehicle fuel consumption and CO2 emissions and the optimal departure time saves on fuel consumption and reduces CO2 emissions by up to 5.4%, and (3) extra driver wages have significant effects on routing and departure time slot decisions. PMID:29466370

Joint optimization of green vehicle scheduling and routing problem with time-varying speeds.

PubMed

Zhang, Dezhi; Wang, Xin; Li, Shuangyan; Ni, Nan; Zhang, Zhuo

2018-01-01

Based on an analysis of the congestion effect and changes in the speed of vehicle flow during morning and evening peaks in a large- or medium-sized city, the piecewise function is used to capture the rules of the time-varying speed of vehicles, which are very important in modelling their fuel consumption and CO2 emission. A joint optimization model of the green vehicle scheduling and routing problem with time-varying speeds is presented in this study. Extra wages during nonworking periods and soft time-window constraints are considered. A heuristic algorithm based on the adaptive large neighborhood search algorithm is also presented. Finally, a numerical simulation example is provided to illustrate the optimization model and its algorithm. Results show that, (1) the shortest route is not necessarily the route that consumes the least energy, (2) the departure time influences the vehicle fuel consumption and CO2 emissions and the optimal departure time saves on fuel consumption and reduces CO2 emissions by up to 5.4%, and (3) extra driver wages have significant effects on routing and departure time slot decisions.

Evolutionary optimality applied to Drosophila experiments: hypothesis of constrained reproductive efficiency.

PubMed

Novoseltsev, V N; Arking, R; Novoseltseva, J A; Yashin, A I

2002-06-01

The general purpose of the paper is to test evolutionary optimality theories with experimental data on reproduction, energy consumption, and longevity in a particular Drosophila genotype. We describe the resource allocation in Drosophila females in terms of the oxygen consumption rates devoted to reproduction and to maintenance. The maximum ratio of the component spent on reproduction to the total rate of oxygen consumption, which can be realized by the female reproductive machinery, is called metabolic reproductive efficiency (MRE). We regard MRE as an evolutionary constraint. We demonstrate that MRE may be evaluated for a particular Drosophila phenotype given the fecundity pattern, the age-related pattern of oxygen consumption rate, and the longevity. We use a homeostatic model of aging to simulate a life history of a representative female fly, which describes the control strain in the long-term experiments with the Wayne State Drosophila genotype. We evaluate the theoretically optimal trade-offs in this genotype. Then we apply the Van Noordwijk-de Jong resource acquisition and allocation model, Kirkwood's disposable soma theory. and the Partridge-Barton optimality approach to test if the experimentally observed trade-offs may be regarded as close to the theoretically optimal ones. We demonstrate that the two approaches by Partridge-Barton and Kirkwood allow a positive answer to the question, whereas the Van Noordwijk-de Jong approach may be used to illustrate the optimality. We discuss the prospects of applying the proposed technique to various Drosophila experiments, in particular those including manipulations affecting fecundity.

State-of-The-Art of Modeling Methodologies and Optimization Operations in Integrated Energy System

NASA Astrophysics Data System (ADS)

Zheng, Zhan; Zhang, Yongjun

2017-08-01

Rapid advances in low carbon technologies and smart energy communities are reshaping future patterns. Uncertainty in energy productions and demand sides are paving the way towards decentralization management. Current energy infrastructures could not meet with supply and consumption challenges, along with emerging environment and economic requirements. Integrated Energy System(IES) whereby electric power, natural gas, heating couples with each other demonstrates that such a significant technique would gradually become one of main comprehensive and optimal energy solutions with high flexibility, friendly renewables absorption and improving efficiency. In these global energy trends, we summarize this literature review. Firstly the accurate definition and characteristics of IES have been presented. Energy subsystem and coupling elements modeling issues are analyzed. It is pointed out that decomposed and integrated analysis methods are the key algorithms for IES optimization operations problems, followed by exploring the IES market mechanisms. Finally several future research tendencies of IES, such as dynamic modeling, peer-to-peer trading, couple market design, sare under discussion.

H∞ memory feedback control with input limitation minimization for offshore jacket platform stabilization

NASA Astrophysics Data System (ADS)

Yang, Jia Sheng

2018-06-01

In this paper, we investigate a H∞ memory controller with input limitation minimization (HMCIM) for offshore jacket platforms stabilization. The main objective of this study is to reduce the control consumption as well as protect the actuator when satisfying the requirement of the system performance. First, we introduce a dynamic model of offshore platform with low order main modes based on mode reduction method in numerical analysis. Then, based on H∞ control theory and matrix inequality techniques, we develop a novel H∞ memory controller with input limitation. Furthermore, a non-convex optimization model to minimize input energy consumption is proposed. Since it is difficult to solve this non-convex optimization model by optimization algorithm, we use a relaxation method with matrix operations to transform this non-convex optimization model to be a convex optimization model. Thus, it could be solved by a standard convex optimization solver in MATLAB or CPLEX. Finally, several numerical examples are given to validate the proposed models and methods.

Optimal Location through Distributed Algorithm to Avoid Energy Hole in Mobile Sink WSNs

PubMed Central

Qing-hua, Li; Wei-hua, Gui; Zhi-gang, Chen

2014-01-01

In multihop data collection sensor network, nodes near the sink need to relay on remote data and, thus, have much faster energy dissipation rate and suffer from premature death. This phenomenon causes energy hole near the sink, seriously damaging the network performance. In this paper, we first compute energy consumption of each node when sink is set at any point in the network through theoretical analysis; then we propose an online distributed algorithm, which can adjust sink position based on the actual energy consumption of each node adaptively to get the actual maximum lifetime. Theoretical analysis and experimental results show that the proposed algorithms significantly improve the lifetime of wireless sensor network. It lowers the network residual energy by more than 30% when it is dead. Moreover, the cost for moving the sink is relatively smaller. PMID:24895668

An Analysis of the DER Adoption Climate in Japan UsingOptimization Results for Prototype Buildings with U.S. Comparisons

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhou, Nan; Marnay, Chris; Firestone, Ryan

2006-06-16

This research demonstrates economically optimal distributedenergy resource (DER) system choice using the DER choice and operationsoptimization program, the Distributed Energy Resources Customer AdoptionModel (DER-CAM). DER-CAM finds the optimal combination of installedequipment given prevailing utility tariffs and fuel prices, siteelectrical and thermal loads (including absorption cooling), and a menuof available equipment. It provides a global optimization, albeitidealized, that shows how site useful energy loads can be served atminimum cost. Five prototype Japanese commercial buildings are examinedand DER-CAM is applied to select the economically optimal DER system foreach. Based on the optimization results, energy and emission reductionsare evaluated. Significant decreases in fuelmore » consumption, carbonemissions, and energy costs were seen in the DER-CAM results. Savingswere most noticeable in the prototype sports facility, followed by thehospital, hotel, and office building. Results show that DER with combinedheat and power equipment is a promising efficiency and carbon mitigationstrategy, but that precise system design is necessary. Furthermore, aJapan-U.S. comparison study of policy, technology, and utility tariffsrelevant to DER installation is presented.« less

An Optimization and Assessment on DG adoption in JapanesePrototype Buildings

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhou, Nan; Marnay, Chris; Firestone, Ryan

2005-11-30

This research investigates a method of choosing economicallyoptimal DER, expanding on prior studies at the Berkeley Lab using the DERdesign optimization program, the Distributed Energy Resources CustomerAdoption Model (DER-CAM). DER-CAM finds the optimal combination ofinstalled equipment from available DER technologies, given prevailingutility tariffs, site electrical and thermal loads, and a menu ofavailable equipment. It provides a global optimization, albeit idealized,that shows how the site energy load scan be served at minimum cost byselection and operation of on-site generation, heat recovery, andcooling. Five prototype Japanese commercial buildings are examined andDER-CAM applied to select thee conomically optimal DER system for each.The fivemore » building types are office, hospital, hotel, retail, and sportsfacility. Based on the optimization results, energy and emissionreductions are evaluated. Furthermore, a Japan-U.S. comparison study ofpolicy, technology, and utility tariffs relevant to DER installation ispresented. Significant decreases in fuel consumption, carbon emissions,and energy costs were seen in the DER-CAM results. Savings were mostnoticeable in the sports facility, followed by the hospital, hotel, andoffice building.« less

Energy Performance Monitoring and Optimization System for DoD Campuses

DTIC Science & Technology

2014-02-01

EPMO system exceeded the energy consumption reduction target of 20% and improved occupant thermal comfort by reducing the number of instances outside... thermal comfort constraints, and plant efficiency EW2011-42 Final Report 8 February 2014 in the same framework [30-33]. In this framework, 4-hour...conjunction with information such as: thermal comfort constraints, equipment constraints, energy performance objectives. All the information is

Model-based minimization algorithm of a supercritical helium loop consumption subject to operational constraints

NASA Astrophysics Data System (ADS)

Bonne, F.; Bonnay, P.; Girard, A.; Hoa, C.; Lacroix, B.; Le Coz, Q.; Nicollet, S.; Poncet, J.-M.; Zani, L.

2017-12-01

Supercritical helium loops at 4.2 K are the baseline cooling strategy of tokamaks superconducting magnets (JT-60SA, ITER, DEMO, etc.). This loops work with cryogenic circulators that force a supercritical helium flow through the superconducting magnets in order that the temperature stay below the working range all along their length. This paper shows that a supercritical helium loop associated with a saturated liquid helium bath can satisfy temperature constraints in different ways (playing on bath temperature and on the supercritical flow), but that only one is optimal from an energy point of view (every Watt consumed at 4.2 K consumes at least 220 W of electrical power). To find the optimal operational conditions, an algorithm capable of minimizing an objective function (energy consumption at 5 bar, 5 K) subject to constraints has been written. This algorithm works with a supercritical loop model realized with the Simcryogenics [2] library. This article describes the model used and the results of constrained optimization. It will be possible to see that the changes in operating point on the temperature of the magnet (e.g. in case of a change in the plasma configuration) involves large changes on the cryodistribution optimal operating point. Recommendations will be made to ensure that the energetic consumption is kept as low as possible despite the changing operating point. This work is partially supported by EUROfusion Consortium through the Euratom Research and Training Program 20142018 under Grant 633053.

Energy demand and environmental implications in urban transport — Case of Delhi

NASA Astrophysics Data System (ADS)

Bose, Ranjan Kumar

A simple model of passenger transport in the city of Delhi has been developed using a computer-based software called—Long Range Energy Alternatives Planning (LEAP) and the associated Environmental Database (EDB) model. The hierarchical structure of LEAP represents the traffic patterns in terms of passenger travel demand, mode (rail/road), type of vehicle and occupancy (persons per vehicle). Transport database in Delhi together with fuel consumption values for the vehicle types, formed the basis of the transport demand and energy consumption calculations. Emission factors corresponding to the actual vehicle types and driving conditions in Delhi is introduced into the EDB and linked to the energy consumption values for estimating total emission of CO, HC, NO x, SO 2 Pb and TSP. The LEAP model is used to estimate total energy demand and the vehicular emissions for the base year-1990/91 and extrapolate for the future—1994/95, 2000/01, 2004/05 and 2009/10, respectively. The model is run under five alternative scenarios to study the impact of different urban transport policy initiatives that would reduce total energy requirement in the transport sector of Delhi and also reduce emission. The prime objective is to arrive at an optimal transport policy which limits the future growth of fuel consumption as well as air pollution.

Power optimization of ultrasonic friction-modulation tactile interfaces.

PubMed

Wiertlewski, Michael; Colgate, J Edward

2015-01-01

Ultrasonic friction-modulation devices provide rich tactile sensation on flat surfaces and have the potential to restore tangibility to touchscreens. To date, their adoption into consumer electronics has been in part limited by relatively high power consumption, incompatible with the requirements of battery-powered devices. This paper introduces a method that optimizes the energy efficiency and performance of this class of devices. It considers optimal energy transfer to the impedance provided by the finger interacting with the surface. Constitutive equations are determined from the mode shape of the interface and the piezoelectric coupling of the actuator. The optimization procedure employs a lumped parameter model to simplify the treatment of the problem. Examples and an experimental study show the evolution of the optimal design as a function of the impedance of the finger.

Energy-efficient routing, modulation and spectrum allocation in elastic optical networks

NASA Astrophysics Data System (ADS)

Tan, Yanxia; Gu, Rentao; Ji, Yuefeng

2017-07-01

With tremendous growth in bandwidth demand, energy consumption problem in elastic optical networks (EONs) becomes a hot topic with wide concern. The sliceable bandwidth-variable transponder in EON, which can transmit/receive multiple optical flows, was recently proposed to improve a transponder's flexibility and save energy. In this paper, energy-efficient routing, modulation and spectrum allocation (EE-RMSA) in EONs with sliceable bandwidth-variable transponder is studied. To decrease the energy consumption, we develop a Mixed Integer Linear Programming (MILP) model with corresponding EE-RMSA algorithm for EONs. The MILP model jointly considers the modulation format and optical grooming in the process of routing and spectrum allocation with the objective of minimizing the energy consumption. With the help of genetic operators, the EE-RMSA algorithm iteratively optimizes the feasible routing path, modulation format and spectrum resources solutions by explore the whole search space. In order to save energy, the optical-layer grooming strategy is designed to transmit the lightpath requests. Finally, simulation results verify that the proposed scheme is able to reduce the energy consumption of the network while maintaining the blocking probability (BP) performance compare with the existing First-Fit-KSP algorithm, Iterative Flipping algorithm and EAMGSP algorithm especially in large network topology. Our results also demonstrate that the proposed EE-RMSA algorithm achieves almost the same performance as MILP on an 8-node network.

Optimization design of submerged propeller in oxidation ditch by computational fluid dynamics and comparison with experiments.

PubMed

Zhang, Yuquan; Zheng, Yuan; Fernandez-Rodriguez, E; Yang, Chunxia; Zhu, Yantao; Liu, Huiwen; Jiang, Hao

The operating condition of a submerged propeller has a significant impact on flow field and energy consumption of the oxidation ditch. An experimentally validated numerical model, based on the computational fluid dynamics (CFD) tool, is presented to optimize the operating condition by considering two important factors: flow field and energy consumption. Performance demonstration and comparison of different operating conditions were carried out in a Carrousel oxidation ditch at the Yingtang wastewater treatment plants in Anhui Province, China. By adjusting the position and rotating speed together with the number of submerged propellers, problems of sludge deposit and the low velocity in the bend could be solved in a most cost-effective way. The simulated results were acceptable compared with the experimental data and the following results were obtained. The CFD model characterized flow pattern and energy consumption in the full-scale oxidation ditch. The predicted flow field values were within -1.28 ± 7.14% difference from the measured values. By determining three sets of propellers under the rotating speed of 6.50 rad/s with one located 5 m from the first curved wall, after numerical simulation and actual measurement, not only the least power density but also the requirement of the flow pattern could be realized.

A hierarchical approach for the design improvements of an Organocat biorefinery.

PubMed

Abdelaziz, Omar Y; Gadalla, Mamdouh A; El-Halwagi, Mahmoud M; Ashour, Fatma H

2015-04-01

Lignocellulosic biomass has emerged as a potentially attractive renewable energy source. Processing technologies of such biomass, particularly its primary separation, still lack economic justification due to intense energy requirements. Establishing an economically viable and energy efficient biorefinery scheme is a significant challenge. In this work, a systematic approach is proposed for improving basic/existing biorefinery designs. This approach is based on enhancing the efficiency of mass and energy utilization through the use of a hierarchical design approach that involves mass and energy integration. The proposed procedure is applied to a novel biorefinery called Organocat to minimize its energy and mass consumption and total annualized cost. An improved heat exchanger network with minimum energy consumption of 4.5 MJ/kgdry biomass is designed. An optimal recycle network with zero fresh water usage and minimum waste discharge is also constructed, making the process more competitive and economically attractive. Copyright © 2015 Elsevier Ltd. All rights reserved.

Improving indoor air quality through botanical air filtration in energy efficient residences

NASA Astrophysics Data System (ADS)

Newkirk, Daniel W.

According to the U.S. EPA, the average American spends 90% of their time indoors where pollutants are two to five times more prevalent than outside. The consequences of these pollutants are estimated to cost the U.S. 125 billion dollars in lost health and productivity. Background literature suggests botanical air filtration may be able to solve this problem by leveraging the natural ability of plants to purify indoor air. By improving indoor air quality, energy consumption can also be reduced by bringing in less outside air to dilute contaminants within the space. A botanical air filter, called the Biowall, was designed and grown aeroponically in a sealed environmental chamber. Precise measurements of air temperature, air humidity, air quality and energy consumption were made under various lighting levels, plant species and watering strategies to optimize its performance. It was found to reduce indoor air pollutants 60 percent and has the potential to reduce heating and cooling energy consumption by 20 to 30 percent.

Prospects for reduced energy transports: A preliminary analysis

NASA Technical Reports Server (NTRS)

Ardema, M. D.; Harper, M.; Smith, C. L.; Waters, M. H.; Williams, L. J.

1974-01-01

The recent energy crisis and subsequent substantial increase in fuel prices have provided increased incentive to reduce the fuel consumption of civil transport aircraft. At the present time many changes in operational procedures have been introduced to decrease fuel consumption of the existing fleet. In the future, however, it may become desirable or even necessary to introduce new fuel-conservative aircraft designs. This paper reports the results of a preliminary study of new near-term fuel conservative aircraft. A parametric study was made to determine the effects of cruise Mach number and fuel cost on the optimum configuration characteristics and on economic performance. For each design, the wing geometry was optimized to give maximum return on investment at a particular fuel cost. Based on the results of the parametric study, a nominal reduced energy configuration was selected. Compared with existing transport designs, the reduced energy design has a higher aspect ratio wing with lower sweep, and cruises at a lower Mach number. It has about 30% less fuel consumption on a seat-mile basis.

Suboptimal artificial potential function sliding mode control for spacecraft rendezvous with obstacle avoidance

NASA Astrophysics Data System (ADS)

Cao, Lu; Qiao, Dong; Xu, Jingwen

2018-02-01

Sub-Optimal Artificial Potential Function Sliding Mode Control (SOAPF-SMC) is proposed for the guidance and control of spacecraft rendezvous considering the obstacles avoidance, which is derived based on the theories of artificial potential function (APF), sliding mode control (SMC) and state dependent riccati equation (SDRE) technique. This new methodology designs a new improved APF to describe the potential field. It can guarantee the value of potential function converge to zero at the desired state. Moreover, the nonlinear terminal sliding mode is introduced to design the sliding mode surface with the potential gradient of APF, which offer a wide variety of controller design alternatives with fast and finite time convergence. Based on the above design, the optimal control theory (SDRE) is also employed to optimal the shape parameter of APF, in order to add some degree of optimality in reducing energy consumption. The new methodology is applied to spacecraft rendezvous with the obstacles avoidance problem, which is simulated to compare with the traditional artificial potential function sliding mode control (APF-SMC) and SDRE to evaluate the energy consumption and control precision. It is demonstrated that the presented method can avoiding dynamical obstacles whilst satisfying the requirements of autonomous rendezvous. In addition, it can save more energy than the traditional APF-SMC and also have better control accuracy than the SDRE.

Integrating water use into Southern California's power dispatch: an evaluation of the potential for cost-effective water conservation

NASA Astrophysics Data System (ADS)

Bolorinos, J.; Ajami, N.; Yu, Y.; Rajagopal, R.

2016-12-01

Urban water supply and energy systems in the arid Southwestern United States are closely linked. Freshwater use by the electricity sector in particular represents a sizable portion of total water consumption in the region. Nonetheless, the dispatch of water and energy resources is managed separately, and no research to-date has examined the water conservation potential presented by the electricity sector. This study gauges the potential water savings that could be achieved including water use in the power dispatch process in Southern California by simulating a DC Optimal Power Flow for a simplified model of the region's power network. The simulation uses historical power consumption data, historical power production data and water use data from the US Geological Survey, the California Energy Commission and the US Energy Information Administration to estimate freshwater consumption by the region's thermoelectric power generation fleet. Preliminary results indicate that power system freshwater consumption could be reduced by as much as 20% at a minimal cost penalty, with potential for even greater savings. Model results show that Southern California's power system has the ability to competitively shift the use of some of the region's water resources from electricity to urban consumption, and suggests that water use should be incorporated into the policy-making process to enhance the efficient use of the state's interconnected water and energy resources.

Bi-level Optimization Method of Air-conditioning System Based on Office Building Energy Storage Characteristics

NASA Astrophysics Data System (ADS)

Wang, Qingze; Chen, Xingying; Ji, Li; Liao, Yingchen; Yu, Kun

2017-05-01

The air-conditioning system of office building is a large power consumption terminal equipment, whose unreasonable operation mode leads to low energy efficiency. Realizing the optimization of the air-conditioning system has become one of the important research contents of the electric power demand response. In this paper, in order to save electricity cost and improve energy efficiency, bi-level optimization method of air-conditioning system based on TOU price is put forward by using the energy storage characteristics of the office building itself. In the upper level, the operation mode of the air-conditioning system is optimized in order to minimize the uses’ electricity cost in the premise of ensuring user’ comfort according to the information of outdoor temperature and TOU price, and the cooling load of the air-conditioning is output to the lower level; In the lower level, the distribution mode of cooling load among the multi chillers is optimized in order to maximize the energy efficiency according to the characteristics of each chiller. Finally, the experimental results under different modes demonstrate that the strategy can improve the energy efficiency of chillers and save the electricity cost for users.

Optimal Control of Connected and Automated Vehicles at Roundabouts

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhao, Liuhui; Malikopoulos, Andreas; Rios-Torres, Jackeline

Connectivity and automation in vehicles provide the most intriguing opportunity for enabling users to better monitor transportation network conditions and make better operating decisions to improve safety and reduce pollution, energy consumption, and travel delays. This study investigates the implications of optimally coordinating vehicles that are wirelessly connected to each other and to an infrastructure in roundabouts to achieve a smooth traffic flow without stop-and-go driving. We apply an optimization framework and an analytical solution that allows optimal coordination of vehicles for merging in such traffic scenario. The effectiveness of the efficiency of the proposed approach is validated through simulationmore » and it is shown that coordination of vehicles can reduce total travel time by 3~49% and fuel consumption by 2~27% with respect to different traffic levels. In addition, network throughput is improved by up to 25% due to elimination of stop-and-go driving behavior.« less

Pruning-Based, Energy-Optimal, Deterministic I/O Device Scheduling for Hard Real-Time Systems

DTIC Science & Technology

2005-02-01

However, DPM via I/O device scheduling for hard real - time systems has received relatively little attention. In this paper,we present an offline I/O...polynomial time. We present experimental results to show that EDS and MDO reduce the energy consumption of I/O devices significantly for hard real - time systems .

Energy management of a university campus utilizing short-term load forecasting with an artificial neural network

NASA Astrophysics Data System (ADS)

Palchak, David

Electrical load forecasting is a tool that has been utilized by distribution designers and operators as a means for resource planning and generation dispatch. The techniques employed in these predictions are proving useful in the growing market of consumer, or end-user, participation in electrical energy consumption. These predictions are based on exogenous variables, such as weather, and time variables, such as day of week and time of day as well as prior energy consumption patterns. The participation of the end-user is a cornerstone of the Smart Grid initiative presented in the Energy Independence and Security Act of 2007, and is being made possible by the emergence of enabling technologies such as advanced metering infrastructure. The optimal application of the data provided by an advanced metering infrastructure is the primary motivation for the work done in this thesis. The methodology for using this data in an energy management scheme that utilizes a short-term load forecast is presented. The objective of this research is to quantify opportunities for a range of energy management and operation cost savings of a university campus through the use of a forecasted daily electrical load profile. The proposed algorithm for short-term load forecasting is optimized for Colorado State University's main campus, and utilizes an artificial neural network that accepts weather and time variables as inputs. The performance of the predicted daily electrical load is evaluated using a number of error measurements that seek to quantify the best application of the forecast. The energy management presented utilizes historical electrical load data from the local service provider to optimize the time of day that electrical loads are being managed. Finally, the utilization of forecasts in the presented energy management scenario is evaluated based on cost and energy savings.

A novel load balanced energy conservation approach in WSN using biogeography based optimization

NASA Astrophysics Data System (ADS)

Kaushik, Ajay; Indu, S.; Gupta, Daya

2017-09-01

Clustering sensor nodes is an effective technique to reduce energy consumption of the sensor nodes and maximize the lifetime of Wireless sensor networks. Balancing load of the cluster head is an important factor in long run operation of WSNs. In this paper we propose a novel load balancing approach using biogeography based optimization (LB-BBO). LB-BBO uses two separate fitness functions to perform load balancing of equal and unequal load respectively. The proposed method is simulated using matlab and compared with existing methods. The proposed method shows better performance than all the previous works implemented for energy conservation in WSN

Voltage scheduling for low power/energy

NASA Astrophysics Data System (ADS)

Manzak, Ali

2001-07-01

Power considerations have become an increasingly dominant factor in the design of both portable and desk-top systems. An effective way to reduce power consumption is to lower the supply voltage since voltage is quadratically related to power. This dissertation considers the problem of lowering the supply voltage at (i) the system level and at (ii) the behavioral level. At the system level, the voltage of the variable voltage processor is dynamically changed with the work load. Processors with limited sized buffers as well as those with very large buffers are considered. Given the task arrival times, deadline times, execution times, periods and switching activities, task scheduling algorithms that minimize energy or peak power are developed for the processors equipped with very large buffers. A relation between the operating voltages of the tasks for minimum energy/power is determined using the Lagrange multiplier method, and an iterative algorithm that utilizes this relation is developed. Experimental results show that the voltage assignment obtained by the proposed algorithm is very close (0.1% error) to that of the optimal energy assignment and the optimal peak power (1% error) assignment. Next, on-line and off-fine minimum energy task scheduling algorithms are developed for processors with limited sized buffers. These algorithms have polynomial time complexity and present optimal (off-line) and close-to-optimal (on-line) solutions. A procedure to calculate the minimum buffer size given information about the size of the task (maximum, minimum), execution time (best case, worst case) and deadlines is also presented. At the behavioral level, resources operating at multiple voltages are used to minimize power while maintaining the throughput. Such a scheme has the advantage of allowing modules on the critical paths to be assigned to the highest voltage levels (thus meeting the required timing constraints) while allowing modules on non-critical paths to be assigned to lower voltage levels (thus reducing the power consumption). A polynomial time resource and latency constrained scheduling algorithm is developed to distribute the available slack among the nodes such that power consumption is minimum. The algorithm is iterative and utilizes the slack based on the Lagrange multiplier method.

QoS and energy aware cooperative routing protocol for wildfire monitoring wireless sensor networks.

PubMed

Maalej, Mohamed; Cherif, Sofiane; Besbes, Hichem

2013-01-01

Wireless sensor networks (WSN) are presented as proper solution for wildfire monitoring. However, this application requires a design of WSN taking into account the network lifetime and the shadowing effect generated by the trees in the forest environment. Cooperative communication is a promising solution for WSN which uses, at each hop, the resources of multiple nodes to transmit its data. Thus, by sharing resources between nodes, the transmission quality is enhanced. In this paper, we use the technique of reinforcement learning by opponent modeling, optimizing a cooperative communication protocol based on RSSI and node energy consumption in a competitive context (RSSI/energy-CC), that is, an energy and quality-of-service aware-based cooperative communication routing protocol. Simulation results show that the proposed algorithm performs well in terms of network lifetime, packet delay, and energy consumption.

Energy-efficient container handling using hybrid model predictive control

NASA Astrophysics Data System (ADS)

Xin, Jianbin; Negenborn, Rudy R.; Lodewijks, Gabriel

2015-11-01

The performance of container terminals needs to be improved to adapt the growth of containers while maintaining sustainability. This paper provides a methodology for determining the trajectory of three key interacting machines for carrying out the so-called bay handling task, involving transporting containers between a vessel and the stacking area in an automated container terminal. The behaviours of the interacting machines are modelled as a collection of interconnected hybrid systems. Hybrid model predictive control (MPC) is proposed to achieve optimal performance, balancing the handling capacity and energy consumption. The underlying control problem is hereby formulated as a mixed-integer linear programming problem. Simulation studies illustrate that a higher penalty on energy consumption indeed leads to improved sustainability using less energy. Moreover, simulations illustrate how the proposed energy-efficient hybrid MPC controller performs under different types of uncertainties.

A rapid and low energy consumption method to decolorize the high concentration triphenylmethane dye wastewater: operational parameters optimization for the ultrasonic-assisted ozone oxidation process.

PubMed

Zhou, Xian-Jiao; Guo, Wan-Qian; Yang, Shan-Shan; Ren, Nan-Qi

2012-02-01

This research set up an ultrasonic-assisted ozone oxidation process (UAOOP) to decolorize the triphenylmethane dyes wastewater. Five factors - temperature, initial pH, reaction time, ultrasonic power (low frequency 20 kHz), and ozone concentration - were investigated. Response surface methodology was used to find out the major factors influencing color removal rate and the interactions between these factors, and optimized the operating parameters as well. Under the experimental conditions: reaction temperature 39.81 °C, initial pH 5.29, ultrasonic power 60 W and ozone concentration 0.17 g/L, the highest color removals were achieved with 10 min reaction time and the initial concentration of the MG solution was 1000 mg/L. The optimal results indicated that the UAOOP was a rapid, efficient and low energy consumption technique to decolorize the high concentration MG wastewater. The predicted model was approximately in accordance with the experimental cases with correlation coefficients R(2) and R(adj)(2) of 0.9103 and 0.8386. Crown Copyright © 2011. Published by Elsevier Ltd. All rights reserved.

A Power-Optimized Cooperative MAC Protocol for Lifetime Extension in Wireless Sensor Networks.

PubMed

Liu, Kai; Wu, Shan; Huang, Bo; Liu, Feng; Xu, Zhen

2016-10-01

In wireless sensor networks, in order to satisfy the requirement of long working time of energy-limited nodes, we need to design an energy-efficient and lifetime-extended medium access control (MAC) protocol. In this paper, a node cooperation mechanism that one or multiple nodes with higher channel gain and sufficient residual energy help a sender relay its data packets to its recipient is employed to achieve this objective. We first propose a transmission power optimization algorithm to prolong network lifetime by optimizing the transmission powers of the sender and its cooperative nodes to maximize their minimum residual energy after their data packet transmissions. Based on it, we propose a corresponding power-optimized cooperative MAC protocol. A cooperative node contention mechanism is designed to ensure that the sender can effectively select a group of cooperative nodes with the lowest energy consumption and the best channel quality for cooperative transmissions, thus further improving the energy efficiency. Simulation results show that compared to typical MAC protocol with direct transmissions and energy-efficient cooperative MAC protocol, the proposed cooperative MAC protocol can efficiently improve the energy efficiency and extend the network lifetime.

A Power-Optimized Cooperative MAC Protocol for Lifetime Extension in Wireless Sensor Networks

PubMed Central

Liu, Kai; Wu, Shan; Huang, Bo; Liu, Feng; Xu, Zhen

2016-01-01

In wireless sensor networks, in order to satisfy the requirement of long working time of energy-limited nodes, we need to design an energy-efficient and lifetime-extended medium access control (MAC) protocol. In this paper, a node cooperation mechanism that one or multiple nodes with higher channel gain and sufficient residual energy help a sender relay its data packets to its recipient is employed to achieve this objective. We first propose a transmission power optimization algorithm to prolong network lifetime by optimizing the transmission powers of the sender and its cooperative nodes to maximize their minimum residual energy after their data packet transmissions. Based on it, we propose a corresponding power-optimized cooperative MAC protocol. A cooperative node contention mechanism is designed to ensure that the sender can effectively select a group of cooperative nodes with the lowest energy consumption and the best channel quality for cooperative transmissions, thus further improving the energy efficiency. Simulation results show that compared to typical MAC protocol with direct transmissions and energy-efficient cooperative MAC protocol, the proposed cooperative MAC protocol can efficiently improve the energy efficiency and extend the network lifetime. PMID:27706079

Adaptive MCS selection and resource planning for energy-efficient communication in LTE-M based IoT sensing platform.

PubMed

Dao, Nhu-Ngoc; Park, Minho; Kim, Joongheon; Cho, Sungrae

2017-01-01

As an important part of IoTization trends, wireless sensing technologies have been involved in many fields of human life. In cellular network evolution, the long term evolution advanced (LTE-A) networks including machine-type communication (MTC) features (named LTE-M) provide a promising infrastructure for a proliferation of Internet of things (IoT) sensing platform. However, LTE-M may not be optimally exploited for directly supporting such low-data-rate devices in terms of energy efficiency since it depends on core technologies of LTE that are originally designed for high-data-rate services. Focusing on this circumstance, we propose a novel adaptive modulation and coding selection (AMCS) algorithm to address the energy consumption problem in the LTE-M based IoT-sensing platform. The proposed algorithm determines the optimal pair of MCS and the number of primary resource blocks (#PRBs), at which the transport block size is sufficient to packetize the sensing data within the minimum transmit power. In addition, a quantity-oriented resource planning (QORP) technique that utilizes these optimal MCS levels as main criteria for spectrum allocation has been proposed for better adapting to the sensing node requirements. The simulation results reveal that the proposed approach significantly reduces the energy consumption of IoT sensing nodes and #PRBs up to 23.09% and 25.98%, respectively.

Adaptive MCS selection and resource planning for energy-efficient communication in LTE-M based IoT sensing platform

PubMed Central

Dao, Nhu-Ngoc; Park, Minho; Kim, Joongheon

2017-01-01

As an important part of IoTization trends, wireless sensing technologies have been involved in many fields of human life. In cellular network evolution, the long term evolution advanced (LTE-A) networks including machine-type communication (MTC) features (named LTE-M) provide a promising infrastructure for a proliferation of Internet of things (IoT) sensing platform. However, LTE-M may not be optimally exploited for directly supporting such low-data-rate devices in terms of energy efficiency since it depends on core technologies of LTE that are originally designed for high-data-rate services. Focusing on this circumstance, we propose a novel adaptive modulation and coding selection (AMCS) algorithm to address the energy consumption problem in the LTE-M based IoT-sensing platform. The proposed algorithm determines the optimal pair of MCS and the number of primary resource blocks (#PRBs), at which the transport block size is sufficient to packetize the sensing data within the minimum transmit power. In addition, a quantity-oriented resource planning (QORP) technique that utilizes these optimal MCS levels as main criteria for spectrum allocation has been proposed for better adapting to the sensing node requirements. The simulation results reveal that the proposed approach significantly reduces the energy consumption of IoT sensing nodes and #PRBs up to 23.09% and 25.98%, respectively. PMID:28796804

Automated Dynamic Demand Response Implementation on a Micro-grid

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kuppannagari, Sanmukh R.; Kannan, Rajgopal; Chelmis, Charalampos

In this paper, we describe a system for real-time automated Dynamic and Sustainable Demand Response with sparse data consumption prediction implemented on the University of Southern California campus microgrid. Supply side approaches to resolving energy supply-load imbalance do not work at high levels of renewable energy penetration. Dynamic Demand Response (D 2R) is a widely used demand-side technique to dynamically adjust electricity consumption during peak load periods. Our D 2R system consists of accurate machine learning based energy consumption forecasting models that work with sparse data coupled with fast and sustainable load curtailment optimization algorithms that provide the ability tomore » dynamically adapt to changing supply-load imbalances in near real-time. Our Sustainable DR (SDR) algorithms attempt to distribute customer curtailment evenly across sub-intervals during a DR event and avoid expensive demand peaks during a few sub-intervals. It also ensures that each customer is penalized fairly in order to achieve the targeted curtailment. We develop near linear-time constant-factor approximation algorithms along with Polynomial Time Approximation Schemes (PTAS) for SDR curtailment that minimizes the curtailment error defined as the difference between the target and achieved curtailment values. Our SDR curtailment problem is formulated as an Integer Linear Program that optimally matches customers to curtailment strategies during a DR event while also explicitly accounting for customer strategy switching overhead as a constraint. We demonstrate the results of our D 2R system using real data from experiments performed on the USC smartgrid and show that 1) our prediction algorithms can very accurately predict energy consumption even with noisy or missing data and 2) our curtailment algorithms deliver DR with extremely low curtailment errors in the 0.01-0.05 kWh range.« less

Modified method to improve the design of Petlyuk distillation columns

PubMed Central

2014-01-01

Background A response surface analysis was performed to study the effect of the composition and feeding thermal conditions of ternary mixtures on the number of theoretical stages and the energy consumption of Petlyuk columns. A modification of the pre-design algorithm was necessary for this purpose. Results The modified algorithm provided feasible results in 100% of the studied cases, compared with only 8.89% for the current algorithm. The proposed algorithm allowed us to attain the desired separations, despite the type of mixture and the operating conditions in the feed stream, something that was not possible with the traditional pre-design method. The results showed that the type of mixture had great influence on the number of stages and on energy consumption. A higher number of stages and a lower consumption of energy were attained with mixtures rich in the light component, while higher energy consumption occurred when the mixture was rich in the heavy component. Conclusions The proposed strategy expands the search of an optimal design of Petlyuk columns within a feasible region, which allow us to find a feasible design that meets output specifications and low thermal loads. PMID:25061476

Internal heat gain from different light sources in the building lighting systems

NASA Astrophysics Data System (ADS)

Suszanowicz, Dariusz

2017-10-01

EU directives and the Construction Law have for some time required investors to report the energy consumption of buildings, and this has indeed caused low energy consumption buildings to proliferate. Of particular interest, internal heat gains from installed lighting affect the final energy consumption for heating of both public and residential buildings. This article presents the results of analyses of the electricity consumption and the luminous flux and the heat flux emitted by different types of light sources used in buildings. Incandescent light, halogen, compact fluorescent bulbs, and LED bulbs from various manufacturers were individually placed in a closed and isolated chamber, and the parameters for their functioning under identical conditions were recorded. The heat flux emitted by 1 W nominal power of each light source was determined. Based on the study results, the empirical coefficients of heat emission and energy efficiency ratios for different types of lighting sources (dependent lamp power and the light output) were designated. In the heat balance of the building, the designated rates allow for precise determination of the internal heat gains coming from lighting systems using various light sources and also enable optimization of lighting systems of buildings that are used in different ways.

The energy trilogy: An integrated sustainability model to bridge wastewater treatment plant energy and emissions gaps

NASA Astrophysics Data System (ADS)

Al-Talibi, A. Adhim

An estimated 4% of national energy consumption is used for drinking water and wastewater services. Despite the awareness and optimization initiatives for energy conservation, energy consumption is on the rise owing to population and urbanization expansion and to commercial and industrial business advancement. The principal concern is since energy consumption grows, the higher will be the energy production demand, leading to an increase in CO2 footprints and the contribution to global warming potential. This research is in the area of energy-water nexus, focusing on wastewater treatment plant (WWTP) energy trilogy -- the group of three related entities, which includes processes: (1) consuming energy, (2) producing energy, and (3) the resulting -- CO2 equivalents. Detailed and measurable energy information is not readily obtained for wastewater facilities, specifically during facility preliminary design phases. These limitations call for data-intensive research approach on GHG emissions quantification, plant efficiencies and source reduction techniques. To achieve these goals, this research introduced a model integrating all plant processes and their pertinent energy sources. In a comprehensive and "Energy Source-to-Effluent Discharge" pattern, this model is capable of bridging the gaps of WWTP energy, facilitating plant designers' decision-making for meeting energy assessment, sustainability and the environmental regulatory compliance. Protocols for estimating common emissions sources are available such as for fuels, whereas, site-specific emissions for other sources have to be developed and are captured in this research. The dissertation objectives were met through an extensive study of the relevant literature, models and tools, originating comprehensive lists of processes and energy sources for WWTPs, locating estimation formulas for each source, identifying site specific emissions factors, and linking the sources in a mathematical model for site specific CO2 e determination. The model was verified and showed a good agreement with billed and measured data from a base case study. In a next phase, a supplemental computational tool can be created for conducting plant energy design comparisons and plant energy and emissions parameters assessments. The main conclusions drawn from this research is that current approaches are severely limited, not covering plant's design phase and not fully considering the balance of energy consumed (EC), energy produced (EP) and the resulting CO2 e emission integration. Finally their results are not representative. This makes reported governmental and institutional national energy consumption figures incomplete and/or misleading, since they are mainly considering energy consumptions from electricity and some fuels or certain processes only. The distinction of the energy trilogy model over existing approaches is based on the following: (1) the ET energy model is unprecedented, prepared to fit WWTP energy assessment during the design and rehabilitation phases, (2) links the energy trilogy eliminating the need for using several models or tools, (3) removes the need for on-site expensive energy measurements or audits, (4) offers alternatives for energy optimization during plant's life-cycle, and (5) ensures reliable GHG emissions inventory reporting for permitting and regulatory compliance.

Performance analysis of CO(2) emissions and energy efficiency of metal industries in China.

PubMed

Shao, Chaofeng; Guan, Yang; Wan, Zheng; Chu, Chunli; Ju, Meiting

2014-02-15

Nonferrous metal industries play an important role in China's national economy and are some of the country's largest energy consumers. To better understand the nature of CO(2) emissions from these industries and to further move towards low-carbon development in this industry sector, this study investigates the CO(2) emissions of 12 nonferrous metal industries from 2003 to 2010 based on their life-cycle assessments. It then classifies these industries into four "emission-efficiency" types through cluster analysis. The results show that (1) the industrial economy and energy consumption of China's nonferrous metal industries have grown rapidly, although their recent energy consumption rate shows a declining trend. (2) The copper, aluminum, zinc, lead, and magnesium industries, classified as high-emission industries, are the main contributors of CO(2) emissions. The results have implications for policy decisions that aim to enhance energy efficiency, particularly for promoting the transformation of low-efficiency industries to high-efficiency ones. The study also highlights the important role of policy development in technological innovations, optimization, and upgrades, the reduction of coal proportion in energy consumption, and the advancement of new energy sources. Copyright © 2014 Elsevier Ltd. All rights reserved.

Energy modelling in sensor networks

NASA Astrophysics Data System (ADS)

Schmidt, D.; Krämer, M.; Kuhn, T.; Wehn, N.

2007-06-01

Wireless sensor networks are one of the key enabling technologies for the vision of ambient intelligence. Energy resources for sensor nodes are very scarce. A key challenge is the design of energy efficient communication protocols. Models of the energy consumption are needed to accurately simulate the efficiency of a protocol or application design, and can also be used for automatic energy optimizations in a model driven design process. We propose a novel methodology to create models for sensor nodes based on few simple measurements. In a case study the methodology was used to create models for MICAz nodes. The models were integrated in a simulation environment as well as in a SDL runtime framework of a model driven design process. Measurements on a test application that was created automatically from an SDL specification showed an 80% reduction in energy consumption compared to an implementation without power saving strategies.

Bioenergetic response by steelhead to variation in diet, thermal habitat, and climate in the north Pacific Ocean

USGS Publications Warehouse

Atcheson, Margaret E.; Myers, Katherine W.; Beauchamp, David A.; Mantua, Nathan J.

2012-01-01

Energetic responses of steelhead Oncorhynchus mykiss to climate-driven changes in marine conditions are expected to affect the species’ ocean distribution, feeding, growth, and survival. With a unique 18-year data series (1991–2008) for steelhead sampled in the open ocean, we simulated interannual variation in prey consumption and growth efficiency of steelhead using a bioenergetics model to evaluate the temperature-dependent growth response of steelhead to past climate events and to estimate growth potential of steelhead under future climate scenarios. Our results showed that annual ocean growth of steelhead is highly variable depending on prey quality, consumption rates, total consumption, and thermal experience. At optimal growing temperatures, steelhead can compensate for a low-energy diet by increasing consumption rates and consuming more prey, if available. Our findings suggest that steelhead have a narrow temperature window in which to achieve optimal growth, which is strongly influenced by climate-driven changes in ocean temperature.

Optimization methods applied to hybrid vehicle design

NASA Technical Reports Server (NTRS)

Donoghue, J. F.; Burghart, J. H.

1983-01-01

The use of optimization methods as an effective design tool in the design of hybrid vehicle propulsion systems is demonstrated. Optimization techniques were used to select values for three design parameters (battery weight, heat engine power rating and power split between the two on-board energy sources) such that various measures of vehicle performance (acquisition cost, life cycle cost and petroleum consumption) were optimized. The apporach produced designs which were often significant improvements over hybrid designs already reported on in the literature. The principal conclusions are as follows. First, it was found that the strategy used to split the required power between the two on-board energy sources can have a significant effect on life cycle cost and petroleum consumption. Second, the optimization program should be constructed so that performance measures and design variables can be easily changed. Third, the vehicle simulation program has a significant effect on the computer run time of the overall optimization program; run time can be significantly reduced by proper design of the types of trips the vehicle takes in a one year period. Fourth, care must be taken in designing the cost and constraint expressions which are used in the optimization so that they are relatively smooth functions of the design variables. Fifth, proper handling of constraints on battery weight and heat engine rating, variables which must be large enough to meet power demands, is particularly important for the success of an optimization study. Finally, the principal conclusion is that optimization methods provide a practical tool for carrying out the design of a hybrid vehicle propulsion system.

Thermal energy storage – overview and specific insight into nitrate salts for sensible and latent heat storage

PubMed Central

Bauer, Thomas; Martin, Claudia; Eck, Markus; Wörner, Antje

2015-01-01

Summary Thermal energy storage (TES) is capable to reduce the demand of conventional energy sources for two reasons: First, they prevent the mismatch between the energy supply and the power demand when generating electricity from renewable energy sources. Second, utilization of waste heat in industrial processes by thermal energy storage reduces the final energy consumption. This review focuses mainly on material aspects of alkali nitrate salts. They include thermal properties, thermal decomposition processes as well as a new method to develop optimized salt systems. PMID:26199853

Thermal energy storage - overview and specific insight into nitrate salts for sensible and latent heat storage.

PubMed

Pfleger, Nicole; Bauer, Thomas; Martin, Claudia; Eck, Markus; Wörner, Antje

2015-01-01

Thermal energy storage (TES) is capable to reduce the demand of conventional energy sources for two reasons: First, they prevent the mismatch between the energy supply and the power demand when generating electricity from renewable energy sources. Second, utilization of waste heat in industrial processes by thermal energy storage reduces the final energy consumption. This review focuses mainly on material aspects of alkali nitrate salts. They include thermal properties, thermal decomposition processes as well as a new method to develop optimized salt systems.

Inert gas ion source program

NASA Technical Reports Server (NTRS)

Ramsey, W. D.

1978-01-01

THe original 12 cm hexagonal magneto-electrostatic containment discharge chamber has been optimized for argon and xenon operation. Argon mass utilization efficiencies of 65 to 77 percent were achieved at keeper-plus-main discharge energy consumptions of 200 to 458 eV/ion, respectively. Xenon performance of 84 to 96 percent mass utilization was realized at 203 to 350 eV/ion. The optimization process and test results are discussed.

Optimized design of total energy systems: The RETE project

NASA Astrophysics Data System (ADS)

Alia, P.; Dallavalle, F.; Denard, C.; Sanson, F.; Veneziani, S.; Spagni, G.

1980-05-01

The RETE (Reggio Emilia Total Energy) project is discussed. The total energy system (TES) was developed to achieve the maximum quality matching on the thermal energy side between plant and user and perform an open scheme on the electrical energy side by connection with the Italian electrical network. The most significant qualitative considerations at the basis of the plant economic energy optimization and the selection of the operating criterion most fitting the user consumption characteristics and the external system constraints are reported. The design methodology described results in a TES that: in energy terms achieves a total efficiency evaluated on a yearly basis to be equal to about 78 percent and a fuel saving of about 28 percent and in economic terms allows a recovery of the investment required as to conventional solutions, in about seven years.

A Network Coding Based Hybrid ARQ Protocol for Underwater Acoustic Sensor Networks

PubMed Central

Wang, Hao; Wang, Shilian; Zhang, Eryang; Zou, Jianbin

2016-01-01

Underwater Acoustic Sensor Networks (UASNs) have attracted increasing interest in recent years due to their extensive commercial and military applications. However, the harsh underwater channel causes many challenges for the design of reliable underwater data transport protocol. In this paper, we propose an energy efficient data transport protocol based on network coding and hybrid automatic repeat request (NCHARQ) to ensure reliability, efficiency and availability in UASNs. Moreover, an adaptive window length estimation algorithm is designed to optimize the throughput and energy consumption tradeoff. The algorithm can adaptively change the code rate and can be insensitive to the environment change. Extensive simulations and analysis show that NCHARQ significantly reduces energy consumption with short end-to-end delay. PMID:27618044

Drops of energy: conserving urban water to reduce greenhouse gas emissions.

PubMed

Zhou, Yuanchun; Zhang, Bing; Wang, Haikun; Bi, Jun

2013-10-01

Water and energy are two essential resources of modern civilization and are inherently linked. Indeed, the optimization of the water supply system would reduce energy demands and greenhouse gas emissions in the municipal water sector. This research measured the climatic cobenefit of water conservation based on a water flow analysis. The results showed that the estimated energy consumption of the total water system in Changzhou, China, reached approximately 10% of the city's total energy consumption, whereas the industrial sector was found to be more energy intensive than other sectors within the entire water system, accounting for nearly 70% of the total energy use of the water system. In addition, four sustainable water management scenarios would bring the cobenefit of reducing the total energy use of the water system by 13.9%, and 77% of the energy savings through water conservation was indirect. To promote sustainable water management and reduce greenhouse gas emissions, China would require its water price system, both for freshwater and recycled water, to be reformed.

Power optimization in body sensor networks: the case of an autonomous wireless EMG sensor powered by PV-cells.

PubMed

Penders, J; Pop, V; Caballero, L; van de Molengraft, J; van Schaijk, R; Vullers, R; Van Hoof, C

2010-01-01

Recent advances in ultra-low-power circuits and energy harvesters are making self-powered body sensor nodes a reality. Power optimization at the system and application level is crucial in achieving ultra-low-power consumption for the entire system. This paper reviews system-level power optimization techniques, and illustrates their impact on the case of autonomous wireless EMG monitoring. The resulting prototype, an Autonomous wireless EMG sensor power by PV-cells, is presented.

Pulse Consumption, Satiety, and Weight Management1

PubMed Central

McCrory, Megan A.; Hamaker, Bruce R.; Lovejoy, Jennifer C.; Eichelsdoerfer, Petra E.

2010-01-01

The prevalence of obesity has reached epidemic proportions, making finding effective solutions to reduce obesity a public health priority. One part of the solution could be for individuals to increase consumption of nonoilseed pulses (dry beans, peas, chickpeas, and lentils), because they have nutritional attributes thought to benefit weight control, including slowly digestible carbohydrates, high fiber and protein contents, and moderate energy density. Observational studies consistently show an inverse relationship between pulse consumption and BMI or risk for obesity, but many do not control for potentially confounding dietary and other lifestyle factors. Short-term (≤1 d) experimental studies using meals controlled for energy, but not those controlled for available carbohydrate, show that pulse consumption increases satiety over 2–4 h, suggesting that at least part of the effect of pulses on satiety is mediated by available carbohydrate amount or composition. Randomized controlled trials generally support a beneficial effect of pulses on weight loss when pulse consumption is coupled with energy restriction, but not without energy restriction. However, few randomized trials have been conducted and most were short term (3–8 wk for whole pulses and 4–12 wk for pulse extracts). Overall, there is some indication of a beneficial effect of pulses on short-term satiety and weight loss during intentional energy restriction, but more studies are needed in this area, particularly those that are longer term (≥1 y), investigate the optimal amount of pulses to consume for weight control, and include behavioral elements to help overcome barriers to pulse consumption. PMID:22043448

Development of an Integrated Process, Modeling and Simulation Platform for Performance-Based Design of Low-Energy and High IEQ Buildings

ERIC Educational Resources Information Center

Chen, Yixing

2013-01-01

The objective of this study was to develop a "Virtual Design Studio (VDS)": a software platform for integrated, coordinated and optimized design of green building systems with low energy consumption, high indoor environmental quality (IEQ), and high level of sustainability. The VDS is intended to assist collaborating architects,…

Analysis of the impact path on factors of China's energy-related CO2 emissions: a path analysis with latent variables.

PubMed

Chen, Wenhui; Lei, Yalin

2017-02-01

Identifying the impact path on factors of CO 2 emissions is crucial for the government to take effective measures to reduce carbon emissions. The most existing research focuses on the total influence of factors on CO 2 emissions without differentiating between the direct and indirect influence. Moreover, scholars have addressed the relationships among energy consumption, economic growth, and CO 2 emissions rather than estimating all the causal relationships simultaneously. To fill this research gaps and explore overall driving factors' influence mechanism on CO 2 emissions, this paper utilizes a path analysis model with latent variables (PA-LV) to estimate the direct and indirect effect of factors on China's energy-related carbon emissions and to investigate the causal relationships among variables. Three key findings emanate from the analysis: (1) The change in the economic growth pattern inhibits the growth rate of CO 2 emissions by reducing the energy intensity; (2) adjustment of industrial structure contributes to energy conservation and CO 2 emission reduction by raising the proportion of the tertiary industry; and (3) the growth of CO 2 emissions impacts energy consumption and energy intensity negatively, which results in a negative impact indirectly on itself. To further control CO 2 emissions, the Chinese government should (1) adjust the industrial structure and actively develop its tertiary industry to improve energy efficiency and develop low-carbon economy, (2) optimize population shifts to avoid excessive population growth and reduce energy consumption, and (3) promote urbanization steadily to avoid high energy consumption and low energy efficiency.

Energy-Saving Optimization of Water Supply Pumping Station Life Cycle Based on BIM Technology

NASA Astrophysics Data System (ADS)

Qun, Miao; Wang, Jiayuan; Liu, Chao

2017-12-01

In the urban water supply system, pump station is the main unit of energy consumption. In the background of pushing forward the informatization in China, using BIM technology in design, construction and operations of water supply pumping station, can break through the limitations of the traditional model and effectively achieve the goal of energy conservation and emissions reduction. This work researches the way to solve energy-saving optimization problems in the process of whole life cycle of water supply pumping station based on BIM technology, and put forward the feasible strategies of BIM application in order to realize the healthy and sustainable development goals by establishing the BIM model of water supply pumping station of Qingdao Guzhenkou water supply project.

Creating Robust STEM Research Ecosystems in Schools: Joining the Dots for Young Researchers

NASA Astrophysics Data System (ADS)

Pritchard, M.; Ibarra, D. L.

2017-12-01

Developing an intelligent curiosity about the world in school-aged learners is one of the key purposes of education. Nurturing this intelligent curiosity in a systematic and integrated manner is essential for rigorous, scientific literacy, which in turn inspires advanced research and innovation in post-school life. STEM (Science, Technology, Engineering, and Mathematics) education has been widely adopted as the conceptual framework to achieve this goal. For young learners, their experienced world is largely confined to the spaces within the boundaries of educational institutions. This type of environment might be perceived as sterile or even hostile to genuine research, as institutional endeavor is largely shaped by examination syllabi. This can be changed by viewing the school as a living laboratory of processes and products, all of which offer enormous potential for meaningful and valuable student-led STEM research. Creating research-focused ecosystems within schools, however, requires considerable effort to create a learning culture that defragments knowledge systems and connects isolated pools of inquiry. The existing parameters and processes of school ecosystems, such as energy generation, consumption, waste creation and disposal offer opportunities for school students to utilize STEM-related skills observe and measure their own ecological footprint, undertake research into these living processes in an integrated manner, and develop solutions to create closed loops of optimally managed and measured consumption with the institution. For example, connecting a deep understanding of the principles of renewable energy generation with close, real-time monitoring of classroom energy usage creates the opportunity to develop a higher level of user awareness and more optimized consumption habits. Food waste, when composted and recycled on-site, similarly offers the potential to connect the sociological issue of excess consumption with a scientific understanding of aerobic and anaerobic decomposition and the subsequent purposes to which the product of the process might be put. In adopting a STEM framework to examine and shape school ecosystems, young researchers develop the capacity to observe, measure, analyze, troubleshoot, and optimize their own environment.

Smart sensing to drive real-time loads scheduling algorithm in a domotic architecture

NASA Astrophysics Data System (ADS)

Santamaria, Amilcare Francesco; Raimondo, Pierfrancesco; De Rango, Floriano; Vaccaro, Andrea

2014-05-01

Nowadays the focus on power consumption represent a very important factor regarding the reduction of power consumption with correlated costs and the environmental sustainability problems. Automatic control load based on power consumption and use cycle represents the optimal solution to costs restraint. The purpose of these systems is to modulate the power request of electricity avoiding an unorganized work of the loads, using intelligent techniques to manage them based on real time scheduling algorithms. The goal is to coordinate a set of electrical loads to optimize energy costs and consumptions based on the stipulated contract terms. The proposed algorithm use two new main notions: priority driven loads and smart scheduling loads. The priority driven loads can be turned off (stand by) according to a priority policy established by the user if the consumption exceed a defined threshold, on the contrary smart scheduling loads are scheduled in a particular way to don't stop their Life Cycle (LC) safeguarding the devices functions or allowing the user to freely use the devices without the risk of exceeding the power threshold. The algorithm, using these two kind of notions and taking into account user requirements, manages loads activation and deactivation allowing the completion their operation cycle without exceeding the consumption threshold in an off-peak time range according to the electricity fare. This kind of logic is inspired by industrial lean manufacturing which focus is to minimize any kind of power waste optimizing the available resources.

Diet and Macronutrient Optimization in Wild Ursids: A Comparison of Grizzly Bears with Sympatric and Allopatric Black Bears.

PubMed

Costello, Cecily M; Cain, Steven L; Pils, Shannon; Frattaroli, Leslie; Haroldson, Mark A; van Manen, Frank T

2016-01-01

When fed ad libitum, ursids can maximize mass gain by selecting mixed diets wherein protein provides 17 ± 4% of digestible energy, relative to carbohydrates or lipids. In the wild, this ability is likely constrained by seasonal food availability, limits of intake rate as body size increases, and competition. By visiting locations of 37 individuals during 274 bear-days, we documented foods consumed by grizzly (Ursus arctos) and black bears (Ursus americanus) in Grand Teton National Park during 2004-2006. Based on published nutritional data, we estimated foods and macronutrients as percentages of daily energy intake. Using principal components and cluster analyses, we identified 14 daily diet types. Only 4 diets, accounting for 21% of days, provided protein levels within the optimal range. Nine diets (75% of days) led to over-consumption of protein, and 1 diet (3% of days) led to under-consumption. Highest protein levels were associated with animal matter (i.e., insects, vertebrates), which accounted for 46-47% of daily energy for both species. As predicted: 1) daily diets dominated by high-energy vertebrates were positively associated with grizzly bears and mean percent protein intake was positively associated with body mass; 2) diets dominated by low-protein fruits were positively associated with smaller-bodied black bears; and 3) mean protein was highest during spring, when high-energy plant foods were scarce, however it was also higher than optimal during summer and fall. Contrary to our prediction: 4) allopatric black bears did not exhibit food selection for high-energy foods similar to grizzly bears. Although optimal gain of body mass was typically constrained, bears usually opted for the energetically superior trade-off of consuming high-energy, high-protein foods. Given protein digestion efficiency similar to obligate carnivores, this choice likely supported mass gain, consistent with studies showing monthly increases in percent body fat among bears in this region.

Diet and Macronutrient Optimization in Wild Ursids: A Comparison of Grizzly Bears with Sympatric and Allopatric Black Bears

PubMed Central

Costello, Cecily M.; Cain, Steven L.; Pils, Shannon; Frattaroli, Leslie; Haroldson, Mark A.; van Manen, Frank T.

2016-01-01

When fed ad libitum, ursids can maximize mass gain by selecting mixed diets wherein protein provides 17 ± 4% of digestible energy, relative to carbohydrates or lipids. In the wild, this ability is likely constrained by seasonal food availability, limits of intake rate as body size increases, and competition. By visiting locations of 37 individuals during 274 bear-days, we documented foods consumed by grizzly (Ursus arctos) and black bears (Ursus americanus) in Grand Teton National Park during 2004–2006. Based on published nutritional data, we estimated foods and macronutrients as percentages of daily energy intake. Using principal components and cluster analyses, we identified 14 daily diet types. Only 4 diets, accounting for 21% of days, provided protein levels within the optimal range. Nine diets (75% of days) led to over-consumption of protein, and 1 diet (3% of days) led to under-consumption. Highest protein levels were associated with animal matter (i.e., insects, vertebrates), which accounted for 46–47% of daily energy for both species. As predicted: 1) daily diets dominated by high-energy vertebrates were positively associated with grizzly bears and mean percent protein intake was positively associated with body mass; 2) diets dominated by low-protein fruits were positively associated with smaller-bodied black bears; and 3) mean protein was highest during spring, when high-energy plant foods were scarce, however it was also higher than optimal during summer and fall. Contrary to our prediction: 4) allopatric black bears did not exhibit food selection for high-energy foods similar to grizzly bears. Although optimal gain of body mass was typically constrained, bears usually opted for the energetically superior trade-off of consuming high-energy, high-protein foods. Given protein digestion efficiency similar to obligate carnivores, this choice likely supported mass gain, consistent with studies showing monthly increases in percent body fat among bears in this region. PMID:27192407

Diet and macronutrient optimization in wild ursids: A comparison of grizzly bears with sympatric and allopatric black bears

USGS Publications Warehouse

Costello, Cecily M.; Cain, Steven L.; Pils, Shannon R; Frattaroli, Leslie; Haroldson, Mark A.; van Manen, Frank T.

2016-01-01

When fed ad libitum, ursids can maximize mass gain by selecting mixed diets wherein protein provides 17 ± 4% of digestible energy, relative to carbohydrates or lipids. In the wild, this ability is likely constrained by seasonal food availability, limits of intake rate as body size increases, and competition. By visiting locations of 37 individuals during 274 bear-days, we documented foods consumed by grizzly (Ursus arctos) and black bears (Ursus americanus) in Grand Teton National Park during 2004–2006. Based on published nutritional data, we estimated foods and macronutrients as percentages of daily energy intake. Using principal components and cluster analyses, we identified 14 daily diet types. Only 4 diets, accounting for 21% of days, provided protein levels within the optimal range. Nine diets (75% of days) led to over-consumption of protein, and 1 diet (3% of days) led to under-consumption. Highest protein levels were associated with animal matter (i.e., insects, vertebrates), which accounted for 46–47% of daily energy for both species. As predicted: 1) daily diets dominated by high-energy vertebrates were positively associated with grizzly bears and mean percent protein intake was positively associated with body mass; 2) diets dominated by low-protein fruits were positively associated with smaller-bodied black bears; and 3) mean protein was highest during spring, when high-energy plant foods were scarce, however it was also higher than optimal during summer and fall. Contrary to our prediction: 4) allopatric black bears did not exhibit food selection for high-energy foods similar to grizzly bears. Although optimal gain of body mass was typically constrained, bears usually opted for the energetically superior trade-off of consuming high-energy, high-protein foods. Given protein digestion efficiency similar to obligate carnivores, this choice likely supported mass gain, consistent with studies showing monthly increases in percent body fat among bears in this region.

A Game Theoretic Approach for Balancing Energy Consumption in Clustered Wireless Sensor Networks.

PubMed

Yang, Liu; Lu, Yinzhi; Xiong, Lian; Tao, Yang; Zhong, Yuanchang

2017-11-17

Clustering is an effective topology control method in wireless sensor networks (WSNs), since it can enhance the network lifetime and scalability. To prolong the network lifetime in clustered WSNs, an efficient cluster head (CH) optimization policy is essential to distribute the energy among sensor nodes. Recently, game theory has been introduced to model clustering. Each sensor node is considered as a rational and selfish player which will play a clustering game with an equilibrium strategy. Then it decides whether to act as the CH according to this strategy for a tradeoff between providing required services and energy conservation. However, how to get the equilibrium strategy while maximizing the payoff of sensor nodes has rarely been addressed to date. In this paper, we present a game theoretic approach for balancing energy consumption in clustered WSNs. With our novel payoff function, realistic sensor behaviors can be captured well. The energy heterogeneity of nodes is considered by incorporating a penalty mechanism in the payoff function, so the nodes with more energy will compete for CHs more actively. We have obtained the Nash equilibrium (NE) strategy of the clustering game through convex optimization. Specifically, each sensor node can achieve its own maximal payoff when it makes the decision according to this strategy. Through plenty of simulations, our proposed game theoretic clustering is proved to have a good energy balancing performance and consequently the network lifetime is greatly enhanced.

Energy Efficiency Maximization for WSNs with Simultaneous Wireless Information and Power Transfer

PubMed Central

Yu, Hongyan; Zhang, Yongqiang; Yang, Yuanyuan; Ji, Luyue

2017-01-01

Recently, the simultaneous wireless information and power transfer (SWIPT) technique has been regarded as a promising approach to enhance performance of wireless sensor networks with limited energy supply. However, from a green communication perspective, energy efficiency optimization for SWIPT system design has not been investigated in Wireless Rechargeable Sensor Networks (WRSNs). In this paper, we consider the tradeoffs between energy efficiency and three factors including spectral efficiency, the transmit power and outage target rate for two different modes, i.e., power splitting (PS) and time switching modes (TS), at the receiver. Moreover, we formulate the energy efficiency maximization problem subject to the constraints of minimum Quality of Service (QoS), minimum harvested energy and maximum transmission power as non-convex optimization problem. In particular, we focus on optimizing power control and power allocation policy in PS and TS modes to maximize energy efficiency of data transmission. For PS and TS modes, we propose the corresponding algorithm to characterize a non-convex optimization problem that takes into account the circuit power consumption and the harvested energy. By exploiting nonlinear fractional programming and Lagrangian dual decomposition, we propose suboptimal iterative algorithms to obtain the solutions of non-convex optimization problems. Furthermore, we derive the outage probability and effective throughput from the scenarios that the transmitter does not or partially know the channel state information (CSI) of the receiver. Simulation results illustrate that the proposed optimal iterative algorithm can achieve optimal solutions within a small number of iterations and various tradeoffs between energy efficiency and spectral efficiency, transmit power and outage target rate, respectively. PMID:28820496

Energy Efficiency Maximization for WSNs with Simultaneous Wireless Information and Power Transfer.

PubMed

Yu, Hongyan; Zhang, Yongqiang; Guo, Songtao; Yang, Yuanyuan; Ji, Luyue

2017-08-18

Recently, the simultaneous wireless information and power transfer (SWIPT) technique has been regarded as a promising approach to enhance performance of wireless sensor networks with limited energy supply. However, from a green communication perspective, energy efficiency optimization for SWIPT system design has not been investigated in Wireless Rechargeable Sensor Networks (WRSNs). In this paper, we consider the tradeoffs between energy efficiency and three factors including spectral efficiency, the transmit power and outage target rate for two different modes, i.e., power splitting (PS) and time switching modes (TS), at the receiver. Moreover, we formulate the energy efficiency maximization problem subject to the constraints of minimum Quality of Service (QoS), minimum harvested energy and maximum transmission power as non-convex optimization problem. In particular, we focus on optimizing power control and power allocation policy in PS and TS modes to maximize energy efficiency of data transmission. For PS and TS modes, we propose the corresponding algorithm to characterize a non-convex optimization problem that takes into account the circuit power consumption and the harvested energy. By exploiting nonlinear fractional programming and Lagrangian dual decomposition, we propose suboptimal iterative algorithms to obtain the solutions of non-convex optimization problems. Furthermore, we derive the outage probability and effective throughput from the scenarios that the transmitter does not or partially know the channel state information (CSI) of the receiver. Simulation results illustrate that the proposed optimal iterative algorithm can achieve optimal solutions within a small number of iterations and various tradeoffs between energy efficiency and spectral efficiency, transmit power and outage target rate, respectively.

Optimization to reduce fuel consumption in charge depleting mode

DOEpatents

Roos, Bryan Nathaniel; Martini, Ryan D.

2014-08-26

A powertrain includes an internal combustion engine, a motor utilizing electrical energy from an energy storage device, and a plug-in connection. A Method for controlling the powertrain includes monitoring a fuel cut mode, ceasing a fuel flow to the engine based upon the fuel cut mode, and through a period of operation including acceleration of the powertrain, providing an entirety of propelling torque to the powertrain with the electrical energy from the energy storage device based upon the fuel cut mode.

Markov Chain Model-Based Optimal Cluster Heads Selection for Wireless Sensor Networks

PubMed Central

Ahmed, Gulnaz; Zou, Jianhua; Zhao, Xi; Sadiq Fareed, Mian Muhammad

2017-01-01

The longer network lifetime of Wireless Sensor Networks (WSNs) is a goal which is directly related to energy consumption. This energy consumption issue becomes more challenging when the energy load is not properly distributed in the sensing area. The hierarchal clustering architecture is the best choice for these kind of issues. In this paper, we introduce a novel clustering protocol called Markov chain model-based optimal cluster heads (MOCHs) selection for WSNs. In our proposed model, we introduce a simple strategy for the optimal number of cluster heads selection to overcome the problem of uneven energy distribution in the network. The attractiveness of our model is that the BS controls the number of cluster heads while the cluster heads control the cluster members in each cluster in such a restricted manner that a uniform and even load is ensured in each cluster. We perform an extensive range of simulation using five quality measures, namely: the lifetime of the network, stable and unstable region in the lifetime of the network, throughput of the network, the number of cluster heads in the network, and the transmission time of the network to analyze the proposed model. We compare MOCHs against Sleep-awake Energy Efficient Distributed (SEED) clustering, Artificial Bee Colony (ABC), Zone Based Routing (ZBR), and Centralized Energy Efficient Clustering (CEEC) using the above-discussed quality metrics and found that the lifetime of the proposed model is almost 1095, 2630, 3599, and 2045 rounds (time steps) greater than SEED, ABC, ZBR, and CEEC, respectively. The obtained results demonstrate that the MOCHs is better than SEED, ABC, ZBR, and CEEC in terms of energy efficiency and the network throughput. PMID:28241492

Optimized design and control of an off grid solar PV/hydrogen fuel cell power system for green buildings

NASA Astrophysics Data System (ADS)

Ghenai, C.; Bettayeb, M.

2017-11-01

Modelling, simulation, optimization and control strategies are used in this study to design a stand-alone solar PV/Fuel Cell/Battery/Generator hybrid power system to serve the electrical load of a commercial building. The main objective is to design an off grid energy system to meet the desired electric load of the commercial building with high renewable fraction, low emissions and low cost of energy. The goal is to manage the energy consumption of the building, reduce the associate cost and to switch from grid-tied fossil fuel power system to an off grid renewable and cleaner power system. Energy audit was performed in this study to determine the energy consumption of the building. Hourly simulations, modelling and optimization were performed to determine the performance and cost of the hybrid power configurations using different control strategies. The results show that the hybrid off grid solar PV/Fuel Cell/Generator/Battery/Inverter power system offers the best performance for the tested system architectures. From the total energy generated from the off grid hybrid power system, 73% is produced from the solar PV, 24% from the fuel cell and 3% from the backup Diesel generator. The produced power is used to meet all the AC load of the building without power shortage (<0.1%). The hybrid power system produces 18.2% excess power that can be used to serve the thermal load of the building. The proposed hybrid power system is sustainable, economically viable and environmentally friendly: High renewable fraction (66.1%), low levelized cost of energy (92 /MWh), and low carbon dioxide emissions (24 kg CO2/MWh) are achieved.

Towards a generalized energy prediction model for machine tools

PubMed Central

Bhinge, Raunak; Park, Jinkyoo; Law, Kincho H.; Dornfeld, David A.; Helu, Moneer; Rachuri, Sudarsan

2017-01-01

Energy prediction of machine tools can deliver many advantages to a manufacturing enterprise, ranging from energy-efficient process planning to machine tool monitoring. Physics-based, energy prediction models have been proposed in the past to understand the energy usage pattern of a machine tool. However, uncertainties in both the machine and the operating environment make it difficult to predict the energy consumption of the target machine reliably. Taking advantage of the opportunity to collect extensive, contextual, energy-consumption data, we discuss a data-driven approach to develop an energy prediction model of a machine tool in this paper. First, we present a methodology that can efficiently and effectively collect and process data extracted from a machine tool and its sensors. We then present a data-driven model that can be used to predict the energy consumption of the machine tool for machining a generic part. Specifically, we use Gaussian Process (GP) Regression, a non-parametric machine-learning technique, to develop the prediction model. The energy prediction model is then generalized over multiple process parameters and operations. Finally, we apply this generalized model with a method to assess uncertainty intervals to predict the energy consumed to machine any part using a Mori Seiki NVD1500 machine tool. Furthermore, the same model can be used during process planning to optimize the energy-efficiency of a machining process. PMID:28652687

Towards a generalized energy prediction model for machine tools.

PubMed

Bhinge, Raunak; Park, Jinkyoo; Law, Kincho H; Dornfeld, David A; Helu, Moneer; Rachuri, Sudarsan

2017-04-01

Energy prediction of machine tools can deliver many advantages to a manufacturing enterprise, ranging from energy-efficient process planning to machine tool monitoring. Physics-based, energy prediction models have been proposed in the past to understand the energy usage pattern of a machine tool. However, uncertainties in both the machine and the operating environment make it difficult to predict the energy consumption of the target machine reliably. Taking advantage of the opportunity to collect extensive, contextual, energy-consumption data, we discuss a data-driven approach to develop an energy prediction model of a machine tool in this paper. First, we present a methodology that can efficiently and effectively collect and process data extracted from a machine tool and its sensors. We then present a data-driven model that can be used to predict the energy consumption of the machine tool for machining a generic part. Specifically, we use Gaussian Process (GP) Regression, a non-parametric machine-learning technique, to develop the prediction model. The energy prediction model is then generalized over multiple process parameters and operations. Finally, we apply this generalized model with a method to assess uncertainty intervals to predict the energy consumed to machine any part using a Mori Seiki NVD1500 machine tool. Furthermore, the same model can be used during process planning to optimize the energy-efficiency of a machining process.

Comprehensive analysis of individual pulp fiber bonds quantifies the mechanisms of fiber bonding in paper

PubMed Central

Hirn, Ulrich; Schennach, Robert

2015-01-01

The process of papermaking requires substantial amounts of energy and wood consumption, which contributes to larger environmental costs. In order to optimize the production of papermaking to suit its many applications in material science and engineering, a quantitative understanding of bonding forces between the individual pulp fibers is of importance. Here we show the first approach to quantify the bonding energies contributed by the individual bonding mechanisms. We calculated the impact of the following mechanisms necessary for paper formation: mechanical interlocking, interdiffusion, capillary bridges, hydrogen bonding, Van der Waals forces, and Coulomb forces on the bonding energy. Experimental results quantify the area in molecular contact necessary for bonding. Atomic force microscopy experiments derive the impact of mechanical interlocking. Capillary bridges also contribute to the bond. A model based on the crystal structure of cellulose leads to values for the chemical bonds. In contrast to general believe which favors hydrogen bonding Van der Waals bonds play the most important role according to our model. Comparison with experimentally derived bond energies support the presented model. This study characterizes bond formation between pulp fibers leading to insight that could be potentially used to optimize the papermaking process, while reducing energy and wood consumption. PMID:26000898

A Localization Method for Underwater Wireless Sensor Networks Based on Mobility Prediction and Particle Swarm Optimization Algorithms

PubMed Central

Zhang, Ying; Liang, Jixing; Jiang, Shengming; Chen, Wei

2016-01-01

Due to their special environment, Underwater Wireless Sensor Networks (UWSNs) are usually deployed over a large sea area and the nodes are usually floating. This results in a lower beacon node distribution density, a longer time for localization, and more energy consumption. Currently most of the localization algorithms in this field do not pay enough consideration on the mobility of the nodes. In this paper, by analyzing the mobility patterns of water near the seashore, a localization method for UWSNs based on a Mobility Prediction and a Particle Swarm Optimization algorithm (MP-PSO) is proposed. In this method, the range-based PSO algorithm is used to locate the beacon nodes, and their velocities can be calculated. The velocity of an unknown node is calculated by using the spatial correlation of underwater object’s mobility, and then their locations can be predicted. The range-based PSO algorithm may cause considerable energy consumption and its computation complexity is a little bit high, nevertheless the number of beacon nodes is relatively smaller, so the calculation for the large number of unknown nodes is succinct, and this method can obviously decrease the energy consumption and time cost of localizing these mobile nodes. The simulation results indicate that this method has higher localization accuracy and better localization coverage rate compared with some other widely used localization methods in this field. PMID:26861348

Multi-Sensor Information Fusion for Optimizing Electric Bicycle Routes Using a Swarm Intelligence Algorithm

PubMed Central

Villarubia, Gabriel; De Paz, Juan F.; Bajo, Javier

2017-01-01

The use of electric bikes (e-bikes) has grown in popularity, especially in large cities where overcrowding and traffic congestion are common. This paper proposes an intelligent engine management system for e-bikes which uses the information collected from sensors to optimize battery energy and time. The intelligent engine management system consists of a built-in network of sensors in the e-bike, which is used for multi-sensor data fusion; the collected data is analysed and fused and on the basis of this information the system can provide the user with optimal and personalized assistance. The user is given recommendations related to battery consumption, sensors, and other parameters associated with the route travelled, such as duration, speed, or variation in altitude. To provide a user with these recommendations, artificial neural networks are used to estimate speed and consumption for each of the segments of a route. These estimates are incorporated into evolutionary algorithms in order to make the optimizations. A comparative analysis of the results obtained has been conducted for when routes were travelled with and without the optimization system. From the experiments, it is evident that the use of an engine management system results in significant energy and time savings. Moreover, user satisfaction increases as the level of assistance adapts to user behavior and the characteristics of the route. PMID:29088087

Multi-Sensor Information Fusion for Optimizing Electric Bicycle Routes Using a Swarm Intelligence Algorithm.

PubMed

De La Iglesia, Daniel H; Villarrubia, Gabriel; De Paz, Juan F; Bajo, Javier

2017-10-31

The use of electric bikes (e-bikes) has grown in popularity, especially in large cities where overcrowding and traffic congestion are common. This paper proposes an intelligent engine management system for e-bikes which uses the information collected from sensors to optimize battery energy and time. The intelligent engine management system consists of a built-in network of sensors in the e-bike, which is used for multi-sensor data fusion; the collected data is analysed and fused and on the basis of this information the system can provide the user with optimal and personalized assistance. The user is given recommendations related to battery consumption, sensors, and other parameters associated with the route travelled, such as duration, speed, or variation in altitude. To provide a user with these recommendations, artificial neural networks are used to estimate speed and consumption for each of the segments of a route. These estimates are incorporated into evolutionary algorithms in order to make the optimizations. A comparative analysis of the results obtained has been conducted for when routes were travelled with and without the optimization system. From the experiments, it is evident that the use of an engine management system results in significant energy and time savings. Moreover, user satisfaction increases as the level of assistance adapts to user behavior and the characteristics of the route.

Energy-Efficient Transmissions for Remote Wireless Sensor Networks: An Integrated HAP/Satellite Architecture for Emergency Scenarios

PubMed Central

Dong, Feihong; Li, Hongjun; Gong, Xiangwu; Liu, Quan; Wang, Jingchao

2015-01-01

A typical application scenario of remote wireless sensor networks (WSNs) is identified as an emergency scenario. One of the greatest design challenges for communications in emergency scenarios is energy-efficient transmission, due to scarce electrical energy in large-scale natural and man-made disasters. Integrated high altitude platform (HAP)/satellite networks are expected to optimally meet emergency communication requirements. In this paper, a novel integrated HAP/satellite (IHS) architecture is proposed, and three segments of the architecture are investigated in detail. The concept of link-state advertisement (LSA) is designed in a slow flat Rician fading channel. The LSA is received and processed by the terminal to estimate the link state information, which can significantly reduce the energy consumption at the terminal end. Furthermore, the transmission power requirements of the HAPs and terminals are derived using the gradient descent and differential equation methods. The energy consumption is modeled at both the source and system level. An innovative and adaptive algorithm is given for the energy-efficient path selection. The simulation results validate the effectiveness of the proposed adaptive algorithm. It is shown that the proposed adaptive algorithm can significantly improve energy efficiency when combined with the LSA and the energy consumption estimation. PMID:26404292

Energy-Efficient Transmissions for Remote Wireless Sensor Networks: An Integrated HAP/Satellite Architecture for Emergency Scenarios.

PubMed

Dong, Feihong; Li, Hongjun; Gong, Xiangwu; Liu, Quan; Wang, Jingchao

2015-09-03

A typical application scenario of remote wireless sensor networks (WSNs) is identified as an emergency scenario. One of the greatest design challenges for communications in emergency scenarios is energy-efficient transmission, due to scarce electrical energy in large-scale natural and man-made disasters. Integrated high altitude platform (HAP)/satellite networks are expected to optimally meet emergency communication requirements. In this paper, a novel integrated HAP/satellite (IHS) architecture is proposed, and three segments of the architecture are investigated in detail. The concept of link-state advertisement (LSA) is designed in a slow flat Rician fading channel. The LSA is received and processed by the terminal to estimate the link state information, which can significantly reduce the energy consumption at the terminal end. Furthermore, the transmission power requirements of the HAPs and terminals are derived using the gradient descent and differential equation methods. The energy consumption is modeled at both the source and system level. An innovative and adaptive algorithm is given for the energy-efficient path selection. The simulation results validate the effectiveness of the proposed adaptive algorithm. It is shown that the proposed adaptive algorithm can significantly improve energy efficiency when combined with the LSA and the energy consumption estimation.

Modeling the energy performance of event-driven wireless sensor network by using static sink and mobile sink.

PubMed

Chen, Jiehui; Salim, Mariam B; Matsumoto, Mitsuji

2010-01-01

Wireless Sensor Networks (WSNs) designed for mission-critical applications suffer from limited sensing capacities, particularly fast energy depletion. Regarding this, mobile sinks can be used to balance the energy consumption in WSNs, but the frequent location updates of the mobile sinks can lead to data collisions and rapid energy consumption for some specific sensors. This paper explores an optimal barrier coverage based sensor deployment for event driven WSNs where a dual-sink model was designed to evaluate the energy performance of not only static sensors, but Static Sink (SS) and Mobile Sinks (MSs) simultaneously, based on parameters such as sensor transmission range r and the velocity of the mobile sink v, etc. Moreover, a MS mobility model was developed to enable SS and MSs to effectively collaborate, while achieving spatiotemporal energy performance efficiency by using the knowledge of the cumulative density function (cdf), Poisson process and M/G/1 queue. The simulation results verified that the improved energy performance of the whole network was demonstrated clearly and our eDSA algorithm is more efficient than the static-sink model, reducing energy consumption approximately in half. Moreover, we demonstrate that our results are robust to realistic sensing models and also validate the correctness of our results through extensive simulations.

Modeling the Energy Performance of Event-Driven Wireless Sensor Network by Using Static Sink and Mobile Sink

PubMed Central

Chen, Jiehui; Salim, Mariam B.; Matsumoto, Mitsuji

2010-01-01

Wireless Sensor Networks (WSNs) designed for mission-critical applications suffer from limited sensing capacities, particularly fast energy depletion. Regarding this, mobile sinks can be used to balance the energy consumption in WSNs, but the frequent location updates of the mobile sinks can lead to data collisions and rapid energy consumption for some specific sensors. This paper explores an optimal barrier coverage based sensor deployment for event driven WSNs where a dual-sink model was designed to evaluate the energy performance of not only static sensors, but Static Sink (SS) and Mobile Sinks (MSs) simultaneously, based on parameters such as sensor transmission range r and the velocity of the mobile sink v, etc. Moreover, a MS mobility model was developed to enable SS and MSs to effectively collaborate, while achieving spatiotemporal energy performance efficiency by using the knowledge of the cumulative density function (cdf), Poisson process and M/G/1 queue. The simulation results verified that the improved energy performance of the whole network was demonstrated clearly and our eDSA algorithm is more efficient than the static-sink model, reducing energy consumption approximately in half. Moreover, we demonstrate that our results are robust to realistic sensing models and also validate the correctness of our results through extensive simulations. PMID:22163503

A New Wavelength Optimization and Energy-Saving Scheme Based on Network Coding in Software-Defined WDM-PON Networks

NASA Astrophysics Data System (ADS)

Ren, Danping; Wu, Shanshan; Zhang, Lijing

2016-09-01

In view of the characteristics of the global control and flexible monitor of software-defined networks (SDN), we proposes a new optical access network architecture dedicated to Wavelength Division Multiplexing-Passive Optical Network (WDM-PON) systems based on SDN. The network coding (NC) technology is also applied into this architecture to enhance the utilization of wavelength resource and reduce the costs of light source. Simulation results show that this scheme can optimize the throughput of the WDM-PON network, greatly reduce the system time delay and energy consumption.

Assessment of Distributed Generation Potential in JapaneseBuildings

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhou, Nan; Marnay, Chris; Firestone, Ryan

2005-05-25

To meet growing energy demands, energy efficiency, renewable energy, and on-site generation coupled with effective utilization of exhaust heat will all be required. Additional benefit can be achieved by integrating these distributed technologies into distributed energy resource (DER) systems (or microgrids). This research investigates a method of choosing economically optimal DER, expanding on prior studies at the Berkeley Lab using the DER design optimization program, the Distributed Energy Resources Customer Adoption Model (DER-CAM). DER-CAM finds the optimal combination of installed equipment from available DER technologies, given prevailing utility tariffs, site electrical and thermal loads, and a menu of available equipment.more » It provides a global optimization, albeit idealized, that shows how the site energy loads can be served at minimum cost by selection and operation of on-site generation, heat recovery, and cooling. Five prototype Japanese commercial buildings are examined and DER-CAM applied to select the economically optimal DER system for each. The five building types are office, hospital, hotel, retail, and sports facility. Based on the optimization results, energy and emission reductions are evaluated. Furthermore, a Japan-U.S. comparison study of policy, technology, and utility tariffs relevant to DER installation is presented. Significant decreases in fuel consumption, carbon emissions, and energy costs were seen in the DER-CAM results. Savings were most noticeable in the sports facility (a very favourable CHP site), followed by the hospital, hotel, and office building.« less

Delay-aware adaptive sleep mechanism for green wireless-optical broadband access networks

NASA Astrophysics Data System (ADS)

Wang, Ruyan; Liang, Alei; Wu, Dapeng; Wu, Dalei

2017-07-01

Wireless-Optical Broadband Access Network (WOBAN) is capacity-high, reliable, flexible, and ubiquitous, as it takes full advantage of the merits from both optical communication and wireless communication technologies. Similar to other access networks, the high energy consumption poses a great challenge for building up WOBANs. To shot this problem, we can make some load-light Optical Network Units (ONUs) sleep to reduce the energy consumption. Such operation, however, causes the increased packet delay. Jointly considering the energy consumption and transmission delay, we propose a delay-aware adaptive sleep mechanism. Specifically, we develop a new analytical method to evaluate the transmission delay and queuing delay over the optical part, instead of adopting M/M/1 queuing model. Meanwhile, we also analyze the access delay and queuing delay of the wireless part. Based on such developed delay models, we mathematically derive ONU's optimal sleep time. In addition, we provide numerous simulation results to show the effectiveness of the proposed mechanism.

Effects of intra-aortic balloon pump counterpulsation on left ventricular mechanoenergetics in a porcine model of acute ischemic heart failure.

PubMed

Malliaras, Konstantinos; Charitos, Efstratios; Diakos, Nikolaos; Pozios, Iraklis; Papalois, Apostolos; Terrovitis, John; Nanas, John

2014-12-01

We investigated the effects of intra-aortic balloon pump (IABP) counterpulsation on left ventricular (LV) contractility, relaxation, and energy consumption and probed the underlying physiologic mechanisms in 12 farm pigs, using an ischemia-reperfusion model of acute heart failure. During both ischemia and reperfusion, IABP support unloaded the LV, decreased LV energy consumption (pressure-volume area, stroke work), and concurrently improved LV mechanical performance (ejection fraction, stroke volume, cardiac output). During reperfusion exclusively, IABP also improved LV relaxation (tau) and contractility (Emax, PRSW). The beneficial effects of IABP support on LV relaxation and contractility correlated with IABP-induced augmentation of coronary blood flow. In conclusion, we find that during both ischemia and reperfusion, IABP support optimizes LV energetic performance (decreases energy consumption and concurrently improves mechanical performance) by LV unloading. During reperfusion exclusively, IABP support also improves LV contractility and active relaxation, possibly due to a synergistic effect of unloading and augmentation of coronary blood flow.

Life cycle based analysis of demands and emissions for residential water-using appliances.

PubMed

Lee, Mengshan; Tansel, Berrin

2012-06-30

Environmental impacts of energy and water demand and greenhouse gas emissions from three residential water-using appliances were analyzed using life cycle assessment (LCA) based approach in collaboration of economic input-output model. This study especially focused on indirect consumption and environmental impacts from end-use/demand phase of each appliance. Water-related activities such as water supply, water heating and wastewater treatment were included in the analysis. The results showed that environmental impacts from end-use/demand phase are most significant for the water system, particularly for the energy demand for water heating (73% for clothes washer and 93% for showerheads). Reducing water/hot water consumption during the end-use/demand phase is expected to improve the overall water-related energy burden and water use sustainability. In the analysis of optimal lifespan for appliances, the estimated values (8-21 years) using energy consumption balance approach were found to be lower than that using other methods (10-25 years). This implies that earlier replacement with efficiency models is encouraged to minimize the environmental impacts of the product. Copyright © 2012 Elsevier Ltd. All rights reserved.

COD capture: a feasible option towards energy self-sufficient domestic wastewater treatment

PubMed Central

Wan, Junfeng; Gu, Jun; Zhao, Qian; Liu, Yu

2016-01-01

Although the activated sludge process, one of the most remarkable engineering inventions in the 20th century, has made significant contribution to wastewater reclamation in the past 100 years, its high energy consumption is posing a serious impact and challenge on the current wastewater industry worldwide and is also inevitably linked to the issue of global climate change. In this study, we argued that substantial improvement in the energy efficiency might be no longer achievable through further optimization of the activated sludge process. Instead, we should devote more effort to the development or the adoption of novel treatment configurations and emerging technologies. Of which an example is A-B process which can significantly improve the energy recovery potential at A-stage, while markedly reduces energy consumption at B-stage. Various configurations of A-B process with energy analysis are thus discussed. It appears highly possible to achieve an overall energy gain in WWTPs with A-B process as a core. PMID:27121339

[Copper recovery from artificial bioleaching lixivium of waste printed circuit boards].

PubMed

Cheng, Dan; Zhu, Neng-Wu; Wu, Ping-Xiao; Zou, Ding-Hui; Xing, Yi-Jia

2014-04-01

The key step to realize metal recovery from bioleaching solutions is the recovery of copper from bioleaching lixivium of waste printed circuit boards in high-grade form. The influences of cathode material, current density, initial pH and initial copper ion concentration on the efficiency and energy consumption of copper recovery from artificial bioleaching lixivium under condition of constant current were investigated using an electro-deposition approach. The results showed that the larger specific surface area of the cathode material (carbon felt) led to the higher copper recovery efficiency (the recovery efficiencies of the anode and the cathode chambers were 96.56% and 99.25%, respectively) and the smaller the total and unit mass product energy consumption (the total and unit mass product energy consumptions were 0.022 kW x h and 15.71 kW x h x kg(-1), respectively). The copper recovery efficiency and energy consumption increased with the increase of current density. When the current density was 155.56 mA x cm(-2), the highest copper recovery efficiencies in the anode and cathode chambers reached 98.51% and 99.37%, respectively. Accordingly, the highest total and unit mass product energy consumptions were 0.037 kW x h and 24.34 kW x h x kg(-1), respectively. The copper recovery efficiency was also significantly affected by the initial copper ion concentration. The increase of the initial copper ion concentration would lead to faster decrease of copper ion concentration, higher total energy consumption, and lower unit mass product consumption. However, the initial pH had no significant effect on the copper recovery efficiency. Under the optimal conditions (carbon felt for cathode materials, current density of 111.11 mA x cm(-2), initial pH of 2.0, and initial copper ion concentration of 10 g x L(-1)), the copper recovery efficiencies of the anode and cathode chambers were 96.75% and 99.35%, and the total and unit mass product energy consumptions were 0.021 kW x h and 14.61 kW x h x kg(-1), respectively. The deposited copper on the cathode material was fascicularly distributed and no oxygen was detected.

Waveguide and active region structure optimization for low-divergence InAs/InGaAs quantum dot comb lasers

NASA Astrophysics Data System (ADS)

Korenev, Vladimir V.; Savelyev, Artem V.; Zhukov, Alexey E.; Maximov, Mikhail V.; Omelchenko, Alexander V.

2015-05-01

Ways to improve beam divergence and energy consumption of quantum dot lasers emitting via the ground-state optical transitions by optimization of the key parameters of laser active region are discussed. It is shown that there exist an optimal cavity length, dispersion of inhomogeneous broadening and number of QD layers in active region allowing to obtain lasing spectrum of a given width at minimum injection current. The planar dielectric waveguide of the laser is optimized by analytical means for a better trade-off between high Γ-factor and low beam divergence.

Dynamic Allocation of SPM Based on Time-Slotted Cache Conflict Graph for System Optimization

NASA Astrophysics Data System (ADS)

Wu, Jianping; Ling, Ming; Zhang, Yang; Mei, Chen; Wang, Huan

This paper proposes a novel dynamic Scratch-pad Memory allocation strategy to optimize the energy consumption of the memory sub-system. Firstly, the whole program execution process is sliced into several time slots according to the temporal dimension; thereafter, a Time-Slotted Cache Conflict Graph (TSCCG) is introduced to model the behavior of Data Cache (D-Cache) conflicts within each time slot. Then, Integer Nonlinear Programming (INP) is implemented, which can avoid time-consuming linearization process, to select the most profitable data pages. Virtual Memory System (VMS) is adopted to remap those data pages, which will cause severe Cache conflicts within a time slot, to SPM. In order to minimize the swapping overhead of dynamic SPM allocation, a novel SPM controller with a tightly coupled DMA is introduced to issue the swapping operations without CPU's intervention. Last but not the least, this paper discusses the fluctuation of system energy profit based on different MMU page size as well as the Time Slot duration quantitatively. According to our design space exploration, the proposed method can optimize all of the data segments, including global data, heap and stack data in general, and reduce the total energy consumption by 27.28% on average, up to 55.22% with a marginal performance promotion. And comparing to the conventional static CCG (Cache Conflicts Graph), our approach can obtain 24.7% energy profit on average, up to 30.5% with a sight boost in performance.

Toward the lowest energy consumption and emission in biofuel production: combination of ideal reactors and robust hosts.

PubMed

Xu, Ke; Lv, Bo; Huo, Yi-Xin; Li, Chun

2018-04-01

Rising feedstock costs, low crude oil prices, and other macroeconomic factors have threatened biofuel fermentation industries. Energy-efficient reactors, which provide controllable and stable biological environment, are important for the large-scale production of renewable and sustainable biofuels, and their optimization focus on the reduction of energy consumption and waste gas emission. The bioreactors could either be aerobic or anaerobic, and photobioreactors were developed for the culture of algae or microalgae. Due to the cost of producing large-volume bioreactors, various modeling strategies were developed for bioreactor design. The achievement of ideal biofuel reactor relies on not only the breakthrough of reactor design, but also the creation of super microbial factories with highest productivity and metabolic pathway flux. Copyright © 2017 Elsevier Ltd. All rights reserved.

An exergy approach to efficiency evaluation of desalination

NASA Astrophysics Data System (ADS)

Ng, Kim Choon; Shahzad, Muhammad Wakil; Son, Hyuk Soo; Hamed, Osman A.

2017-05-01

This paper presents an evaluation process efficiency based on the consumption of primary energy for all types of practical desalination methods available hitherto. The conventional performance ratio has, thus far, been defined with respect to the consumption of derived energy, such as the electricity or steam, which are susceptible to the conversion losses of power plants and boilers that burned the input primary fuels. As derived energies are usually expressed by the units, either kWh or Joules, these units cannot differentiate the grade of energy supplied to the processes accurately. In this paper, the specific energy consumption is revisited for the efficacy of all large-scale desalination plants. In today's combined production of electricity and desalinated water, accomplished with advanced cogeneration concept, the input exergy of fuels is utilized optimally and efficiently in a temperature cascaded manner. By discerning the exergy destruction successively in the turbines and desalination processes, the relative contribution of primary energy to the processes can be accurately apportioned to the input primary energy. Although efficiency is not a law of thermodynamics, however, a common platform for expressing the figures of merit explicit to the efficacy of desalination processes can be developed meaningfully that has the thermodynamic rigor up to the ideal or thermodynamic limit of seawater desalination for all scientists and engineers to aspire to.

Node Self-Deployment Algorithm Based on Pigeon Swarm Optimization for Underwater Wireless Sensor Networks

PubMed Central

Yu, Shanen; Xu, Yiming; Jiang, Peng; Wu, Feng; Xu, Huan

2017-01-01

At present, free-to-move node self-deployment algorithms aim at event coverage and cannot improve network coverage under the premise of considering network connectivity, network reliability and network deployment energy consumption. Thus, this study proposes pigeon-based self-deployment algorithm (PSA) for underwater wireless sensor networks to overcome the limitations of these existing algorithms. In PSA, the sink node first finds its one-hop nodes and maximizes the network coverage in its one-hop region. The one-hop nodes subsequently divide the network into layers and cluster in each layer. Each cluster head node constructs a connected path to the sink node to guarantee network connectivity. Finally, the cluster head node regards the ratio of the movement distance of the node to the change in the coverage redundancy ratio as the target function and employs pigeon swarm optimization to determine the positions of the nodes. Simulation results show that PSA improves both network connectivity and network reliability, decreases network deployment energy consumption, and increases network coverage. PMID:28338615

Optimal Operation of Variable Speed Pumping System in China's Eastern Route Project of S-to-N Water Diversion Project

NASA Astrophysics Data System (ADS)

Cheng, Jilin; Zhang, Lihua; Zhang, Rentian; Gong, Yi; Zhu, Honggeng; Deng, Dongsheng; Feng, Xuesong; Qiu, Jinxian

2010-06-01

A dynamic planning model for optimizing operation of variable speed pumping system, aiming at minimum power consumption, was proposed to achieve economic operation. The No. 4 Jiangdu Pumping Station, a source pumping station in China's Eastern Route of South-to-North Water Diversion Project, is taken as a study case. Since the sump water level of Jiangdu Pumping Station is affected by the tide of Yangtze River, the daily-average heads of the pumping system varies yearly from 3.8m to 7.8m and the tide level difference in one day up to 1.2m. Comparisons of operation electricity cost between optimized variable speed and fixed speed operations of pumping system were made. When the full load operation mode is adopted, whether or not electricity prices in peak-valley periods are considered, the benefits of variable speed operation cannot compensate the energy consumption of the VFD. And when the pumping system operates in part load and the peak-valley electricity prices are considered, the pumping system should cease operation or lower its rotational speed in peak load hours since the electricity price are much higher, and to the contrary the pumping system should raise its rotational speed in valley load hours to pump more water. The computed results show that if the pumping system operates in 80% or 60% loads, the energy consumption cost of specified volume of water will save 14.01% and 26.69% averagely by means of optimal variable speed operation, and the investment on VFD will be paid back in 2 or 3 years. However, if the pumping system operates in 80% or 60% loads and the energy cost is calculated in non peak-valley electricity price, the repayment will be lengthened up to 18 years. In China's S-to-N Water Diversion Project, when the market operation and peak-valley electricity prices are taken into effect to supply water and regulate water levels in regulation reservoirs as Hongzehu Lake, Luomahu Lake, etc. the economic operation of water-diversion pumping stations will be vital, and the adoption of VFDs to achieve optimal operation may be a good choice.

Reverse Osmosis Optimization

DOE Office of Scientific and Technical Information (OSTI.GOV)

McMordie Stoughton, Kate; Duan, Xiaoli; Wendel, Emily M.

This technology evaluation was prepared by Pacific Northwest National Laboratory on behalf of the U.S. Department of Energy’s Federal Energy Management Program (FEMP). ¬The technology evaluation assesses techniques for optimizing reverse osmosis (RO) systems to increase RO system performance and water efficiency. This evaluation provides a general description of RO systems, the influence of RO systems on water use, and key areas where RO systems can be optimized to reduce water and energy consumption. The evaluation is intended to help facility managers at Federal sites understand the basic concepts of the RO process and system optimization options, enabling them tomore » make informed decisions during the system design process for either new projects or recommissioning of existing equipment. This evaluation is focused on commercial-sized RO systems generally treating more than 80 gallons per hour.¬« less

Reverse Osmosis Optimization

DOE Office of Scientific and Technical Information (OSTI.GOV)

None

This technology evaluation was prepared by Pacific Northwest National Laboratory on behalf of the U.S. Department of Energy’s Federal Energy Management Program (FEMP). The technology evaluation assesses techniques for optimizing reverse osmosis (RO) systems to increase RO system performance and water efficiency. This evaluation provides a general description of RO systems, the influence of RO systems on water use, and key areas where RO systems can be optimized to reduce water and energy consumption. The evaluation is intended to help facility managers at Federal sites understand the basic concepts of the RO process and system optimization options, enabling them tomore » make informed decisions during the system design process for either new projects or recommissioning of existing equipment. This evaluation is focused on commercial-sized RO systems generally treating more than 80 gallons per hour.« less

Control of Smart Building Using Advanced SCADA

NASA Astrophysics Data System (ADS)

Samuel, Vivin Thomas

For complete control of the building, a proper SCADA implementation and the optimization strategy has to be build. For better communication and efficiency a proper channel between the Communication protocol and SCADA has to be designed. This paper concentrate mainly between the communication protocol, and the SCADA implementation, for a better optimization and energy savings is derived to large scale industrial buildings. The communication channel used in order to completely control the building remotely from a distant place. For an efficient result we consider the temperature values and the power ratings of the equipment so that while controlling the equipment, we are setting a threshold values for FDD technique implementation. Building management system became a vital source for any building to maintain it and for safety purpose. Smart buildings, refers to various distinct features, where the complete automation system, office building controls, data center controls. ELC's are used to communicate the load values of the building to the remote server from a far location with the help of an Ethernet communication channel. Based on the demand fluctuation and the peak voltage, the loads operate differently increasing the consumption rate thus results in the increase in the annual consumption bill. In modern days, saving energy and reducing the consumption bill is most essential for any building for a better and long operation. The equipment - monitored regularly and optimization strategy is implemented for cost reduction automation system. Thus results in the reduction of annual cost reduction and load lifetime increase.

Energy Aware Cluster-Based Routing in Flying Ad-Hoc Networks.

PubMed

Aadil, Farhan; Raza, Ali; Khan, Muhammad Fahad; Maqsood, Muazzam; Mehmood, Irfan; Rho, Seungmin

2018-05-03

Flying ad-hoc networks (FANETs) are a very vibrant research area nowadays. They have many military and civil applications. Limited battery energy and the high mobility of micro unmanned aerial vehicles (UAVs) represent their two main problems, i.e., short flight time and inefficient routing. In this paper, we try to address both of these problems by means of efficient clustering. First, we adjust the transmission power of the UAVs by anticipating their operational requirements. Optimal transmission range will have minimum packet loss ratio (PLR) and better link quality, which ultimately save the energy consumed during communication. Second, we use a variant of the K-Means Density clustering algorithm for selection of cluster heads. Optimal cluster heads enhance the cluster lifetime and reduce the routing overhead. The proposed model outperforms the state of the art artificial intelligence techniques such as Ant Colony Optimization-based clustering algorithm and Grey Wolf Optimization-based clustering algorithm. The performance of the proposed algorithm is evaluated in term of number of clusters, cluster building time, cluster lifetime and energy consumption.

Designing train-speed trajectory with energy efficiency and service quality

NASA Astrophysics Data System (ADS)

Jia, Jiannan; Yang, Kai; Yang, Lixing; Gao, Yuan; Li, Shukai

2018-05-01

With the development of automatic train operations, optimal trajectory design is significant to the performance of train operations in railway transportation systems. Considering energy efficiency and service quality, this article formulates a bi-objective train-speed trajectory optimization model to minimize simultaneously the energy consumption and travel time in an inter-station section. This article is distinct from previous studies in that more sophisticated train driving strategies characterized by the acceleration/deceleration gear, the cruising speed, and the speed-shift site are specifically considered. For obtaining an optimal train-speed trajectory which has equal satisfactory degree on both objectives, a fuzzy linear programming approach is applied to reformulate the objectives. In addition, a genetic algorithm is developed to solve the proposed train-speed trajectory optimization problem. Finally, a series of numerical experiments based on a real-world instance of Beijing-Tianjin Intercity Railway are implemented to illustrate the practicability of the proposed model as well as the effectiveness of the solution methodology.

Analysis of power management and system latency in wireless sensor networks

NASA Astrophysics Data System (ADS)

Oswald, Matthew T.; Rohwer, Judd A.; Forman, Michael A.

2004-08-01

Successful power management in a wireless sensor network requires optimization of the protocols which affect energy-consumption on each node and the aggregate effects across the larger network. System optimization for a given deployment scenario requires an analysis and trade off of desired node and network features with their associated costs. The sleep protocol for an energy-efficient wireless sensor network for event detection, target classification, and target tracking developed at Sandia National Laboratories is presented. The dynamic source routing (DSR) algorithm is chosen to reduce network maintenance overhead, while providing a self-configuring and self-healing network architecture. A method for determining the optimal sleep time is developed and presented, providing reference data which spans several orders of magnitude. Message timing diagrams show, that a node in a five-node cluster, employing an optimal cyclic single-radio sleep protocol, consumes 3% more energy and incurs a 16-s increase latency than nodes employing the more complex dual-radio STEM protocol.

Optimizing cutting conditions on sustainable machining of aluminum alloy to minimize power consumption

NASA Astrophysics Data System (ADS)

Nur, Rusdi; Suyuti, Muhammad Arsyad; Susanto, Tri Agus

2017-06-01

Aluminum is widely utilized in the industrial sector. There are several advantages of aluminum, i.e. good flexibility and formability, high corrosion resistance and electrical conductivity, and high heat. Despite of these characteristics, however, pure aluminum is rarely used because of its lacks of strength. Thus, most of the aluminum used in the industrial sectors was in the form of alloy form. Sustainable machining can be considered to link with the transformation of input materials and energy/power demand into finished goods. Machining processes are responsible for environmental effects accepting to their power consumption. The cutting conditions have been optimized to minimize the cutting power, which is the power consumed for cutting. This paper presents an experimental study of sustainable machining of Al-11%Si base alloy that was operated without any cooling system to assess the capacity in reducing power consumption. The cutting force was measured and the cutting power was calculated. Both of cutting force and cutting power were analyzed and modeled by using the central composite design (CCD). The result of this study indicated that the cutting speed has an effect on machining performance and that optimum cutting conditions have to be determined, while sustainable machining can be followed in terms of minimizing power consumption and cutting force. The model developed from this study can be used for evaluation process and optimization to determine optimal cutting conditions for the performance of the whole process.

Dynamic Hierarchical Energy-Efficient Method Based on Combinatorial Optimization for Wireless Sensor Networks.

PubMed

Chang, Yuchao; Tang, Hongying; Cheng, Yongbo; Zhao, Qin; Yuan, Baoqing Li andXiaobing

2017-07-19

Routing protocols based on topology control are significantly important for improving network longevity in wireless sensor networks (WSNs). Traditionally, some WSN routing protocols distribute uneven network traffic load to sensor nodes, which is not optimal for improving network longevity. Differently to conventional WSN routing protocols, we propose a dynamic hierarchical protocol based on combinatorial optimization (DHCO) to balance energy consumption of sensor nodes and to improve WSN longevity. For each sensor node, the DHCO algorithm obtains the optimal route by establishing a feasible routing set instead of selecting the cluster head or the next hop node. The process of obtaining the optimal route can be formulated as a combinatorial optimization problem. Specifically, the DHCO algorithm is carried out by the following procedures. It employs a hierarchy-based connection mechanism to construct a hierarchical network structure in which each sensor node is assigned to a special hierarchical subset; it utilizes the combinatorial optimization theory to establish the feasible routing set for each sensor node, and takes advantage of the maximum-minimum criterion to obtain their optimal routes to the base station. Various results of simulation experiments show effectiveness and superiority of the DHCO algorithm in comparison with state-of-the-art WSN routing algorithms, including low-energy adaptive clustering hierarchy (LEACH), hybrid energy-efficient distributed clustering (HEED), genetic protocol-based self-organizing network clustering (GASONeC), and double cost function-based routing (DCFR) algorithms.

RGCA: A Reliable GPU Cluster Architecture for Large-Scale Internet of Things Computing Based on Effective Performance-Energy Optimization

PubMed Central

Chen, Qingkui; Zhao, Deyu; Wang, Jingjuan

2017-01-01

This paper aims to develop a low-cost, high-performance and high-reliability computing system to process large-scale data using common data mining algorithms in the Internet of Things (IoT) computing environment. Considering the characteristics of IoT data processing, similar to mainstream high performance computing, we use a GPU (Graphics Processing Unit) cluster to achieve better IoT services. Firstly, we present an energy consumption calculation method (ECCM) based on WSNs. Then, using the CUDA (Compute Unified Device Architecture) Programming model, we propose a Two-level Parallel Optimization Model (TLPOM) which exploits reasonable resource planning and common compiler optimization techniques to obtain the best blocks and threads configuration considering the resource constraints of each node. The key to this part is dynamic coupling Thread-Level Parallelism (TLP) and Instruction-Level Parallelism (ILP) to improve the performance of the algorithms without additional energy consumption. Finally, combining the ECCM and the TLPOM, we use the Reliable GPU Cluster Architecture (RGCA) to obtain a high-reliability computing system considering the nodes’ diversity, algorithm characteristics, etc. The results show that the performance of the algorithms significantly increased by 34.1%, 33.96% and 24.07% for Fermi, Kepler and Maxwell on average with TLPOM and the RGCA ensures that our IoT computing system provides low-cost and high-reliability services. PMID:28777325

RGCA: A Reliable GPU Cluster Architecture for Large-Scale Internet of Things Computing Based on Effective Performance-Energy Optimization.

PubMed

Fang, Yuling; Chen, Qingkui; Xiong, Neal N; Zhao, Deyu; Wang, Jingjuan

2017-08-04

This paper aims to develop a low-cost, high-performance and high-reliability computing system to process large-scale data using common data mining algorithms in the Internet of Things (IoT) computing environment. Considering the characteristics of IoT data processing, similar to mainstream high performance computing, we use a GPU (Graphics Processing Unit) cluster to achieve better IoT services. Firstly, we present an energy consumption calculation method (ECCM) based on WSNs. Then, using the CUDA (Compute Unified Device Architecture) Programming model, we propose a Two-level Parallel Optimization Model (TLPOM) which exploits reasonable resource planning and common compiler optimization techniques to obtain the best blocks and threads configuration considering the resource constraints of each node. The key to this part is dynamic coupling Thread-Level Parallelism (TLP) and Instruction-Level Parallelism (ILP) to improve the performance of the algorithms without additional energy consumption. Finally, combining the ECCM and the TLPOM, we use the Reliable GPU Cluster Architecture (RGCA) to obtain a high-reliability computing system considering the nodes' diversity, algorithm characteristics, etc. The results show that the performance of the algorithms significantly increased by 34.1%, 33.96% and 24.07% for Fermi, Kepler and Maxwell on average with TLPOM and the RGCA ensures that our IoT computing system provides low-cost and high-reliability services.

Improving Energy Efficiency Via Optimized Charge Motion and Slurry Flow in Plant Scale Sag Mills

DOE Office of Scientific and Technical Information (OSTI.GOV)

Raj K. Rajamani

2006-07-21

A research team from the University of Utah is working to make inroads into saving energy in these SAG mills. In 2003, Industries of the Future Program of the Department of Energy tasked the University of Utah team to build a partnership between the University and the mining industry for the specific purpose of reducing energy consumption in SAG mills. A partnership was formed with Cortez Gold Mines, Outokumpu Technology, Kennecott Utah Copper Corporation, and Process Engineering Resources Inc. At Cortez Gold Operations the shell and pulp lifters of the semiautogenous grinding mill was redesigned. The redesigned shell lifter hasmore » been in operation for over three years and the redesigned pulp lifter has been in operation for over nine months now. This report summarizes the dramatic reductions in energy consumption. Even though the energy reductions are very large, it is safe to say that a 20% minimum reduction would be achieved in any future installations of this technology.« less

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mittal, Sparsh; Zhang, Zhao

With each CMOS technology generation, leakage energy consumption has been dramatically increasing and hence, managing leakage power consumption of large last-level caches (LLCs) has become a critical issue in modern processor design. In this paper, we present EnCache, a novel software-based technique which uses dynamic profiling-based cache reconfiguration for saving cache leakage energy. EnCache uses a simple hardware component called profiling cache, which dynamically predicts energy efficiency of an application for 32 possible cache configurations. Using these estimates, system software reconfigures the cache to the most energy efficient configuration. EnCache uses dynamic cache reconfiguration and hence, it does not requiremore » offline profiling or tuning the parameter for each application. Furthermore, EnCache optimizes directly for the overall memory subsystem (LLC and main memory) energy efficiency instead of the LLC energy efficiency alone. The experiments performed with an x86-64 simulator and workloads from SPEC2006 suite confirm that EnCache provides larger energy saving than a conventional energy saving scheme. For single core and dual-core system configurations, the average savings in memory subsystem energy over a shared baseline configuration are 30.0% and 27.3%, respectively.« less

A Game Theoretic Approach for Balancing Energy Consumption in Clustered Wireless Sensor Networks

PubMed Central

Lu, Yinzhi; Xiong, Lian; Tao, Yang; Zhong, Yuanchang

2017-01-01

Clustering is an effective topology control method in wireless sensor networks (WSNs), since it can enhance the network lifetime and scalability. To prolong the network lifetime in clustered WSNs, an efficient cluster head (CH) optimization policy is essential to distribute the energy among sensor nodes. Recently, game theory has been introduced to model clustering. Each sensor node is considered as a rational and selfish player which will play a clustering game with an equilibrium strategy. Then it decides whether to act as the CH according to this strategy for a tradeoff between providing required services and energy conservation. However, how to get the equilibrium strategy while maximizing the payoff of sensor nodes has rarely been addressed to date. In this paper, we present a game theoretic approach for balancing energy consumption in clustered WSNs. With our novel payoff function, realistic sensor behaviors can be captured well. The energy heterogeneity of nodes is considered by incorporating a penalty mechanism in the payoff function, so the nodes with more energy will compete for CHs more actively. We have obtained the Nash equilibrium (NE) strategy of the clustering game through convex optimization. Specifically, each sensor node can achieve its own maximal payoff when it makes the decision according to this strategy. Through plenty of simulations, our proposed game theoretic clustering is proved to have a good energy balancing performance and consequently the network lifetime is greatly enhanced. PMID:29149075

Energy optimization analysis of the more electric aircraft

NASA Astrophysics Data System (ADS)

Liu, Yitao; Deng, Junxiang; Liu, Chao; Li, Sen

2018-02-01

The More Electric Aircraft (MEA) underlines the utilization of the electrical power to drive the non-propulsive aircraft systems. The critical features of the MEA including no-bleed engine architecture and advanced electrical system are introduced. Energy and exergy analysis is conducted for the MEA, and comparison of the effectiveness and efficiency of the energy usage between conventional aircraft and the MEA is conducted. The results indicate that one of the advantages of the MEA architecture is the greater efficiency gained in terms of reduced fuel consumption.

An Energy-Efficient Approach to Enhance Virtual Sensors Provisioning in Sensor Clouds Environments

PubMed Central

Filho, Raimir Holanda; Rabêlo, Ricardo de Andrade L.; de Carvalho, Carlos Giovanni N.; Mendes, Douglas Lopes de S.; Costa, Valney da Gama

2018-01-01

Virtual sensors provisioning is a central issue for sensors cloud middleware since it is responsible for selecting physical nodes, usually from Wireless Sensor Networks (WSN) of different owners, to handle user’s queries or applications. Recent works perform provisioning by clustering sensor nodes based on the correlation measurements and then selecting as few nodes as possible to preserve WSN energy. However, such works consider only homogeneous nodes (same set of sensors). Therefore, those works are not entirely appropriate for sensor clouds, which in most cases comprises heterogeneous sensor nodes. In this paper, we propose ACxSIMv2, an approach to enhance the provisioning task by considering heterogeneous environments. Two main algorithms form ACxSIMv2. The first one, ACASIMv1, creates multi-dimensional clusters of sensor nodes, taking into account the measurements correlations instead of the physical distance between nodes like most works on literature. Then, the second algorithm, ACOSIMv2, based on an Ant Colony Optimization system, selects an optimal set of sensors nodes from to respond user’s queries while attending all parameters and preserving the overall energy consumption. Results from initial experiments show that the approach reduces significantly the sensor cloud energy consumption compared to traditional works, providing a solution to be considered in sensor cloud scenarios. PMID:29495406

An Energy-Efficient Approach to Enhance Virtual Sensors Provisioning in Sensor Clouds Environments.

PubMed

Lemos, Marcus Vinícius de S; Filho, Raimir Holanda; Rabêlo, Ricardo de Andrade L; de Carvalho, Carlos Giovanni N; Mendes, Douglas Lopes de S; Costa, Valney da Gama

2018-02-26

Virtual sensors provisioning is a central issue for sensors cloud middleware since it is responsible for selecting physical nodes, usually from Wireless Sensor Networks (WSN) of different owners, to handle user's queries or applications. Recent works perform provisioning by clustering sensor nodes based on the correlation measurements and then selecting as few nodes as possible to preserve WSN energy. However, such works consider only homogeneous nodes (same set of sensors). Therefore, those works are not entirely appropriate for sensor clouds, which in most cases comprises heterogeneous sensor nodes. In this paper, we propose ACxSIMv2, an approach to enhance the provisioning task by considering heterogeneous environments. Two main algorithms form ACxSIMv2. The first one, ACASIMv1, creates multi-dimensional clusters of sensor nodes, taking into account the measurements correlations instead of the physical distance between nodes like most works on literature. Then, the second algorithm, ACOSIMv2, based on an Ant Colony Optimization system, selects an optimal set of sensors nodes from to respond user's queries while attending all parameters and preserving the overall energy consumption. Results from initial experiments show that the approach reduces significantly the sensor cloud energy consumption compared to traditional works, providing a solution to be considered in sensor cloud scenarios.

An energy-efficient rate adaptive media access protocol (RA-MAC) for long-lived sensor networks.

PubMed

Hu, Wen; Chen, Quanjun; Corke, Peter; O'Rourke, Damien

2010-01-01

We introduce an energy-efficient Rate Adaptive Media Access Control (RA-MAC) algorithm for long-lived Wireless Sensor Networks (WSNs). Previous research shows that the dynamic and lossy nature of wireless communications is one of the major challenges to reliable data delivery in WSNs. RA-MAC achieves high link reliability in such situations by dynamically trading off data rate for channel gain. The extra gain that can be achieved reduces the packet loss rate which contributes to reduced energy expenditure through a reduced numbers of retransmissions. We achieve this at the expense of raw bit rate which generally far exceeds the application's link requirement. To minimize communication energy consumption, RA-MAC selects the optimal data rate based on the estimated link quality at each data rate and an analytical model of the energy consumption. Our model shows how the selected data rate depends on different channel conditions in order to minimize energy consumption. We have implemented RA-MAC in TinyOS for an off-the-shelf sensor platform (the TinyNode) on top of a state-of-the-art WSN Media Access Control Protocol, SCP-MAC, and evaluated its performance by comparing our implementation with the original SCP-MAC using both simulation and experiment.

Use of nonlinear programming to optimize performance response to energy density in broiler feed formulation.

PubMed

Guevara, V R

2004-02-01

A nonlinear programming optimization model was developed to maximize margin over feed cost in broiler feed formulation and is described in this paper. The model identifies the optimal feed mix that maximizes profit margin. Optimum metabolizable energy level and performance were found by using Excel Solver nonlinear programming. Data from an energy density study with broilers were fitted to quadratic equations to express weight gain, feed consumption, and the objective function income over feed cost in terms of energy density. Nutrient:energy ratio constraints were transformed into equivalent linear constraints. National Research Council nutrient requirements and feeding program were used for examining changes in variables. The nonlinear programming feed formulation method was used to illustrate the effects of changes in different variables on the optimum energy density, performance, and profitability and was compared with conventional linear programming. To demonstrate the capabilities of the model, I determined the impact of variation in prices. Prices for broiler, corn, fish meal, and soybean meal were increased and decreased by 25%. Formulations were identical in all other respects. Energy density, margin, and diet cost changed compared with conventional linear programming formulation. This study suggests that nonlinear programming can be more useful than conventional linear programming to optimize performance response to energy density in broiler feed formulation because an energy level does not need to be set.

Architectural-level power estimation and experimentation

NASA Astrophysics Data System (ADS)

Ye, Wu

With the emergence of a plethora of embedded and portable applications and ever increasing integration levels, power dissipation of integrated circuits has moved to the forefront as a design constraint. Recent years have also seen a significant trend towards designs starting at the architectural (or RT) level. Those demand accurate yet fast RT level power estimation methodologies and tools. This thesis addresses issues and experiments associate with architectural level power estimation. An execution driven, cycle-accurate RT level power simulator, SimplePower, was developed using transition-sensitive energy models. It is based on the architecture of a five-stage pipelined RISC datapath for both 0.35mum and 0.8mum technology and can execute the integer subset of the instruction set of SimpleScalar . SimplePower measures the energy consumed in the datapath, memory and on-chip buses. During the development of SimplePower , a partitioning power modeling technique was proposed to model the energy consumed in complex functional units. The accuracy of this technique was validated with HSPICE simulation results for a register file and a shifter. A novel, selectively gated pipeline register optimization technique was proposed to reduce the datapath energy consumption. It uses the decoded control signals to selectively gate the data fields of the pipeline registers. Simulation results show that this technique can reduce the datapath energy consumption by 18--36% for a set of benchmarks. A low-level back-end compiler optimization, register relabeling, was applied to reduce the on-chip instruction cache data bus switch activities. Its impact was evaluated by SimplePower. Results show that it can reduce the energy consumed in the instruction data buses by 3.55--16.90%. A quantitative evaluation was conducted for the impact of six state-of-art high-level compilation techniques on both datapath and memory energy consumption. The experimental results provide a valuable insight for designers to develop future power-aware compilation frameworks for embedded systems.

The optimal operation of cooling tower systems with variable-frequency control

NASA Astrophysics Data System (ADS)

Cao, Yong; Huang, Liqing; Cui, Zhiguo; Liu, Jing

2018-02-01

This study investigates the energy performance of chiller and cooling tower systems integrated with variable-frequency control for cooling tower fans and condenser water pumps. With regard to an example chiller system serving an office building, Chiller and cooling towers models were developed to assess how different variable-frequency control methods of cooling towers fans and condenser water pumps influence the trade-off between the chiller power, pump power and fan power under various operating conditions. The matching relationship between the cooling tower fans frequency and condenser water pumps frequency at optimal energy consumption of the system is introduced to achieve optimum system performance.

The potential of value management (VM) to improve the consideration of energy efficiency within pre-construction

NASA Astrophysics Data System (ADS)

Tahir, Mohamad Zamhari; Nawi, Mohd Nasrun Mohd; Rajemi, Mohamad Farizal

2016-08-01

Energy demand and consumption in buildings will rise rapidly in the near future because of several social economics factors and this situation occurs not only in developed countries but also in developing countries such as Malaysia. There is demand towards building with energy efficiency features at this time, however most of the current buildings types are still being constructed with conventional designs, thus contribute to inefficient of energy consumption during the operation stage of the building. This paper presents the concept and the application of Value Management (VM) approach and its potential to improve consideration of energy efficiency within pre-construction process. Based on the relevant literatures, VM has provides an efficient and effective delivery system to fulfill the objectives and client's requirements. Generally in this paper, VM is discussed and scrutinized with reference to previous studies to see how these concepts contribute to better optimize the energy consumption in a building by seeking the best value energy efficiency through the design and construction process. This paper will not draw any conclusion but rather a preliminary research to propose the most energy efficiency measures to reliably accomplish a function that will meet the client's needs, desires and expectations. For further research in future, simple quantitative industry survey and VM workshops will be conducted to validate and further improve the research.

The Power of Flexibility: Autonomous Agents That Conserve Energy in Commercial Buildings

NASA Astrophysics Data System (ADS)

Kwak, Jun-young

Agent-based systems for energy conservation are now a growing area of research in multiagent systems, with applications ranging from energy management and control on the smart grid, to energy conservation in residential buildings, to energy generation and dynamic negotiations in distributed rural communities. Contributing to this area, my thesis presents new agent-based models and algorithms aiming to conserve energy in commercial buildings. More specifically, my thesis provides three sets of algorithmic contributions. First, I provide online predictive scheduling algorithms to handle massive numbers of meeting/event scheduling requests considering flexibility , which is a novel concept for capturing generic user constraints while optimizing the desired objective. Second, I present a novel BM-MDP ( Bounded-parameter Multi-objective Markov Decision Problem) model and robust algorithms for multi-objective optimization under uncertainty both at the planning and execution time. The BM-MDP model and its robust algorithms are useful in (re)scheduling events to achieve energy efficiency in the presence of uncertainty over user's preferences. Third, when multiple users contribute to energy savings, fair division of credit for such savings to incentivize users for their energy saving activities arises as an important question. I appeal to cooperative game theory and specifically to the concept of Shapley value for this fair division. Unfortunately, scaling up this Shapley value computation is a major hindrance in practice. Therefore, I present novel approximation algorithms to efficiently compute the Shapley value based on sampling and partitions and to speed up the characteristic function computation. These new models have not only advanced the state of the art in multiagent algorithms, but have actually been successfully integrated within agents dedicated to energy efficiency: SAVES, TESLA and THINC. SAVES focuses on the day-to-day energy consumption of individuals and groups in commercial buildings by reactively suggesting energy conserving alternatives. TESLA takes a long-range planning perspective and optimizes overall energy consumption of a large number of group events or meetings together. THINC provides an end-to-end integration within a single agent of energy efficient scheduling, rescheduling and credit allocation. While SAVES, TESLA and THINC thus differ in their scope and applicability, they demonstrate the utility of agent-based systems in actually reducing energy consumption in commercial buildings. I evaluate my algorithms and agents using extensive analysis on data from over 110,000 real meetings/events at multiple educational buildings including the main libraries at the University of Southern California. I also provide results on simulations and real-world experiments, clearly demonstrating the power of agent technology to assist human users in saving energy in commercial buildings.

How to dip nectar: optimal time apportionment in natural viscous fluid transport

NASA Astrophysics Data System (ADS)

Wu, Jianing; Shi, Guanya; Zhao, Yiwei; Yan, Shaoze

2018-06-01

The mouthparts of some animals are highly evolved fluid transporters. Most honeybees dip viscous nectar in a cyclic fashion by using protrusible tongues with active hairs that can erect rhythmically. The glossal hairs flatten when the tongue extends into the nectar, and then erect outwards like an umbrella to catch nectar while retracting. This paper examines the potential capability of honeybees in allocating the duration of the tongue protraction and retraction phases for the sake of energy saving. A physical model is established to analyze energy consumption induced by viscous drag, considering tongue kinematics and variation of the surface profile in different phases of tongue movements. The results indicate that the theoretically optimal time apportionment ratio at which the energy consumption is the minimum, is directly related to the square root of the tongue’s diameter ratio between the protraction and retraction phase. Through dipping observations, we validate that the duration for the protraction and retraction phases show high accordance with the theoretical prediction. These findings not only broaden the insights into honeybee’s foraging strategy but inspire the design of high-performance microfluidic pumps with dynamic surfaces to transport viscous fluid.

Electrification pathways for Kenya-linking spatial electrification analysis and medium to long term energy planning

NASA Astrophysics Data System (ADS)

Moksnes, Nandi; Korkovelos, Alexandros; Mentis, Dimitrios; Howells, Mark

2017-09-01

In September 2015 UN announced 17 Sustainable Development goals (SDG) from which goal number 7 envisions universal access to modern energy services for all by 2030. In Kenya only about 46% of the population currently has access to electricity. This paper analyses hypothetical scenarios, and selected implications, investigating pathways that would allow the country to reach its electrification targets by 2030. Two modelling tools were used for the purposes of this study, namely OnSSET and OSeMOSYS. The tools were soft-linked in order to capture both the spatial and temporal dynamics of their nature. Two electricity demand scenarios were developed representing low and high end user consumption goals respectively. Indicatively, results show that geothermal, coal, hydro and natural gas would consist the optimal energy mix for the centralized national grid. However, in the case of the low demand scenario a high penetration of stand-alone systems is evident in the country, reaching out to approximately 47% of the electrified population. Increasing end user consumption leads to a shift in the optimal technology mix, with higher penetration of mini-grid technologies and grid extension.

Comparison of phthalic acid removal from aqueous solution by electrochemical methods: Optimization, kinetic and sludge study.

PubMed

Sandhwar, Vishal Kumar; Prasad, Basheshwar

2017-12-01

In this work, comparative study between electrochemical processes such as electrocoagulation (EC), peroxi-coagulation (PC) and peroxi-electrocoagulation (PEC) was performed for the removal of phthalic acid (PA) and chemical oxygen demand (COD) from aqueous medium. Initially, acid treatment was studied at various pH (1-3) and temperature (10-55 °C). Subsequently, the supernatant was re-treated by electrochemical processes such as EC, PC and PEC separately. Independent parameters viz. pH, current density (CD), electrolyte concentration (m), electrode gap (g), H 2 O 2 concentration and electrolysis time (t) were optimized by Central Composite Design (CCD) for these electrochemical processes. All three processes were compared based on removal, energy consumption, kinetic analysis, operating cost and sludge characteristics. In this study, PEC process was found more efficient among EC, PC and PEC processes in order to get maximum removal, minimum energy consumption and minimum operating cost. Maximum removal of PA- 68.21%, 74.36%, 82.25% & COD- 64.79%, 68.15%, 75.21% with energy consumption - 120.95, 97.51, 65.68 (kWh/kg COD removed) were attained through EC, PC and PEC processes respectively at their corresponding optimum conditions. Results indicated that PA and COD removals are in order of PEC > PC > EC under optimum conditions. First order kinetic model was found able to describe the degradation kinetics and provided best correlation for the removal rate within the acceptable error range. Copyright © 2017 Elsevier Ltd. All rights reserved.

Power-rate-distortion analysis for wireless video communication under energy constraint

NASA Astrophysics Data System (ADS)

He, Zhihai; Liang, Yongfang; Ahmad, Ishfaq

2004-01-01

In video coding and streaming over wireless communication network, the power-demanding video encoding operates on the mobile devices with limited energy supply. To analyze, control, and optimize the rate-distortion (R-D) behavior of the wireless video communication system under the energy constraint, we need to develop a power-rate-distortion (P-R-D) analysis framework, which extends the traditional R-D analysis by including another dimension, the power consumption. Specifically, in this paper, we analyze the encoding mechanism of typical video encoding systems and develop a parametric video encoding architecture which is fully scalable in computational complexity. Using dynamic voltage scaling (DVS), a hardware technology recently developed in CMOS circuits design, the complexity scalability can be translated into the power consumption scalability of the video encoder. We investigate the rate-distortion behaviors of the complexity control parameters and establish an analytic framework to explore the P-R-D behavior of the video encoding system. Both theoretically and experimentally, we show that, using this P-R-D model, the encoding system is able to automatically adjust its complexity control parameters to match the available energy supply of the mobile device while maximizing the picture quality. The P-R-D model provides a theoretical guideline for system design and performance optimization in wireless video communication under energy constraint, especially over the wireless video sensor network.

IEA EBC Annex 66: Definition and simulation of occupant behavior in buildings

DOE PAGES

Yan, Da; Hong, Tianzhen; Dong, Bing; ...

2017-09-28

Here, more than 30% of the total primary energy in the world is consumed in buildings. It is crucial to reduce building energy consumption in order to preserve energy resources and mitigate global climate change. Building performance simulations have been widely used for the estimation and optimization of building performance, providing reference values for the assessment of building energy consumption and the effects of energy-saving technologies. Among the various factors influencing building energy consumption, occupant behavior has drawn increasing attention. Occupant behavior includes occupant presence, movement, and interaction with building energy devices and systems. However, there are gaps in occupantmore » behavior modeling as different energy modelers have employed varied data and tools to simulate occupant behavior, therefore producing different and incomparable results. Aiming to address these gaps, the International Energy Agency (IEA) Energy in Buildings and Community (EBC) Programme Annex 66 has established a scientific methodological framework for occupant behavior research, including data collection, behavior model representation, modeling and evaluation approaches, and the integration of behavior modeling tools with building performance simulation programs. Annex 66 also includes case studies and application guidelines to assist in building design, operation, and policymaking, using interdisciplinary approaches to reduce energy use in buildings and improve occupant comfort and productivity. This paper highlights the key research issues, methods, and outcomes pertaining to Annex 66, and offers perspectives on future research needs to integrate occupant behavior with the building life cycle.« less

IEA EBC Annex 66: Definition and simulation of occupant behavior in buildings

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yan, Da; Hong, Tianzhen; Dong, Bing

Here, more than 30% of the total primary energy in the world is consumed in buildings. It is crucial to reduce building energy consumption in order to preserve energy resources and mitigate global climate change. Building performance simulations have been widely used for the estimation and optimization of building performance, providing reference values for the assessment of building energy consumption and the effects of energy-saving technologies. Among the various factors influencing building energy consumption, occupant behavior has drawn increasing attention. Occupant behavior includes occupant presence, movement, and interaction with building energy devices and systems. However, there are gaps in occupantmore » behavior modeling as different energy modelers have employed varied data and tools to simulate occupant behavior, therefore producing different and incomparable results. Aiming to address these gaps, the International Energy Agency (IEA) Energy in Buildings and Community (EBC) Programme Annex 66 has established a scientific methodological framework for occupant behavior research, including data collection, behavior model representation, modeling and evaluation approaches, and the integration of behavior modeling tools with building performance simulation programs. Annex 66 also includes case studies and application guidelines to assist in building design, operation, and policymaking, using interdisciplinary approaches to reduce energy use in buildings and improve occupant comfort and productivity. This paper highlights the key research issues, methods, and outcomes pertaining to Annex 66, and offers perspectives on future research needs to integrate occupant behavior with the building life cycle.« less

Approaches to Enable Demand Response by Industrial Loads for Ancillary Services Provision

NASA Astrophysics Data System (ADS)

Zhang, Xiao

Demand response has gained significant attention in recent years as it demonstrates potentials to enhance the power system's operational flexibility in a cost-effective way. Industrial loads such as aluminum smelters, steel manufacturers, and cement plants demonstrate advantages in supporting power system operation through demand response programs, because of their intensive power consumption, already existing advanced monitoring and control infrastructure, and the strong economic incentive due to the high energy costs. In this thesis, we study approaches to efficiently integrate each of these types of manufacturing processes as demand response resources. The aluminum smelting process is able to change its power consumption both accurately and quickly by controlling the pots' DC voltage, without affecting the production quality. Hence, an aluminum smelter has both the motivation and the ability to participate in demand response. First, we focus on determining the optimal regulation capacity that such a manufacturing plant should provide. Next, we focus on determining its optimal bidding strategy in the day-ahead energy and ancillary services markets. Electric arc furnaces (EAFs) in steel manufacturing consume a large amount of electric energy. However, a steel plant can take advantage of time-based electricity prices by optimally arranging energy-consuming activities to avoid peak hours. We first propose scheduling methods that incorporate the EAFs' flexibilities to reduce the electricity cost. We then propose methods to make the computations more tractable. Finally, we extend the scheduling formulations to enable the provision of spinning reserve. Cement plants are able to quickly adjust their power consumption rate by switching on/off the crushers. However, switching on/off the loading units only achieves discrete power changes, which restricts the load from offering valuable ancillary services such as regulation and load following, as continuous power changes are required for these services. We propose methods that enable these services with the support of an on-site energy storage device. As demonstrated by the case studies, the proposed approaches are effective and can generate practical production instructions for the industrial loads. This thesis not only provides methods to enable demand response by industrial loads but also potentially encourages industrial loads to be active in electricity markets.

Power Allocation Based on Data Classification in Wireless Sensor Networks

PubMed Central

Wang, Houlian; Zhou, Gongbo

2017-01-01

Limited node energy in wireless sensor networks is a crucial factor which affects the monitoring of equipment operation and working conditions in coal mines. In addition, due to heterogeneous nodes and different data acquisition rates, the number of arriving packets in a queue network can differ, which may lead to some queue lengths reaching the maximum value earlier compared with others. In order to tackle these two problems, an optimal power allocation strategy based on classified data is proposed in this paper. Arriving data is classified into dissimilar classes depending on the number of arriving packets. The problem is formulated as a Lyapunov drift optimization with the objective of minimizing the weight sum of average power consumption and average data class. As a result, a suboptimal distributed algorithm without any knowledge of system statistics is presented. The simulations, conducted in the perfect channel state information (CSI) case and the imperfect CSI case, reveal that the utility can be pushed arbitrarily close to optimal by increasing the parameter V, but with a corresponding growth in the average delay, and that other tunable parameters W and the classification method in the interior of utility function can trade power optimality for increased average data class. The above results show that data in a high class has priorities to be processed than data in a low class, and energy consumption can be minimized in this resource allocation strategy. PMID:28498346

Recovery of water and minerals from shale gas produced water by membrane distillation crystallization.

PubMed

Kim, Junghyun; Kim, Jungwon; Hong, Seungkwan

2018-02-01

Shale gas produced water (SGPW) treatment imposes greater technical challenges because of its high concentration of various contaminants. Membrane distillation crystallization (MDC) has a great potential to manage SGPW since it is capable of recovering both water and minerals at high rates, up to near a zero liquid discharge (ZLD) condition. To evaluate the feasibility of MDC for SGPW treatment, MDC performance indicators, such as water recovery rate, solid production rate (SPR) and specific energy consumption (SEC), were systematically investigated, to our knowledge for the first time, by using actual SGPW from Eagle Ford Shale (USA). The main operating parameters including feed cross-flow velocity (CFV) and crystallization temperature (T Cr ) were optimized by performing a series of MDC experiments. The results reported that water and minerals were effectively recovered with 84% of recovery rate and 2.72 kg/m 2 day of SPR under respective optimal operating conditions. Furthermore, the scale mechanism was firstly identified as limiting factor for MDC performance degradation. Lastly, SEC of MDC was estimated to be as low as 28.2 kWh/m 3 under ideal optimal operating conditions. Our experimental observations demonstrated that MDC could sustainably and effectively recover water and mineral with low energy consumption from SGPW by optimizing operating condition. Copyright © 2017 Elsevier Ltd. All rights reserved.

Energy management and cooperation in microgrids

NASA Astrophysics Data System (ADS)

Rahbar, Katayoun

Microgrids are key components of future smart power grids, which integrate distributed renewable energy generators to efficiently serve the load demand locally. However, random and intermittent characteristics of renewable energy generations may hinder the reliable operation of microgrids. This thesis is thus devoted to investigating new strategies for microgrids to optimally manage their energy consumption, energy storage system (ESS) and cooperation in real time to achieve the reliable and cost-effective operation. This thesis starts with a single microgrid system. The optimal energy scheduling and ESS management policy is derived to minimize the energy cost of the microgrid resulting from drawing conventional energy from the main grid under both the off-line and online setups, where the renewable energy generation/load demand are assumed to be non-causally known and causally known at the microgrid, respectively. The proposed online algorithm is designed based on the optimal off-line solution and works under arbitrary (even unknown) realizations of future renewable energy generation/load demand. Therefore, it is more practically applicable as compared to solutions based on conventional techniques such as dynamic programming and stochastic programming that require the prior knowledge of renewable energy generation and load demand realizations/distributions. Next, for a group of microgrids that cooperate in energy management, we study efficient methods for sharing energy among them for both fully and partially cooperative scenarios, where microgrids are of common interests and self-interested, respectively. For the fully cooperative energy management, the off-line optimization problem is first formulated and optimally solved, where a distributed algorithm is proposed to minimize the total (sum) energy cost of microgrids. Inspired by the results obtained from the off-line optimization, efficient online algorithms are proposed for the real-time energy management, which are of low complexity and work given arbitrary realizations of renewable energy generation/load demand. On the other hand, for self-interested microgrids, the partially cooperative energy management is formulated and a distributed algorithm is proposed to optimize the energy cooperation such that energy costs of individual microgrids reduce simultaneously over the case without energy cooperation while limited information is shared among the microgrids and the central controller.

Research and application of an intelligent control system in central air-conditioning based on energy consumption simulation

NASA Astrophysics Data System (ADS)

Cao, Ling; Che, Wenbin

2018-05-01

For the central air-conditioning energy-saving, it is common practice to use a wide range of PTD controllers in engineering to optimize energy savings. However, the shortcomings of the PTD controller have also been magnified on this issue, such as: calculation accuracy is not enough, the calculation time is too long. Particle swarm optimization has the advantage of fast convergence. This paper is based on Particle Swarm Optimization apply in PTD controller tuning parameters in order to achieve the purpose of saving energy while ensuring comfort. The algorithm proposed in this paper can adjust the weight according to the change of population fitness, reduce the weights of particles with lower fitness and enhance the weights of particles with higher fitness in the population, and fully release the population vitality. The method in this paper is validated by the TRNSYS model based on the central air-conditioning system. The experimental results show that the room temperature fluctuation is small, the overshoot is small, the adjustment speed is fast, and the energy-saving fluctuates at 10%.

Decision making based on data analysis and optimization algorithm applied for cogeneration systems integration into a grid

NASA Astrophysics Data System (ADS)

Asmar, Joseph Al; Lahoud, Chawki; Brouche, Marwan

2018-05-01

Cogeneration and trigeneration systems can contribute to the reduction of primary energy consumption and greenhouse gas emissions in residential and tertiary sectors, by reducing fossil fuels demand and grid losses with respect to conventional systems. The cogeneration systems are characterized by a very high energy efficiency (80 to 90%) as well as a less polluting aspect compared to the conventional energy production. The integration of these systems into the energy network must simultaneously take into account their economic and environmental challenges. In this paper, a decision-making strategy will be introduced and is divided into two parts. The first one is a strategy based on a multi-objective optimization tool with data analysis and the second part is based on an optimization algorithm. The power dispatching of the Lebanese electricity grid is then simulated and considered as a case study in order to prove the compatibility of the cogeneration power calculated by our decision-making technique. In addition, the thermal energy produced by the cogeneration systems which capacity is selected by our technique shows compatibility with the thermal demand for district heating.

Modelisation temporelle de la consommation electrique en analyse du cycle de vie appliquee au contexte des TIC

NASA Astrophysics Data System (ADS)

Maurice, Elsa

Fossil fuels are a scarce energy resource. Since the industrial revolution, mankind uses and abuses of non-renewable energies. They are responsible for many environmental damages. The production of energy is one of the main challenges for a global sustainable development. In our society, we can witness an exponential increase of the usage of the systems of Information and Communication Technologies (ICT) such as Internet, phone calls, etc. The ICT development allows the creation and optimization of many smart systems, the pooling of services, and it also helps damping the climate change. However, because of their electric consumption, the ICT are also responsible for some green house gases (GHG) emissions: 3% in total. This fact gives them the willingness to change in order to limit their GHG emissions. In order to properly evaluate and optimize the ICT services, it is necessary to use some methods of evaluation that comply with the specificity of these systems. Currently, the methods used to evaluate the GHG emissions are not adapted to dynamic systems, which include the ICT systems. The variations of the production of electricity in a day or even a month are not yet taken into account. This problem is far from being restricted to the modelling of GHG emissions, it widens to the global variation in production and consumption of electricity. The Life Cycle Assessment (LCA) method grants useful and complete tools to analyse their environmental impacts, but, as with the GHG computation methods, it should be dynamically adapted. In the ICT framework, the first step to solve this LCA problem is to be able to model the variations in time of the electricity production. This master thesis introduces a new way to include the variation in time of the consumption and production of electricity in LCA methods. First, it generates an historical hourly database of the electricity production and import-export of three Canadian states: Alberta, Ontario and Quebec. Then it develops a model in function of time to predict their electricity consumption. This study is done for a project implementing a " cloud computing " service in between these states. The consumption model then provides information to optimize the best place and time to make use of ICT services such as Internet messaging or server maintenance. This first-ever implementation of time parameter allows more precision and vision in LCA data. The disintegration of electrical inventory flows in LCA refines the effects of the electricity production both historically and in real time. Some short-term predictions for the state of Quebec electrical exportations and importations were also computed in this thesis. The goal is to foresee and optimize in real time the ICT services use. The origin of a kilowatt-hour consumed in Quebec depends on the import-export variable with its surrounding states. This parameter relies mainly on the price of the electricity, the weather and the need for the state of Quebec in energy. This allows to plot a time-varying estimate of the environmental consequences for the consumption of a kilowatt-hour in Quebec. This can then be used to limit the GHG emission of ICT services like " cloud-computing " or " smart-grids ". A smart trade-off between electricity consumption and environmental issues will lead to a more efficient sustainable development.

Energy Efficient Image/Video Data Transmission on Commercial Multi-Core Processors

PubMed Central

Lee, Sungju; Kim, Heegon; Chung, Yongwha; Park, Daihee

2012-01-01

In transmitting image/video data over Video Sensor Networks (VSNs), energy consumption must be minimized while maintaining high image/video quality. Although image/video compression is well known for its efficiency and usefulness in VSNs, the excessive costs associated with encoding computation and complexity still hinder its adoption for practical use. However, it is anticipated that high-performance handheld multi-core devices will be used as VSN processing nodes in the near future. In this paper, we propose a way to improve the energy efficiency of image and video compression with multi-core processors while maintaining the image/video quality. We improve the compression efficiency at the algorithmic level or derive the optimal parameters for the combination of a machine and compression based on the tradeoff between the energy consumption and the image/video quality. Based on experimental results, we confirm that the proposed approach can improve the energy efficiency of the straightforward approach by a factor of 2∼5 without compromising image/video quality. PMID:23202181

Simulation of Energy Savings in Automotive Coatings Processes

NASA Astrophysics Data System (ADS)

Gerini Romagnoli, Marco

Recently, the automakers have become more and more aware of the environmental and economic impact of their manufacturing processes. The paint shop is the largest energy user in a vehicle manufacturing plant, and one way to reduce costs and energy usage is the optimization of this area. This project aims at providing a tool to model and simulate a paint shop, in order to run and analyze some scenarios and case studies, helping to take strategic decisions. Analytical computations and real data were merged to build a tool that can be used by FCA for their Sterling Heights plant. Convection and conduction heat losses were modeled for the dip processes and the ovens. Thermal balances were used to compute the consumptions of booths, decks and ovens, while pump and fan energy consumptions were modeled for each sub-process. The user acts on a calendar, scheduling a year of production, and the model predicts the energy consumption of the paint shop. Five scenarios were run to test different conditions and the influence of scheduling on the energy consumption. Two different sets of production schedules have been evaluated, the first one fulfilling the production requirement in one shift of 10 hours, at high rate, the second one using two 7-hour-long shifts at medium production rate. It was found that the unit cost was minimized in the warmest months of spring and fall, and system shutdown was a crucial factor to reduce energy consumption. A fifth hypothetical scenario was run, with a 4 month continuous production and an 8 month total shutdown, which reduced the energy consumption to a half of the best realistic scenario. When the plant was run in a two-shifts configuration, the cost to coat a vehicle was found to be 29 with weekend shutdown, and 39 without. In the one-shift configuration, the cost was slightly higher, but the difference was less than 5%. While the fifth scenario showed a consistent reduction of the unit cost, inventory and logistic expenses deriving from the production strategy make this scenario almost impossible to realize. A sensitivity analysis was run on several parameters influencing the energy consumption of the paint shop, and the booths set point temperature was found to be the most significant factor.

Analysis of optimal design of low temperature economizer

NASA Astrophysics Data System (ADS)

Song, J. H.; Wang, S.

2017-11-01

This paper has studied the Off-design characteristic of low temperature economizer system based on thermodynamics analysis. Based on the data from one 1000 MW coal-fired unit, two modes of operation are contrasted and analyzed. One is to fix exhaust gas temperature and the other one is to take into account both of the average temperature difference and the exhaust gas temperature. Meanwhile, the cause of energy saving effect change is explored. Result shows that: in mode 1, the amount of decrease in coal consumption reduces from 1.11 g/kWh (under full load) to 0.54 g/kWh (under half load), and in mode 2, when the load decreases from 90% to 50%, the decrease in coal consumption reduces from 1.29 g/kWh to 0.84 g/kWh. From the result, under high load, the energy saving effect is superior, and under lower work load, energy saving effect declines rapidly when load is reduced. When load changes, the temperature difference of heat transfer, gas flow, the flue gas heat rejection and the waste heat recovery change. The energy saving effect corresponding changes result in that the energy saving effect under high load is superior and more stable. However, rational adjustment to the temperature of outlet gas can alleviate the decline of the energy saving effect under low load. The result provides theoretical analysis data for the optimal design and operation of low temperature economizer system of power plant.

An Efficient Interactive Model for On-Demand Sensing-As-A-Servicesof Sensor-Cloud

PubMed Central

Dinh, Thanh; Kim, Younghan

2016-01-01

This paper proposes an efficient interactive model for the sensor-cloud to enable the sensor-cloud to efficiently provide on-demand sensing services for multiple applications with different requirements at the same time. The interactive model is designed for both the cloud and sensor nodes to optimize the resource consumption of physical sensors, as well as the bandwidth consumption of sensing traffic. In the model, the sensor-cloud plays a key role in aggregating application requests to minimize the workloads required for constrained physical nodes while guaranteeing that the requirements of all applications are satisfied. Physical sensor nodes perform their sensing under the guidance of the sensor-cloud. Based on the interactions with the sensor-cloud, physical sensor nodes adapt their scheduling accordingly to minimize their energy consumption. Comprehensive experimental results show that our proposed system achieves a significant improvement in terms of the energy consumption of physical sensors, the bandwidth consumption from the sink node to the sensor-cloud, the packet delivery latency, reliability and scalability, compared to current approaches. Based on the obtained results, we discuss the economical benefits and how the proposed system enables a win-win model in the sensor-cloud. PMID:27367689

An Efficient Interactive Model for On-Demand Sensing-As-A-Servicesof Sensor-Cloud.

PubMed

Dinh, Thanh; Kim, Younghan

2016-06-28

This paper proposes an efficient interactive model for the sensor-cloud to enable the sensor-cloud to efficiently provide on-demand sensing services for multiple applications with different requirements at the same time. The interactive model is designed for both the cloud and sensor nodes to optimize the resource consumption of physical sensors, as well as the bandwidth consumption of sensing traffic. In the model, the sensor-cloud plays a key role in aggregating application requests to minimize the workloads required for constrained physical nodes while guaranteeing that the requirements of all applications are satisfied. Physical sensor nodes perform their sensing under the guidance of the sensor-cloud. Based on the interactions with the sensor-cloud, physical sensor nodes adapt their scheduling accordingly to minimize their energy consumption. Comprehensive experimental results show that our proposed system achieves a significant improvement in terms of the energy consumption of physical sensors, the bandwidth consumption from the sink node to the sensor-cloud, the packet delivery latency, reliability and scalability, compared to current approaches. Based on the obtained results, we discuss the economical benefits and how the proposed system enables a win-win model in the sensor-cloud.

A Distribution-class Locational Marginal Price (DLMP) Index for Enhanced Distribution Systems

NASA Astrophysics Data System (ADS)

Akinbode, Oluwaseyi Wemimo

The smart grid initiative is the impetus behind changes that are expected to culminate into an enhanced distribution system with the communication and control infrastructure to support advanced distribution system applications and resources such as distributed generation, energy storage systems, and price responsive loads. This research proposes a distribution-class analog of the transmission LMP (DLMP) as an enabler of the advanced applications of the enhanced distribution system. The DLMP is envisioned as a control signal that can incentivize distribution system resources to behave optimally in a manner that benefits economic efficiency and system reliability and that can optimally couple the transmission and the distribution systems. The DLMP is calculated from a two-stage optimization problem; a transmission system OPF and a distribution system OPF. An iterative framework that ensures accurate representation of the distribution system's price sensitive resources for the transmission system problem and vice versa is developed and its convergence problem is discussed. As part of the DLMP calculation framework, a DCOPF formulation that endogenously captures the effect of real power losses is discussed. The formulation uses piecewise linear functions to approximate losses. This thesis explores, with theoretical proofs, the breakdown of the loss approximation technique when non-positive DLMPs/LMPs occur and discusses a mixed integer linear programming formulation that corrects the breakdown. The DLMP is numerically illustrated in traditional and enhanced distribution systems and its superiority to contemporary pricing mechanisms is demonstrated using price responsive loads. Results show that the impact of the inaccuracy of contemporary pricing schemes becomes significant as flexible resources increase. At high elasticity, aggregate load consumption deviated from the optimal consumption by up to about 45 percent when using a flat or time-of-use rate. Individual load consumption deviated by up to 25 percent when using a real-time price. The superiority of the DLMP is more pronounced when important distribution network conditions are not reflected by contemporary prices. The individual load consumption incentivized by the real-time price deviated by up to 90 percent from the optimal consumption in a congested distribution network. While the DLMP internalizes congestion management, the consumption incentivized by the real-time price caused overloads.

CFD simulation of fluid dynamic and biokinetic processes within activated sludge reactors under intermittent aeration regime.

PubMed

Sánchez, F; Rey, H; Viedma, A; Nicolás-Pérez, F; Kaiser, A S; Martínez, M

2018-08-01

Due to the aeration system, biological reactors are the most energy-consuming facilities of convectional WWTPs. Many biological reactors work under intermittent aeration regime; the optimization of the aeration process (air diffuser layout, air flow rate per diffuser, aeration length …) is necessary to ensure an efficient performance; satisfying the effluent requirements with the minimum energy consumption. This work develops a CFD modelling of an activated sludge reactor (ASR) which works under intermittent aeration regime. The model considers the fluid dynamic and biological processes within the ASR. The biological simulation, which is transient, takes into account the intermittent aeration regime. The CFD modelling is employed for the selection of the aeration system of an ASR. Two different aeration configurations are simulated. The model evaluates the aeration power consumption necessary to satisfy the effluent requirements. An improvement of 2.8% in terms of energy consumption is achieved by modifying the air diffuser layout. An analysis of the influence of the air flow rate per diffuser on the ASR performance is carried out. The results show a reduction of 14.5% in the energy consumption of the aeration system when the air flow rate per diffuser is reduced. The model provides an insight into the aeration inefficiencies produced within ASRs. Copyright © 2018 Elsevier Ltd. All rights reserved.

Performance analysis of a continuous serpentine flow reactor for electrochemical oxidation of synthetic and real textile wastewater: Energy consumption, mass transfer coefficient and economic analysis.

PubMed

Pillai, Indu M Sasidharan; Gupta, Ashok K

2017-05-15

A continuous flow electrochemical reactor was developed, and its application was tested for the treatment of textile wastewater. A parallel plate configuration with serpentine flow was chosen for the continuous flow reactor. Uniparameter optimization was carried out for electrochemical oxidation of synthetic and real textile wastewater (collected from the inlet of the effluent treatment plant). Chemical Oxygen Demand (COD) removal efficiency of 90% was achieved for synthetic textile wastewater (initial COD - 780 mg L -1 ) at a flow rate of 500 mL h -1 (retention time of 6 h) and a current density of 1.15 mA cm -2 and the energy consumption for the degradation was 9.2 kWh (kg COD) -1 . The complete degradation of real textile wastewater (initial COD of 368 mg L -1 ) was obtained at a current density of 1.15 mA cm -2 , NaCl concentration of 1 g L -1 and retention time of 6 h. Energy consumption and mass transfer coefficient of the reactions were calculated. The continuous flow reactor performed better than batch reactor with reference to energy consumption and economy. The overall treatment cost for complete COD removal of real textile wastewater was 5.83 USD m -3 . Copyright © 2017 Elsevier Ltd. All rights reserved.

Reducing aeration energy consumption in a large-scale membrane bioreactor: Process simulation and engineering application.

PubMed

Sun, Jianyu; Liang, Peng; Yan, Xiaoxu; Zuo, Kuichang; Xiao, Kang; Xia, Junlin; Qiu, Yong; Wu, Qing; Wu, Shijia; Huang, Xia; Qi, Meng; Wen, Xianghua

2016-04-15

Reducing the energy consumption of membrane bioreactors (MBRs) is highly important for their wider application in wastewater treatment engineering. Of particular significance is reducing aeration in aerobic tanks to reduce the overall energy consumption. This study proposed an in situ ammonia-N-based feedback control strategy for aeration in aerobic tanks; this was tested via model simulation and through a large-scale (50,000 m(3)/d) engineering application. A full-scale MBR model was developed based on the activated sludge model (ASM) and was calibrated to the actual MBR. The aeration control strategy took the form of a two-step cascaded proportion-integration (PI) feedback algorithm. Algorithmic parameters were optimized via model simulation. The strategy achieved real-time adjustment of aeration amounts based on feedback from effluent quality (i.e., ammonia-N). The effectiveness of the strategy was evaluated through both the model platform and the full-scale engineering application. In the former, the aeration flow rate was reduced by 15-20%. In the engineering application, the aeration flow rate was reduced by 20%, and overall specific energy consumption correspondingly reduced by 4% to 0.45 kWh/m(3)-effluent, using the present practice of regulating the angle of guide vanes of fixed-frequency blowers. Potential energy savings are expected to be higher for MBRs with variable-frequency blowers. This study indicated that the ammonia-N-based aeration control strategy holds promise for application in full-scale MBRs. Copyright © 2016 Elsevier Ltd. All rights reserved.

Using a water-food-energy nexus approach for optimal irrigation management during drought events in Nebraska

NASA Astrophysics Data System (ADS)

Campana, P. E.; Zhang, J.; Yao, T.; Melton, F. S.; Yan, J.

2017-12-01

Climate change and drought have severe impacts on the agricultural sector affecting crop yields, water availability, and energy consumption for irrigation. Monitoring, assessing and mitigating the effects of climate change and drought on the agricultural and energy sectors are fundamental challenges that require investigation for water, food, and energy security issues. Using an integrated water-food-energy nexus approach, this study is developing a comprehensive drought management system through integration of real-time drought monitoring with real-time irrigation management. The spatially explicit model developed, GIS-OptiCE, can be used for simulation, multi-criteria optimization and generation of forecasts to support irrigation management. To demonstrate the value of the approach, the model has been applied to one major corn region in Nebraska to study the effects of the 2012 drought on crop yield and irrigation water/energy requirements as compared to a wet year such as 2009. The water-food-energy interrelationships evaluated show that significant water volumes and energy are required to halt the negative effects of drought on the crop yield. The multi-criteria optimization problem applied in this study indicates that the optimal solutions of irrigation do not necessarily correspond to those that would produce the maximum crop yields, depending on both water and economic constraints. In particular, crop pricing forecasts are extremely important to define the optimal irrigation management strategy. The model developed shows great potential in precision agriculture by providing near real-time data products including information on evapotranspiration, irrigation volumes, energy requirements, predicted crop growth, and nutrient requirements.

Modeling and optimization of Quality of Service routing in Mobile Ad hoc Networks

NASA Astrophysics Data System (ADS)

Rafsanjani, Marjan Kuchaki; Fatemidokht, Hamideh; Balas, Valentina Emilia

2016-01-01

Mobile ad hoc networks (MANETs) are a group of mobile nodes that are connected without using a fixed infrastructure. In these networks, nodes communicate with each other by forming a single-hop or multi-hop network. To design effective mobile ad hoc networks, it is important to evaluate the performance of multi-hop paths. In this paper, we present a mathematical model for a routing protocol under energy consumption and packet delivery ratio of multi-hop paths. In this model, we use geometric random graphs rather than random graphs. Our proposed model finds effective paths that minimize the energy consumption and maximizes the packet delivery ratio of the network. Validation of the mathematical model is performed through simulation.

Activity recognition using dynamic multiple sensor fusion in body sensor networks.

PubMed

Gao, Lei; Bourke, Alan K; Nelson, John

2012-01-01

Multiple sensor fusion is a main research direction for activity recognition. However, there are two challenges in those systems: the energy consumption due to the wireless transmission and the classifier design because of the dynamic feature vector. This paper proposes a multi-sensor fusion framework, which consists of the sensor selection module and the hierarchical classifier. The sensor selection module adopts the convex optimization to select the sensor subset in real time. The hierarchical classifier combines the Decision Tree classifier with the Naïve Bayes classifier. The dataset collected from 8 subjects, who performed 8 scenario activities, was used to evaluate the proposed system. The results show that the proposed system can obviously reduce the energy consumption while guaranteeing the recognition accuracy.

Life cycle assessment of biodiesel production from algal bio-crude oils extracted under subcritical water conditions.

PubMed

Ponnusamy, Sundaravadivelnathan; Reddy, Harvind Kumar; Muppaneni, Tapaswy; Downes, Cara Meghan; Deng, Shuguang

2014-10-01

A life cycle assessment study is performed for the energy requirements and greenhouse gas emissions in an algal biodiesel production system. Subcritical water (SCW) extraction was applied for extracting bio-crude oil from algae, and conventional transesterification method was used for converting the algal oil to biodiesel. 58MJ of energy is required to produce 1kg of biodiesel without any co-products management, of which 36% was spent on cultivation and 56% on lipid extraction. SCW extraction with thermal energy recovery reduces the energy consumption by 3-5 folds when compared to the traditional solvent extraction. It is estimated that 1kg of algal biodiesel fixes about 0.6kg of CO2. An optimized case considering the energy credits from co-products could further reduce the total energy demand. The energy demand for producing 1kg of biodiesel in the optimized case is 28.23MJ. Copyright © 2014 Elsevier Ltd. All rights reserved.

Dynamic Hierarchical Energy-Efficient Method Based on Combinatorial Optimization for Wireless Sensor Networks

PubMed Central

Tang, Hongying; Cheng, Yongbo; Zhao, Qin; Li, Baoqing; Yuan, Xiaobing

2017-01-01

Routing protocols based on topology control are significantly important for improving network longevity in wireless sensor networks (WSNs). Traditionally, some WSN routing protocols distribute uneven network traffic load to sensor nodes, which is not optimal for improving network longevity. Differently to conventional WSN routing protocols, we propose a dynamic hierarchical protocol based on combinatorial optimization (DHCO) to balance energy consumption of sensor nodes and to improve WSN longevity. For each sensor node, the DHCO algorithm obtains the optimal route by establishing a feasible routing set instead of selecting the cluster head or the next hop node. The process of obtaining the optimal route can be formulated as a combinatorial optimization problem. Specifically, the DHCO algorithm is carried out by the following procedures. It employs a hierarchy-based connection mechanism to construct a hierarchical network structure in which each sensor node is assigned to a special hierarchical subset; it utilizes the combinatorial optimization theory to establish the feasible routing set for each sensor node, and takes advantage of the maximum–minimum criterion to obtain their optimal routes to the base station. Various results of simulation experiments show effectiveness and superiority of the DHCO algorithm in comparison with state-of-the-art WSN routing algorithms, including low-energy adaptive clustering hierarchy (LEACH), hybrid energy-efficient distributed clustering (HEED), genetic protocol-based self-organizing network clustering (GASONeC), and double cost function-based routing (DCFR) algorithms. PMID:28753962

Performative building envelope design correlated to solar radiation and cooling energy consumption

NASA Astrophysics Data System (ADS)

Jacky, Thiodore; Santoni

2017-11-01

Climate change as an ongoing anthropogenic environmental challenge is predominantly caused by an amplification in the amount of greenhouse gases (GHGs), notably carbon dioxide (CO2) in building sector. Global CO2 emissions are emitted from HVAC (Heating, Ventilation, and Air Conditioning) occupation to provide thermal comfort in building. In fact, the amount of energy used for cooling or heating building is implication of building envelope design. Building envelope acts as interface layer of heat transfer between outdoor environment and the interior of a building. It appears as wall, window, roof and external shading device. This paper examines performance of various design strategy on building envelope to limit solar radiation and reduce cooling loads in tropical climate. The design strategies are considering orientation, window to wall ratio, material properties, and external shading device. This research applied simulation method using Autodesk Ecotect to investigate simultaneously between variations of wall and window ratio, shading device composition and the implication to the amount of solar radiation, cooling energy consumption. Comparative analysis on the data will determine logical variation between opening and shading device composition and cooling energy consumption. Optimizing the building envelope design is crucial strategy for reducing CO2 emissions and long-term energy reduction in building sector. Simulation technology as feedback loop will lead to better performative building envelope.

Residential Consumption Scheduling Based on Dynamic User Profiling

NASA Astrophysics Data System (ADS)

Mangiatordi, Federica; Pallotti, Emiliano; Del Vecchio, Paolo; Capodiferro, Licia

Deployment of household appliances and of electric vehicles raises the electricity demand in the residential areas and the impact of the building's electrical power. The variations of electricity consumption across the day, may affect both the design of the electrical generation facilities and the electricity bill, mainly when a dynamic pricing is applied. This paper focuses on an energy management system able to control the day-ahead electricity demand in a residential area, taking into account both the variability of the energy production costs and the profiling of the users. The user's behavior is dynamically profiled on the basis of the tasks performed during the previous days and of the tasks foreseen for the current day. Depending on the size and on the flexibility in time of the user tasks, home inhabitants are grouped in, one over N, energy profiles, using a k-means algorithm. For a fixed energy generation cost, each energy profile is associated to a different hourly energy cost. The goal is to identify any bad user profile and to make it pay a highest bill. A bad profile example is when a user applies a lot of consumption tasks and low flexibility in task reallocation time. The proposed energy management system automatically schedules the tasks, solving a multi-objective optimization problem based on an MPSO strategy. The goals, when identifying bad users profiles, are to reduce the peak to average ratio in energy demand, and to minimize the energy costs, promoting virtuous behaviors.

DUV light source sustainability achievements and next steps

NASA Astrophysics Data System (ADS)

Roman, Yzzer; Cacouris, Ted; Raju, Kumar Raja Guvindan; Kanawade, Dinesh; Gillespie, Walt; Luo, Siqi; Mason, Eric; Manley, David; Das, Saptaparna

2018-03-01

Key sustainability opportunities have been executed in support of corporate initiatives to reduce the environmental footprint and decrease the running cost of DUV light sources. Previously, substantial neon savings were demonstrated over several years through optimized gas management technologies. Beyond this work, Cymer is developing the XLGR 100, a self-contained neon recycling system, to enable minimal gas consumption. The high efficiency results of the XLGR 100 in a production factory are validated in this paper. Cymer has also developed new light source modules with 33% longer life in an effort to reduce raw and associated resource consumption. In addition, a progress report is included regarding the improvements developed to reduce light source energy consumption.

Optimal Rate Schedules with Data Sharing in Energy Harvesting Communication Systems.

PubMed

Wu, Weiwei; Li, Huafan; Shan, Feng; Zhao, Yingchao

2017-12-20

Despite the abundant research on energy-efficient rate scheduling polices in energy harvesting communication systems, few works have exploited data sharing among multiple applications to further enhance the energy utilization efficiency, considering that the harvested energy from environments is limited and unstable. In this paper, to overcome the energy shortage of wireless devices at transmitting data to a platform running multiple applications/requesters, we design rate scheduling policies to respond to data requests as soon as possible by encouraging data sharing among data requests and reducing the redundancy. We formulate the problem as a transmission completion time minimization problem under constraints of dynamical data requests and energy arrivals. We develop offline and online algorithms to solve this problem. For the offline setting, we discover the relationship between two problems: the completion time minimization problem and the energy consumption minimization problem with a given completion time. We first derive the optimal algorithm for the min-energy problem and then adopt it as a building block to compute the optimal solution for the min-completion-time problem. For the online setting without future information, we develop an event-driven online algorithm to complete the transmission as soon as possible. Simulation results validate the efficiency of the proposed algorithm.

Optimal Rate Schedules with Data Sharing in Energy Harvesting Communication Systems

PubMed Central

Wu, Weiwei; Li, Huafan; Shan, Feng; Zhao, Yingchao

2017-01-01

Despite the abundant research on energy-efficient rate scheduling polices in energy harvesting communication systems, few works have exploited data sharing among multiple applications to further enhance the energy utilization efficiency, considering that the harvested energy from environments is limited and unstable. In this paper, to overcome the energy shortage of wireless devices at transmitting data to a platform running multiple applications/requesters, we design rate scheduling policies to respond to data requests as soon as possible by encouraging data sharing among data requests and reducing the redundancy. We formulate the problem as a transmission completion time minimization problem under constraints of dynamical data requests and energy arrivals. We develop offline and online algorithms to solve this problem. For the offline setting, we discover the relationship between two problems: the completion time minimization problem and the energy consumption minimization problem with a given completion time. We first derive the optimal algorithm for the min-energy problem and then adopt it as a building block to compute the optimal solution for the min-completion-time problem. For the online setting without future information, we develop an event-driven online algorithm to complete the transmission as soon as possible. Simulation results validate the efficiency of the proposed algorithm. PMID:29261135

Parameters optimization for the energy management system of hybrid electric vehicle

NASA Astrophysics Data System (ADS)

Tseng, Chyuan-Yow; Hung, Yi-Hsuan; Tsai, Chien-Hsiung; Huang, Yu-Jen

2007-12-01

Hybrid electric vehicle (HEV) has been widely studied recently due to its high potential in reduction of fuel consumption, exhaust emission, and lower noise. Because of comprised of two power sources, the HEV requires an energy management system (EMS) to distribute optimally the power sources for various driving conditions. The ITRI in Taiwan has developed a HEV consisted of a 2.2L internal combustion engine (ICE), a 18KW motor/generator (M/G), a 288V battery pack, and a continuous variable transmission (CVT). The task of the present study is to design an energy management strategy of the EMS for the HEV. Due to the nonlinear nature and the fact of unknown system model of the system, a kind of simplex method based energy management strategy is proposed for the HEV system. The simplex method is a kind of optimization strategy which is generally used to find out the optimal parameters for un-modeled systems. The way to apply the simplex method for the design of the EMS is presented. The feasibility of the proposed method was verified by perform numerical simulation on the FTP75 drive cycles.

Optimization on drying conditions of a solar electrohydrodynamic drying system based on desirability concept

PubMed Central

Dalvand, Mohammad Jafar; Mohtasebi, Seyed Saeid; Rafiee, Shahin

2014-01-01

The purpose of this article was to present a new drying method for agricultural products. Electrohydrodynamic (EHD) has been applied for drying of agricultural materials due to several advantages such as energy saving, low cost equipment, low drying temperatures, and superior material quality. To evaluate this method, an EHD dryer based on solar (photovoltaic) energy was designed and fabricated. Moreover, the optimum condition for the EHD drying of kiwi fruit was studied by applying the Box–Behnken design of response surface methodology. The desirability function was applied for optimization in case of single objective and multiobjective functions. By using the multiobjective optimization method, maximum desirability value of 0.865 was obtained based on the following: applied voltage of 15 kV, field strength of 5.2 kV cm−1, without forced air stream, and finally a combination of 17 discharge electrodes (needles). The results indicated that increasing the applied voltage from 6 to 15 kV, moisture ratio (MR) decreased, though energy efficiency and energy consumption were increasing. On the other hand, field strength of 5.2 kV cm−1 was the optimal point in terms of MR. PMID:25493195

Optimization on drying conditions of a solar electrohydrodynamic drying system based on desirability concept.

PubMed

Dalvand, Mohammad Jafar; Mohtasebi, Seyed Saeid; Rafiee, Shahin

2014-11-01

The purpose of this article was to present a new drying method for agricultural products. Electrohydrodynamic (EHD) has been applied for drying of agricultural materials due to several advantages such as energy saving, low cost equipment, low drying temperatures, and superior material quality. To evaluate this method, an EHD dryer based on solar (photovoltaic) energy was designed and fabricated. Moreover, the optimum condition for the EHD drying of kiwi fruit was studied by applying the Box-Behnken design of response surface methodology. The desirability function was applied for optimization in case of single objective and multiobjective functions. By using the multiobjective optimization method, maximum desirability value of 0.865 was obtained based on the following: applied voltage of 15 kV, field strength of 5.2 kV cm(-1), without forced air stream, and finally a combination of 17 discharge electrodes (needles). The results indicated that increasing the applied voltage from 6 to 15 kV, moisture ratio (MR) decreased, though energy efficiency and energy consumption were increasing. On the other hand, field strength of 5.2 kV cm(-1) was the optimal point in terms of MR.

Optimization of the Electrochemical Extraction and Recovery of Metals from Electronic Waste Using Response Surface Methodology

DOE Office of Scientific and Technical Information (OSTI.GOV)

Diaz, Luis A.; Clark, Gemma G.; Lister, Tedd E.

The rapid growth of the electronic waste can be viewed both as an environmental threat and as an attractive source of minerals that can reduce the mining of natural resources, and stabilize the market of critical materials, such as rare earths. Here in this article surface response methodology was used to optimize a previously developed electrochemical recovery process for base metals from electronic waste using a mild oxidant (Fe 3+). Through this process an effective extraction of base metals can be achieved enriching the concentration of precious metals and significantly reducing environmental impacts and operational costs associated with the wastemore » generation and chemical consumption. The optimization was performed using a bench-scale system specifically designed for this process. Operational parameters such as flow rate, applied current density and iron concentration were optimized to reduce the specific energy consumption of the electrochemical recovery process to 1.94 kWh per kg of metal recovered at a processing rate of 3.3 g of electronic waste per hour.« less

Optimization of the Electrochemical Extraction and Recovery of Metals from Electronic Waste Using Response Surface Methodology

DOE PAGES

Diaz, Luis A.; Clark, Gemma G.; Lister, Tedd E.

2017-06-08

The rapid growth of the electronic waste can be viewed both as an environmental threat and as an attractive source of minerals that can reduce the mining of natural resources, and stabilize the market of critical materials, such as rare earths. Here in this article surface response methodology was used to optimize a previously developed electrochemical recovery process for base metals from electronic waste using a mild oxidant (Fe 3+). Through this process an effective extraction of base metals can be achieved enriching the concentration of precious metals and significantly reducing environmental impacts and operational costs associated with the wastemore » generation and chemical consumption. The optimization was performed using a bench-scale system specifically designed for this process. Operational parameters such as flow rate, applied current density and iron concentration were optimized to reduce the specific energy consumption of the electrochemical recovery process to 1.94 kWh per kg of metal recovered at a processing rate of 3.3 g of electronic waste per hour.« less

Application configuration selection for energy-efficient execution on multicore systems

DOE PAGES

Wang, Shinan; Luo, Bing; Shi, Weisong; ...

2015-09-21

Balanced performance and energy consumption are incorporated in the design of modern computer systems. Several runtime factors, such as concurrency levels, thread mapping strategies, and dynamic voltage and frequency scaling (DVFS) should be considered in order to achieve optimal energy efficiency fora workload. Selecting appropriate run-time factors, however, is one of the most challenging tasks because the run-time factors are architecture-specific and workload-specific. And while most existing works concentrate on either static analysis of the workload or run-time prediction results, we present a hybrid two-step method that utilizes concurrency levels and DVFS settings to achieve the energy efficiency configuration formore » a worldoad. The experimental results based on a Xeon E5620 server with NPB and PARSEC benchmark suites show that the model is able to predict the energy efficient configuration accurately. On average, an additional 10% EDP (Energy Delay Product) saving is obtained by using run-time DVFS for the entire system. An off-line optimal solution is used to compare with the proposed scheme. Finally, the experimental results show that the average extra EDP saved by the optimal solution is within 5% on selective parallel benchmarks.« less

RECOVERY ACT: DYNAMIC ENERGY CONSUMPTION MANAGEMENT OF ROUTING TELECOM AND DATA CENTERS THROUGH REAL-TIME OPTIMAL CONTROL (RTOC): Final Scientific/Technical Report

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ron Moon

This final scientific report documents the Industrial Technology Program (ITP) Stage 2 Concept Development effort on Data Center Energy Reduction and Management Through Real-Time Optimal Control (RTOC). Society is becoming increasingly dependent on information technology systems, driving exponential growth in demand for data center processing and an insatiable appetite for energy. David Raths noted, 'A 50,000-square-foot data center uses approximately 4 megawatts of power, or the equivalent of 57 barrels of oil a day1.' The problem has become so severe that in some cases, users are giving up raw performance for a better balance between performance and energy efficiency. Historically,more » power systems for data centers were crudely sized to meet maximum demand. Since many servers operate at 60%-90% of maximum power while only utilizing an average of 5% to 15% of their capability, there are huge inefficiencies in the consumption and delivery of power in these data centers. The goal of the 'Recovery Act: Decreasing Data Center Energy Use through Network and Infrastructure Control' is to develop a state of the art approach for autonomously and intelligently reducing and managing data center power through real-time optimal control. Advances in microelectronics and software are enabling the opportunity to realize significant data center power savings through the implementation of autonomous power management control algorithms. The first step to realizing these savings was addressed in this study through the successful creation of a flexible and scalable mathematical model (equation) for data center behavior and the formulation of an acceptable low technical risk market introduction strategy leveraging commercial hardware and software familiar to the data center market. Follow-on Stage 3 Concept Development efforts include predictive modeling and simulation of algorithm performance, prototype demonstrations with representative data center equipment to verify requisite performance and continued commercial partnering agreement formation to ensure uninterrupted development, and deployment of the real-time optimal control algorithm. As a software implementable technique for reducing power consumption, the RTOC has two very desirable traits supporting rapid prototyping and ultimately widespread dissemination. First, very little capital is required for implementation. No major infrastructure modifications are required and there is no need to purchase expensive capital equipment. Second, the RTOC can be rolled out incrementally. Therefore, the effectiveness can be proven without a large scale initial roll out. Through the use of the Impact Projections Model provided by the DOE, monetary savings in excess of $100M in 2020 and billions by 2040 are predicted. In terms of energy savings, the model predicts a primary energy displacement of 260 trillion BTUs (33 trillion kWh), or a 50% reduction in server power consumption. The model also predicts a corresponding reduction of pollutants such as SO2 and NOx in excess of 100,000 metric tonnes assuming the RTOC is fully deployed. While additional development and prototyping is required to validate these predictions, the relative low cost and ease of implementation compared to large capital projects makes it an ideal candidate for further investigation.« less

Stochastic multi-objective model for optimal energy exchange optimization of networked microgrids with presence of renewable generation under risk-based strategies.

PubMed

Gazijahani, Farhad Samadi; Ravadanegh, Sajad Najafi; Salehi, Javad

2018-02-01

The inherent volatility and unpredictable nature of renewable generations and load demand pose considerable challenges for energy exchange optimization of microgrids (MG). To address these challenges, this paper proposes a new risk-based multi-objective energy exchange optimization for networked MGs from economic and reliability standpoints under load consumption and renewable power generation uncertainties. In so doing, three various risk-based strategies are distinguished by using conditional value at risk (CVaR) approach. The proposed model is specified as a two-distinct objective function. The first function minimizes the operation and maintenance costs, cost of power transaction between upstream network and MGs as well as power loss cost, whereas the second function minimizes the energy not supplied (ENS) value. Furthermore, the stochastic scenario-based approach is incorporated into the approach in order to handle the uncertainty. Also, Kantorovich distance scenario reduction method has been implemented to reduce the computational burden. Finally, non-dominated sorting genetic algorithm (NSGAII) is applied to minimize the objective functions simultaneously and the best solution is extracted by fuzzy satisfying method with respect to risk-based strategies. To indicate the performance of the proposed model, it is performed on the modified IEEE 33-bus distribution system and the obtained results show that the presented approach can be considered as an efficient tool for optimal energy exchange optimization of MGs. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

[Synergistic emission reduction of chief air pollutants and greenhouse gases-based on scenario simulations of energy consumptions in Beijing].

PubMed

Xie, Yuan-bo; Li, Wei

2013-05-01

It is one of the common targets and important tasks for energy management and environmental control of Beijing to improve urban air quality while reducing the emissions of greenhouse gases (GHG). Here, based on the interim and long term developmental planning and energy structure of the city, three energy consumption scenarios in low, moderate and high restrictions were designed by taking the potential energy saving policies and environmental targets into account. The long-range energy alternatives planning (LEAP) model was employed to predict and evaluate reduction effects of the chief air pollutants and GHG during 2010 to 2020 under the three given scenarios. The results showed that if urban energy consumption system was optimized or adjusted by exercising energy saving and emission reduction and pollution control measures, the predicted energy uses will be reduced by 10 to 30 million tons of coal equivalents by 2020. Under the two energy scenarios with moderate and high restrictions, the anticipated emissions of SO2, NOx, PM10, PM2.5, VOC and GHG will be respectively reduced to 71 to 100.2, 159.2 to 218.7, 89.8 to 133.8, 51.4 to 96.0, 56.4 to 74.8 and 148 200 to 164 700 thousand tons. Correspondingly, when compared with the low-restriction scenario, the reducing rate will be 53% to 67% , 50% to 64% , 33% to 55% , 25% to 60% , 41% to 55% and 26% to 34% respectively. Furthermore, based on a study of synergistic emission reduction of the air pollutants and GHG, it was proposed that the adjustment and control of energy consumptions shall be intensively developed in the three sectors of industry, transportation and services. In this way the synergistic reduction of the emissions of chief air pollutants and GHG will be achieved; meanwhile the pressures of energy demands may be deliberately relieved.

LED lighting – modification of growth, metabolism, yield and flour composition in wheat by spectral quality and intensity

USDA-ARS?s Scientific Manuscript database

The use of light-emitting diode (LED) technology for plant cultivation under controlled environmental conditions can result in significant reductions in energy consumption. However, there is still a lack of detailed information on the lighting conditions required for optimal growth of different plan...

Evaluating and optimizing horticultural regimes in space plant growth facilities

NASA Technical Reports Server (NTRS)

Berkovich, Y. A.; Chetirkin, P. V.; Wheeler, R. M.; Sager, J. C.

2004-01-01

In designing innovative space plant growth facilities (SPGF) for long duration space flight, various limitations must be addressed including onboard resources: volume, energy consumption, heat transfer and crew labor expenditure. The required accuracy in evaluating on board resources by using the equivalent mass methodology and applying it to the design of such facilities is not precise. This is due to the uncertainty of the structure and not completely understanding the properties of all associated hardware, including the technology in these systems. We present a simple criteria of optimization for horticultural regimes in SPGF: Qmax = max [M x (EBI)2/(V x E x T], where M is the crop harvest in terms of total dry biomass in the plant growth system; EBI is the edible biomass index (harvest index), V is volume occupied by the crop; E is the crop light energy supply during growth; T is the crop growth duration. The criterion reflects directly on the consumption of onboard resources for crop production. c2004 COSPAR. Published by Elsevier Ltd. All rights reserved.

Eco-friendly copper recovery process from waste printed circuit boards using Fe³⁺/Fe²⁺ redox system.

PubMed

Fogarasi, Szabolcs; Imre-Lucaci, Florica; Egedy, Attila; Imre-Lucaci, Árpád; Ilea, Petru

2015-06-01

The present study aimed at developing an original and environmentally friendly process for the recovery of copper from waste printed circuit boards (WPCBs) by chemical dissolution with Fe(3+) combined with the simultaneous electrowinning of copper and oxidant regeneration. The recovery of copper was achieved in an original set-up consisting of a three chamber electrochemical reactor (ER) connected in series with a chemical reactor (CR) equipped with a perforated rotating drum. Several experiments were performed in order to identify the optimal flow rate for the dissolution of copper in the CR and to ensure the lowest energy consumption for copper electrodeposition in the ER. The optimal hydrodynamic conditions were provided at 400 mL/min, leading to the 75% dissolution of metals and to a low specific energy consumption of 1.59 kW h/kg Cu for the electrodeposition process. In most experiments, the copper content of the obtained cathodic deposits was over 99.9%. Copyright © 2015 Elsevier Ltd. All rights reserved.

A CPS Based Optimal Operational Control System for Fused Magnesium Furnace

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chai, Tian-you; Wu, Zhi-wei; Wang, Hong

Fused magnesia smelting for fused magnesium furnace (FMF) is an energy intensive process with high temperature and comprehensive complexities. Its operational index namely energy consumption per ton (ECPT) is defined as the consumed electrical energy per ton of acceptable quality and is difficult to measure online. Moreover, the dynamics of ECPT cannot be precisely modelled mathematically. The model parameters of the three-phase currents of the electrodes such as the molten pool level, its variation rate and resistance are uncertain and nonlinear functions of the changes in both the smelting process and the raw materials composition. In this paper, an integratedmore » optimal operational control algorithm proposed is composed of a current set-point control, a current switching control and a self-optimized tuning mechanism. The tight conjoining of and coordination between the computational resources including the integrated optimal operational control, embedded software, industrial cloud, wireless communication and the physical resources of FMF constitutes a cyber-physical system (CPS) based embedded optimal operational control system. Successful application of this system has been made for a production line with ten fused magnesium furnaces in a factory in China, leading to a significant reduced ECPT.« less

Ubiquitous Green Computing Techniques for High Demand Applications in Smart Environments

PubMed Central

Zapater, Marina; Sanchez, Cesar; Ayala, Jose L.; Moya, Jose M.; Risco-Martín, José L.

2012-01-01

Ubiquitous sensor network deployments, such as the ones found in Smart cities and Ambient intelligence applications, require constantly increasing high computational demands in order to process data and offer services to users. The nature of these applications imply the usage of data centers. Research has paid much attention to the energy consumption of the sensor nodes in WSNs infrastructures. However, supercomputing facilities are the ones presenting a higher economic and environmental impact due to their very high power consumption. The latter problem, however, has been disregarded in the field of smart environment services. This paper proposes an energy-minimization workload assignment technique, based on heterogeneity and application-awareness, that redistributes low-demand computational tasks from high-performance facilities to idle nodes with low and medium resources in the WSN infrastructure. These non-optimal allocation policies reduce the energy consumed by the whole infrastructure and the total execution time. PMID:23112621

Smart Transportation CO2 Emission Reduction Strategies

NASA Astrophysics Data System (ADS)

Tarulescu, S.; Tarulescu, R.; Soica, A.; Leahu, C. I.

2017-10-01

Transport represents the sector with the fastest growing greenhouse gas emissions around the world. The main global objective is to reduce energy usage and associated greenhouse gas emissions from the transportation sector. For this study it was analyzed the road transportation system from Brasov Metropolitan area. The study was made for the transportation route that connects Ghimbav city to the main surrounding objectives. In this study ware considered four optimization measures: vehicle fleet renewal; building the detour belt for the city; road increasing the average travel speed; making bicycle lanes; and implementing an urban public transport system for Ghimbav. For each measure it was used a mathematical model to calculate the energy consumption and carbon emissions from the road transportation sector. After all four measures was analyzed is calculated the general energy consumption and CO2 reduction if this are applied from year 2017 to 2020.

[Design and Implementation of a Novel Networked Sleep Monitoring System].

PubMed

Tian, Yu; Yan, Zhuangzhi; Tao, Jia'an

2015-03-01

To meet the need of cost-effective multi-biosignal monitoring devices nowadays, we designed a system based on super low power MCU. It can collect, record and transfer several signals including ECG, Oxygen saturation, thoracic and abdominal wall expansion, oronasal airflow signal. The data files can be stored on a flash chip and transferred to a computer by a USB module. In addition, the sensing data can be sent wirelessly in real time. Considering that long term work of wireless module consumes much energy, we present a low-power optimization method based on delay constraint. Lower energy consumption comes at the cost of little delay. Experimental results show that it can effectively decrease the energy consumption without changing wireless module and transfer protocol. Besides, our system is powered by two dry batteries and can work at least 8 hours throughout a whole night.

Ubiquitous green computing techniques for high demand applications in Smart environments.

PubMed

Zapater, Marina; Sanchez, Cesar; Ayala, Jose L; Moya, Jose M; Risco-Martín, José L

2012-01-01

Ubiquitous sensor network deployments, such as the ones found in Smart cities and Ambient intelligence applications, require constantly increasing high computational demands in order to process data and offer services to users. The nature of these applications imply the usage of data centers. Research has paid much attention to the energy consumption of the sensor nodes in WSNs infrastructures. However, supercomputing facilities are the ones presenting a higher economic and environmental impact due to their very high power consumption. The latter problem, however, has been disregarded in the field of smart environment services. This paper proposes an energy-minimization workload assignment technique, based on heterogeneity and application-awareness, that redistributes low-demand computational tasks from high-performance facilities to idle nodes with low and medium resources in the WSN infrastructure. These non-optimal allocation policies reduce the energy consumed by the whole infrastructure and the total execution time.

Effort Optimization in Minimizing Food Related Greenhouse Gas Emissions, a look at "Organic" and "Local"

NASA Astrophysics Data System (ADS)

Bowen, E.; Martin, P. A.; Eshel, G.

2008-12-01

The adverse environmental effects, especially energy use and resultant GHG emissions, of food production and consumption are becoming more widely appreciated and increasingly well documented. Our insights into the thorny problem of how to mitigate some of those effects, however, are far less evolved. Two of the most commonly advocated strategies are "organic" and "local", referring, respectively, to growing food without major inputs of fossil fuel based synthetic fertilizers and pesticides and to food consumption near its agricultural origin. Indeed, both agrochemical manufacture and transportation of produce to market make up a significant percentage of energy use in agriculture. While there can be unique environmental benefits to each strategy, "organic" and "local" each may potentially result in energy and emissions savings relative to conventionally grown produce. Here, we quantify the potential energy and greenhouse gas emissions savings associated with "organic" and "local". We take note of energy use and actual GHG costs of the major synthetic fertilizers and transportation by various modes routinely employed in agricultural distribution chains, and compare them for ~35 frequently consumed nutritional mainstays. We present new, current, lower-bound energy and greenhouse gas efficiency estimates for these items and compare energy consumption and GHG emissions incurred during producing those food items to consumption and emissions resulting from transporting them, considering travel distances ranging from local to continental and transportation modes ranging from (most efficient) rail to (least efficient) air. In performing those calculations, we demonstrate the environmental superiority of either local or organic over conventional foods, and illuminate the complexities involved in entertaining the timely yet currently unanswered, and previously unanswerable, question of "Which is Environmentally Superior, Organic or Local?". More broadly, we put forth a database that amounts to a general blueprint for rigorous comparative evaluation of any competing diets.

Optimizing energy for a ‘green’ vaccine supply chain

PubMed Central

Lloyd, John; McCarney, Steve; Ouhichi, Ramzi; Lydon, Patrick; Zaffran, Michel

2015-01-01

This paper describes an approach piloted in the Kasserine region of Tunisia to increase the energy efficiency of the distribution of vaccines and temperature sensitive drugs. The objectives of an approach, known as the ‘net zero energy’ (NZE) supply chain were demonstrated within the first year of operation. The existing distribution system was modified to store vaccines and medicines in the same buildings and to transport them according to pre-scheduled and optimized delivery circuits. Electric utility vehicles, dedicated to the integrated delivery of vaccines and medicines, improved the regularity and reliability of the supply chains. Solar energy, linked to the electricity grid at regional and district stores, supplied over 100% of consumption meeting all energy needs for storage, cooling and transportation. Significant benefits to the quality and costs of distribution were demonstrated. Supply trips were scheduled, integrated and reliable, energy consumption was reduced, the recurrent cost of electricity was eliminated and the release of carbon to the atmosphere was reduced. Although the initial capital cost of scaling up implementation of NZE remain high today, commercial forecasts predict cost reduction for solar energy and electric vehicles that may permit a step-wise implementation over the next 7–10 years. Efficiency in the use of energy and in the deployment of transport is already a critical component of distribution logistics in both private and public sectors of industrialized countries. The NZE approach has an intensified rationale in countries where energy costs threaten the maintenance of public health services in areas of low population density. In these countries where the mobility of health personnel and timely arrival of supplies is at risk, NZE has the potential to reduce energy costs and release recurrent budget to other needs of service delivery while also improving the supply chain. PMID:25444811

Sensitivity of Mission Energy Consumption to Turboelectric Distributed Propulsion Design Assumptions on the N3-X Hybrid Wing Body Aircraft

NASA Technical Reports Server (NTRS)

Felder, James L.; Tong, Michael T.; Chu, Julio

2012-01-01

In a previous study by the authors it was shown that the N3-X, a 300 passenger hybrid wing body (HWB) aircraft with a turboelectric distributed propulsion (TeDP) system, was able to meet the NASA Subsonic Fixed Wing (SFW) project goal for N+3 generation aircraft of at least a 60% reduction in total energy consumption as compared to the best in class current generation aircraft. This previous study combined technology assumptions that represented the highest anticipated values that could be matured to technology readiness level (TRL) 4-6 by 2030. This paper presents the results of a sensitivity analysis of the total mission energy consumption to reductions in each key technology assumption. Of the parameters examined, the mission total energy consumption was most sensitive to changes to total pressure loss in the propulsor inlet. The baseline inlet internal pressure loss is assumed to be an optimistic 0.5%. An inlet pressure loss of 3% increases the total energy consumption 9%. However changes to reduce inlet pressure loss can result in additional distortion to the fan which can reduce fan efficiency or vice versa. It is very important that the inlet and fan be analyzed and optimized as a single unit. The turboshaft hot section is assumed to be made of ceramic matrix composite (CMC) with a 3000 F maximum material temperature. Reducing the maximum material temperature to 2700 F increases the mission energy consumption by only 1.5%. Thus achieving a 3000 F temperature in CMCs is important but not central to achieving the energy consumption objective of the N3-X/TeDP. A key parameter in the efficiency of superconducting motors and generators is the size of the superconducting filaments in the stator. The size of the superconducting filaments in the baseline model is assumed to be 10 microns. A 40 micron filament, which represents current technology, results in a 200% increase in AC losses in the motor and generator stators. This analysis shows that for a system with 40 micron filaments the higher stator losses plus the added weight and power of larger cryocoolers results in a 4% increase in mission energy consumption. If liquid hydrogen is used to cool the superconductors the 40 micron fibers results in a 200% increase in hydrogen required for cooling. Each pound of hydrogen used as fuel displaces 3 pounds of jet fuel. For the N3-X on the reference mission the additional hydrogen due to the increase stator losses reduces the total fuel weight 10%. The lighter fuel load and attendant vehicle resizing reduces the total energy consumption more than the higher stator losses increase it. As a result with hydrogen cooling there is a slight reduction in mission energy consumption with increasing stator losses. This counter intuitive result highlights the need to consider the full system impact of changes rather than just at the component or subsystem level.

Energy and water quality management systems for water utility's operations: a review.

PubMed

Cherchi, Carla; Badruzzaman, Mohammad; Oppenheimer, Joan; Bros, Christopher M; Jacangelo, Joseph G

2015-04-15

Holistic management of water and energy resources is critical for water utilities facing increasing energy prices, water supply shortage and stringent regulatory requirements. In the early 1990s, the concept of an integrated Energy and Water Quality Management System (EWQMS) was developed as an operational optimization framework for solving water quality, water supply and energy management problems simultaneously. Approximately twenty water utilities have implemented an EWQMS by interfacing commercial or in-house software optimization programs with existing control systems. For utilities with an installed EWQMS, operating cost savings of 8-15% have been reported due to higher use of cheaper tariff periods and better operating efficiencies, resulting in the reduction in energy consumption of ∼6-9%. This review provides the current state-of-knowledge on EWQMS typical structural features and operational strategies and benefits and drawbacks are analyzed. The review also highlights the challenges encountered during installation and implementation of EWQMS and identifies the knowledge gaps that should motivate new research efforts. Copyright © 2015 Elsevier Ltd. All rights reserved.

Analysis of wireless sensor network topology and estimation of optimal network deployment by deterministic radio channel characterization.

PubMed

Aguirre, Erik; Lopez-Iturri, Peio; Azpilicueta, Leire; Astrain, José Javier; Villadangos, Jesús; Falcone, Francisco

2015-02-05

One of the main challenges in the implementation and design of context-aware scenarios is the adequate deployment strategy for Wireless Sensor Networks (WSNs), mainly due to the strong dependence of the radiofrequency physical layer with the surrounding media, which can lead to non-optimal network designs. In this work, radioplanning analysis for WSN deployment is proposed by employing a deterministic 3D ray launching technique in order to provide insight into complex wireless channel behavior in context-aware indoor scenarios. The proposed radioplanning procedure is validated with a testbed implemented with a Mobile Ad Hoc Network WSN following a chain configuration, enabling the analysis and assessment of a rich variety of parameters, such as received signal level, signal quality and estimation of power consumption. The adoption of deterministic radio channel techniques allows the design and further deployment of WSNs in heterogeneous wireless scenarios with optimized behavior in terms of coverage, capacity, quality of service and energy consumption.

Microgrid to enable optimal distributed energy retail and end-user demand response

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jin, Ming; Feng, Wei; Marnay, Chris

In the face of unprecedented challenges in environmental sustainability and grid resilience, there is an increasingly held consensus regarding the adoption of distributed and renewable energy resources such as microgrids (MGs), and the utilization of flexible electric loads by demand response (DR) to potentially drive a necessary paradigm shift in energy production and consumption patterns. However, the potential value of distributed generation and demand flexibility has not yet been fully realized in the operation of MGs. This study investigates the pricing and operation strategy with DR for a MG retailer in an integrated energy system (IES). Based on co-optimizing retailmore » rates and MG dispatch formulated as a mixed integer quadratic programming (MIQP) problem, our model devises a dynamic pricing scheme that reflects the cost of generation and promotes DR, in tandem with an optimal dispatch plan that exploits spark spread and facilitates the integration of renewables, resulting in improved retailer profits and system stability. Main issues like integrated energy coupling and customer bill reduction are addressed during pricing to ensure rates competitiveness and customer protection. By evaluating on real datasets, the system is demonstrated to optimally coordinate storage, renewables, and combined heat and power (CHP), reduce carbon dioxide emission while maintaining profits, and effectively alleviate the PV curtailment problem. Finally, the model can be used by retailers and MG operators to optimize their operations, as well as regulators to design new utility rates in support of the ongoing transformation of energy systems.« less

Microgrid to enable optimal distributed energy retail and end-user demand response

DOE PAGES

Jin, Ming; Feng, Wei; Marnay, Chris; ...

2018-06-07

In the face of unprecedented challenges in environmental sustainability and grid resilience, there is an increasingly held consensus regarding the adoption of distributed and renewable energy resources such as microgrids (MGs), and the utilization of flexible electric loads by demand response (DR) to potentially drive a necessary paradigm shift in energy production and consumption patterns. However, the potential value of distributed generation and demand flexibility has not yet been fully realized in the operation of MGs. This study investigates the pricing and operation strategy with DR for a MG retailer in an integrated energy system (IES). Based on co-optimizing retailmore » rates and MG dispatch formulated as a mixed integer quadratic programming (MIQP) problem, our model devises a dynamic pricing scheme that reflects the cost of generation and promotes DR, in tandem with an optimal dispatch plan that exploits spark spread and facilitates the integration of renewables, resulting in improved retailer profits and system stability. Main issues like integrated energy coupling and customer bill reduction are addressed during pricing to ensure rates competitiveness and customer protection. By evaluating on real datasets, the system is demonstrated to optimally coordinate storage, renewables, and combined heat and power (CHP), reduce carbon dioxide emission while maintaining profits, and effectively alleviate the PV curtailment problem. Finally, the model can be used by retailers and MG operators to optimize their operations, as well as regulators to design new utility rates in support of the ongoing transformation of energy systems.« less

Minimization of power consumption during charging of superconducting accelerating cavities

NASA Astrophysics Data System (ADS)

Bhattacharyya, Anirban Krishna; Ziemann, Volker; Ruber, Roger; Goryashko, Vitaliy

2015-11-01

The radio frequency cavities, used to accelerate charged particle beams, need to be charged to their nominal voltage after which the beam can be injected into them. The standard procedure for such cavity filling is to use a step charging profile. However, during initial stages of such a filling process a substantial amount of the total energy is wasted in reflection for superconducting cavities because of their extremely narrow bandwidth. The paper presents a novel strategy to charge cavities, which reduces total energy reflection. We use variational calculus to obtain analytical expression for the optimal charging profile. Energies, reflected and required, and generator peak power are also compared between the charging schemes and practical aspects (saturation, efficiency and gain characteristics) of power sources (tetrodes, IOTs and solid state power amplifiers) are also considered and analysed. The paper presents a methodology to successfully identify the optimal charging scheme for different power sources to minimize total energy requirement.

A DDS-Based Energy Management Framework for Small Microgrid Operation and Control

DOE PAGES

Youssef, Tarek A.; El Hariri, Mohamad; Elsayed, Ahmed T.; ...

2017-09-26

The smart grid is seen as a power system with realtime communication and control capabilities between the consumer and the utility. This modern platform facilitates the optimization in energy usage based on several factors including environmental, price preferences, and system technical issues. In this paper a real-time energy management system (EMS) for microgrids or nanogrids was developed. The developed system involves an online optimization scheme to adapt its parameters based on previous, current, and forecasted future system states. The communication requirements for all EMS modules were analyzed and are all integrated over a data distribution service (DDS) Ethernet network withmore » appropriate quality of service (QoS) profiles. In conclusion, the developed EMS was emulated with actual residential energy consumption and irradiance data from Miami, Florida and proved its effectiveness in reducing consumers’ bills and achieving flat peak load profiles.« less

Bio-mimic optimization strategies in wireless sensor networks: a survey.

PubMed

Adnan, Md Akhtaruzzaman; Abdur Razzaque, Mohammd; Ahmed, Ishtiaque; Isnin, Ismail Fauzi

2013-12-24

For the past 20 years, many authors have focused their investigations on wireless sensor networks. Various issues related to wireless sensor networks such as energy minimization (optimization), compression schemes, self-organizing network algorithms, routing protocols, quality of service management, security, energy harvesting, etc., have been extensively explored. The three most important issues among these are energy efficiency, quality of service and security management. To get the best possible results in one or more of these issues in wireless sensor networks optimization is necessary. Furthermore, in number of applications (e.g., body area sensor networks, vehicular ad hoc networks) these issues might conflict and require a trade-off amongst them. Due to the high energy consumption and data processing requirements, the use of classical algorithms has historically been disregarded. In this context contemporary researchers started using bio-mimetic strategy-based optimization techniques in the field of wireless sensor networks. These techniques are diverse and involve many different optimization algorithms. As far as we know, most existing works tend to focus only on optimization of one specific issue of the three mentioned above. It is high time that these individual efforts are put into perspective and a more holistic view is taken. In this paper we take a step in that direction by presenting a survey of the literature in the area of wireless sensor network optimization concentrating especially on the three most widely used bio-mimetic algorithms, namely, particle swarm optimization, ant colony optimization and genetic algorithm. In addition, to stimulate new research and development interests in this field, open research issues, challenges and future research directions are highlighted.

A Survey on an Energy-Efficient and Energy-Balanced Routing Protocol for Wireless Sensor Networks.

PubMed

Ogundile, Olayinka O; Alfa, Attahiru S

2017-05-10

Wireless sensor networks (WSNs) form an important part of industrial application. There has been growing interest in the potential use of WSNs in applications such as environment monitoring, disaster management, health care monitoring, intelligence surveillance and defence reconnaissance. In these applications, the sensor nodes (SNs) are envisaged to be deployed in sizeable numbers in an outlying area, and it is quite difficult to replace these SNs after complete deployment in many scenarios. Therefore, as SNs are predominantly battery powered devices, the energy consumption of the nodes must be properly managed in order to prolong the network lifetime and functionality to a rational time. Different energy-efficient and energy-balanced routing protocols have been proposed in literature over the years. The energy-efficient routing protocols strive to increase the network lifetime by minimizing the energy consumption in each SN. On the other hand, the energy-balanced routing protocols protract the network lifetime by uniformly balancing the energy consumption among the nodes in the network. There have been various survey papers put forward by researchers to review the performance and classify the different energy-efficient routing protocols for WSNs. However, there seems to be no clear survey emphasizing the importance, concepts, and principles of load-balanced energy routing protocols for WSNs. In this paper, we provide a clear picture of both the energy-efficient and energy-balanced routing protocols for WSNs. More importantly, this paper presents an extensive survey of the different state-of-the-art energy-efficient and energy-balanced routing protocols. A taxonomy is introduced in this paper to classify the surveyed energy-efficient and energy-balanced routing protocols based on their proposed mode of communication towards the base station (BS). In addition, we classified these routing protocols based on the solution types or algorithms, and the input decision variables defined in the routing algorithm. The strengths and weaknesses of the choice of the decision variables used in the design of these energy-efficient and energy-balanced routing protocols are emphasised. Finally, we suggest possible research directions in order to optimize the energy consumption in sensor networks.

A Survey on an Energy-Efficient and Energy-Balanced Routing Protocol for Wireless Sensor Networks

PubMed Central

Ogundile, Olayinka O.; Alfa, Attahiru S.

2017-01-01

Wireless sensor networks (WSNs) form an important part of industrial application. There has been growing interest in the potential use of WSNs in applications such as environment monitoring, disaster management, health care monitoring, intelligence surveillance and defence reconnaissance. In these applications, the sensor nodes (SNs) are envisaged to be deployed in sizeable numbers in an outlying area, and it is quite difficult to replace these SNs after complete deployment in many scenarios. Therefore, as SNs are predominantly battery powered devices, the energy consumption of the nodes must be properly managed in order to prolong the network lifetime and functionality to a rational time. Different energy-efficient and energy-balanced routing protocols have been proposed in literature over the years. The energy-efficient routing protocols strive to increase the network lifetime by minimizing the energy consumption in each SN. On the other hand, the energy-balanced routing protocols protract the network lifetime by uniformly balancing the energy consumption among the nodes in the network. There have been various survey papers put forward by researchers to review the performance and classify the different energy-efficient routing protocols for WSNs. However, there seems to be no clear survey emphasizing the importance, concepts, and principles of load-balanced energy routing protocols for WSNs. In this paper, we provide a clear picture of both the energy-efficient and energy-balanced routing protocols for WSNs. More importantly, this paper presents an extensive survey of the different state-of-the-art energy-efficient and energy-balanced routing protocols. A taxonomy is introduced in this paper to classify the surveyed energy-efficient and energy-balanced routing protocols based on their proposed mode of communication towards the base station (BS). In addition, we classified these routing protocols based on the solution types or algorithms, and the input decision variables defined in the routing algorithm. The strengths and weaknesses of the choice of the decision variables used in the design of these energy-efficient and energy-balanced routing protocols are emphasised. Finally, we suggest possible research directions in order to optimize the energy consumption in sensor networks. PMID:28489054

Optimal Design and Operation of Permanent Irrigation Systems

NASA Astrophysics Data System (ADS)

Oron, Gideon; Walker, Wynn R.

1981-01-01

Solid-set pressurized irrigation system design and operation are studied with optimization techniques to determine the minimum cost distribution system. The principle of the analysis is to divide the irrigation system into subunits in such a manner that the trade-offs among energy, piping, and equipment costs are selected at the minimum cost point. The optimization procedure involves a nonlinear, mixed integer approach capable of achieving a variety of optimal solutions leading to significant conclusions with regard to the design and operation of the system. Factors investigated include field geometry, the effect of the pressure head, consumptive use rates, a smaller flow rate in the pipe system, and outlet (sprinkler or emitter) discharge.

Optimal design of reverse osmosis module networks

DOE Office of Scientific and Technical Information (OSTI.GOV)

Maskan, F.; Wiley, D.E.; Johnston, L.P.M.

2000-05-01

The structure of individual reverse osmosis modules, the configuration of the module network, and the operating conditions were optimized for seawater and brackish water desalination. The system model included simple mathematical equations to predict the performance of the reverse osmosis modules. The optimization problem was formulated as a constrained multivariable nonlinear optimization. The objective function was the annual profit for the system, consisting of the profit obtained from the permeate, capital cost for the process units, and operating costs associated with energy consumption and maintenance. Optimization of several dual-stage reverse osmosis systems were investigated and compared. It was found thatmore » optimal network designs are the ones that produce the most permeate. It may be possible to achieve economic improvements by refining current membrane module designs and their operating pressures.« less

Biomass-to-electricity: analysis and optimization of the complete pathway steam explosion--enzymatic hydrolysis--anaerobic digestion with ICE vs SOFC as biogas users.

PubMed

Santarelli, M; Barra, S; Sagnelli, F; Zitella, P

2012-11-01

The paper deals with the energy analysis and optimization of a complete biomass-to-electricity energy pathway, starting from raw biomass towards the production of renewable electricity. The first step (biomass-to-biogas) is based on a real pilot plant located in Environment Park S.p.A. (Torino, Italy) with three main steps ((1) impregnation; (2) steam explosion; (3) enzymatic hydrolysis), completed by a two-step anaerobic fermentation. In the second step (biogas-to-electricity), the paper considers two technologies: internal combustion engines and a stack of solid oxide fuel cells. First, the complete pathway has been modeled and validated through experimental data. After, the model has been used for an analysis and optimization of the complete thermo-chemical and biological process, with the objective function of maximization of the energy balance at minimum consumption. The comparison between ICE and SOFC shows the better performance of the integrated plants based on SOFC. Copyright © 2012 Elsevier Ltd. All rights reserved.

Attack on centrifugal costs

DOE Office of Scientific and Technical Information (OSTI.GOV)

Murray, P.F.

1986-03-01

The Monsanto Chocolate Bayou plant has had an aggressive and successful energy conservation program. The combined efforts have resulted in a 80% reduction in unit energy consumption compared to 1972. The approach of using system audits to optimize fluid systems was developed. Since most of the fluid movers are centrifugal, the name Centrifugal Savings Task Force was adopted. There are three tools that are particularly valuable in optimizing fluid systems. First, a working level understanding of the Affinity Laws seems a must. In addition, the performance curves for the fluid movers is needed. The last need is accurate system fieldmore » data. Systems effectively managed at the Chocolate Bayou plant were process air improvement, feed-water pressure reduction, combustion air blower turbine speed control, and cooling tower pressure reduction. Optimization of centrifugal systems is an often-overlooked opportunity for energy savings. The basic guidelines are to move only the fluid needed, and move it at as low a pressure as possible.« less

Model Based Optimization of Integrated Low Voltage DC-DC Converter for Energy Harvesting Applications

NASA Astrophysics Data System (ADS)

Jayaweera, H. M. P. C.; Muhtaroğlu, Ali

2016-11-01

A novel model based methodology is presented to determine optimal device parameters for the fully integrated ultra low voltage DC-DC converter for energy harvesting applications. The proposed model feasibly contributes to determine the maximum efficient number of charge pump stages to fulfill the voltage requirement of the energy harvester application. The proposed DC-DC converter based power consumption model enables the analytical derivation of the charge pump efficiency when utilized simultaneously with the known LC tank oscillator behavior under resonant conditions, and voltage step up characteristics of the cross-coupled charge pump topology. The verification of the model has been done using a circuit simulator. The optimized system through the established model achieves more than 40% maximum efficiency yielding 0.45 V output with single stage, 0.75 V output with two stages, and 0.9 V with three stages for 2.5 kΩ, 3.5 kΩ and 5 kΩ loads respectively using 0.2 V input.

Microgrid energy dispatching for industrial zones with renewable generations and electric vehicles via stochastic optimization and learning

NASA Astrophysics Data System (ADS)

Zhang, Kai; Li, Jingzhi; He, Zhubin; Yan, Wanfeng

2018-07-01

In this paper, a stochastic optimization framework is proposed to address the microgrid energy dispatching problem with random renewable generation and vehicle activity pattern, which is closer to the practical applications. The patterns of energy generation, consumption and storage availability are all random and unknown at the beginning, and the microgrid controller design (MCD) is formulated as a Markov decision process (MDP). Hence, an online learning-based control algorithm is proposed for the microgrid, which could adapt the control policy with increasing knowledge of the system dynamics and converges to the optimal algorithm. We adopt the linear approximation idea to decompose the original value functions as the summation of each per-battery value function. As a consequence, the computational complexity is significantly reduced from exponential growth to linear growth with respect to the size of battery states. Monte Carlo simulation of different scenarios demonstrates the effectiveness and efficiency of our algorithm.

Optimal Operation Method of Smart House by Controllable Loads based on Smart Grid Topology

NASA Astrophysics Data System (ADS)

Yoza, Akihiro; Uchida, Kosuke; Yona, Atsushi; Senju, Tomonobu

2013-08-01

From the perspective of global warming suppression and depletion of energy resources, renewable energy such as wind generation (WG) and photovoltaic generation (PV) are getting attention in distribution systems. Additionally, all electrification apartment house or residence such as DC smart house have increased in recent years. However, due to fluctuating power from renewable energy sources and loads, supply-demand balancing fluctuations of power system become problematic. Therefore, "smart grid" has become very popular in the worldwide. This article presents a methodology for optimal operation of a smart grid to minimize the interconnection point power flow fluctuations. To achieve the proposed optimal operation, we use distributed controllable loads such as battery and heat pump. By minimizing the interconnection point power flow fluctuations, it is possible to reduce the maximum electric power consumption and the electric cost. This system consists of photovoltaics generator, heat pump, battery, solar collector, and load. In order to verify the effectiveness of the proposed system, MATLAB is used in simulations.

Optimal Renewable Energy Integration into Refinery with CO2 Emissions Consideration: An Economic Feasibility Study

NASA Astrophysics Data System (ADS)

Alnifro, M.; Taqvi, S. T.; Ahmad, M. S.; Bensaida, K.; Elkamel, A.

2017-08-01

With increasing global energy demand and declining energy return on energy invested (EROEI) of crude oil, global energy consumption by the O&G industry has increased drastically over the past few years. In addition, this energy increase has led to an increase GHG emissions, resulting in adverse environmental effects. On the other hand, electricity generation through renewable resources have become relatively cost competitive to fossil based energy sources in a much ‘cleaner’ way. In this study, renewable energy is integrated optimally into a refinery considering costs and CO2 emissions. Using Aspen HYSYS, a refinery in the Middle East was simulated to estimate the energy demand by different processing units. An LP problem was formulated based on existing solar energy systems and wind potential in the region. The multi-objective function, minimizing cost as well as CO2 emissions, was solved using GAMS to determine optimal energy distribution from each energy source to units within the refinery. Additionally, an economic feasibility study was carried out to determine the viability of renewable energy technology project implementation to overcome energy requirement of the refinery. Electricity generation through all renewable energy sources considered (i.e. solar PV, solar CSP and wind) were found feasible based on their low levelized cost of electricity (LCOE). The payback period for a Solar CSP project, with an annual capacity of about 411 GWh and a lifetime of 30 years, was found to be 10 years. In contrast, the payback period for Solar PV and Wind were calculated to be 7 and 6 years, respectively. This opens up possibilities for integrating renewables into the refining sector as well as optimizing multiple energy carrier systems within the crude oil industry

The potential for distributed generation in Japanese prototype buildings: A DER-CAM analysis of policy, tariff design, building energy use, and technology development (English Version)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhou, Nan; Marnay, Chris; Firestone, Ryan

The August 2003 blackout of the northeastern U.S. and CANADA caused great economic losses and inconvenience to New York City and other affected areas. The blackout was a warning to the rest of the world that the ability of conventional power systems to meet growing electricity demand is questionable. Failure of large power systems can lead to serious emergencies. Introduction of on-site generation, renewable energy such as solar and wind power and the effective utilization of exhaust heat is needed, to meet the growing energy demands of the residential and commercial sectors. Additional benefit can be achieved by integrating thesemore » distributed technologies into distributed energy resource (DER) systems. This work demonstrates a method for choosing and designing economically optimal DER systems. An additional purpose of this research is to establish a database of energy tariffs, DER technology cost and performance characteristics, and building energy consumption for Japan. This research builds on prior DER studies at the Ernest Orlando Lawrence Berkeley National Laboratory (LBNL) and with their associates in the Consortium for Electric Reliability Technology Solutions (CERTS) and operation, including the development of the microgrid concept, and the DER selection optimization program, the Distributed Energy Resources Customer Adoption Model (DER-CAM). DER-CAM is a tool designed to find the optimal combination of installed equipment and an idealized operating schedule to minimize a site's energy bills, given performance and cost data on available DER technologies, utility tariffs, and site electrical and thermal loads over a test period, usually an historic year. Since hourly electric and thermal energy data are rarely available, they are typically developed by building simulation for each of six end use loads used to model the building: electric-only loads, space heating, space cooling, refrigeration, water heating, and natural-gas-only loads. DER-CAM provides a global optimization, albeit idealized, that shows how the necessary useful energy loads can be provided for at minimum cost by selection and operation of on-site generation, heat recovery, cooling, and efficiency improvements. This study examines five prototype commercial buildings and uses DER-CAM to select the economically optimal DER system for each. The five building types are office, hospital, hotel, retail, and sports facility. Each building type was considered for both 5,000 and 10,000 square meter floor sizes. The energy consumption of these building types is based on building energy simulation and published literature. Based on the optimization results, energy conservation and the emissions reduction were also evaluated. Furthermore, a comparison study between Japan and the U.S. has been conducted covering the policy, technology and the utility tariffs effects on DER systems installations. This study begins with an examination of existing DER research. Building energy loads were then generated through simulation (DOE-2) and scaled to match available load data in the literature. Energy tariffs in Japan and the U.S. were then compared: electricity prices did not differ significantly, while commercial gas prices in Japan are much higher than in the U.S. For smaller DER systems, the installation costs in Japan are more than twice those in the U.S., but this difference becomes smaller with larger systems. In Japan, DER systems are eligible for a 1/3 rebate of installation costs, while subsidies in the U.S. vary significantly by region and application. For 10,000 m{sup 2} buildings, significant decreases in fuel consumption, carbon emissions, and energy costs were seen in the economically optimal results. This was most noticeable in the sports facility, followed the hospital and hotel. This research demonstrates that office buildings can benefit from CHP, in contrast to popular opinion. For hospitals and sports facilities, the use of waste heat is particularly effective for water and space heating. For the other building types, waste heat is most effectively used for both heating and cooling. The same examination was done for the 5,000 m{sup 2} buildings. Although CHP installation capacity is smaller and the payback periods are longer, economic, fuel efficiency, and environmental benefits are still seen. While these benefits remain even when subsidies are removed, the increased installation costs lead to lower levels of installation capacity and thus benefit.« less

Fuel cells, batteries and super-capacitors stand-alone power systems management using optimal/flatness based-control

DOE Office of Scientific and Technical Information (OSTI.GOV)

Benaouadj, M.; Aboubou, A.; Bahri, M.

2016-07-25

In this work, an optimal control (under constraints) based on the Pontryagin’s maximum principle is used to optimally manage energy flows in a basic PEM (Proton Exchange Membrane) fuel cells system associated to lithium-ion batteries and supercapacitors through a common DC bus having a voltage to stabilize using the differential flatness approach. The adaptation of voltage levels between different sources and load is ensured by use of three DC-DC converters, one boost connected to the PEM fuel cells, while the two others are buck/boost and connected to the lithiumion batteries and supercapacitors. The aim of this paper is to developmore » an energy management strategy that is able to satisfy the following objectives: Impose the power requested by a habitat (representing the load) according to a proposed daily consumption profile, Keep fuel cells working at optimal power delivery conditions, Maintain constant voltage across the common DC bus, Stabilize the batteries voltage and stored quantity of charge at desired values given by the optimal control. Results obtained under MATLAB/Simulink environment prove that the cited objectives are satisfied, validating then, effectiveness and complementarity between the optimal and flatness concepts proposed for energy management. Note that this study is currently in experimentally validation within MSE Laboratory.« less

[Thermal energy utilization analysis and energy conservation measures of fluidized bed dryer].

PubMed

Xing, Liming; Zhao, Zhengsheng

2012-07-01

To propose measures for enhancing thermal energy utilization by analyzing drying process and operation principle of fluidized bed dryers,in order to guide optimization and upgrade of fluidized bed drying equipment. Through a systematic analysis on drying process and operation principle of fluidized beds,the energy conservation law was adopted to calculate thermal energy of dryers. The thermal energy of fluidized bed dryers is mainly used to make up for thermal consumption of water evaporation (Qw), hot air from outlet equipment (Qe), thermal consumption for heating and drying wet materials (Qm) and heat dissipation to surroundings through hot air pipelines and cyclone separators. Effective measures and major approaches to enhance thermal energy utilization of fluidized bed dryers were to reduce exhaust gas out by the loss of heat Qe, recycle dryer export air quantity of heat, preserve heat for dry towers, hot air pipes and cyclone separators, dehumidify clean air in inlets and reasonably control drying time and air temperature. Such technical parameters such air supply rate, air inlet temperature and humidity, material temperature and outlet temperature and humidity are set and controlled to effectively save energy during the drying process and reduce the production cost.

The role of nuts in the optimal diet: time for a critical appraisal?

PubMed

Russo, P; Siani, A

2012-12-01

During the last decades, nuts have attracted the attention of researchers for their potential benefits in cardiovascular prevention. We discuss here some aspects of the assumed beneficial effects of nuts, weighing them against potential harm. Epidemiological observations and controlled intervention trials consistently suggest that nuts consumption is associated with improved serum lipid profile, thus helping decrease cardiovascular risk. Being nuts an energy dense food, their impact on energy balance and body weight should be considered. In particular, the claim that adding nuts to the habitual diet, thus increasing calorie intake, does not cause body fat accumulation still needs evidence and biological plausibility. The potential risk associated with the relatively frequent occurrence of allergic reactions following the consumption of nuts is also discussed. Copyright © 2012 Elsevier Ltd. All rights reserved.

Cooperative Position Aware Mobility Pattern of AUVs for Avoiding Void Zones in Underwater WSNs.

PubMed

Javaid, Nadeem; Ejaz, Mudassir; Abdul, Wadood; Alamri, Atif; Almogren, Ahmad; Niaz, Iftikhar Azim; Guizani, Nadra

2017-03-13

In this paper, we propose two schemes; position-aware mobility pattern (PAMP) and cooperative PAMP (Co PAMP). The first one is an optimization scheme that avoids void hole occurrence and minimizes the uncertainty in the position estimation of glider's. The second one is a cooperative routing scheme that reduces the packet drop ratio by using the relay cooperation. Both techniques use gliders that stay at sojourn positions for a predefined time, at sojourn position self-confidence (s-confidence) and neighbor-confidence (n-confidence) regions that are estimated for balanced energy consumption. The transmission power of a glider is adjusted according to those confidence regions. Simulation results show that our proposed schemes outperform the compared existing one in terms of packet delivery ratio, void zones and energy consumption.

Active optimal control strategies for increasing the efficiency of photovoltaic cells

NASA Astrophysics Data System (ADS)

Aljoaba, Sharif Zidan Ahmad

Energy consumption has increased drastically during the last century. Currently, the worldwide energy consumption is about 17.4 TW and is predicted to reach 25 TW by 2035. Solar energy has emerged as one of the potential renewable energy sources. Since its first physical recognition in 1887 by Adams and Day till nowadays, research in solar energy is continuously developing. This has lead to many achievements and milestones that introduced it as one of the most reliable and sustainable energy sources. Recently, the International Energy Agency declared that solar energy is predicted to be one of the major electricity production energy sources by 2035. Enhancing the efficiency and lifecycle of photovoltaic (PV) modules leads to significant cost reduction. Reducing the temperature of the PV module improves its efficiency and enhances its lifecycle. To better understand the PV module performance, it is important to study the interaction between the output power and the temperature. A model that is capable of predicting the PV module temperature and its effects on the output power considering the individual contribution of the solar spectrum wavelengths significantly advances the PV module edsigns toward higher efficiency. In this work, a thermoelectrical model is developed to predict the effects of the solar spectrum wavelengths on the PV module performance. The model is characterized and validated under real meteorological conditions where experimental temperature and output power of the PV module measurements are shown to agree with the predicted results. The model is used to validate the concept of active optical filtering. Since this model is wavelength-based, it is used to design an active optical filter for PV applications. Applying this filter to the PV module is expected to increase the output power of the module by filtering the spectrum wavelengths. The active filter performance is optimized, where different cutoff wavelengths are used to maximize the module output power. It is predicted that if the optimized active optical filter is applied to the PV module, the module efficiency is predicted to increase by about 1%. Different technologies are considered for physical implementation of the active optical filter.

Design and Implementation of a Wireless Sensor and Actuator Network to Support the Intelligent Control of Efficient Energy Usage.

PubMed

Blanco, Jesús; García, Andrés; Morenas, Javier de Las

2018-06-09

Energy saving has become a major concern for the developed society of our days. This paper presents a Wireless Sensor and Actuator Network (WSAN) designed to provide support to an automatic intelligent system, based on the Internet of Things (IoT), which enables a responsible consumption of energy. The proposed overall system performs an efficient energetic management of devices, machines and processes, optimizing their operation to achieve a reduction in their overall energy usage at any given time. For this purpose, relevant data is collected from intelligent sensors, which are in-stalled at the required locations, as well as from the energy market through the Internet. This information is analysed to provide knowledge about energy utilization, and to improve efficiency. The system takes autonomous decisions automatically, based on the available information and the specific requirements in each case. The proposed system has been implanted and tested in a food factory. Results show a great optimization of energy efficiency and a substantial improvement on energy and costs savings.

Method of electric powertrain matching for battery-powered electric cars

NASA Astrophysics Data System (ADS)

Ning, Guobao; Xiong, Lu; Zhang, Lijun; Yu, Zhuoping

2013-05-01

The current match method of electric powertrain still makes use of longitudinal dynamics, which can't realize maximum capacity for on-board energy storage unit and can't reach lowest equivalent fuel consumption as well. Another match method focuses on improving available space considering reasonable layout of vehicle to enlarge rated energy capacity for on-board energy storage unit, which can keep the longitudinal dynamics performance almost unchanged but can't reach lowest fuel consumption. Considering the characteristics of driving motor, method of electric powertrain matching utilizing conventional longitudinal dynamics for driving system and cut-and-try method for energy storage system is proposed for passenger cars converted from traditional ones. Through combining the utilization of vehicle space which contributes to the on-board energy amount, vehicle longitudinal performance requirements, vehicle equivalent fuel consumption level, passive safety requirements and maximum driving range requirement together, a comprehensive optimal match method of electric powertrain for battery-powered electric vehicle is raised. In simulation, the vehicle model and match method is built in Matlab/simulink, and the Environmental Protection Agency (EPA) Urban Dynamometer Driving Schedule (UDDS) is chosen as a test condition. The simulation results show that 2.62% of regenerative energy and 2% of energy storage efficiency are increased relative to the traditional method. The research conclusions provide theoretical and practical solutions for electric powertrain matching for modern battery-powered electric vehicles especially for those converted from traditional ones, and further enhance dynamics of electric vehicles.

Low-energy transfers to cislunar periodic orbits visiting triangular libration points

NASA Astrophysics Data System (ADS)

Lei, Hanlun; Xu, Bo

2018-01-01

This paper investigates the cislunar periodic orbits that pass through triangular libration points of the Earth-Moon system and studies the techniques on design low-energy transfer trajectories. In order to compute periodic orbits, families of impulsive transfers between triangular libration points are taken to generate the initial guesses of periodic orbits, and multiple shooting techniques are applied to solving the problem. Then, varieties of periodic orbits in cislunar space are obtained, and stability analysis shows that the majority of them are unstable. Among these periodic orbits, an unstable periodic orbit in near 3:2 resonance with the Moon is taken as the nominal orbit of an assumed mission. As the stable manifolds of the target orbit could approach the Moon, low-energy transfer trajectories can be designed by combining lunar gravity assist with the invariant manifold structure of the target orbit. In practice, both the natural and perturbed invariant manifolds are considered to obtain the low-energy transfers, which are further refined to the Sun-perturbed Earth-Moon system. Results indicate that (a) compared to the case of natural invariant manifolds, the optimal transfers using perturbed invariant manifolds could reduce flight time at least 50 days, (b) compared to the cheapest direct transfer, the optimal low-energy transfer obtained by combining lunar gravity assist and invariant manifolds could save on-board fuel consumption more than 200 m/s, and (c) by taking advantage of the gravitational perturbation of the Sun, the low-energy transfers could save more fuel consumption than the corresponding ones obtained in the Earth-Moon system.

Cluster Size Optimization in Sensor Networks with Decentralized Cluster-Based Protocols

PubMed Central

Amini, Navid; Vahdatpour, Alireza; Xu, Wenyao; Gerla, Mario; Sarrafzadeh, Majid

2011-01-01

Network lifetime and energy-efficiency are viewed as the dominating considerations in designing cluster-based communication protocols for wireless sensor networks. This paper analytically provides the optimal cluster size that minimizes the total energy expenditure in such networks, where all sensors communicate data through their elected cluster heads to the base station in a decentralized fashion. LEACH, LEACH-Coverage, and DBS comprise three cluster-based protocols investigated in this paper that do not require any centralized support from a certain node. The analytical outcomes are given in the form of closed-form expressions for various widely-used network configurations. Extensive simulations on different networks are used to confirm the expectations based on the analytical results. To obtain a thorough understanding of the results, cluster number variability problem is identified and inspected from the energy consumption point of view. PMID:22267882

Stochastic Control of Energy Efficient Buildings: A Semidefinite Programming Approach

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ma, Xiao; Dong, Jin; Djouadi, Seddik M

2015-01-01

The key goal in energy efficient buildings is to reduce energy consumption of Heating, Ventilation, and Air- Conditioning (HVAC) systems while maintaining a comfortable temperature and humidity in the building. This paper proposes a novel stochastic control approach for achieving joint performance and power control of HVAC. We employ a constrained Stochastic Linear Quadratic Control (cSLQC) by minimizing a quadratic cost function with a disturbance assumed to be Gaussian. The problem is formulated to minimize the expected cost subject to a linear constraint and a probabilistic constraint. By using cSLQC, the problem is reduced to a semidefinite optimization problem, wheremore » the optimal control can be computed efficiently by Semidefinite programming (SDP). Simulation results are provided to demonstrate the effectiveness and power efficiency by utilizing the proposed control approach.« less

DOE Office of Scientific and Technical Information (OSTI.GOV)

Youssef, Tarek A.; El Hariri, Mohamad; Elsayed, Ahmed T.

The smart grid is seen as a power system with realtime communication and control capabilities between the consumer and the utility. This modern platform facilitates the optimization in energy usage based on several factors including environmental, price preferences, and system technical issues. In this paper a real-time energy management system (EMS) for microgrids or nanogrids was developed. The developed system involves an online optimization scheme to adapt its parameters based on previous, current, and forecasted future system states. The communication requirements for all EMS modules were analyzed and are all integrated over a data distribution service (DDS) Ethernet network withmore » appropriate quality of service (QoS) profiles. In conclusion, the developed EMS was emulated with actual residential energy consumption and irradiance data from Miami, Florida and proved its effectiveness in reducing consumers’ bills and achieving flat peak load profiles.« less

Simulation model for assessing the efficiency of a combined power installation based on a geothermal heat pump and a vacuum solar collector

NASA Astrophysics Data System (ADS)

Vaysman, Ya I.; Surkov, AA; Surkova, Yu I.; Kychkin, AV

2017-06-01

The article is devoted to the use of renewable energy sources and the assessment of the feasibility of their use in the climatic conditions of the Western Urals. A simulation model that calculates the efficiency of a combined power installations (CPI) was (RES) developed. The CPI consists of the geothermal heat pump (GHP) and the vacuum solar collector (VCS) and is based on the research model. This model allows solving a wide range of problems in the field of energy and resource efficiency, and can be applied to other objects using RES. Based on the research recommendations for optimizing the management and the application of CPI were given. The optimization system will give a positive effect in the energy and resource consumption of low-rise residential buildings projects.

Unified Performance and Power Modeling of Scientific Workloads

DOE Office of Scientific and Technical Information (OSTI.GOV)

Song, Shuaiwen; Barker, Kevin J.; Kerbyson, Darren J.

2013-11-17

It is expected that scientific applications executing on future large-scale HPC must be optimized not only in terms of performance, but also in terms of power consumption. As power and energy become increasingly constrained resources, researchers and developers must have access to tools that will allow for accurate prediction of both performance and power consumption. Reasoning about performance and power consumption in concert will be critical for achieving maximum utilization of limited resources on future HPC systems. To this end, we present a unified performance and power model for the Nek-Bone mini-application developed as part of the DOE's CESAR Exascalemore » Co-Design Center. Our models consider the impact of computation, point-to-point communication, and collective communication« less

Optimizing lighting, thermal performance, and energy production of building facades by using automated blinds and PV cells

NASA Astrophysics Data System (ADS)

Alzoubi, Hussain Hendi

Energy consumption in buildings has recently become a major concern for environmental designers. Within this field, daylighting and solar energy design are attractive strategies for saving energy. This study seeks the integrity and the optimality of building envelopes' performance. It focuses on the transparent parts of building facades, specifically, the windows and their shading devices. It suggests a new automated method of utilizing solar energy while keeping optimal solutions for indoor daylighting. The method utilizes a statistical approach to produce mathematical equations based on physical experimentation. A full-scale mock-up representing an actual office was built. Heat gain and lighting levels were measured empirically and correlated with blind angles. Computational methods were used to estimate the power production from photovoltaic cells. Mathematical formulas were derived from the results of the experiments; these formulas were utilized to construct curves as well as mathematical equations for the purpose of optimization. The mathematical equations resulting from the optimization process were coded using Java programming language to enable future users to deal with generic locations of buildings with a broader context of various climatic conditions. For the purpose of optimization by automation under different climatic conditions, a blind control system was developed based on the findings of this study. This system calibrates the blind angles instantaneously based upon the sun position, the indoor daylight, and the power production from the photovoltaic cells. The functions of this system guarantee full control of the projected solar energy on buildings' facades for indoor lighting and heat gain. In winter, the system automatically blows heat into the space, whereas it expels heat from the space during the summer season. The study showed that the optimality of building facades' performance is achievable for integrated thermal, energy, and lighting models in buildings. There are blind angles that produce maximum energy from the photovoltaic cells while keeping indoor light within the acceptable limits that prevent undesired heat gain in summer.

Optimizing cellulose fibrillation for the production of cellulose nanofibrils by a disk grinder

Treesearch

Chuanshuang Hu; Yu Zhao; Kecheng Li; J.Y. Zhu; Roland Gleisner

2015-01-01

The fibrillation of a bleached kraft eucalyptus pulp was investigated by means of a laboratory-scale disk grinder for the production of cellulose nanofibrils (CNF), while the parameters disk rotating speed, solid loading, and fibrillation duration were varied. The cumulative energy consumption was monitored during fibrillation. The degree of polymerization (DP) and...

Network switching strategy for energy conservation in heterogeneous networks.

PubMed

Song, Yujae; Choi, Wooyeol; Baek, Seungjae

2017-01-01

In heterogeneous networks (HetNets), the large-scale deployment of small base stations (BSs) together with traditional macro BSs is an economical and efficient solution that is employed to address the exponential growth in mobile data traffic. In dense HetNets, network switching, i.e., handovers, plays a critical role in connecting a mobile terminal (MT) to the best of all accessible networks. In the existing literature, a handover decision is made using various handover metrics such as the signal-to-noise ratio, data rate, and movement speed. However, there are few studies on handovers that focus on energy efficiency in HetNets. In this paper, we propose a handover strategy that helps to minimize energy consumption at BSs in HetNets without compromising the quality of service (QoS) of each MT. The proposed handover strategy aims to capture the effect of the stochastic behavior of handover parameters and the expected energy consumption due to handover execution when making a handover decision. To identify the validity of the proposed handover strategy, we formulate a handover problem as a constrained Markov decision process (CMDP), by which the effects of the stochastic behaviors of handover parameters and consequential handover energy consumption can be accurately reflected when making a handover decision. In the CMDP, the aim is to minimize the energy consumption to service an MT over the lifetime of its connection, and the constraint is to guarantee the QoS requirements of the MT given in terms of the transmission delay and call-dropping probability. We find an optimal policy for the CMDP using a combination of the Lagrangian method and value iteration. Simulation results verify the validity of the proposed handover strategy.

Linear triangular optimization technique and pricing scheme in residential energy management systems

NASA Astrophysics Data System (ADS)

Anees, Amir; Hussain, Iqtadar; AlKhaldi, Ali Hussain; Aslam, Muhammad

2018-06-01

This paper presents a new linear optimization algorithm for power scheduling of electric appliances. The proposed system is applied in a smart home community, in which community controller acts as a virtual distribution company for the end consumers. We also present a pricing scheme between community controller and its residential users based on real-time pricing and likely block rates. The results of the proposed optimization algorithm demonstrate that by applying the anticipated technique, not only end users can minimise the consumption cost, but it can also reduce the power peak to an average ratio which will be beneficial for the utilities as well.

A Data-Gathering Scheme with Joint Routing and Compressive Sensing Based on Modified Diffusion Wavelets in Wireless Sensor Networks.

PubMed

Gu, Xiangping; Zhou, Xiaofeng; Sun, Yanjing

2018-02-28

Compressive sensing (CS)-based data gathering is a promising method to reduce energy consumption in wireless sensor networks (WSNs). Traditional CS-based data-gathering approaches require a large number of sensor nodes to participate in each CS measurement task, resulting in high energy consumption, and do not guarantee load balance. In this paper, we propose a sparser analysis that depends on modified diffusion wavelets, which exploit sensor readings' spatial correlation in WSNs. In particular, a novel data-gathering scheme with joint routing and CS is presented. A modified ant colony algorithm is adopted, where next hop node selection takes a node's residual energy and path length into consideration simultaneously. Moreover, in order to speed up the coverage rate and avoid the local optimal of the algorithm, an improved pheromone impact factor is put forward. More importantly, theoretical proof is given that the equivalent sensing matrix generated can satisfy the restricted isometric property (RIP). The simulation results demonstrate that the modified diffusion wavelets' sparsity affects the sensor signal and has better reconstruction performance than DFT. Furthermore, our data gathering with joint routing and CS can dramatically reduce the energy consumption of WSNs, balance the load, and prolong the network lifetime in comparison to state-of-the-art CS-based methods.

Electrooxidation of industrial wastewater containing 1,4-dioxane in the presence of different salts.

PubMed

Barndõk, H; Hermosilla, D; Cortijo, L; Torres, E; Blanco, A

2014-04-01

The treatment of 1,4-dioxane solution by electrochemical oxidation on boron-doped diamond was studied using a central composite design and the response surface methodology to investigate the use of SO4 (2-) and HCO3 (-) as supporting electrolytes considering the applied electric current, initial chemical oxygen demand (COD) value, and treatment time. Two industrial effluents containing bicarbonate alkalinity, one just carrying 1,4-dioxane (S1), and another one including 1,4-dioxane and 2-methyl-1,3-dioxolane (S2), were treated under optimized conditions and subsequently subjected to biodegradability assays with a Pseudomonas putida culture. Electrooxidation was compared with ozone oxidation (O3) and its combination with hydrogen peroxide (O3/H2O2). Regarding the experimental design, the optimal compromise for maximum COD removal at minimum energy consumption was shown at the maximum tested concentrations of SO4 (2-) and HCO3 (-) (41.6 and 32.8 mEq L(-1), respectively) and the maximum selected initial COD (750 mg L(-1)), applying a current density of 11.9 mA cm(-2) for 3.8 h. Up to 98 % of the COD was removed in the electrooxidation treatment of S1 effluent using 114 kWh per kg of removed COD and about 91 % of the COD from S2 wastewater applying 49 kWh per kg of removed COD. The optimal biodegradability enhancement was achieved after 1 h of electrooxidation treatment. In comparison with O3 and O3/H2O2 alternatives, electrochemical oxidation achieved the fastest degradation rate per oxidant consumption unit, and it also resulted to be the most economical treatment in terms of energy consumption and price per unit of removed COD.

Energy-neutral sustainable nutrient recovery incorporated with the wastewater purification process in an enlarged microbial nutrient recovery cell

NASA Astrophysics Data System (ADS)

Sun, Dongya; Gao, Yifan; Hou, Dianxun; Zuo, Kuichang; Chen, Xi; Liang, Peng; Zhang, Xiaoyuan; Ren, Zhiyong Jason; Huang, Xia

2018-04-01

Recovery of nutrient resources from the wastewater is now an inevitable strategy to maintain the supply of both nutrient and water for our huge population. While the intensive energy consumption in conventional nutrient recovery technologies still remained as the bottleneck towards the sustainable nutrient recycle. This study proposed an enlarged microbial nutrient recovery cell (EMNRC) which was powered by the energy contained in wastewater and achieved multi-cycle nutrient recovery incorporated with in situ wastewater treatment. With the optimal recovery solution of 3 g/L NaCl and the optimal volume ratio of wastewater to recovery solution of 10:1, >89% of phosphorus and >62% of ammonium nitrogen were recovered into struvite. An extremely low water input ratio of <1% was required to obtain the recovered fertilizer and the purified water. It was proved the EMNRC system was a promising technology which could utilize the chemical energy contained in wastewater itself and energy-neutrally recover nutrient during the continuous wastewater purification process.

Energy-efficiency based classification of the manufacturing workstation

NASA Astrophysics Data System (ADS)

Frumuşanu, G.; Afteni, C.; Badea, N.; Epureanu, A.

2017-08-01

EU Directive 92/75/EC established for the first time an energy consumption labelling scheme, further implemented by several other directives. As consequence, nowadays many products (e.g. home appliances, tyres, light bulbs, houses) have an EU Energy Label when offered for sale or rent. Several energy consumption models of manufacturing equipments have been also developed. This paper proposes an energy efficiency - based classification of the manufacturing workstation, aiming to characterize its energetic behaviour. The concept of energy efficiency of the manufacturing workstation is defined. On this base, a classification methodology has been developed. It refers to specific criteria and their evaluation modalities, together to the definition & delimitation of energy efficiency classes. The energy class position is defined after the amount of energy needed by the workstation in the middle point of its operating domain, while its extension is determined by the value of the first coefficient from the Taylor series that approximates the dependence between the energy consume and the chosen parameter of the working regime. The main domain of interest for this classification looks to be the optimization of the manufacturing activities planning and programming. A case-study regarding an actual lathe classification from energy efficiency point of view, based on two different approaches (analytical and numerical) is also included.

Joint Mobile Data Collection and Wireless Energy Transfer in Wireless Rechargeable Sensor Networks.

PubMed

Zhong, Ping; Li, Ya-Ting; Liu, Wei-Rong; Duan, Gui-Hua; Chen, Ying-Wen; Xiong, Neal

2017-08-16

In wireless rechargeable sensor networks (WRSNs), there is a way to use mobile vehicles to charge node and collect data. It is a rational pattern to use two types of vehicles, one is for energy charging, and the other is for data collecting. These two types of vehicles, data collection vehicles (DCVs) and wireless charging vehicles (WCVs), are employed to achieve high efficiency in both data gathering and energy consumption. To handle the complex scheduling problem of multiple vehicles in large-scale networks, a twice-partition algorithm based on center points is proposed to divide the network into several parts. In addition, an anchor selection algorithm based on the tradeoff between neighbor amount and residual energy, named AS-NAE, is proposed to collect the zonal data. It can reduce the data transmission delay and the energy consumption for DCVs' movement in the zonal. Besides, we design an optimization function to achieve maximum data throughput by adjusting data rate and link rate of each node. Finally, the effectiveness of proposed algorithm is validated by numerical simulation results in WRSNs.

Joint Mobile Data Collection and Wireless Energy Transfer in Wireless Rechargeable Sensor Networks

PubMed Central

Li, Ya-Ting; Liu, Wei-Rong; Duan, Gui-Hua; Chen, Ying-Wen

2017-01-01

In wireless rechargeable sensor networks (WRSNs), there is a way to use mobile vehicles to charge node and collect data. It is a rational pattern to use two types of vehicles, one is for energy charging, and the other is for data collecting. These two types of vehicles, data collection vehicles (DCVs) and wireless charging vehicles (WCVs), are employed to achieve high efficiency in both data gathering and energy consumption. To handle the complex scheduling problem of multiple vehicles in large-scale networks, a twice-partition algorithm based on center points is proposed to divide the network into several parts. In addition, an anchor selection algorithm based on the tradeoff between neighbor amount and residual energy, named AS-NAE, is proposed to collect the zonal data. It can reduce the data transmission delay and the energy consumption for DCVs’ movement in the zonal. Besides, we design an optimization function to achieve maximum data throughput by adjusting data rate and link rate of each node. Finally, the effectiveness of proposed algorithm is validated by numerical simulation results in WRSNs. PMID:28813029

Energy-Aware Topology Control Strategy for Human-Centric Wireless Sensor Networks

PubMed Central

Meseguer, Roc; Molina, Carlos; Ochoa, Sergio F.; Santos, Rodrigo

2014-01-01

The adoption of mobile and ubiquitous solutions that involve participatory or opportunistic sensing increases every day. This situation has highlighted the relevance of optimizing the energy consumption of these solutions, because their operation depends on the devices' battery lifetimes. This article presents a study that intends to understand how the prediction of topology control messages in human-centric wireless sensor networks can be used to help reduce the energy consumption of the participating devices. In order to do that, five research questions have been defined and a study based on simulations was conducted to answer these questions. The obtained results help identify suitable mobile computing scenarios where the prediction of topology control messages can be used to save energy of the network nodes. These results also allow estimating the percentage of energy saving that can be expected, according to the features of the work scenario and the participants behavior. Designers of mobile collaborative applications that involve participatory or opportunistic sensing, can take advantage of these findings to increase the autonomy of their solutions. PMID:24514884

Evaluating the Power Consumption of Wireless Sensor Network Applications Using Models

PubMed Central

Dâmaso, Antônio; Freitas, Davi; Rosa, Nelson; Silva, Bruno; Maciel, Paulo

2013-01-01

Power consumption is the main concern in developing Wireless Sensor Network (WSN) applications. Consequently, several strategies have been proposed for investigating the power consumption of this kind of application. These strategies can help to predict the WSN lifetime, provide recommendations to application developers and may optimize the energy consumed by the WSN applications. While measurement is a known and precise strategy for power consumption evaluation, it is very costly, tedious and may be unfeasible considering the (usual) large number of WSN nodes. Furthermore, due to the inherent dynamism of WSNs, the instrumentation required by measurement techniques makes difficult their use in several different scenarios. In this context, this paper presents an approach for evaluating the power consumption of WSN applications by using simulation models along with a set of tools to automate the proposed approach. Starting from a programming language code, we automatically generate consumption models used to predict the power consumption of WSN applications. In order to evaluate the proposed approach, we compare the results obtained by using the generated models against ones obtained by measurement. PMID:23486217

Evaluating the power consumption of wireless sensor network applications using models.

PubMed

Dâmaso, Antônio; Freitas, Davi; Rosa, Nelson; Silva, Bruno; Maciel, Paulo

2013-03-13

Power consumption is the main concern in developing Wireless Sensor Network (WSN) applications. Consequently, several strategies have been proposed for investigating the power consumption of this kind of application. These strategies can help to predict the WSN lifetime, provide recommendations to application developers and may optimize the energy consumed by the WSN applications. While measurement is a known and precise strategy for power consumption evaluation, it is very costly, tedious and may be unfeasible considering the (usual) large number of WSN nodes. Furthermore, due to the inherent dynamism of WSNs, the instrumentation required by measurement techniques makes difficult their use in several different scenarios. In this context, this paper presents an approach for evaluating the power consumption of WSN applications by using simulation models along with a set of tools to automate the proposed approach. Starting from a programming language code, we automatically generate consumption models used to predict the power consumption of WSN applications. In order to evaluate the proposed approach, we compare the results obtained by using the generated models against ones obtained by measurement.

Comparison of microwave hydrodistillation and solvent-free microwave extraction of essential oil from Melaleuca leucadendra Linn

NASA Astrophysics Data System (ADS)

Ismanto, A. W.; Kusuma, H. S.; Mahfud, M.

2017-12-01

The comparison of solvent-free microwave extraction (SFME) and microwave hydrodistillation (MHD) in the extraction of essential oil from Melaleuca leucadendra Linn. was examined. Dry cajuput leaves were used in this study. The purpose of this study is also to determine optimal condition (microwave power). The relative electric consumption of SFME and MHD methods are both showing 0,1627 kWh/g and 0,3279 kWh/g. The results showed that solvent-free microwave extraction methods able to reduce energy consumption and can be regarded as a green technique for extraction of cajuput oil.

Box-Behnken statistical design to optimize thermal performance of energy storage systems

NASA Astrophysics Data System (ADS)

Jalalian, Iman Joz; Mohammadiun, Mohammad; Moqadam, Hamid Hashemi; Mohammadiun, Hamid

2018-05-01

Latent heat thermal storage (LHTS) is a technology that can help to reduce energy consumption for cooling applications, where the cold is stored in phase change materials (PCMs). In the present study a comprehensive theoretical and experimental investigation is performed on a LHTES system containing RT25 as phase change material (PCM). Process optimization of the experimental conditions (inlet air temperature and velocity and number of slabs) was carried out by means of Box-Behnken design (BBD) of Response surface methodology (RSM). Two parameters (cooling time and COP value) were chosen to be the responses. Both of the responses were significantly influenced by combined effect of inlet air temperature with velocity and number of slabs. Simultaneous optimization was performed on the basis of the desirability function to determine the optimal conditions for the cooling time and COP value. Maximum cooling time (186 min) and COP value (6.04) were found at optimum process conditions i.e. inlet temperature of (32.5), air velocity of (1.98) and slab number of (7).

Night ventilation control strategies in office buildings

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wang, Zhaojun; Yi, Lingli; Gao, Fusheng

2009-10-15

In moderate climates night ventilation is an effective and energy-efficient approach to improve the indoor thermal environment for office buildings during the summer months, especially for heavyweight construction. However, is night ventilation a suitable strategy for office buildings with lightweight construction located in cold climates? In order to answer this question, the whole energy-consumption analysis software EnergyPlus was used to simulate the indoor thermal environment and energy consumption in typical office buildings with night mechanical ventilation in three cities in northern China. The summer outdoor climate data was analyzed, and three typical design days were chosen. The most important factorsmore » influencing night ventilation performance such as ventilation rates, ventilation duration, building mass and climatic conditions were evaluated. When night ventilation operation time is closer to active cooling time, the efficiency of night ventilation is higher. With night ventilation rate of 10 ach, the mean radiant temperature of the indoor surface decreased by up to 3.9 C. The longer the duration of operation, the more efficient the night ventilation strategy becomes. The control strategies for three locations are given in the paper. Based on the optimized strategies, the operation consumption and fees are calculated. The results show that more energy is saved in office buildings cooled by a night ventilation system in northern China than ones that do not employ this strategy. (author)« less

On the Optimization of a Probabilistic Data Aggregation Framework for Energy Efficiency in Wireless Sensor Networks.

PubMed

Kafetzoglou, Stella; Aristomenopoulos, Giorgos; Papavassiliou, Symeon

2015-08-11

Among the key aspects of the Internet of Things (IoT) is the integration of heterogeneous sensors in a distributed system that performs actions on the physical world based on environmental information gathered by sensors and application-related constraints and requirements. Numerous applications of Wireless Sensor Networks (WSNs) have appeared in various fields, from environmental monitoring, to tactical fields, and healthcare at home, promising to change our quality of life and facilitating the vision of sensor network enabled smart cities. Given the enormous requirements that emerge in such a setting-both in terms of data and energy-data aggregation appears as a key element in reducing the amount of traffic in wireless sensor networks and achieving energy conservation. Probabilistic frameworks have been introduced as operational efficient and performance effective solutions for data aggregation in distributed sensor networks. In this work, we introduce an overall optimization approach that improves and complements such frameworks towards identifying the optimal probability for a node to aggregate packets as well as the optimal aggregation period that a node should wait for performing aggregation, so as to minimize the overall energy consumption, while satisfying certain imposed delay constraints. Primal dual decomposition is employed to solve the corresponding optimization problem while simulation results demonstrate the operational efficiency of the proposed approach under different traffic and topology scenarios.

Energy Consumption Research of Mobile Data Collection Protocol for Underwater Nodes Using an USV.

PubMed

Lv, Zhichao; Zhang, Jie; Jin, Jiucai; Li, Qi; Gao, Baoru

2018-04-16

The Unmanned Surface Vehicle (USV) integrated with an acoustic modem is a novel mobile vehicle for data collection, which has an advantage in terms of mobility, efficiency, and collection cost. In the scenario of data collection, the USV is controlled autonomously along the planning trajectory and the data of underwater nodes are dynamically collected. In order to improve the efficiency of data collection and extend the life of the underwater nodes, a mobile data collection protocol for underwater nodes using the USV was proposed. In the protocol, the stop-and-wait ARQ transmission mechanism is adopted, where the duty cycle is designed considering the ratio between the sleep mode and the detection mode, and the transmission ratio is defined by the duty cycle, wake-up signal cycles, and USV’s speed. According to protocol, the evaluation index for energy consumption is constructed based on the duty cycle and the transmission ratio. The energy consumption of the protocol is simulated and analyzed using the mobile communication experiment data of USV, taking into consideration USV’s speed, data sequence length, and duty cycle. Optimized protocol parameters are identified, which in turn denotes the proposed protocol’s feasibility and effectiveness.

Planning and management of cloud computing networks

NASA Astrophysics Data System (ADS)

Larumbe, Federico

The evolution of the Internet has a great impact on a big part of the population. People use it to communicate, query information, receive news, work, and as entertainment. Its extraordinary usefulness as a communication media made the number of applications and technological resources explode. However, that network expansion comes at the cost of an important power consumption. If the power consumption of telecommunication networks and data centers is considered as the power consumption of a country, it would rank at the 5 th place in the world. Furthermore, the number of servers in the world is expected to grow by a factor of 10 between 2013 and 2020. This context motivates us to study techniques and methods to allocate cloud computing resources in an optimal way with respect to cost, quality of service (QoS), power consumption, and environmental impact. The results we obtained from our test cases show that besides minimizing capital expenditures (CAPEX) and operational expenditures (OPEX), the response time can be reduced up to 6 times, power consumption by 30%, and CO2 emissions by a factor of 60. Cloud computing provides dynamic access to IT resources as a service. In this paradigm, programs are executed in servers connected to the Internet that users access from their computers and mobile devices. The first advantage of this architecture is to reduce the time of application deployment and interoperability, because a new user only needs a web browser and does not need to install software on local computers with specific operating systems. Second, applications and information are available from everywhere and with any device with an Internet access. Also, servers and IT resources can be dynamically allocated depending on the number of users and workload, a feature called elasticity. This thesis studies the resource management of cloud computing networks and is divided in three main stages. We start by analyzing the planning of cloud computing networks to get a comprehensive vision. The first question to be solved is what are the optimal data center locations. We found that the location of each data center has a big impact on cost, QoS, power consumption, and greenhouse gas emissions. An optimization problem with a multi-criteria objective function is proposed to decide jointly the optimal location of data centers and software components, link capacities, and information routing. Once the network planning has been analyzed, the problem of dynamic resource provisioning in real time is addressed. In this context, virtualization is a key technique in cloud computing because each server can be shared by multiple Virtual Machines (VMs) and the total power consumption can be reduced. In the same line of location problems, we propose a Green Cloud Broker that optimizes VM placement across multiple data centers. In fact, when multiple data centers are considered, response time can be reduced by placing VMs close to users, cost can be minimized, power consumption can be optimized by using energy efficient data centers, and CO2 emissions can be decreased by choosing data centers provided with renewable energy sources. The third stage of the analysis is the short-term management of a cloud data center. In particular, a method is proposed to assign VMs to servers by considering communication traffic among VMs. Cloud data centers receive new applications over time and these applications need on-demand resource provisioning. Each application is composed of multiple types of VMs that interact among themselves. A program called scheduler must place each new VM in a server and that impacts the QoS and power consumption. Our method places VMs that communicate among themselves in servers that are close to each other in the network topology, thus reducing communication delay and increasing the throughput available among VMs. Furthermore, the power consumption of each type of server is considered and the most efficient ones are chosen to place the VMs. The number of VMs of each application can be dynamically changed to match the workload and servers not needed in a particular period can be suspended to save energy. The methodology developed is based on Mixed Integer Programming (MIP) models to formalize the problems and use state of the art optimization solvers. Then, heuristics are developed to solve cases with more than 1,000 potential data center locations for the planning problem, 1,000 nodes for the cloud broker, and 128,000 servers for the VM placement problem. Solutions with very short optimality gaps, between 0% and 1.95%, are obtained, and execution time in the order of minutes for the planning problem and less than a second for real time cases. We consider that this thesis on resource provisioning of cloud computing networks includes important contributions on this research area, and innovative commercial applications based on the proposed methods have promising future.

Optimization of a Solar Photovoltaic Applied to Greenhouses

NASA Astrophysics Data System (ADS)

Nakoul, Z.; Bibi-Triki, N.; Kherrous, A.; Bessenouci, M. Z.; Khelladi, S.

The global energy consumption and in our country is increasing. The bulk of world energy comes from fossil fuels, whose reserves are doomed to exhaustion and are the leading cause of pollution and global warming through the greenhouse effect. This is not the case of renewable energy that are inexhaustible and from natural phenomena. For years, unanimously, solar energy is in the first rank of renewable energies .The study of energetic aspect of a solar power plant is the best way to find the optimum of its performances. The study on land with real dimensions requires a long time and therefore is very costly, and more results are not always generalizable. To avoid these drawbacks we opted for a planned study on computer only, using the software 'Matlab' by modeling different components for a better sizing and simulating all energies to optimize profitability taking into account the cost. The result of our work applied to sites of Tlemcen and Bouzareah led us to conclude that the energy required is a determining factor in the choice of components of a PV solar power plant.

Optimal translational swimming of a sphere at low Reynolds number.

PubMed

Felderhof, B U; Jones, R B

2014-08-01

Swimming velocity and rate of dissipation of a sphere with surface distortions are discussed on the basis of the Stokes equations of low-Reynolds-number hydrodynamics. At first the surface distortions are assumed to cause an irrotational axisymmetric flow pattern. The efficiency of swimming is optimized within this class of flows. Subsequently, more general axisymmetric polar flows with vorticity are considered. This leads to a considerably higher maximum efficiency. An additional measure of swimming performance is proposed based on the energy consumption for given amplitude of stroke.

Bio-Mimic Optimization Strategies in Wireless Sensor Networks: A Survey

PubMed Central

Adnan, Md. Akhtaruzzaman; Razzaque, Mohammd Abdur; Ahmed, Ishtiaque; Isnin, Ismail Fauzi

2014-01-01

For the past 20 years, many authors have focused their investigations on wireless sensor networks. Various issues related to wireless sensor networks such as energy minimization (optimization), compression schemes, self-organizing network algorithms, routing protocols, quality of service management, security, energy harvesting, etc., have been extensively explored. The three most important issues among these are energy efficiency, quality of service and security management. To get the best possible results in one or more of these issues in wireless sensor networks optimization is necessary. Furthermore, in number of applications (e.g., body area sensor networks, vehicular ad hoc networks) these issues might conflict and require a trade-off amongst them. Due to the high energy consumption and data processing requirements, the use of classical algorithms has historically been disregarded. In this context contemporary researchers started using bio-mimetic strategy-based optimization techniques in the field of wireless sensor networks. These techniques are diverse and involve many different optimization algorithms. As far as we know, most existing works tend to focus only on optimization of one specific issue of the three mentioned above. It is high time that these individual efforts are put into perspective and a more holistic view is taken. In this paper we take a step in that direction by presenting a survey of the literature in the area of wireless sensor network optimization concentrating especially on the three most widely used bio-mimetic algorithms, namely, particle swarm optimization, ant colony optimization and genetic algorithm. In addition, to stimulate new research and development interests in this field, open research issues, challenges and future research directions are highlighted. PMID:24368702

Portfolio-Scale Optimization of Customer Energy Efficiency Incentive and Marketing: Cooperative Research and Development Final Report, CRADA Number CRD-13-535

DOE Office of Scientific and Technical Information (OSTI.GOV)

Brackney, Larry J.

North East utility National Grid (NGrid) is developing a portfolio-scale application of OpenStudio designed to optimize incentive and marketing expenditures for their energy efficiency (EE) programs. NGrid wishes to leverage a combination of geographic information systems (GIS), public records, customer data, and content from the Building Component Library (BCL) to form a JavaScript Object Notation (JSON) input file that is consumed by an OpenStudio-based expert system for automated model generation. A baseline model for each customer building will be automatically tuned using electricity and gas consumption data, and a set of energy conservation measures (ECMs) associated with each NGrid incentivemore » program will be applied to the model. The simulated energy performance and return on investment (ROI) will be compared with customer hurdle rates and available incentives to A) optimize the incentive required to overcome the customer hurdle rate and B) determine if marketing activity associated with the specific ECM is warranted for that particular customer. Repeated across their portfolio, this process will enable NGrid to substantially optimize their marketing and incentive expenditures, targeting those customers that will likely adopt and benefit from specific EE programs.« less

The Nitrogen Footprint Tool Network: A Multi-Institution Program To Reduce Nitrogen Pollution

PubMed Central

Leach, Allison M.; Leary, Neil; Baron, Jill; Compton, Jana E.; Galloway, James N.; Hastings, Meredith G.; Kimiecik, Jacob; Lantz-Trissel, Jonathan; de la Reguera, Elizabeth; Ryals, Rebecca

2017-01-01

Abstract Anthropogenic sources of reactive nitrogen have local and global impacts on air and water quality and detrimental effects on human and ecosystem health. This article uses the Nitrogen Footprint Tool (NFT) to determine the amount of nitrogen (N) released as a result of institutional consumption. The sectors accounted for include food (consumption and upstream production), energy, transportation, fertilizer, research animals, and agricultural research. The NFT is then used for scenario analysis to manage and track reductions, which are driven by the consumption behaviors of both the institution itself and its constituent individuals. In this article, the first seven completed institution nitrogen footprint results are presented. The Nitrogen Footprint Tool Network aims to develop footprints for many institutions to encourage widespread upper-level management strategies that will create significant reductions in reactive nitrogen released to the environment. Energy use and food purchases are the two largest sectors contributing to institution nitrogen footprints. Ongoing efforts by institutions to reduce greenhouse gas emissions also help to reduce the nitrogen footprint, but the impact of food production on nitrogen pollution has not been directly addressed by the higher education sustainability community. The Nitrogen Footprint Tool Network found that institutions could reduce their nitrogen footprints by optimizing food purchasing to reduce consumption of animal products and minimize food waste, as well as by reducing dependence on fossil fuels for energy. PMID:29350216

The Nitrogen Footprint Tool network: A multi-institution program to reduce nitrogen pollution

USGS Publications Warehouse

Castner, Elizabeth A.; Leah, Allison M.; Leary, Neal; Baron, Jill S.; Compton, Jana E.; Galloway, James N.; Hastings, Meredith G.; Kimiecik, Jacob; Lantz-Trissel, Jonathan; de la Riguera, Elizabeth; Ryals, Rebecca

2017-01-01

Anthropogenic sources of reactive nitrogen have local and global impacts on air and water quality and detrimental effects on human and ecosystem health. This paper uses the nitrogen footprint tool (NFT) to determine the amount of nitrogen (N) released as a result of institutional consumption. The sectors accounted for include food (consumption and upstream production), energy, transportation, fertilizer, research animals, and agricultural research. The NFT is then used for scenario analysis to manage and track reductions, which are driven by the consumption behaviors of both the institution itself and its constituent individuals. In this paper, the first seven completed institution nitrogen footprint results are presented. The institution NFT network aims to develop footprints for many institutions to encourage widespread upper-level management strategies that will create significant reductions in reactive nitrogen released to the environment. Energy use and food purchases are the two largest sectors contributing to institution nitrogen footprints. Ongoing efforts by institutions to reduce greenhouse gas emissions also help to reduce the nitrogen footprint, but the impact of food production on nitrogen pollution has not been directly addressed by the higher-ed sustainability community. The NFT Network found that institutions could reduce their nitrogen footprints by optimizing food purchasing to reduce consumption of animal products and minimize food waste, as well as reducing dependence on fossil fuels for energy.

Union Carbide's PECOP cops $500,000 fuel cut

DOE Office of Scientific and Technical Information (OSTI.GOV)

Crawford, E.

1979-10-29

Union Carbide's Plant Energy Cost Optimization Program (POCOP) is saving $500,000 a year at a Taft, Louisiana chemical complex. Day-to-day decisions affecting fuel costs and plant operations are based on a system of computerized data-gathering and processing. Although Carbide's system is not unique, it is more extensive and more comprehensive than the systems used by other chemical companies. The plant has decreased its energy consumption 12% below the 1972 level while increasing production by 30%. The system was initiated in response to the shift from raw materials to energy as the major production cost.

Eco-friendly copper recovery process from waste printed circuit boards using Fe{sup 3+}/Fe{sup 2+} redox system

DOE Office of Scientific and Technical Information (OSTI.GOV)

Fogarasi, Szabolcs; Imre-Lucaci, Florica; Egedy, Attila

2015-06-15

Highlights: • We developed an ecofriendly mediated electrochemical process for copper recovery. • The recovery of copper was achieved without mechanical pretreatment of the samples. • We identified the optimal flow rate for the leaching and electrowinning of copper. • The copper content of the obtained cathodic deposits was over 99.9%. - Abstract: The present study aimed at developing an original and environmentally friendly process for the recovery of copper from waste printed circuit boards (WPCBs) by chemical dissolution with Fe{sup 3+} combined with the simultaneous electrowinning of copper and oxidant regeneration. The recovery of copper was achieved in anmore » original set-up consisting of a three chamber electrochemical reactor (ER) connected in series with a chemical reactor (CR) equipped with a perforated rotating drum. Several experiments were performed in order to identify the optimal flow rate for the dissolution of copper in the CR and to ensure the lowest energy consumption for copper electrodeposition in the ER. The optimal hydrodynamic conditions were provided at 400 mL/min, leading to the 75% dissolution of metals and to a low specific energy consumption of 1.59 kW h/kg Cu for the electrodeposition process. In most experiments, the copper content of the obtained cathodic deposits was over 99.9%.« less

Replacement policy of residential lighting optimized for cost, energy, and greenhouse gas emissions

NASA Astrophysics Data System (ADS)

Liu, Lixi; Keoleian, Gregory A.; Saitou, Kazuhiro

2017-11-01

Accounting for 10% of the electricity consumption in the US, artificial lighting represents one of the easiest ways to cut household energy bills and greenhouse gas (GHG) emissions by upgrading to energy-efficient technologies such as compact fluorescent lamps (CFL) and light emitting diodes (LED). However, given the high initial cost and rapidly improving trajectory of solid-state lighting today, estimating the right time to switch over to LEDs from a cost, primary energy, and GHG emissions perspective is not a straightforward problem. This is an optimal replacement problem that depends on many determinants, including how often the lamp is used, the state of the initial lamp, and the trajectories of lighting technology and of electricity generation. In this paper, multiple replacement scenarios of a 60 watt-equivalent A19 lamp are analyzed and for each scenario, a few replacement policies are recommended. For example, at an average use of 3 hr day-1 (US average), it may be optimal both economically and energetically to delay the adoption of LEDs until 2020 with the use of CFLs, whereas purchasing LEDs today may be optimal in terms of GHG emissions. In contrast, incandescent and halogen lamps should be replaced immediately. Based on expected LED improvement, upgrading LED lamps before the end of their rated lifetime may provide cost and environmental savings over time by taking advantage of the higher energy efficiency of newer models.

Energy Dissipation and Transport in Carbon Nanotube Devices

NASA Astrophysics Data System (ADS)

Pop, Eric

2011-03-01

Power consumption is a significant challenge in electronics, often limiting the performance of integrated circuits from mobile devices to massive data centers. Carbon nanotubes have emerged as potentially energy-efficient future devices and interconnects, with both large mobility and thermal conductivity. This talk will focus on understanding and controlling energy dissipation [1-3] and transport [4-6] in carbon nanotubes, with applications to low-energy devices, interconnects, heat sinks, and memory elements. Experiments have been used to gain new insight into the fundamental behavior of such devices, and to better inform practical device models. The results suggest much room for energy optimization in nanoelectronics through the design of geometry, interfaces, and materials..

Assessing actual evapotranspiration via surface energy balance aiming to optimize water and energy consumption in large scale pressurized irrigation systems

NASA Astrophysics Data System (ADS)

Awada, H.; Ciraolo, G.; Maltese, A.; Moreno Hidalgo, M. A.; Provenzano, G.; Còrcoles, J. I.

2017-10-01

Satellite imagery provides a dependable basis for computational models that aimed to determine actual evapotranspiration (ET) by surface energy balance. Satellite-based models enables quantifying ET over large areas for a wide range of applications, such as monitoring water distribution, managing irrigation and assessing irrigation systems' performance. With the aim to evaluate the energy and water consumption of a large scale on-turn pressurized irrigation system in the district of Aguas Nuevas, Albacete, Spain, the satellite-based image-processing model SEBAL was used for calculating actual ET. The model has been applied to quantify instantaneous, daily, and seasonal actual ET over high- resolution Landsat images for the peak water demand season (May to September) and for the years 2006 - 2008. The model provided a direct estimation of the distribution of main energy fluxes, at the instant when the satellite overpassed over each field of the district. The image acquisition day Evapotranspiration (ET24) was obtained from instantaneous values by assuming a constant evaporative fraction (Λ) for the entire day of acquisition; then, monthly and seasonal ET were estimated from the daily evapotranspiration (ETdaily) assuming that ET24 varies in proportion to reference ET (ETr) at the meteorological station, thus accounting for day to day variation in meteorological forcing. The comparison between the hydrants water consumption and the actual evapotranspiration, considering an irrigation efficiency of 85%, showed that a considerable amount of water and energy can be saved at district level.

An Energy Balanced and Lifetime Extended Routing Protocol for Underwater Sensor Networks.

PubMed

Wang, Hao; Wang, Shilian; Zhang, Eryang; Lu, Luxi

2018-05-17

Energy limitation is an adverse problem in designing routing protocols for underwater sensor networks (UWSNs). To prolong the network lifetime with limited battery power, an energy balanced and efficient routing protocol, called energy balanced and lifetime extended routing protocol (EBLE), is proposed in this paper. The proposed EBLE not only balances traffic loads according to the residual energy, but also optimizes data transmissions by selecting low-cost paths. Two phases are operated in the EBLE data transmission process: (1) candidate forwarding set selection phase and (2) data transmission phase. In candidate forwarding set selection phase, nodes update candidate forwarding nodes by broadcasting the position and residual energy level information. The cost value of available nodes is calculated and stored in each sensor node. Then in data transmission phase, high residual energy and relatively low-cost paths are selected based on the cost function and residual energy level information. We also introduce detailed analysis of optimal energy consumption in UWSNs. Numerical simulation results on a variety of node distributions and data load distributions prove that EBLE outperforms other routing protocols (BTM, BEAR and direct transmission) in terms of network lifetime and energy efficiency.

Efficiency enhancement for natural gas liquefaction with CO2 capture and sequestration through cycles innovation and process optimization

NASA Astrophysics Data System (ADS)

Alabdulkarem, Abdullah

Liquefied natural gas (LNG) plants are energy intensive. As a result, the power plants operating these LNG plants emit high amounts of CO2 . To mitigate global warming that is caused by the increase in atmospheric CO2, CO2 capture and sequestration (CCS) using amine absorption is proposed. However, the major challenge of implementing this CCS system is the associated power requirement, increasing power consumption by about 15--25%. Therefore, the main scope of this work is to tackle this challenge by minimizing CCS power consumption as well as that of the entire LNG plant though system integration and rigorous optimization. The power consumption of the LNG plant was reduced through improving the process of liquefaction itself. In this work, a genetic algorithm (GA) was used to optimize a propane pre-cooled mixed-refrigerant (C3-MR) LNG plant modeled using HYSYS software. An optimization platform coupling Matlab with HYSYS was developed. New refrigerant mixtures were found, with savings in power consumption as high as 13%. LNG plants optimization with variable natural gas feed compositions was addressed and the solution was proposed through applying robust optimization techniques, resulting in a robust refrigerant which can liquefy a range of natural gas feeds. The second approach for reducing the power consumption is through process integration and waste heat utilization in the integrated CCS system. Four waste heat sources and six potential uses were uncovered and evaluated using HYSYS software. The developed models were verified against experimental data from the literature with good agreement. Net available power enhancement in one of the proposed CCS configuration is 16% more than the conventional CCS configuration. To reduce the CO2 pressurization power into a well for enhanced oil recovery (EOR) applications, five CO2 pressurization methods were explored. New CO2 liquefaction cycles were developed and modeled using HYSYS software. One of the developed liquefaction cycles using NH3 as a refrigerant resulted in 5% less power consumption than the conventional multi-stage compression cycle. Finally, a new concept of providing the CO2 regeneration heat is proposed. The proposed concept is using a heat pump to provide the regeneration heat as well as process heat and CO2 liquefaction heat. Seven configurations of heat pumps integrated with CCS were developed. One of the heat pumps consumes 24% less power than the conventional system or 59% less total equivalent power demand than the conventional system with steam extraction and CO2 compression.

Study on key technologies of optimization of big data for thermal power plant performance

NASA Astrophysics Data System (ADS)

Mao, Mingyang; Xiao, Hong

2018-06-01

Thermal power generation accounts for 70% of China's power generation, the pollutants accounted for 40% of the same kind of emissions, thermal power efficiency optimization needs to monitor and understand the whole process of coal combustion and pollutant migration, power system performance data show explosive growth trend, The purpose is to study the integration of numerical simulation of big data technology, the development of thermal power plant efficiency data optimization platform and nitrogen oxide emission reduction system for the thermal power plant to improve efficiency, energy saving and emission reduction to provide reliable technical support. The method is big data technology represented by "multi-source heterogeneous data integration", "large data distributed storage" and "high-performance real-time and off-line computing", can greatly enhance the energy consumption capacity of thermal power plants and the level of intelligent decision-making, and then use the data mining algorithm to establish the boiler combustion mathematical model, mining power plant boiler efficiency data, combined with numerical simulation technology to find the boiler combustion and pollutant generation rules and combustion parameters of boiler combustion and pollutant generation Influence. The result is to optimize the boiler combustion parameters, which can achieve energy saving.

Data-driven reinforcement learning–based real-time energy management system for plug-in hybrid electric vehicles

DOE PAGES

Qi, Xuewei; Wu, Guoyuan; Boriboonsomsin, Kanok; ...

2016-01-01

Plug-in hybrid electric vehicles (PHEVs) show great promise in reducing transportation-related fossil fuel consumption and greenhouse gas emissions. Designing an efficient energy management system (EMS) for PHEVs to achieve better fuel economy has been an active research topic for decades. Most of the advanced systems rely either on a priori knowledge of future driving conditions to achieve the optimal but not real-time solution (e.g., using a dynamic programming strategy) or on only current driving situations to achieve a real-time but nonoptimal solution (e.g., rule-based strategy). This paper proposes a reinforcement learning–based real-time EMS for PHEVs to address the trade-off betweenmore » real-time performance and optimal energy savings. The proposed model can optimize the power-split control in real time while learning the optimal decisions from historical driving cycles. Here, a case study on a real-world commute trip shows that about a 12% fuel saving can be achieved without considering charging opportunities; further, an 8% fuel saving can be achieved when charging opportunities are considered, compared with the standard binary mode control strategy.« less

Optimization of Supersonic Transport Trajectories

NASA Technical Reports Server (NTRS)

Ardema, Mark D.; Windhorst, Robert; Phillips, James

1998-01-01

This paper develops a near-optimal guidance law for generating minimum fuel, time, or cost fixed-range trajectories for supersonic transport aircraft. The approach uses a choice of new state variables along with singular perturbation techniques to time-scale decouple the dynamic equations into multiple equations of single order (second order for the fast dynamics). Application of the maximum principle to each of the decoupled equations, as opposed to application to the original coupled equations, avoids the two point boundary value problem and transforms the problem from one of a functional optimization to one of multiple function optimizations. It is shown that such an approach produces well known aircraft performance results such as minimizing the Brequet factor for minimum fuel consumption and the energy climb path. Furthermore, the new state variables produce a consistent calculation of flight path angle along the trajectory, eliminating one of the deficiencies in the traditional energy state approximation. In addition, jumps in the energy climb path are smoothed out by integration of the original dynamic equations at constant load factor. Numerical results performed for a supersonic transport design show that a pushover dive followed by a pullout at nominal load factors are sufficient maneuvers to smooth the jump.

Data-driven reinforcement learning–based real-time energy management system for plug-in hybrid electric vehicles

DOE Office of Scientific and Technical Information (OSTI.GOV)

Qi, Xuewei; Wu, Guoyuan; Boriboonsomsin, Kanok

Plug-in hybrid electric vehicles (PHEVs) show great promise in reducing transportation-related fossil fuel consumption and greenhouse gas emissions. Designing an efficient energy management system (EMS) for PHEVs to achieve better fuel economy has been an active research topic for decades. Most of the advanced systems rely either on a priori knowledge of future driving conditions to achieve the optimal but not real-time solution (e.g., using a dynamic programming strategy) or on only current driving situations to achieve a real-time but nonoptimal solution (e.g., rule-based strategy). This paper proposes a reinforcement learning–based real-time EMS for PHEVs to address the trade-off betweenmore » real-time performance and optimal energy savings. The proposed model can optimize the power-split control in real time while learning the optimal decisions from historical driving cycles. Here, a case study on a real-world commute trip shows that about a 12% fuel saving can be achieved without considering charging opportunities; further, an 8% fuel saving can be achieved when charging opportunities are considered, compared with the standard binary mode control strategy.« less

Improving the energy efficiency of telecommunication networks

NASA Astrophysics Data System (ADS)

Lange, Christoph; Gladisch, Andreas

2011-05-01

The energy consumption of telecommunication networks has gained increasing interest throughout the recent past: Besides its environmental implications it has been identified to be a major contributor to operational expenditures of network operators. Targeting at sustainable telecommunication networks, thus, it is important to find appropriate strategies for improving their energy efficiency before the background of rapidly increasing traffic volumes. Besides the obvious benefits of increasing energy efficiency of network elements by leveraging technology progress, load-adaptive network operation is a very promising option, i.e. using network resources only to an extent and for the time they are actually needed. In contrast, current network operation takes almost no advantage of the strongly time-variant behaviour of the network traffic load. Mechanisms for energy-aware load-adaptive network operation can be subdivided in techniques based on local autonomous or per-link decisions and in techniques relying on coordinated decisions incorporating information from several links. For the transformation from current network structures and operation paradigms towards energy-efficient and sustainable networks it will be essential to use energy-optimized network elements as well as including the overall energy consumption in network design and planning phases together with the energy-aware load-adaptive operation. In load-adaptive operation it will be important to establish the optimum balance between local and overarching power management concepts in telecommunication networks.

Impact of Climate Change on Energy Production, Distribution, and Consumption in Russia

NASA Astrophysics Data System (ADS)

Klimenko, V. V.; Klimenko, A. V.; Tereshin, A. G.; Fedotova, E. V.

2018-05-01

An assessment of the overall impact of the observed and expected climatic changes on energy production, distribution, and consumption in Russia is presented. Climate model results of various complexity and evaluation data on the vulnerability of various energy production sectors to climate change are presented. It is shown that, due to the increase of air temperature, the efficiency of electricity production at thermal and nuclear power plants declines. According to the climate model results, the production of electricity at TPPs and NPPs by 2050 could be reduced by 6 billion kW h due to the temperature increase. At the same time, as a result of simulation, the expected increase in the rainfall amount and river runoff in Russia by 2050 could lead to an increase in the output of HPP by 4-6% as compared with the current level, i.e., by 8 billion kW h. For energy transmission and distribution, the climate warming will mean an increase in transmission losses, which, according to estimates, may amount to approximately 1 billion kW h by 2050. The increase of air temperature in summer will require higher energy consumption for air conditioning, which will increase by approximately 6 billion kW h by 2050. However, in total, the optimal energy consumption in Russia, corresponding to the postindustrial level, will decrease by 2050 by approximately 150 billion kW h as a result of climate- induced changes. The maximum global warming impact is focused on the heat demand sector. As a result of a decrease in the heating degree-days by 2050, the need for space heating is expected to fall by 10-15%, which will cause a fuel conservation sufficient for generating approximately 140 billion kW h of electricity. Hence, a conclusion about the positive direct impact of climate change on the Russia's energy sector follows, which is constituted in the additional available energy resource of approximately 300 billion kW h per year.

A Distributed and Energy-Efficient Algorithm for Event K-Coverage in Underwater Sensor Networks

PubMed Central

Jiang, Peng; Xu, Yiming; Liu, Jun

2017-01-01

For event dynamic K-coverage algorithms, each management node selects its assistant node by using a greedy algorithm without considering the residual energy and situations in which a node is selected by several events. This approach affects network energy consumption and balance. Therefore, this study proposes a distributed and energy-efficient event K-coverage algorithm (DEEKA). After the network achieves 1-coverage, the nodes that detect the same event compete for the event management node with the number of candidate nodes and the average residual energy, as well as the distance to the event. Second, each management node estimates the probability of its neighbor nodes’ being selected by the event it manages with the distance level, the residual energy level, and the number of dynamic coverage event of these nodes. Third, each management node establishes an optimization model that uses expectation energy consumption and the residual energy variance of its neighbor nodes and detects the performance of the events it manages as targets. Finally, each management node uses a constrained non-dominated sorting genetic algorithm (NSGA-II) to obtain the Pareto set of the model and the best strategy via technique for order preference by similarity to an ideal solution (TOPSIS). The algorithm first considers the effect of harsh underwater environments on information collection and transmission. It also considers the residual energy of a node and a situation in which the node is selected by several other events. Simulation results show that, unlike the on-demand variable sensing K-coverage algorithm, DEEKA balances and reduces network energy consumption, thereby prolonging the network’s best service quality and lifetime. PMID:28106837

A Distributed and Energy-Efficient Algorithm for Event K-Coverage in Underwater Sensor Networks.

PubMed

Jiang, Peng; Xu, Yiming; Liu, Jun

2017-01-19

For event dynamic K-coverage algorithms, each management node selects its assistant node by using a greedy algorithm without considering the residual energy and situations in which a node is selected by several events. This approach affects network energy consumption and balance. Therefore, this study proposes a distributed and energy-efficient event K-coverage algorithm (DEEKA). After the network achieves 1-coverage, the nodes that detect the same event compete for the event management node with the number of candidate nodes and the average residual energy, as well as the distance to the event. Second, each management node estimates the probability of its neighbor nodes' being selected by the event it manages with the distance level, the residual energy level, and the number of dynamic coverage event of these nodes. Third, each management node establishes an optimization model that uses expectation energy consumption and the residual energy variance of its neighbor nodes and detects the performance of the events it manages as targets. Finally, each management node uses a constrained non-dominated sorting genetic algorithm (NSGA-II) to obtain the Pareto set of the model and the best strategy via technique for order preference by similarity to an ideal solution (TOPSIS). The algorithm first considers the effect of harsh underwater environments on information collection and transmission. It also considers the residual energy of a node and a situation in which the node is selected by several other events. Simulation results show that, unlike the on-demand variable sensing K-coverage algorithm, DEEKA balances and reduces network energy consumption, thereby prolonging the network's best service quality and lifetime.

Study on two operating conditions of a full-scale oxidation ditch for optimization of energy consumption and effluent quality by using CFD model.

PubMed

Yang, Yin; Yang, Jiakuan; Zuo, Jiaolan; Li, Ye; He, Shu; Yang, Xiao; Zhang, Kai

2011-05-01

The operating condition of an oxidation ditch (OD) has significant impact on energy consumption and effluent quality of wastewater treatment plants (WWTPs). An experimentally validated numerical tool, based on computational fluid dynamics (CFD) model, was proposed to optimize the operating condition by considering two important factors: flow field and dissolved oxygen (DO) concentration profiles. The model is capable of predicting flow pattern and oxygen mass transfer characteristics in ODs equipped with surface aerators and submerged impellers. Performance demonstration and comparison of two operating conditions (existing and improved) were carried out in two full-scale Carrousel ODs at the Ping Dingshan WWTP in Henan, China. A moving wall model and a fan model were designed to simulate surface aerators and submerged impellers, respectively. Oxygen mass transfer in the ditch was predicted by using a unit analysis method. In aeration zones, the mass inlets representing the surface aerators were set as one source of DO. In the whole straight channel, the oxygen consumption was modeled by using modified BOD-DO model. The following results were obtained: (1) the CFD model characterized flow pattern and DO concentration profiles in the full-scale OD. The predicted flow field values were within 1.98 ± 4.28% difference from the actual measured values while the predicted DO concentration values were within -4.71 ± 4.15% of the measured ones, (2) a surface aerator should be relocated to around 15m from the curve bend entrance to reduce energy loss caused by fierce collisions at the wall of the curve bend, and (3) DO concentration gradients in the OD under the improved operating condition were more favorable for occurrence of simultaneous nitrification and denitrification (SND). Copyright © 2011 Elsevier Ltd. All rights reserved.

Study on the Influence of the Cold-End Cooling Water Thickness on the Generative Performance of TEG

NASA Astrophysics Data System (ADS)

Zhou, Li; Guo, Xuexun; Tan, Gangfeng; Ji, Kangping; Xiao, Longjie

2017-05-01

At present, about 40% of the fuel energy is discharged into air with the exhaust gas when an automobile is working, which is a big waste of energy. A thermoelectric generator (TEG) has the ability to harvest the waste heat energy in the exhaust gas. The traditional TEG cold-end is cooled by the engine cooling system, and although its structure is compact, the TEG weight and the space occupied are important factors restricting its application. In this paper, under the premise of ensuring the TEG maximum net output power and reducing the TEG water consumption as much as possible, the optimization of the TEG water thickness in the normal direction of the cold-end surface (WTNCS) is studied, which results in lighter weight, less space occupied and better automobile fuel economy. First, the thermal characteristics of the target diesel vehicle exhaust gas are evaluated based on the experimental data. Then, according to the thermoelectric generation model and the cold-end heat transfer model, the effect of the WTNCS on the cold-end temperature control stability and the system flow resistance are studied. The results show that the WTNCS influences the TEG cold-end temperature. When the engine works in a stable condition, the cold-end temperature decreases with the decrease of the WTNCS. The optimal value of the WTNCS is 0.02 m and the TEG water consumption is 8.8 L. Comparin it with the traditional vehicle exhaust TEG structure, the power generation increased slightly, but the water consumption decreased by about 39.5%, which can save fuel at0.18 L/h when the vehicle works at the speed of 60 km/h.

Node Deployment Algorithm Based on Connected Tree for Underwater Sensor Networks

PubMed Central

Jiang, Peng; Wang, Xingmin; Jiang, Lurong

2015-01-01

Designing an efficient deployment method to guarantee optimal monitoring quality is one of the key topics in underwater sensor networks. At present, a realistic approach of deployment involves adjusting the depths of nodes in water. One of the typical algorithms used in such process is the self-deployment depth adjustment algorithm (SDDA). This algorithm mainly focuses on maximizing network coverage by constantly adjusting node depths to reduce coverage overlaps between two neighboring nodes, and thus, achieves good performance. However, the connectivity performance of SDDA is irresolute. In this paper, we propose a depth adjustment algorithm based on connected tree (CTDA). In CTDA, the sink node is used as the first root node to start building a connected tree. Finally, the network can be organized as a forest to maintain network connectivity. Coverage overlaps between the parent node and the child node are then reduced within each sub-tree to optimize coverage. The hierarchical strategy is used to adjust the distance between the parent node and the child node to reduce node movement. Furthermore, the silent mode is adopted to reduce communication cost. Simulations show that compared with SDDA, CTDA can achieve high connectivity with various communication ranges and different numbers of nodes. Moreover, it can realize coverage as high as that of SDDA with various sensing ranges and numbers of nodes but with less energy consumption. Simulations under sparse environments show that the connectivity and energy consumption performances of CTDA are considerably better than those of SDDA. Meanwhile, the connectivity and coverage performances of CTDA are close to those depth adjustment algorithms base on connected dominating set (CDA), which is an algorithm similar to CTDA. However, the energy consumption of CTDA is less than that of CDA, particularly in sparse underwater environments. PMID:26184209

Optimization of photo-Fenton process for the treatment of prednisolone.

PubMed

Díez, Aida María; Ribeiro, Ana Sofia; Sanromán, Maria Angeles; Pazos, Marta

2018-03-29

Prednisolone is a widely prescribed synthetic glucocorticoid and stated to be toxic to a number of non-target aquatic organisms. Its extensive consumption generates environmental concern due to its detection in wastewater samples at concentrations ranged from ng/L to μg/L that requests the application of suitable degradation processes. Regarding the actual treatment options, advanced oxidation processes (AOPs) are presented as a viable alternative. In this work, the comparison in terms of pollutant removal and energetic efficiencies, between different AOPs such as Fenton (F), photo-Fenton (UV/F), photolysis (UV), and hydrogen peroxide/photolysis (UV/H 2 O 2 ), was carried out. Light diode emission (LED) was the selected source to provide the UV radiation. The UV/F process revealed the best performance, reaching high levels of both degradation and mineralization with low energy consumption. Its optimization was conducted and the operational parameters were iron and H 2 O 2 concentrations and the working volume. Using the response surface methodology with the Box-Behnken design, the effect of independent variables and their interactions on the process response were effectively evaluated. Different responses were analyzed taking into account the prednisolone removal (TOC and drug abatements) and the energy consumptions associated. The obtained model showed an improvement of the UV/F process when treating smaller volumes and when adding high concentrations of H 2 O 2 and Fe 2+ . The validation of this model was successfully carried out, having only 5% of discrepancy between the model and the experimental results. Finally, the performance of the process when having a real wastewater matrix was also tested, achieving complete mineralization and detoxification after 8 h. In addition, prednisolone degradation products were identified. Finally, the obtained low energy permitted to confirm the viability of the process.

Intelligent systems installed in building of research centre for research purposes

NASA Astrophysics Data System (ADS)

Matusov, Jozef; Mokry, Marian; Kolkova, Zuzana; Sedivy, Stefan

2016-06-01

The attractiveness of intelligent buildings is nowadays directly connected with higher level of comfort and also the economic mode of consumption energy for heating, cooling and the total consumption of electricity for electric devices. The technologies of intelligent buildings compared with conventional solutions allow dynamic optimization in real time and make it easy for operational message. The basic division of functionality in horizontal direction is possible divide in to two areas such as Economical sophisticated residential care about the comfort of people in the building and Security features. The paper deals with description of intelligent systems which has a building of Research Centre. The building has installed the latest technology for utilization of renewable energy and also latest systems of controlling and driving all devices which contribute for economy operation by achieving the highest thermal comfort and overall safety.

Cooperative Position Aware Mobility Pattern of AUVs for Avoiding Void Zones in Underwater WSNs

PubMed Central

Javaid, Nadeem; Ejaz, Mudassir; Abdul, Wadood; Alamri, Atif; Almogren, Ahmad; Niaz, Iftikhar Azim; Guizani, Nadra

2017-01-01

In this paper, we propose two schemes; position-aware mobility pattern (PAMP) and cooperative PAMP (Co PAMP). The first one is an optimization scheme that avoids void hole occurrence and minimizes the uncertainty in the position estimation of glider’s. The second one is a cooperative routing scheme that reduces the packet drop ratio by using the relay cooperation. Both techniques use gliders that stay at sojourn positions for a predefined time, at sojourn position self-confidence (s-confidence) and neighbor-confidence (n-confidence) regions that are estimated for balanced energy consumption. The transmission power of a glider is adjusted according to those confidence regions. Simulation results show that our proposed schemes outperform the compared existing one in terms of packet delivery ratio, void zones and energy consumption. PMID:28335377

An Energy-Efficient Multi-Tier Architecture for Fall Detection Using Smartphones.

PubMed

Guvensan, M Amac; Kansiz, A Oguz; Camgoz, N Cihan; Turkmen, H Irem; Yavuz, A Gokhan; Karsligil, M Elif

2017-06-23

Automatic detection of fall events is vital to providing fast medical assistance to the causality, particularly when the injury causes loss of consciousness. Optimization of the energy consumption of mobile applications, especially those which run 24/7 in the background, is essential for longer use of smartphones. In order to improve energy-efficiency without compromising on the fall detection performance, we propose a novel 3-tier architecture that combines simple thresholding methods with machine learning algorithms. The proposed method is implemented on a mobile application, called uSurvive, for Android smartphones. It runs as a background service and monitors the activities of a person in daily life and automatically sends a notification to the appropriate authorities and/or user defined contacts when it detects a fall. The performance of the proposed method was evaluated in terms of fall detection performance and energy consumption. Real life performance tests conducted on two different models of smartphone demonstrate that our 3-tier architecture with feature reduction could save up to 62% of energy compared to machine learning only solutions. In addition to this energy saving, the hybrid method has a 93% of accuracy, which is superior to thresholding methods and better than machine learning only solutions.

Multiphase porous media modelling: A novel approach to predicting food processing performance.

PubMed

Khan, Md Imran H; Joardder, M U H; Kumar, Chandan; Karim, M A

2018-03-04

The development of a physics-based model of food processing is essential to improve the quality of processed food and optimize energy consumption. Food materials, particularly plant-based food materials, are complex in nature as they are porous and have hygroscopic properties. A multiphase porous media model for simultaneous heat and mass transfer can provide a realistic understanding of transport processes and thus can help to optimize energy consumption and improve food quality. Although the development of a multiphase porous media model for food processing is a challenging task because of its complexity, many researchers have attempted it. The primary aim of this paper is to present a comprehensive review of the multiphase models available in the literature for different methods of food processing, such as drying, frying, cooking, baking, heating, and roasting. A critical review of the parameters that should be considered for multiphase modelling is presented which includes input parameters, material properties, simulation techniques and the hypotheses. A discussion on the general trends in outcomes, such as moisture saturation, temperature profile, pressure variation, and evaporation patterns, is also presented. The paper concludes by considering key issues in the existing multiphase models and future directions for development of multiphase models.

CO2 Reduction Effect of the Utilization of Waste Heat and Solar Heat in City Gas System

NASA Astrophysics Data System (ADS)

Okamura, Tomohito; Matsuhashi, Ryuji; Yoshida, Yoshikuni; Hasegawa, Hideo; Ishitani, Hisashi

We evaluate total energy consumption and CO2 emissions in the phase of the city gas utilization system from obtaining raw materials to consuming the product. First, we develop a simulation model which calculates CO2 emissions for monthly and hourly demands of electricity, heats for air conditioning and hot-water in a typical hospital. Under the given standard capacity and operating time of CGS, energy consumption in the equipments is calculated in detail considering the partial load efficiency and the control by the temperature of exhaust heat. Then, we explored the optimal size and operation of city gas system that minimizes the life cycle CO2 emissions or total cost. The cost-effectiveness is compared between conventional co-generation, solar heat system, and hybrid co-generation utilizing solar heat. We formulate a problem of mixed integer programming that includes integral parameters that express the state of system devices such as on/off of switches. As a result of optimization, the hybrid co-generation can reduce annual CO2 emissions by forty-three percent compared with the system without co-generation. Sensitivity for the scale of CGS on CO2 reduction and cost is also analyzed.

A novel vortex tube-based N2-expander liquefaction process for enhancing the energy efficiency of natural gas liquefaction

NASA Astrophysics Data System (ADS)

Qyyum, Muhammad Abdul; Wei, Feng; Hussain, Arif; Ali, Wahid; Sehee, Oh; Lee, Moonyong

2017-11-01

This research work unfolds a simple, safe, and environment-friendly energy efficient novel vortex tube-based natural gas liquefaction process (LNG). A vortex tube was introduced to the popular N2-expander liquefaction process to enhance the liquefaction efficiency. The process structure and condition were modified and optimized to take a potential advantage of the vortex tube on the natural gas liquefaction cycle. Two commercial simulators ANSYS® and Aspen HYSYS® were used to investigate the application of vortex tube in the refrigeration cycle of LNG process. The Computational fluid dynamics (CFD) model was used to simulate the vortex tube with nitrogen (N2) as a working fluid. Subsequently, the results of the CFD model were embedded in the Aspen HYSYS® to validate the proposed LNG liquefaction process. The proposed natural gas liquefaction process was optimized using the knowledge-based optimization (KBO) approach. The overall energy consumption was chosen as an objective function for optimization. The performance of the proposed liquefaction process was compared with the conventional N2-expander liquefaction process. The vortex tube-based LNG process showed a significant improvement of energy efficiency by 20% in comparison with the conventional N2-expander liquefaction process. This high energy efficiency was mainly due to the isentropic expansion of the vortex tube. It turned out that the high energy efficiency of vortex tube-based process is totally dependent on the refrigerant cold fraction, operating conditions as well as refrigerant cycle configurations.

ePave: A Self-Powered Wireless Sensor for Smart and Autonomous Pavement.

PubMed

Xiao, Jian; Zou, Xiang; Xu, Wenyao

2017-09-26

"Smart Pavement" is an emerging infrastructure for various on-road applications in transportation and road engineering. However, existing road monitoring solutions demand a certain periodic maintenance effort due to battery life limits in the sensor systems. To this end, we present an end-to-end self-powered wireless sensor-ePave-to facilitate smart and autonomous pavements. The ePave system includes a self-power module, an ultra-low-power sensor system, a wireless transmission module and a built-in power management module. First, we performed an empirical study to characterize the piezoelectric module in order to optimize energy-harvesting efficiency. Second, we developed an integrated sensor system with the optimized energy harvester. An adaptive power knob is designated to adjust the power consumption according to energy budgeting. Finally, we intensively evaluated the ePave system in real-world applications to examine the system's performance and explore the trade-off.

IEEE 802.21 Assisted Seamless and Energy Efficient Handovers in Mixed Networks

NASA Astrophysics Data System (ADS)

Liu, Huaiyu; Maciocco, Christian; Kesavan, Vijay; Low, Andy L. Y.

Network selection is the decision process for a mobile terminal to handoff between homogeneous or heterogeneous networks. With multiple available networks, the selection process must evaluate factors like network services/conditions, monetary cost, system conditions, user preferences etc. In this paper, we investigate network selection using a cost function and information provided by IEEE 802.21. The cost function provides flexibility to balance different factors in decision making and our research is focused on improving both seamlessness and energy efficiency of handovers. Our solution is evaluated using real WiFi, WiMax, and 3G signal strength traces. The results show that appropriate networks were selected based on selection policies, handovers were triggered at optimal times to increase overall network connectivity as compared to traditional triggering schemes, while at the same time the energy consumption of multi-radio devices for both on-going operations as well as during handovers is optimized.

ePave: A Self-Powered Wireless Sensor for Smart and Autonomous Pavement

PubMed Central

Xiao, Jian; Zou, Xiang

2017-01-01

“Smart Pavement” is an emerging infrastructure for various on-road applications in transportation and road engineering. However, existing road monitoring solutions demand a certain periodic maintenance effort due to battery life limits in the sensor systems. To this end, we present an end-to-end self-powered wireless sensor—ePave—to facilitate smart and autonomous pavements. The ePave system includes a self-power module, an ultra-low-power sensor system, a wireless transmission module and a built-in power management module. First, we performed an empirical study to characterize the piezoelectric module in order to optimize energy-harvesting efficiency. Second, we developed an integrated sensor system with the optimized energy harvester. An adaptive power knob is designated to adjust the power consumption according to energy budgeting. Finally, we intensively evaluated the ePave system in real-world applications to examine the system’s performance and explore the trade-off. PMID:28954430

Electrical appliance energy consumption control methods and electrical energy consumption systems

DOEpatents

Donnelly, Matthew K [Kennewick, WA; Chassin, David P [Pasco, WA; Dagle, Jeffery E [Richland, WA; Kintner-Meyer, Michael [Richland, WA; Winiarski, David W [Kennewick, WA; Pratt, Robert G [Kennewick, WA; Boberly-Bartis, Anne Marie [Alexandria, VA

2006-03-07

Electrical appliance energy consumption control methods and electrical energy consumption systems are described. In one aspect, an electrical appliance energy consumption control method includes providing an electrical appliance coupled with a power distribution system, receiving electrical energy within the appliance from the power distribution system, consuming the received electrical energy using a plurality of loads of the appliance, monitoring electrical energy of the power distribution system, and adjusting an amount of consumption of the received electrical energy via one of the loads of the appliance from an initial level of consumption to an other level of consumption different than the initial level of consumption responsive to the monitoring.

Electrical appliance energy consumption control methods and electrical energy consumption systems

DOEpatents

Donnelly, Matthew K [Kennewick, WA; Chassin, David P [Pasco, WA; Dagle, Jeffery E [Richland, WA; Kintner-Meyer, Michael [Richland, WA; Winiarski, David W [Kennewick, WA; Pratt, Robert G [Kennewick, WA; Boberly-Bartis, Anne Marie [Alexandria, VA

2008-09-02

Electrical appliance energy consumption control methods and electrical energy consumption systems are described. In one aspect, an electrical appliance energy consumption control method includes providing an electrical appliance coupled with a power distribution system, receiving electrical energy within the appliance from the power distribution system, consuming the received electrical energy using a plurality of loads of the appliance, monitoring electrical energy of the power distribution system, and adjusting an amount of consumption of the received electrical energy via one of the loads of the appliance from an initial level of consumption to an other level of consumption different than the initial level of consumption responsive to the monitoring.

Fuel consumption for various driving styles in conventional and hybrid electric vehicles: Integrating driving cycle predictions with fuel consumption optimization

DOE PAGES

Rios-Torres, Jackeline; Liu, Jun; Khattak, Asad

2018-06-14

Here, improving fuel economy and lowering emissions are key societal goals. Standard driving cycles, pre-designed by the US Environmental Protection Agency (EPA), have long been used to estimate vehicle fuel economy in laboratory-controlled conditions. They have also been used to test and tune different energy management strategies for hybrid electric vehicles (HEVs). This paper aims to estimate fuel consumption for a conventional vehicle and a HEV using personalized driving cycles extracted from real-world data to study the effects of different driving styles and vehicle types on fuel consumption when compared to the estimates based on standard driving cycles. To domore » this, we extracted driving cycles for conventional vehicles and HEVs from a large-scale U.S. survey that contains real-world GPS-based driving records. Next, the driving cycles were assigned to one of three categories: volatile, normal, or calm. Then, the driving cycles were used along with a driver-vehicle simulation that captures driver decisions (vehicle speed during a trip), powertrain, and vehicle dynamics to estimate fuel consumption for conventional vehicles and HEVs with power-split powertrain. To further optimize fuel consumption for HEVs, the Equivalent Consumption Minimization Strategy (ECMS) is applied. The results show that depending on the driving style and the driving scenario, conventional vehicle fuel consumption can vary widely compared with standard EPA driving cycles. Specifically, conventional vehicle fuel consumption was 13% lower in calm urban driving, but almost 34% higher for volatile highway driving compared with standard EPA driving cycles. Interestingly, when a driving cycle is predicted based on the application of case-based reasoning and used to tune the power distribution in a hybrid electric vehicle, its fuel consumption can be reduced by up to 12% in urban driving. Implications and limitations of the findings are discussed.« less

Fuel consumption for various driving styles in conventional and hybrid electric vehicles: Integrating driving cycle predictions with fuel consumption optimization

DOE Office of Scientific and Technical Information (OSTI.GOV)

Rios-Torres, Jackeline; Liu, Jun; Khattak, Asad

Here, improving fuel economy and lowering emissions are key societal goals. Standard driving cycles, pre-designed by the US Environmental Protection Agency (EPA), have long been used to estimate vehicle fuel economy in laboratory-controlled conditions. They have also been used to test and tune different energy management strategies for hybrid electric vehicles (HEVs). This paper aims to estimate fuel consumption for a conventional vehicle and a HEV using personalized driving cycles extracted from real-world data to study the effects of different driving styles and vehicle types on fuel consumption when compared to the estimates based on standard driving cycles. To domore » this, we extracted driving cycles for conventional vehicles and HEVs from a large-scale U.S. survey that contains real-world GPS-based driving records. Next, the driving cycles were assigned to one of three categories: volatile, normal, or calm. Then, the driving cycles were used along with a driver-vehicle simulation that captures driver decisions (vehicle speed during a trip), powertrain, and vehicle dynamics to estimate fuel consumption for conventional vehicles and HEVs with power-split powertrain. To further optimize fuel consumption for HEVs, the Equivalent Consumption Minimization Strategy (ECMS) is applied. The results show that depending on the driving style and the driving scenario, conventional vehicle fuel consumption can vary widely compared with standard EPA driving cycles. Specifically, conventional vehicle fuel consumption was 13% lower in calm urban driving, but almost 34% higher for volatile highway driving compared with standard EPA driving cycles. Interestingly, when a driving cycle is predicted based on the application of case-based reasoning and used to tune the power distribution in a hybrid electric vehicle, its fuel consumption can be reduced by up to 12% in urban driving. Implications and limitations of the findings are discussed.« less

TAS::89 0927::TAS RECOVERY - The Lean Green Energy Controller Machine

DOE Office of Scientific and Technical Information (OSTI.GOV)

Teeter, John; Wang, Gene; Moss, David

Achieving efficiency improvements and providing demand-response programs have been identified as key elements of our national energy initiative. The residential market is the largest, yet most difficult, segment to engage in efforts to meet these objectives. This project developed Energy Management System that engages the consumer and enables Smart Grid services, applications, and business processes to address this need. Our innovative solution provides smart controller providing dynamic optimization of energy consumption for the residential energy consumer. Our solution extends the technical platform to include a cloud based Internet of Things (IoT) aggregation of data sensors and actuators the go beyondmore » energy management and extend to life style services provided through compelling mobile and console based user experiences.« less

Nozzle Mounting Method Optimization Based on Robot Kinematic Analysis

NASA Astrophysics Data System (ADS)

Chen, Chaoyue; Liao, Hanlin; Montavon, Ghislain; Deng, Sihao

2016-08-01

Nowadays, the application of industrial robots in thermal spray is gaining more and more importance. A desired coating quality depends on factors such as a balanced robot performance, a uniform scanning trajectory and stable parameters (e.g. nozzle speed, scanning step, spray angle, standoff distance). These factors also affect the mass and heat transfer as well as the coating formation. Thus, the kinematic optimization of all these aspects plays a key role in order to obtain an optimal coating quality. In this study, the robot performance was optimized from the aspect of nozzle mounting on the robot. An optimized nozzle mounting for a type F4 nozzle was designed, based on the conventional mounting method from the point of view of robot kinematics validated on a virtual robot. Robot kinematic parameters were obtained from the simulation by offline programming software and analyzed by statistical methods. The energy consumptions of different nozzle mounting methods were also compared. The results showed that it was possible to reasonably assign the amount of robot motion to each axis during the process, so achieving a constant nozzle speed. Thus, it is possible optimize robot performance and to economize robot energy.

A "healthy diet-optimal sleep" lifestyle pattern is inversely associated with liver stiffness and insulin resistance in patients with nonalcoholic fatty liver disease.

PubMed

Katsagoni, Christina N; Papatheodoridis, George V; Papageorgiou, Maria-Vasiliki; Ioannidou, Panagiota; Deutsch, Melanie; Alexopoulou, Alexandra; Papadopoulos, Nikolaos; Fragopoulou, Elisabeth; Kontogianni, Meropi D

2017-03-01

Several lifestyle habits have been described as risk factors for nonalcoholic fatty liver disease (NAFLD). Given that both healthy and unhealthy habits tend to cluster, the aim of this study was to identify lifestyle patterns and explore their potential associations with clinical characteristics of individuals with NAFLD. One hundred and thirty-six consecutive patients with ultrasound-proven NAFLD were included. Diet and physical activity level were assessed through appropriate questionnaires. Habitual night sleep hours and duration of midday naps were recorded. Optimal sleep duration was defined as sleep hours ≥ 7 and ≤ 9 h/day. Lifestyle patterns were identified using principal component analysis. Eight components were derived explaining 67% of total variation of lifestyle characteristics. Lifestyle pattern 3, namely high consumption of low-fat dairy products, vegetables, fish, and optimal sleep duration was negatively associated with insulin resistance (β = -1.66, P = 0.008) and liver stiffness (β = -1.62, P = 0.05) after controlling for age, sex, body mass index, energy intake, smoking habits, adiponectin, and tumor necrosis factor-α. Lifestyle pattern 1, namely high consumption of full-fat dairy products, refined cereals, potatoes, red meat, and high television viewing time was positively associated with insulin resistance (β = 1.66, P = 0.005), although this association was weakened after adjusting for adiponectin and tumor necrosis factor-α. A "healthy diet-optimal sleep" lifestyle pattern was beneficially associated with insulin resistance and liver stiffness in NAFLD patients independent of body weight status and energy intake.

Optimization of electrocoagulation (EC) process for the purification of a real industrial wastewater from toxic metals.

PubMed

Gatsios, Evangelos; Hahladakis, John N; Gidarakos, Evangelos

2015-05-01

In the present work, the efficiency evaluation of electrocoagulation (EC) in removing toxic metals from a real industrial wastewater, collected from Aspropyrgos, Athens, Greece was investigated. Manganese (Mn), copper (Cu) and zinc (Zn) at respective concentrations of 5 mg/L, 5 mg/L and 10 mg/L were present in the wastewater (pH=6), originated from the wastes produced by EBO-PYRKAL munitions industry and Hellenic Petroleum Elefsis Refineries. The effect of operational parameters such as electrode combination and distance, applied current, initial pH and initial metal concentration, was studied. The results indicated that Cu and Zn were totally removed in all experiments, while Mn exhibited equally high removal percentages (approximately 90%). Decreasing the initial pH and increasing the distance between electrodes, resulted in a negative effect on the efficiency and energy consumption of the process. On the other hand, increasing the applied current, favored metal removal but resulted in a power consumption increase. Different initial concentrations did not affect metal removal efficiency. The optimal results, regarding both cost and EC efficiency, were obtained with a combination of iron electrodes, at 2 cm distance, at initial current of 0.1 A and pH=6. After 90 min of treatment, maximum removal percentages obtained were 89% for Mn, 100% for Cu and 100% for Zn, at an energy consumption of 2.55 kWh/m(3). Copyright © 2015 Elsevier Ltd. All rights reserved.

Optimal Operation of Data Centers in Future Smart Grid

NASA Astrophysics Data System (ADS)

Ghamkhari, Seyed Mahdi

The emergence of cloud computing has established a growing trend towards building massive, energy-hungry, and geographically distributed data centers. Due to their enormous energy consumption, data centers are expected to have major impact on the electric grid by significantly increasing the load at locations where they are built. However, data centers also provide opportunities to help the grid with respect to robustness and load balancing. For instance, as data centers are major and yet flexible electric loads, they can be proper candidates to offer ancillary services, such as voluntary load reduction, to the smart grid. Also, data centers may better stabilize the price of energy in the electricity markets, and at the same time reduce their electricity cost by exploiting the diversity in the price of electricity in the day-ahead and real-time electricity markets. In this thesis, such potentials are investigated within an analytical profit maximization framework by developing new mathematical models based on queuing theory. The proposed models capture the trade-off between quality-of-service and power consumption in data centers. They are not only accurate, but also they posses convexity characteristics that facilitate joint optimization of data centers' service rates, demand levels and demand bids to different electricity markets. The analysis is further expanded to also develop a unified comprehensive energy portfolio optimization for data centers in the future smart grid. Specifically, it is shown how utilizing one energy option may affect selecting other energy options that are available to a data center. For example, we will show that the use of on-site storage and the deployment of geographical workload distribution can particularly help data centers in utilizing high-risk energy options such as renewable generation. The analytical approach in this thesis takes into account service-level-agreements, risk management constraints, and also the statistical characteristics of the Internet workload and the electricity prices. Using empirical data, the performance of our proposed profit maximization models for data centers are evaluated, and the capability of data centers to benefit from participation in a variety of Demand Response programs is assessed.

Methods for Analysis of Urban Energy Systems: A New York City Case Study

NASA Astrophysics Data System (ADS)

Howard, Bianca

This dissertation describes methods developed for analysis of the New York City energy system. The analysis specifically aims to consider the built environment and its' impacts on greenhouse gas (GHG) emissions. Several contributions to the urban energy systems literature were made. First, estimates of annual energy intensities of the New York building stock were derived using a statistical analysis that leveraged energy consumption and tax assessor data collected by the Office of the Mayor. These estimates provided the basis for an assessment of the spatial distribution of building energy consumption. The energy consumption estimates were then leveraged to estimate the potential for combined heat and power (CHP) systems in New York City at both the building and microgrid scales. In aggregate, given the 2009 non-baseload GHG emissions factors for electricity production, these systems could reduce citywide GHG emissions by 10%. The operational characteristics of CHP systems were explored further considering different prime movers, climates, and GHG emissions factors. A combination of mixed integer linear programing and controlled random search algorithms were the methods used to determine the optimal capacity and operating strategies for the CHP systems under the various scenarios. Lastly a multi-regional unit commitment model of electricity and GHG emissions production for New York State was developed using data collected from several publicly available sources. The model was used to estimate average and marginal GHG emissions factors for New York State and New York City. The analysis found that marginal GHG emissions factors could reduce by 30% to 370 g CO2e/kWh in the next 10 years.

Optimizing energy for a 'green' vaccine supply chain.

PubMed

Lloyd, John; McCarney, Steve; Ouhichi, Ramzi; Lydon, Patrick; Zaffran, Michel

2015-02-11

This paper describes an approach piloted in the Kasserine region of Tunisia to increase the energy efficiency of the distribution of vaccines and temperature sensitive drugs. The objectives of an approach, known as the 'net zero energy' (NZE) supply chain were demonstrated within the first year of operation. The existing distribution system was modified to store vaccines and medicines in the same buildings and to transport them according to pre-scheduled and optimized delivery circuits. Electric utility vehicles, dedicated to the integrated delivery of vaccines and medicines, improved the regularity and reliability of the supply chains. Solar energy, linked to the electricity grid at regional and district stores, supplied over 100% of consumption meeting all energy needs for storage, cooling and transportation. Significant benefits to the quality and costs of distribution were demonstrated. Supply trips were scheduled, integrated and reliable, energy consumption was reduced, the recurrent cost of electricity was eliminated and the release of carbon to the atmosphere was reduced. Although the initial capital cost of scaling up implementation of NZE remain high today, commercial forecasts predict cost reduction for solar energy and electric vehicles that may permit a step-wise implementation over the next 7-10 years. Efficiency in the use of energy and in the deployment of transport is already a critical component of distribution logistics in both private and public sectors of industrialized countries. The NZE approach has an intensified rationale in countries where energy costs threaten the maintenance of public health services in areas of low population density. In these countries where the mobility of health personnel and timely arrival of supplies is at risk, NZE has the potential to reduce energy costs and release recurrent budget to other needs of service delivery while also improving the supply chain. Copyright © 2014 The Authors. Published by Elsevier Ltd.. All rights reserved.

Assessment on the Benefits from Energy Structure Optimization and Coal-fired Emission Control in Beijing: 1998-2013

NASA Astrophysics Data System (ADS)

Zong, Y.; He, K.; Zhang, Q.; Hong, C.

2016-12-01

Coal has long been an important energy type of Beijing's energy consumption. Since 1998, to improve urban air quality, Beijing has vigorously promoted the structure optimization of energy consumption. Primary measures included the implementation of strict emission standards for coal-fired power plant boilers, subsidized replacement and after-treatment retrofit of coal-fired boilers, the mandatory application of low-sulfur coal, and the accelerated use of natural gas, imported electricity and other clean energy. This work attempts to assess the emission reduction benefits on measures of three sectors, including replacing with clean energy and application of end-of-pipe control technologies in power plants, comprehensive control on coal-fired boilers and residential heating renovation. This study employs the model of Multi-resolution Emission Inventory for China (MEIC) to quantify emission reductions from upfront measures. These control measures have effectively reduced local emissions of major air pollutants in Beijing. The total emissions of PM2.5, PM10, SO2 and NOX from power plants in Beijing are estimated to have reduced 14.5 kt, 23.7 kt, 45.0 kt and 7.6 kt from 1998 to 2013, representing reductions of 86%, 87%, 85% and 16%, respectively. Totally, 14.3 kt, 24.0 kt, 136 kt and 48.7kt of PM2.5, PM10, SO2 and NOX emissions have been mitigated due to the comprehensive control measures on coal-fired boilers from 1998 to 2013. Residential heating renovation projects by replacing coal with electricity in Beijing's conventional old house areas contribute to emission reductions of 630 t, 870 t, 2070 t and 790 t for PM2.5, PM10, SO2 and NOX, respectively.

Channel and Timeslot Co-Scheduling with Minimal Channel Switching for Data Aggregation in MWSNs

PubMed Central

Yeoum, Sanggil; Kang, Byungseok; Lee, Jinkyu; Choo, Hyunseung

2017-01-01

Collision-free transmission and efficient data transfer between nodes can be achieved through a set of channels in multichannel wireless sensor networks (MWSNs). While using multiple channels, we have to carefully consider channel interference, channel and time slot (resources) optimization, channel switching delay, and energy consumption. Since sensor nodes operate on low battery power, the energy consumed in channel switching becomes an important challenge. In this paper, we propose channel and time slot scheduling for minimal channel switching in MWSNs, while achieving efficient and collision-free transmission between nodes. The proposed scheme constructs a duty-cycled tree while reducing the amount of channel switching. As a next step, collision-free time slots are assigned to every node based on the minimal data collection delay. The experimental results demonstrate that the validity of our scheme reduces the amount of channel switching by 17.5%, reduces energy consumption for channel switching by 28%, and reduces the schedule length by 46%, as compared to the existing schemes. PMID:28471416

Bringing Solid-State Magnetocaloric Cooling to the Market: A Commercialization Plan

DOE Office of Scientific and Technical Information (OSTI.GOV)

Abu-Heiba, Ahmad; Sikes, Karen; Blackburn, Julia

Air conditioning has become a staple in American life, bringing improved health, productivity, and comfort to 93% of single-family homes as of 2015, compared to only 76% in 1990. This rise in demand has contributed to the 2.51 quads (12.5%) of total annual energy consumption in residential buildings attributable to space cooling (U.S. Energy Information Administration 2017). Accompanying this upward trend in space cooling has been increased refrigerant use, which has historically contributed to ozone depletion, global warming, or both. The Oak Ridge National Laboratory – along with German-based partner Vacuumschmelze GmbH & Co. KG – is working to reducemore » energy consumption and refrigerant use through the development of a next-generation, solid-state magnetocaloric cooling system. The purpose of this study is to investigate market potential of these systems in the United States, including information on the industry landscape, market share and unit shipment projections, optimal price points, and barriers to market entry.« less

State space model approach for forecasting the use of electrical energy (a case study on: PT. PLN (Persero) district of Kroya)

NASA Astrophysics Data System (ADS)

Kurniati, Devi; Hoyyi, Abdul; Widiharih, Tatik

2018-05-01

Time series data is a series of data taken or measured based on observations at the same time interval. Time series data analysis is used to perform data analysis considering the effect of time. The purpose of time series analysis is to know the characteristics and patterns of a data and predict a data value in some future period based on data in the past. One of the forecasting methods used for time series data is the state space model. This study discusses the modeling and forecasting of electric energy consumption using the state space model for univariate data. The modeling stage is began with optimal Autoregressive (AR) order selection, determination of state vector through canonical correlation analysis, estimation of parameter, and forecasting. The result of this research shows that modeling of electric energy consumption using state space model of order 4 with Mean Absolute Percentage Error (MAPE) value 3.655%, so the model is very good forecasting category.

Channel and Timeslot Co-Scheduling with Minimal Channel Switching for Data Aggregation in MWSNs.

PubMed

Yeoum, Sanggil; Kang, Byungseok; Lee, Jinkyu; Choo, Hyunseung

2017-05-04

Collision-free transmission and efficient data transfer between nodes can be achieved through a set of channels in multichannel wireless sensor networks (MWSNs). While using multiple channels, we have to carefully consider channel interference, channel and time slot (resources) optimization, channel switching delay, and energy consumption. Since sensor nodes operate on low battery power, the energy consumed in channel switching becomes an important challenge. In this paper, we propose channel and time slot scheduling for minimal channel switching in MWSNs, while achieving efficient and collision-free transmission between nodes. The proposed scheme constructs a duty-cycled tree while reducing the amount of channel switching. As a next step, collision-free time slots are assigned to every node based on the minimal data collection delay. The experimental results demonstrate that the validity of our scheme reduces the amount of channel switching by 17.5%, reduces energy consumption for channel switching by 28%, and reduces the schedule length by 46%, as compared to the existing schemes.

Development of Energy Models for Production Systems and Processes to Inform Environmentally Benign Decision-Making

NASA Astrophysics Data System (ADS)

Diaz-Elsayed, Nancy

Between 2008 and 2035 global energy demand is expected to grow by 53%. While most industry-level analyses of manufacturing in the United States (U.S.) have traditionally focused on high energy consumers such as the petroleum, chemical, paper, primary metal, and food sectors, the remaining sectors account for the majority of establishments in the U.S. Specifically, of the establishments participating in the Energy Information Administration's Manufacturing Energy Consumption Survey in 2006, the non-energy intensive" sectors still consumed 4*109 GJ of energy, i.e., one-quarter of the energy consumed by the manufacturing sectors, which is enough to power 98 million homes for a year. The increasing use of renewable energy sources and the introduction of energy-efficient technologies in manufacturing operations support the advancement towards a cleaner future, but having a good understanding of how the systems and processes function can reduce the environmental burden even further. To facilitate this, methods are developed to model the energy of manufacturing across three hierarchical levels: production equipment, factory operations, and industry; these methods are used to accurately assess the current state and provide effective recommendations to further reduce energy consumption. First, the energy consumption of production equipment is characterized to provide machine operators and product designers with viable methods to estimate the environmental impact of the manufacturing phase of a product. The energy model of production equipment is tested and found to have an average accuracy of 97% for a product requiring machining with a variable material removal rate profile. However, changing the use of production equipment alone will not result in an optimal solution since machines are part of a larger system. Which machines to use, how to schedule production runs while accounting for idle time, the design of the factory layout to facilitate production, and even the machining parameters --- these decisions affect how much energy is utilized during production. Therefore, at the facility level a methodology is presented for implementing priority queuing while accounting for a high product mix in a discrete event simulation environment. A baseline case is presented and alternative factory designs are suggested, which lead to energy savings of approximately 9%. At the industry level, the majority of energy consumption for manufacturing facilities is utilized for machine drive, process heating, and HVAC. Numerous studies have characterized the energy of manufacturing processes and HVAC equipment, but energy data is often limited for a facility in its entirety since manufacturing companies often lack the appropriate sensors to track it and are hesitant to release this information for confidentiality purposes. Without detailed information about the use of energy in manufacturing sites, the scope of factory studies cannot be adequately defined. Therefore, the breakdown of energy consumption of sectors with discrete production is presented, as well as a case study assessing the electrical energy consumption, greenhouse gas emissions, their associated costs, and labor costs for selected sites in the United States, Japan, Germany, China, and India. By presenting energy models and assessments of production equipment, factory operations, and industry, this dissertation provides a comprehensive assessment of energy trends in manufacturing and recommends methods that can be used beyond these case studies and industries to reduce consumption and contribute to an energy-efficient future.

Optimal Decision Model for Sustainable Hospital Building Renovation—A Case Study of a Vacant School Building Converting into a Community Public Hospital

PubMed Central

Juan, Yi-Kai; Cheng, Yu-Ching; Perng, Yeng-Horng; Castro-Lacouture, Daniel

2016-01-01

Much attention has been paid to hospitals environments since modern pandemics have emerged. The building sector is considered to be the largest world energy consumer, so many global organizations are attempting to create a sustainable environment in building construction by reducing energy consumption. Therefore, maintaining high standards of hygiene while reducing energy consumption has become a major task for hospitals. This study develops a decision model based on genetic algorithms and A* graph search algorithms to evaluate existing hospital environmental conditions and to recommend an optimal scheme of sustainable renovation strategies, considering trade-offs among minimal renovation cost, maximum quality improvement, and low environmental impact. Reusing vacant buildings is a global and sustainable trend. In Taiwan, for example, more and more school space will be unoccupied due to a rapidly declining birth rate. Integrating medical care with local community elder-care efforts becomes important because of the aging population. This research introduces a model that converts a simulated vacant school building into a community public hospital renovation project in order to validate the solutions made by hospital managers and suggested by the system. The result reveals that the system performs well and its solutions are more successful than the actions undertaken by decision-makers. This system can improve traditional hospital building condition assessment while making it more effective and efficient. PMID:27347986

Optimal Decision Model for Sustainable Hospital Building Renovation-A Case Study of a Vacant School Building Converting into a Community Public Hospital.

PubMed

Juan, Yi-Kai; Cheng, Yu-Ching; Perng, Yeng-Horng; Castro-Lacouture, Daniel

2016-06-24

Much attention has been paid to hospitals environments since modern pandemics have emerged. The building sector is considered to be the largest world energy consumer, so many global organizations are attempting to create a sustainable environment in building construction by reducing energy consumption. Therefore, maintaining high standards of hygiene while reducing energy consumption has become a major task for hospitals. This study develops a decision model based on genetic algorithms and A* graph search algorithms to evaluate existing hospital environmental conditions and to recommend an optimal scheme of sustainable renovation strategies, considering trade-offs among minimal renovation cost, maximum quality improvement, and low environmental impact. Reusing vacant buildings is a global and sustainable trend. In Taiwan, for example, more and more school space will be unoccupied due to a rapidly declining birth rate. Integrating medical care with local community elder-care efforts becomes important because of the aging population. This research introduces a model that converts a simulated vacant school building into a community public hospital renovation project in order to validate the solutions made by hospital managers and suggested by the system. The result reveals that the system performs well and its solutions are more successful than the actions undertaken by decision-makers. This system can improve traditional hospital building condition assessment while making it more effective and efficient.

Climate Impacts on Extreme Energy Consumption of Different Types of Buildings

PubMed Central

Li, Mingcai; Shi, Jun; Guo, Jun; Cao, Jingfu; Niu, Jide; Xiong, Mingming

2015-01-01

Exploring changes of building energy consumption and its relationships with climate can provide basis for energy-saving and carbon emission reduction. Heating and cooling energy consumption of different types of buildings during 1981-2010 in Tianjin city, was simulated by using TRNSYS software. Daily or hourly extreme energy consumption was determined by percentile methods, and the climate impact on extreme energy consumption was analyzed. The results showed that days of extreme heating consumption showed apparent decrease during the recent 30 years for residential and large venue buildings, whereas days of extreme cooling consumption increased in large venue building. No significant variations were found for the days of extreme energy consumption for commercial building, although a decreasing trend in extreme heating energy consumption. Daily extreme energy consumption for large venue building had no relationship with climate parameters, whereas extreme energy consumption for commercial and residential buildings was related to various climate parameters. Further multiple regression analysis suggested heating energy consumption for commercial building was affected by maximum temperature, dry bulb temperature, solar radiation and minimum temperature, which together can explain 71.5 % of the variation of the daily extreme heating energy consumption. The daily extreme cooling energy consumption for commercial building was only related to the wet bulb temperature (R2= 0.382). The daily extreme heating energy consumption for residential building was affected by 4 climate parameters, but the dry bulb temperature had the main impact. The impacts of climate on hourly extreme heating energy consumption has a 1-3 hour delay in all three types of buildings, but no delay was found in the impacts of climate on hourly extreme cooling energy consumption for the selected buildings. PMID:25923205

Climate impacts on extreme energy consumption of different types of buildings.

PubMed

Li, Mingcai; Shi, Jun; Guo, Jun; Cao, Jingfu; Niu, Jide; Xiong, Mingming

2015-01-01

Exploring changes of building energy consumption and its relationships with climate can provide basis for energy-saving and carbon emission reduction. Heating and cooling energy consumption of different types of buildings during 1981-2010 in Tianjin city, was simulated by using TRNSYS software. Daily or hourly extreme energy consumption was determined by percentile methods, and the climate impact on extreme energy consumption was analyzed. The results showed that days of extreme heating consumption showed apparent decrease during the recent 30 years for residential and large venue buildings, whereas days of extreme cooling consumption increased in large venue building. No significant variations were found for the days of extreme energy consumption for commercial building, although a decreasing trend in extreme heating energy consumption. Daily extreme energy consumption for large venue building had no relationship with climate parameters, whereas extreme energy consumption for commercial and residential buildings was related to various climate parameters. Further multiple regression analysis suggested heating energy consumption for commercial building was affected by maximum temperature, dry bulb temperature, solar radiation and minimum temperature, which together can explain 71.5 % of the variation of the daily extreme heating energy consumption. The daily extreme cooling energy consumption for commercial building was only related to the wet bulb temperature (R2= 0.382). The daily extreme heating energy consumption for residential building was affected by 4 climate parameters, but the dry bulb temperature had the main impact. The impacts of climate on hourly extreme heating energy consumption has a 1-3 hour delay in all three types of buildings, but no delay was found in the impacts of climate on hourly extreme cooling energy consumption for the selected buildings.

Robust Dynamic Multi-objective Vehicle Routing Optimization Method.

PubMed

Guo, Yi-Nan; Cheng, Jian; Luo, Sha; Gong, Dun-Wei

2017-03-21

For dynamic multi-objective vehicle routing problems, the waiting time of vehicle, the number of serving vehicles, the total distance of routes were normally considered as the optimization objectives. Except for above objectives, fuel consumption that leads to the environmental pollution and energy consumption was focused on in this paper. Considering the vehicles' load and the driving distance, corresponding carbon emission model was built and set as an optimization objective. Dynamic multi-objective vehicle routing problems with hard time windows and randomly appeared dynamic customers, subsequently, were modeled. In existing planning methods, when the new service demand came up, global vehicle routing optimization method was triggered to find the optimal routes for non-served customers, which was time-consuming. Therefore, robust dynamic multi-objective vehicle routing method with two-phase is proposed. Three highlights of the novel method are: (i) After finding optimal robust virtual routes for all customers by adopting multi-objective particle swarm optimization in the first phase, static vehicle routes for static customers are formed by removing all dynamic customers from robust virtual routes in next phase. (ii)The dynamically appeared customers append to be served according to their service time and the vehicles' statues. Global vehicle routing optimization is triggered only when no suitable locations can be found for dynamic customers. (iii)A metric measuring the algorithms' robustness is given. The statistical results indicated that the routes obtained by the proposed method have better stability and robustness, but may be sub-optimum. Moreover, time-consuming global vehicle routing optimization is avoided as dynamic customers appear.

An optimal consumption and investment problem with quadratic utility and negative wealth constraints.

PubMed

Roh, Kum-Hwan; Kim, Ji Yeoun; Shin, Yong Hyun

2017-01-01

In this paper, we investigate the optimal consumption and portfolio selection problem with negative wealth constraints for an economic agent who has a quadratic utility function of consumption and receives a constant labor income. Due to the property of the quadratic utility function, we separate our problem into two cases and derive the closed-form solutions for each case. We also illustrate some numerical implications of the optimal consumption and portfolio.

Analysis and Optimization of Pulse Dynamics for Magnetic Stimulation

PubMed Central

Goetz, Stefan M.; Truong, Cong Nam; Gerhofer, Manuel G.; Peterchev, Angel V.; Herzog, Hans-Georg; Weyh, Thomas

2013-01-01

Magnetic stimulation is a standard tool in brain research and has found important clinical applications in neurology, psychiatry, and rehabilitation. Whereas coil designs and the spatial field properties have been intensively studied in the literature, the temporal dynamics of the field has received less attention. Typically, the magnetic field waveform is determined by available device circuit topologies rather than by consideration of what is optimal for neural stimulation. This paper analyzes and optimizes the waveform dynamics using a nonlinear model of a mammalian axon. The optimization objective was to minimize the pulse energy loss. The energy loss drives power consumption and heating, which are the dominating limitations of magnetic stimulation. The optimization approach is based on a hybrid global-local method. Different coordinate systems for describing the continuous waveforms in a limited parameter space are defined for numerical stability. The optimization results suggest that there are waveforms with substantially higher efficiency than that of traditional pulse shapes. One class of optimal pulses is analyzed further. Although the coil voltage profile of these waveforms is almost rectangular, the corresponding current shape presents distinctive characteristics, such as a slow low-amplitude first phase which precedes the main pulse and reduces the losses. Representatives of this class of waveforms corresponding to different maximum voltages are linked by a nonlinear transformation. The main phase, however, scales with time only. As with conventional magnetic stimulation pulses, briefer pulses result in lower energy loss but require higher coil voltage than longer pulses. PMID:23469168

Compressed Air System Optimization: Case Study Food Industry in Indonesia

NASA Astrophysics Data System (ADS)

Widayati, Endang; Nuzahar, Hasril

2016-01-01

Compressors and compressed air systems was one of the most important utilities in industries or factories. Approximately 10% of the cost of electricity in the industry was used to produce compressed air. Therefore the potential for energy savings in the compressors and compressed air systems had a big challenge. This field was conducted especially in Indonesia food industry or factory. Compressed air system optimization was a technique approach to determine the optimal conditions for the operation of compressors and compressed air systems that included evaluation of the energy needs, supply adjustment, eliminating or reconfiguring the use and operation of inefficient, changing and complementing some equipment and improving operating efficiencies. This technique gave the significant impact for energy saving and costs. The potential savings based on this study through measurement and optimization e.g. system that lowers the pressure of 7.5 barg to 6.8 barg would reduce energy consumption and running costs approximately 4.2%, switch off the compressor GA110 and GA75 was obtained annual savings of USD 52,947 ≈ 455 714 kWh, running GA75 light load or unloaded then obtained annual savings of USD 31,841≈ 270,685 kWh, install new compressor 2x132 kW and 1x 132 kW VSD obtained annual savings of USD 108,325≈ 928,500 kWh. Furthermore it was needed to conduct study of technical aspect of energy saving potential (Investment Grade Audit) and performed Cost Benefit Analysis. This study was one of best practice solutions how to save energy and improve energy performance in compressors and compressed air system.

Chronobiology and nutrition.

PubMed

Tahara, Y; Shibata, S

2013-12-03

Numerous long-term studies have investigated the circadian clock system in mammals, which organizes physiological functions, including metabolism, digestion, and absorption of food, and energy expenditure. Food or nutrition can be a synchronizer for the circadian clock systems, as potent as the external light-dark signal can be. Recent studies have investigated different kinds of food, frequency of consumption, and time of consumption for optimizing body clock and ensuring healthy habits. In this review, we discuss recent studies investigating chronobiology and nutrition, and then summarize available information as "Chrono-nutrition" for the development of a new standardized research strategy. Copyright © 2013 IBRO. Published by Elsevier Ltd. All rights reserved.

Scheduling for energy and reliability management on multiprocessor real-time systems

NASA Astrophysics Data System (ADS)

Qi, Xuan

Scheduling algorithms for multiprocessor real-time systems have been studied for years with many well-recognized algorithms proposed. However, it is still an evolving research area and many problems remain open due to their intrinsic complexities. With the emergence of multicore processors, it is necessary to re-investigate the scheduling problems and design/develop efficient algorithms for better system utilization, low scheduling overhead, high energy efficiency, and better system reliability. Focusing cluster schedulings with optimal global schedulers, we study the utilization bound and scheduling overhead for a class of cluster-optimal schedulers. Then, taking energy/power consumption into consideration, we developed energy-efficient scheduling algorithms for real-time systems, especially for the proliferating embedded systems with limited energy budget. As the commonly deployed energy-saving technique (e.g. dynamic voltage frequency scaling (DVFS)) will significantly affect system reliability, we study schedulers that have intelligent mechanisms to recuperate system reliability to satisfy the quality assurance requirements. Extensive simulation is conducted to evaluate the performance of the proposed algorithms on reduction of scheduling overhead, energy saving, and reliability improvement. The simulation results show that the proposed reliability-aware power management schemes could preserve the system reliability while still achieving substantial energy saving.

A micropower electrocardiogram amplifier.

PubMed

Fay, L; Misra, V; Sarpeshkar, R

2009-10-01

We introduce an electrocardiogram (EKG) preamplifier with a power consumption of 2.8 muW, 8.1 muVrms input-referred noise, and a common-mode rejection ratio of 90 dB. Compared to previously reported work, this amplifier represents a significant reduction in power with little compromise in signal quality. The improvement in performance may be attributed to many optimizations throughout the design including the use of subthreshold transistor operation to improve noise efficiency, gain-setting capacitors versus resistors, half-rail operation wherever possible, optimal power allocations among amplifier blocks, and the sizing of devices to improve matching and reduce noise. We envision that the micropower amplifier can be used as part of a wireless EKG monitoring system powered by rectified radio-frequency energy or other forms of energy harvesting like body vibration and body heat.

Online energy management strategy of fuel cell hybrid electric vehicles based on data fusion approach

NASA Astrophysics Data System (ADS)

Zhou, Daming; Al-Durra, Ahmed; Gao, Fei; Ravey, Alexandre; Matraji, Imad; Godoy Simões, Marcelo

2017-10-01

Energy management strategy plays a key role for Fuel Cell Hybrid Electric Vehicles (FCHEVs), it directly affects the efficiency and performance of energy storages in FCHEVs. For example, by using a suitable energy distribution controller, the fuel cell system can be maintained in a high efficiency region and thus saving hydrogen consumption. In this paper, an energy management strategy for online driving cycles is proposed based on a combination of the parameters from three offline optimized fuzzy logic controllers using data fusion approach. The fuzzy logic controllers are respectively optimized for three typical driving scenarios: highway, suburban and city in offline. To classify patterns of online driving cycles, a Probabilistic Support Vector Machine (PSVM) is used to provide probabilistic classification results. Based on the classification results of the online driving cycle, the parameters of each offline optimized fuzzy logic controllers are then fused using Dempster-Shafer (DS) evidence theory, in order to calculate the final parameters for the online fuzzy logic controller. Three experimental validations using Hardware-In-the-Loop (HIL) platform with different-sized FCHEVs have been performed. Experimental comparison results show that, the proposed PSVM-DS based online controller can achieve a relatively stable operation and a higher efficiency of fuel cell system in real driving cycles.

Steam distribution and energy delivery optimization using wireless sensors

NASA Astrophysics Data System (ADS)

Olama, Mohammed M.; Allgood, Glenn O.; Kuruganti, Teja P.; Sukumar, Sreenivas R.; Djouadi, Seddik M.; Lake, Joe E.

2011-05-01

The Extreme Measurement Communications Center at Oak Ridge National Laboratory (ORNL) explores the deployment of a wireless sensor system with a real-time measurement-based energy efficiency optimization framework in the ORNL campus. With particular focus on the 12-mile long steam distribution network in our campus, we propose an integrated system-level approach to optimize the energy delivery within the steam distribution system. We address the goal of achieving significant energy-saving in steam lines by monitoring and acting on leaking steam valves/traps. Our approach leverages an integrated wireless sensor and real-time monitoring capabilities. We make assessments on the real-time status of the distribution system by mounting acoustic sensors on the steam pipes/traps/valves and observe the state measurements of these sensors. Our assessments are based on analysis of the wireless sensor measurements. We describe Fourier-spectrum based algorithms that interpret acoustic vibration sensor data to characterize flows and classify the steam system status. We are able to present the sensor readings, steam flow, steam trap status and the assessed alerts as an interactive overlay within a web-based Google Earth geographic platform that enables decision makers to take remedial action. We believe our demonstration serves as an instantiation of a platform that extends implementation to include newer modalities to manage water flow, sewage and energy consumption.