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

Genetic Algorithm Optimizes Q-LAW Control Parameters

NASA Technical Reports Server (NTRS)

Lee, Seungwon; von Allmen, Paul; Petropoulos, Anastassios; Terrile, Richard

2008-01-01

A document discusses a multi-objective, genetic algorithm designed to optimize Lyapunov feedback control law (Q-law) parameters in order to efficiently find Pareto-optimal solutions for low-thrust trajectories for electronic propulsion systems. These would be propellant-optimal solutions for a given flight time, or flight time optimal solutions for a given propellant requirement. The approximate solutions are used as good initial solutions for high-fidelity optimization tools. When the good initial solutions are used, the high-fidelity optimization tools quickly converge to a locally optimal solution near the initial solution. Q-law control parameters are represented as real-valued genes in the genetic algorithm. The performances of the Q-law control parameters are evaluated in the multi-objective space (flight time vs. propellant mass) and sorted by the non-dominated sorting method that assigns a better fitness value to the solutions that are dominated by a fewer number of other solutions. With the ranking result, the genetic algorithm encourages the solutions with higher fitness values to participate in the reproduction process, improving the solutions in the evolution process. The population of solutions converges to the Pareto front that is permitted within the Q-law control parameter space.

Changes of parameters during composting of bio-waste collected over four seasons.

PubMed

Hanc, Ales; Ochecova, Pavla; Vasak, Filip

2017-07-01

This study investigated the evolution of several main parameters during the composting of separately collected household bio-waste originating from urban settlements (U-bio-waste) and family houses (F-bio-waste) from four climate seasons. When comparing both types of composts, U-bio-waste compost contained a higher amount of nutrients, however F-bio-waste compost was characterized by greater yield, greater availability of phosphorus and magnesium, and faster stability. In terms of seasons, compost from bio-waste collected in spring contained the highest amount of nutrients, reflecting the high content of nutrients in plant feedstock. Dissolved organic carbon and pH in U- and F-bio-waste compost, respectively, frequently showed close relationships with other parameters. The seasonal variations of most of the parameters in the composts were found to be lower compared to the variations observed in the feedstocks. The greatest seasonal variation was found in nitrate nitrogen, which is the reason for the more frequent analysis of this parameter.

Numerical optimization methods for controlled systems with parameters

NASA Astrophysics Data System (ADS)

Tyatyushkin, A. I.

2017-10-01

First- and second-order numerical methods for optimizing controlled dynamical systems with parameters are discussed. In unconstrained-parameter problems, the control parameters are optimized by applying the conjugate gradient method. A more accurate numerical solution in these problems is produced by Newton's method based on a second-order functional increment formula. Next, a general optimal control problem with state constraints and parameters involved on the righthand sides of the controlled system and in the initial conditions is considered. This complicated problem is reduced to a mathematical programming one, followed by the search for optimal parameter values and control functions by applying a multimethod algorithm. The performance of the proposed technique is demonstrated by solving application problems.

Optimization of fuels from waste composition with application of genetic algorithm.

PubMed

Małgorzata, Wzorek

2014-05-01

The objective of this article is to elaborate a method to optimize the composition of the fuels from sewage sludge (PBS fuel - fuel based on sewage sludge and coal slime, PBM fuel - fuel based on sewage sludge and meat and bone meal, PBT fuel - fuel based on sewage sludge and sawdust). As a tool for an optimization procedure, the use of a genetic algorithm is proposed. The optimization task involves the maximization of mass fraction of sewage sludge in a fuel developed on the basis of quality-based criteria for the use as an alternative fuel used by the cement industry. The selection criteria of fuels composition concerned such parameters as: calorific value, content of chlorine, sulphur and heavy metals. Mathematical descriptions of fuel compositions and general forms of the genetic algorithm, as well as the obtained optimization results are presented. The results of this study indicate that the proposed genetic algorithm offers an optimization tool, which could be useful in the determination of the composition of fuels that are produced from waste.

Gaussian mass optimization for kernel PCA parameters

NASA Astrophysics Data System (ADS)

Liu, Yong; Wang, Zulin

2011-10-01

This paper proposes a novel kernel parameter optimization method based on Gaussian mass, which aims to overcome the current brute force parameter optimization method in a heuristic way. Generally speaking, the choice of kernel parameter should be tightly related to the target objects while the variance between the samples, the most commonly used kernel parameter, doesn't possess much features of the target, which gives birth to Gaussian mass. Gaussian mass defined in this paper has the property of the invariance of rotation and translation and is capable of depicting the edge, topology and shape information. Simulation results show that Gaussian mass leads a promising heuristic optimization boost up for kernel method. In MNIST handwriting database, the recognition rate improves by 1.6% compared with common kernel method without Gaussian mass optimization. Several promising other directions which Gaussian mass might help are also proposed at the end of the paper.

Optimization of Gas Metal Arc Welding Process Parameters

NASA Astrophysics Data System (ADS)

Kumar, Amit; Khurana, M. K.; Yadav, Pradeep K.

2016-09-01

This study presents the application of Taguchi method combined with grey relational analysis to optimize the process parameters of gas metal arc welding (GMAW) of AISI 1020 carbon steels for multiple quality characteristics (bead width, bead height, weld penetration and heat affected zone). An orthogonal array of L9 has been implemented to fabrication of joints. The experiments have been conducted according to the combination of voltage (V), current (A) and welding speed (Ws). The results revealed that the welding speed is most significant process parameter. By analyzing the grey relational grades, optimal parameters are obtained and significant factors are known using ANOVA analysis. The welding parameters such as speed, welding current and voltage have been optimized for material AISI 1020 using GMAW process. To fortify the robustness of experimental design, a confirmation test was performed at selected optimal process parameter setting. Observations from this method may be useful for automotive sub-assemblies, shipbuilding and vessel fabricators and operators to obtain optimal welding conditions.

Backtracking search algorithm in CVRP models for efficient solid waste collection and route optimization.

PubMed

Akhtar, Mahmuda; Hannan, M A; Begum, R A; Basri, Hassan; Scavino, Edgar

2017-03-01

Waste collection is an important part of waste management that involves different issues, including environmental, economic, and social, among others. Waste collection optimization can reduce the waste collection budget and environmental emissions by reducing the collection route distance. This paper presents a modified Backtracking Search Algorithm (BSA) in capacitated vehicle routing problem (CVRP) models with the smart bin concept to find the best optimized waste collection route solutions. The objective function minimizes the sum of the waste collection route distances. The study introduces the concept of the threshold waste level (TWL) of waste bins to reduce the number of bins to be emptied by finding an optimal range, thus minimizing the distance. A scheduling model is also introduced to compare the feasibility of the proposed model with that of the conventional collection system in terms of travel distance, collected waste, fuel consumption, fuel cost, efficiency and CO 2 emission. The optimal TWL was found to be between 70% and 75% of the fill level of waste collection nodes and had the maximum tightness value for different problem cases. The obtained results for four days show a 36.80% distance reduction for 91.40% of the total waste collection, which eventually increases the average waste collection efficiency by 36.78% and reduces the fuel consumption, fuel cost and CO 2 emission by 50%, 47.77% and 44.68%, respectively. Thus, the proposed optimization model can be considered a viable tool for optimizing waste collection routes to reduce economic costs and environmental impacts. Copyright © 2017 Elsevier Ltd. All rights reserved.

Parameter Optimization of PAL-XFEL Injector

NASA Astrophysics Data System (ADS)

Lee, Jaehyun; Ko, In Soo; Han, Jang-Hui; Hong, Juho; Yang, Haeryong; Min, Chang Ki; Kang, Heung-Sik

2018-05-01

A photoinjector is used as the electron source to generate a high peak current and low emittance beam for an X-ray free electron laser (FEL). The beam emittance is one of the critical parameters to determine the FEL performance together with the slice energy spread and the peak current. The Pohang Accelerator Laboratory X-ray Free Electron Laser (PAL-XFEL) was constructed in 2015, and the beam commissioning was carried out in spring 2016. The injector is running routinely for PAL-XFEL user operation. The operational parameters of the injector have been optimized experimentally, and these are somewhat different from the originally designed ones. Therefore, we study numerically the injector parameters based on the empirically optimized parameters and review the present operating condition.

SEMINAR PUBLICATION: OPERATIONAL PARAMETERS FOR HAZARDOUS WASTE COMBUSTION DEVICES

EPA Science Inventory

The information in the document is based on presentations at the EPA-sponsored seminar series on Operational Parameters for Hazardous Waste Combustion Devices. This series consisted of five seminars held in 1992. Hazardous waste combustion devices are regulated under the Resource...

Quantification of parameters influencing methane generation due to biodegradation of municipal solid waste in landfills and laboratory experiments.

PubMed

Fei, Xunchang; Zekkos, Dimitrios; Raskin, Lutgarde

2016-09-01

The energy conversion potential of municipal solid waste (MSW) disposed of in landfills remains largely untapped because of the slow and variable rate of biogas generation, delayed and inefficient biogas collection, leakage of biogas, and landfill practices and infrastructure that are not geared toward energy recovery. A database consisting of methane (CH4) generation data, the major constituent of biogas, from 49 laboratory experiments and field monitoring data from 57 landfills was developed. Three CH4 generation parameters, i.e., waste decay rate (k), CH4 generation potential (L0), and time until maximum CH4 generation rate (tmax), were calculated for each dataset using U.S. EPA's Landfill Gas Emission Model (LandGEM). Factors influencing the derived parameters in laboratory experiments and landfills were investigated using multi-linear regression analysis. Total weight of waste (W) was correlated with biodegradation conditions through a ranked classification scheme. k increased with increasing percentage of readily biodegradable waste (Br0 (%)) and waste temperature, and reduced with increasing W, an indicator of less favorable biodegradation conditions. The values of k obtained in the laboratory were commonly significantly higher than those in landfills and those recommended by LandGEM. The mean value of L0 was 98 and 88L CH4/kg waste for laboratory and field studies, respectively, but was significantly affected by waste composition with ranges from 10 to 300L CH4/kg. tmax increased with increasing percentage of biodegradable waste (B0) and W. The values of tmax in landfills were higher than those in laboratory experiments or those based on LandGEM's recommended parameters. Enhancing biodegradation conditions in landfill cells has a greater impact on improving k and tmax than increasing B0. Optimizing the B0 and Br0 values of landfilled waste increases L0 and reduces tmax. Copyright © 2015 Elsevier Ltd. All rights reserved.

Modeling and verification of process parameters for the production of tannase by Aspergillus oryzae under submerged fermentation using agro-wastes.

PubMed

Varadharajan, Venkatramanan; Vadivel, Sudhan Shanmuga; Ramaswamy, Arulvel; Sundharamurthy, Venkatesaprabhu; Chandrasekar, Priyadharshini

2017-01-01

Tannase production by Aspergillus oryzae using various agro-wastes as substrates by submerged fermentation was studied in this research. The microbe was isolated from degrading corn kernel obtained from the corn fields at Tiruchengode, India. The microbial identification was done using 18S rRNA gene analysis. The agro-wastes chosen for the study were pomegranate rind, Cassia auriculata flower, black gram husk, and tea dust. The process parameters chosen for optimization study were substrate concentration, pH, temperature, and incubation period. During one variable at a time optimization, the pomegranate rind extract produced maximum tannase activity of 138.12 IU/mL and it was chosen as the best substrate for further experiments. The quadratic model was found to be the effective model for prediction of tannase production by A. oryzae. The optimized conditions predicted by response surface methodology (RSM) with genetic algorithm (GA) were 1.996% substrate concentration, pH of 4.89, temperature of 34.91 °C, and an incubation time of 70.65 H with maximum tannase activity of 138.363 IU/mL. The confirmatory experiment under optimized conditions showed tannase activity of 139.22 IU/mL. Hence, RSM-GA pair was successfully used in this study to optimize the process parameters required for the production of tannase using pomegranate rind. © 2015 International Union of Biochemistry and Molecular Biology, Inc.

Chickpea seeds germination rational parameters optimization

NASA Astrophysics Data System (ADS)

Safonova, Yu A.; Ivliev, M. N.; Lemeshkin, A. V.

2018-05-01

The paper presents the influence of chickpea seeds bioactivation parameters on their enzymatic activity experimental results. Optimal bioactivation process modes were obtained by regression-factor analysis: process temperature - 13.6 °C, process duration - 71.5 h. It was found that in the germination process, the proteolytic, amylolytic and lipolytic enzymes activity increased, and the urease enzyme activity is reduced. The dependences of enzyme activity on chickpea seeds germination conditions were obtained by mathematical processing of experimental data. The calculated data are in good agreement with the experimental ones. This confirms the optimization efficiency based on experiments mathematical planning in order to determine the enzymatic activity of chickpea seeds germination optimal parameters of bioactivated seeds.

Optimization of automotive Rankine cycle waste heat recovery under various engine operating condition

NASA Astrophysics Data System (ADS)

Punov, Plamen; Milkov, Nikolay; Danel, Quentin; Perilhon, Christelle; Podevin, Pierre; Evtimov, Teodossi

2017-02-01

An optimization study of the Rankine cycle as a function of diesel engine operating mode is presented. The Rankine cycle here, is studied as a waste heat recovery system which uses the engine exhaust gases as heat source. The engine exhaust gases parameters (temperature, mass flow and composition) were defined by means of numerical simulation in advanced simulation software AVL Boost. Previously, the engine simulation model was validated and the Vibe function parameters were defined as a function of engine load. The Rankine cycle output power and efficiency was numerically estimated by means of a simulation code in Python(x,y). This code includes discretized heat exchanger model and simplified model of the pump and the expander based on their isentropic efficiency. The Rankine cycle simulation revealed the optimum value of working fluid mass flow and evaporation pressure according to the heat source. Thus, the optimal Rankine cycle performance was obtained over the engine operating map.

Capacitated vehicle-routing problem model for scheduled solid waste collection and route optimization using PSO algorithm.

PubMed

Hannan, M A; Akhtar, Mahmuda; Begum, R A; Basri, H; Hussain, A; Scavino, Edgar

2018-01-01

Waste collection widely depends on the route optimization problem that involves a large amount of expenditure in terms of capital, labor, and variable operational costs. Thus, the more waste collection route is optimized, the more reduction in different costs and environmental effect will be. This study proposes a modified particle swarm optimization (PSO) algorithm in a capacitated vehicle-routing problem (CVRP) model to determine the best waste collection and route optimization solutions. In this study, threshold waste level (TWL) and scheduling concepts are applied in the PSO-based CVRP model under different datasets. The obtained results from different datasets show that the proposed algorithmic CVRP model provides the best waste collection and route optimization in terms of travel distance, total waste, waste collection efficiency, and tightness at 70-75% of TWL. The obtained results for 1 week scheduling show that 70% of TWL performs better than all node consideration in terms of collected waste, distance, tightness, efficiency, fuel consumption, and cost. The proposed optimized model can serve as a valuable tool for waste collection and route optimization toward reducing socioeconomic and environmental impacts. Copyright © 2017 Elsevier Ltd. All rights reserved.

Determination of service standard time for liquid waste parameter in certification institution

NASA Astrophysics Data System (ADS)

Sembiring, M. T.; Kusumawaty, D.

2018-02-01

Baristand Industry Medan is a technical implementation unit under the Industrial and Research and Development Agency, the Ministry of Industry. One of the services often used in Baristand Industry Medan is liquid waste testing service. The company set the standard of service 9 working days for testing services. At 2015, 89.66% on testing services liquid waste does not meet the specified standard of services company. The purpose of this research is to specify the standard time of each parameter in testing services liquid waste. The method used is the stopwatch time study. There are 45 test parameters in liquid waste laboratory. The measurement of the time done 4 samples per test parameters using the stopwatch. From the measurement results obtained standard time that the standard Minimum Service test of liquid waste is 13 working days if there is testing E. coli.

A Modified Penalty Parameter Approach for Optimal Estimation of UH with Simultaneous Estimation of Infiltration Parameters

NASA Astrophysics Data System (ADS)

Bhattacharjya, Rajib Kumar

2018-05-01

The unit hydrograph and the infiltration parameters of a watershed can be obtained from observed rainfall-runoff data by using inverse optimization technique. This is a two-stage optimization problem. In the first stage, the infiltration parameters are obtained and the unit hydrograph ordinates are estimated in the second stage. In order to combine this two-stage method into a single stage one, a modified penalty parameter approach is proposed for converting the constrained optimization problem to an unconstrained one. The proposed approach is designed in such a way that the model initially obtains the infiltration parameters and then searches the optimal unit hydrograph ordinates. The optimization model is solved using Genetic Algorithms. A reduction factor is used in the penalty parameter approach so that the obtained optimal infiltration parameters are not destroyed during subsequent generation of genetic algorithms, required for searching optimal unit hydrograph ordinates. The performance of the proposed methodology is evaluated by using two example problems. The evaluation shows that the model is superior, simple in concept and also has the potential for field application.

Hybrid computer optimization of systems with random parameters

NASA Technical Reports Server (NTRS)

White, R. C., Jr.

1972-01-01

A hybrid computer Monte Carlo technique for the simulation and optimization of systems with random parameters is presented. The method is applied to the simultaneous optimization of the means and variances of two parameters in the radar-homing missile problem treated by McGhee and Levine.

Optimization of parameters of special asynchronous electric drives

NASA Astrophysics Data System (ADS)

Karandey, V. Yu; Popov, B. K.; Popova, O. B.; Afanasyev, V. L.

2018-03-01

The article considers the solution of the problem of parameters optimization of special asynchronous electric drives. The solution of the problem will allow one to project and create special asynchronous electric drives for various industries. The created types of electric drives will have optimum mass-dimensional and power parameters. It will allow one to realize and fulfill the set characteristics of management of technological processes with optimum level of expenses of electric energy, time of completing the process or other set parameters. The received decision allows one not only to solve a certain optimizing problem, but also to construct dependences between the optimized parameters of special asynchronous electric drives, for example, with the change of power, current in a winding of the stator or rotor, induction in a gap or steel of magnetic conductors and other parameters. On the constructed dependences, it is possible to choose necessary optimum values of parameters of special asynchronous electric drives and their components without carrying out repeated calculations.

Fine-Tuning ADAS Algorithm Parameters for Optimizing Traffic ...

EPA Pesticide Factsheets

With the development of the Connected Vehicle technology that facilitates wirelessly communication among vehicles and road-side infrastructure, the Advanced Driver Assistance Systems (ADAS) can be adopted as an effective tool for accelerating traffic safety and mobility optimization at various highway facilities. To this end, the traffic management centers identify the optimal ADAS algorithm parameter set that enables the maximum improvement of the traffic safety and mobility performance, and broadcast the optimal parameter set wirelessly to individual ADAS-equipped vehicles. After adopting the optimal parameter set, the ADAS-equipped drivers become active agents in the traffic stream that work collectively and consistently to prevent traffic conflicts, lower the intensity of traffic disturbances, and suppress the development of traffic oscillations into heavy traffic jams. Successful implementation of this objective requires the analysis capability of capturing the impact of the ADAS on driving behaviors, and measuring traffic safety and mobility performance under the influence of the ADAS. To address this challenge, this research proposes a synthetic methodology that incorporates the ADAS-affected driving behavior modeling and state-of-the-art microscopic traffic flow modeling into a virtually simulated environment. Building on such an environment, the optimal ADAS algorithm parameter set is identified through an optimization programming framework to enable th

Sequential ensemble-based optimal design for parameter estimation: SEQUENTIAL ENSEMBLE-BASED OPTIMAL DESIGN

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

Man, Jun; Zhang, Jiangjiang; Li, Weixuan

2016-10-01

The ensemble Kalman filter (EnKF) has been widely used in parameter estimation for hydrological models. The focus of most previous studies was to develop more efficient analysis (estimation) algorithms. On the other hand, it is intuitively understandable that a well-designed sampling (data-collection) strategy should provide more informative measurements and subsequently improve the parameter estimation. In this work, a Sequential Ensemble-based Optimal Design (SEOD) method, coupled with EnKF, information theory and sequential optimal design, is proposed to improve the performance of parameter estimation. Based on the first-order and second-order statistics, different information metrics including the Shannon entropy difference (SD), degrees ofmore » freedom for signal (DFS) and relative entropy (RE) are used to design the optimal sampling strategy, respectively. The effectiveness of the proposed method is illustrated by synthetic one-dimensional and two-dimensional unsaturated flow case studies. It is shown that the designed sampling strategies can provide more accurate parameter estimation and state prediction compared with conventional sampling strategies. Optimal sampling designs based on various information metrics perform similarly in our cases. The effect of ensemble size on the optimal design is also investigated. Overall, larger ensemble size improves the parameter estimation and convergence of optimal sampling strategy. Although the proposed method is applied to unsaturated flow problems in this study, it can be equally applied in any other hydrological problems.« less

Optimal design criteria - prediction vs. parameter estimation

NASA Astrophysics Data System (ADS)

Waldl, Helmut

2014-05-01

G-optimality is a popular design criterion for optimal prediction, it tries to minimize the kriging variance over the whole design region. A G-optimal design minimizes the maximum variance of all predicted values. If we use kriging methods for prediction it is self-evident to use the kriging variance as a measure of uncertainty for the estimates. Though the computation of the kriging variance and even more the computation of the empirical kriging variance is computationally very costly and finding the maximum kriging variance in high-dimensional regions can be time demanding such that we cannot really find the G-optimal design with nowadays available computer equipment in practice. We cannot always avoid this problem by using space-filling designs because small designs that minimize the empirical kriging variance are often non-space-filling. D-optimality is the design criterion related to parameter estimation. A D-optimal design maximizes the determinant of the information matrix of the estimates. D-optimality in terms of trend parameter estimation and D-optimality in terms of covariance parameter estimation yield basically different designs. The Pareto frontier of these two competing determinant criteria corresponds with designs that perform well under both criteria. Under certain conditions searching the G-optimal design on the above Pareto frontier yields almost as good results as searching the G-optimal design in the whole design region. In doing so the maximum of the empirical kriging variance has to be computed only a few times though. The method is demonstrated by means of a computer simulation experiment based on data provided by the Belgian institute Management Unit of the North Sea Mathematical Models (MUMM) that describe the evolution of inorganic and organic carbon and nutrients, phytoplankton, bacteria and zooplankton in the Southern Bight of the North Sea.

Mini-review of the geotechnical parameters of municipal solid waste: Mechanical and biological pre-treated versus raw untreated waste.

PubMed

Petrovic, Igor

2016-09-01

The most viable option for biostabilisation of old sanitary landfills, filled with raw municipal solid waste, is the so-called bioreactor landfill. Even today, bioreactor landfills are viable options in many economically developing countries. However, in order to reduce the biodegradable component of landfilled waste, mechanical and biological treatment has become a widely accepted waste treatment technology, especially in more prosperous countries. Given that mechanical and biological treatment alters the geotechnical properties of raw waste material, the design of sanitary landfills which accepts mechanically and biologically treated waste, should be carried out with a distinct set of geotechnical parameters. However, under the assumption that 'waste is waste', some design engineers might be tempted to use geotechnical parameters of untreated raw municipal solid waste and mechanical and biological pre-treated municipal solid waste interchangeably. Therefore, to provide guidelines for use and to provide an aggregated source of this information, this mini-review provides comparisons of geotechnical parameters of mechanical and biological pre-treated waste and raw untreated waste at various decomposition stages. This comparison reveals reasonable correlations between the hydraulic conductivity values of untreated and mechanical and biological pre-treated municipal solid waste. It is recognised that particle size might have a significant influence on the hydraulic conductivity of both municipal solid waste types. However, the compression ratios and shear strengths of untreated and pre-treated municipal solid waste do not show such strong correlations. Furthermore, another emerging topic that requires appropriate attention is the recovery of resources that are embedded in old landfills. Therefore, the presented results provide a valuable tool for engineers designing landfills for mechanical and biological pre-treated waste or bioreactor landfills for untreated raw

Identification of vehicle suspension parameters by design optimization

NASA Astrophysics Data System (ADS)

Tey, J. Y.; Ramli, R.; Kheng, C. W.; Chong, S. Y.; Abidin, M. A. Z.

2014-05-01

The design of a vehicle suspension system through simulation requires accurate representation of the design parameters. These parameters are usually difficult to measure or sometimes unavailable. This article proposes an efficient approach to identify the unknown parameters through optimization based on experimental results, where the covariance matrix adaptation-evolutionary strategy (CMA-es) is utilized to improve the simulation and experimental results against the kinematic and compliance tests. This speeds up the design and development cycle by recovering all the unknown data with respect to a set of kinematic measurements through a single optimization process. A case study employing a McPherson strut suspension system is modelled in a multi-body dynamic system. Three kinematic and compliance tests are examined, namely, vertical parallel wheel travel, opposite wheel travel and single wheel travel. The problem is formulated as a multi-objective optimization problem with 40 objectives and 49 design parameters. A hierarchical clustering method based on global sensitivity analysis is used to reduce the number of objectives to 30 by grouping correlated objectives together. Then, a dynamic summation of rank value is used as pseudo-objective functions to reformulate the multi-objective optimization to a single-objective optimization problem. The optimized results show a significant improvement in the correlation between the simulated model and the experimental model. Once accurate representation of the vehicle suspension model is achieved, further analysis, such as ride and handling performances, can be implemented for further optimization.

Optimization of solid content, carbon/nitrogen ratio and food/inoculum ratio for biogas production from food waste.

PubMed

Dadaser-Celik, Filiz; Azgin, Sukru Taner; Yildiz, Yalcin Sevki

2016-12-01

Biogas production from food waste has been used as an efficient waste treatment option for years. The methane yields from decomposition of waste are, however, highly variable under different operating conditions. In this study, a statistical experimental design method (Taguchi OA 9 ) was implemented to investigate the effects of simultaneous variations of three parameters on methane production. The parameters investigated were solid content (SC), carbon/nitrogen ratio (C/N) and food/inoculum ratio (F/I). Two sets of experiments were conducted with nine anaerobic reactors operating under different conditions. Optimum conditions were determined using statistical analysis, such as analysis of variance (ANOVA). A confirmation experiment was carried out at optimum conditions to investigate the validity of the results. Statistical analysis showed that SC was the most important parameter for methane production with a 45% contribution, followed by F/I ratio with a 35% contribution. The optimum methane yield of 151 l kg -1 volatile solids (VS) was achieved after 24 days of digestion when SC was 4%, C/N was 28 and F/I were 0.3. The confirmation experiment provided a methane yield of 167 l kg -1 VS after 24 days. The analysis showed biogas production from food waste may be increased by optimization of operating conditions. © The Author(s) 2016.

Industrial waste recycling strategies optimization problem: mixed integer programming model and heuristics

NASA Astrophysics Data System (ADS)

Tang, Jiafu; Liu, Yang; Fung, Richard; Luo, Xinggang

2008-12-01

Manufacturers have a legal accountability to deal with industrial waste generated from their production processes in order to avoid pollution. Along with advances in waste recovery techniques, manufacturers may adopt various recycling strategies in dealing with industrial waste. With reuse strategies and technologies, byproducts or wastes will be returned to production processes in the iron and steel industry, and some waste can be recycled back to base material for reuse in other industries. This article focuses on a recovery strategies optimization problem for a typical class of industrial waste recycling process in order to maximize profit. There are multiple strategies for waste recycling available to generate multiple byproducts; these byproducts are then further transformed into several types of chemical products via different production patterns. A mixed integer programming model is developed to determine which recycling strategy and which production pattern should be selected with what quantity of chemical products corresponding to this strategy and pattern in order to yield maximum marginal profits. The sales profits of chemical products and the set-up costs of these strategies, patterns and operation costs of production are considered. A simulated annealing (SA) based heuristic algorithm is developed to solve the problem. Finally, an experiment is designed to verify the effectiveness and feasibility of the proposed method. By comparing a single strategy to multiple strategies in an example, it is shown that the total sales profit of chemical products can be increased by around 25% through the simultaneous use of multiple strategies. This illustrates the superiority of combinatorial multiple strategies. Furthermore, the effects of the model parameters on profit are discussed to help manufacturers organize their waste recycling network.

TRU Waste Management Program cost/schedule optimization analysis

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

Detamore, J.A.; Raudenbush, M.H.; Wolaver, R.W.

1985-10-01

The cost/schedule optimization task is a necessary function to insure that program goals and plans are optimized from a cost and schedule aspect. Results of this study will offer DOE information with which it can establish, within institutional constraints, the most efficient program for the long-term management and disposal of contact handled transuranic waste (CH-TRU). To this end, a comprehensive review of program cost/schedule tradeoffs has been made, to identify any major cost saving opportunities that may be realized by modification of current program plans. It was decided that all promising scenarios would be explored, and institutional limitations to implementationmore » would be described. Since a virtually limitless number of possible scenarios can be envisioned, it was necessary to distill these possibilities into a manageable number of alternatives. The resultant scenarios were described in the cost/schedule strategy and work plan document. Each scenario was compared with the base case: waste processing at the originating site; transport of CH-TRU wastes in TRUPACT; shipment of drums in 6-Packs; 25 year stored waste workoff; WIPP operational 10/88, with all sites shipping to WIPP beginning 10/88; and no processing at WIPP. Major savings were identified in two alternate scenarios: centralize waste processing at INEL and eliminate rail shipment of TRUPACT. No attempt was made to calculate savings due to combination of scenarios. 1 ref., 5 figs., 1 tab. (MHB)« less

IPO: a tool for automated optimization of XCMS parameters.

PubMed

Libiseller, Gunnar; Dvorzak, Michaela; Kleb, Ulrike; Gander, Edgar; Eisenberg, Tobias; Madeo, Frank; Neumann, Steffen; Trausinger, Gert; Sinner, Frank; Pieber, Thomas; Magnes, Christoph

2015-04-16

Untargeted metabolomics generates a huge amount of data. Software packages for automated data processing are crucial to successfully process these data. A variety of such software packages exist, but the outcome of data processing strongly depends on algorithm parameter settings. If they are not carefully chosen, suboptimal parameter settings can easily lead to biased results. Therefore, parameter settings also require optimization. Several parameter optimization approaches have already been proposed, but a software package for parameter optimization which is free of intricate experimental labeling steps, fast and widely applicable is still missing. We implemented the software package IPO ('Isotopologue Parameter Optimization') which is fast and free of labeling steps, and applicable to data from different kinds of samples and data from different methods of liquid chromatography - high resolution mass spectrometry and data from different instruments. IPO optimizes XCMS peak picking parameters by using natural, stable (13)C isotopic peaks to calculate a peak picking score. Retention time correction is optimized by minimizing relative retention time differences within peak groups. Grouping parameters are optimized by maximizing the number of peak groups that show one peak from each injection of a pooled sample. The different parameter settings are achieved by design of experiments, and the resulting scores are evaluated using response surface models. IPO was tested on three different data sets, each consisting of a training set and test set. IPO resulted in an increase of reliable groups (146% - 361%), a decrease of non-reliable groups (3% - 8%) and a decrease of the retention time deviation to one third. IPO was successfully applied to data derived from liquid chromatography coupled to high resolution mass spectrometry from three studies with different sample types and different chromatographic methods and devices. We were also able to show the potential of IPO to

Optimization for minimum sensitivity to uncertain parameters

NASA Technical Reports Server (NTRS)

Pritchard, Jocelyn I.; Adelman, Howard M.; Sobieszczanski-Sobieski, Jaroslaw

1994-01-01

A procedure to design a structure for minimum sensitivity to uncertainties in problem parameters is described. The approach is to minimize directly the sensitivity derivatives of the optimum design with respect to fixed design parameters using a nested optimization procedure. The procedure is demonstrated for the design of a bimetallic beam for minimum weight with insensitivity to uncertainties in structural properties. The beam is modeled with finite elements based on two dimensional beam analysis. A sequential quadratic programming procedure used as the optimizer supplies the Lagrange multipliers that are used to calculate the optimum sensitivity derivatives. The method was perceived to be successful from comparisons of the optimization results with parametric studies.

Optimal planning for the sustainable utilization of municipal solid waste.

PubMed

Santibañez-Aguilar, José Ezequiel; Ponce-Ortega, José María; Betzabe González-Campos, J; Serna-González, Medardo; El-Halwagi, Mahmoud M

2013-12-01

The increasing generation of municipal solid waste (MSW) is a major problem particularly for large urban areas with insufficient landfill capacities and inefficient waste management systems. Several options associated to the supply chain for implementing a MSW management system are available, however to determine the optimal solution several technical, economic, environmental and social aspects must be considered. Therefore, this paper proposes a mathematical programming model for the optimal planning of the supply chain associated to the MSW management system to maximize the economic benefit while accounting for technical and environmental issues. The optimization model simultaneously selects the processing technologies and their location, the distribution of wastes from cities as well as the distribution of products to markets. The problem was formulated as a multi-objective mixed-integer linear programing problem to maximize the profit of the supply chain and the amount of recycled wastes, where the results are showed through Pareto curves that tradeoff economic and environmental aspects. The proposed approach is applied to a case study for the west-central part of Mexico to consider the integration of MSW from several cities to yield useful products. The results show that an integrated utilization of MSW can provide economic, environmental and social benefits. Copyright © 2013 Elsevier Ltd. All rights reserved.

Conversion of solid organic wastes into oil via Boettcherisca peregrine (Diptera: Sarcophagidae) larvae and optimization of parameters for biodiesel production.

PubMed

Yang, Sen; Li, Qing; Zeng, Qinglan; Zhang, Jibin; Yu, Ziniu; Liu, Ziduo

2012-01-01

The feedstocks for biodiesel production are predominantly from edible oils and the high cost of the feedstocks prevents its large scale application. In this study, we evaluated the oil extracted from Boettcherisca peregrine larvae (BPL) grown on solid organic wastes for biodiesel production. The oil contents detected in the BPL converted from swine manure, fermentation residue and the degreased food waste, were 21.7%, 19.5% and 31.1%, respectively. The acid value of the oil is 19.02 mg KOH/g requiring a two-step transesterification process. The optimized process of 12∶1 methanol/oil (mol/mol) with 1.5% H(2)SO(4) reacted at 70°C for 120 min resulted in a 90.8% conversion rate of free fatty acid (FFA) by esterification, and a 92.3% conversion rate of triglycerides into esters by alkaline transesterification. Properties of the BPL oil-based biodiesel are within the specifications of ASTM D6751, suggesting that the solid organic waste-grown BPL could be a feasible non-food feedstock for biodiesel production.

Conversion of Solid Organic Wastes into Oil via Boettcherisca peregrine (Diptera: Sarcophagidae) Larvae and Optimization of Parameters for Biodiesel Production

PubMed Central

Yang, Sen; Li, Qing; Zeng, Qinglan; Zhang, Jibin; Yu, Ziniu; Liu, Ziduo

2012-01-01

The feedstocks for biodiesel production are predominantly from edible oils and the high cost of the feedstocks prevents its large scale application. In this study, we evaluated the oil extracted from Boettcherisca peregrine larvae (BPL) grown on solid organic wastes for biodiesel production. The oil contents detected in the BPL converted from swine manure, fermentation residue and the degreased food waste, were 21.7%, 19.5% and 31.1%, respectively. The acid value of the oil is 19.02 mg KOH/g requiring a two-step transesterification process. The optimized process of 12∶1 methanol/oil (mol/mol) with 1.5% H2SO4 reacted at 70°C for 120 min resulted in a 90.8% conversion rate of free fatty acid (FFA) by esterification, and a 92.3% conversion rate of triglycerides into esters by alkaline transesterification. Properties of the BPL oil-based biodiesel are within the specifications of ASTM D6751, suggesting that the solid organic waste-grown BPL could be a feasible non-food feedstock for biodiesel production. PMID:23029331

Using multi-criteria decision making for selection of the optimal strategy for municipal solid waste management.

PubMed

Jovanovic, Sasa; Savic, Slobodan; Jovicic, Nebojsa; Boskovic, Goran; Djordjevic, Zorica

2016-09-01

Multi-criteria decision making (MCDM) is a relatively new tool for decision makers who deal with numerous and often contradictory factors during their decision making process. This paper presents a procedure to choose the optimal municipal solid waste (MSW) management system for the area of the city of Kragujevac (Republic of Serbia) based on the MCDM method. Two methods of multiple attribute decision making, i.e. SAW (simple additive weighting method) and TOPSIS (technique for order preference by similarity to ideal solution), respectively, were used to compare the proposed waste management strategies (WMS). Each of the created strategies was simulated using the software package IWM2. Total values for eight chosen parameters were calculated for all the strategies. Contribution of each of the six waste treatment options was valorized. The SAW analysis was used to obtain the sum characteristics for all the waste management treatment strategies and they were ranked accordingly. The TOPSIS method was used to calculate the relative closeness factors to the ideal solution for all the alternatives. Then, the proposed strategies were ranked in form of tables and diagrams obtained based on both MCDM methods. As shown in this paper, the results were in good agreement, which additionally confirmed and facilitated the choice of the optimal MSW management strategy. © The Author(s) 2016.

Environmental Optimization Using the WAste Reduction Algorithm (WAR)

EPA Science Inventory

Traditionally chemical process designs were optimized using purely economic measures such as rate of return. EPA scientists developed the WAste Reduction algorithm (WAR) so that environmental impacts of designs could easily be evaluated. The goal of WAR is to reduce environme...

An optimal generic model for multi-parameters and big data optimizing: a laboratory experimental study

NASA Astrophysics Data System (ADS)

Utama, D. N.; Ani, N.; Iqbal, M. M.

2018-03-01

Optimization is a process for finding parameter (parameters) that is (are) able to deliver an optimal value for an objective function. Seeking an optimal generic model for optimizing is a computer science study that has been being practically conducted by numerous researchers. Generic model is a model that can be technically operated to solve any varieties of optimization problem. By using an object-oriented method, the generic model for optimizing was constructed. Moreover, two types of optimization method, simulated-annealing and hill-climbing, were functioned in constructing the model and compared to find the most optimal one then. The result said that both methods gave the same result for a value of objective function and the hill-climbing based model consumed the shortest running time.

Optimal correction and design parameter search by modern methods of rigorous global optimization

NASA Astrophysics Data System (ADS)

Makino, K.; Berz, M.

2011-07-01

Frequently the design of schemes for correction of aberrations or the determination of possible operating ranges for beamlines and cells in synchrotrons exhibit multitudes of possibilities for their correction, usually appearing in disconnected regions of parameter space which cannot be directly qualified by analytical means. In such cases, frequently an abundance of optimization runs are carried out, each of which determines a local minimum depending on the specific chosen initial conditions. Practical solutions are then obtained through an often extended interplay of experienced manual adjustment of certain suitable parameters and local searches by varying other parameters. However, in a formal sense this problem can be viewed as a global optimization problem, i.e. the determination of all solutions within a certain range of parameters that lead to a specific optimum. For example, it may be of interest to find all possible settings of multiple quadrupoles that can achieve imaging; or to find ahead of time all possible settings that achieve a particular tune; or to find all possible manners to adjust nonlinear parameters to achieve correction of high order aberrations. These tasks can easily be phrased in terms of such an optimization problem; but while mathematically this formulation is often straightforward, it has been common belief that it is of limited practical value since the resulting optimization problem cannot usually be solved. However, recent significant advances in modern methods of rigorous global optimization make these methods feasible for optics design for the first time. The key ideas of the method lie in an interplay of rigorous local underestimators of the objective functions, and by using the underestimators to rigorously iteratively eliminate regions that lie above already known upper bounds of the minima, in what is commonly known as a branch-and-bound approach. Recent enhancements of the Differential Algebraic methods used in particle

Optimization of volatile fatty acid production with co-substrate of food wastes and dewatered excess sludge using response surface methodology.

PubMed

Hong, Chen; Haiyun, Wu

2010-07-01

Central-composite design (CCD) and response surface methodology (RSM) were used to optimize the parameters of volatile fatty acid (VFA) production from food wastes and dewatered excess sludge in a semi-continuous process. The effects of four variables (food wastes composition in the co-substrate of food wastes and excess sludge, hydraulic retention time (HRT), organic loading rate (OLR), and pH) on acidogenesis were evaluated individually and interactively. The optimum condition derived via RSM was food wastes composition, 88.03%; HRT, 8.92 days; OLR, 8.31 g VSS/ld; and pH 6.99. The experimental VFA concentration was 29,099 mg/l under this optimum condition, which was well in agreement with the predicted value of 28,000 mg/l. Copyright (c) 2010 Elsevier Ltd. All rights reserved.

Parameter optimization for surface flux transport models

NASA Astrophysics Data System (ADS)

Whitbread, T.; Yeates, A. R.; Muñoz-Jaramillo, A.; Petrie, G. J. D.

2017-11-01

Accurate prediction of solar activity calls for precise calibration of solar cycle models. Consequently we aim to find optimal parameters for models which describe the physical processes on the solar surface, which in turn act as proxies for what occurs in the interior and provide source terms for coronal models. We use a genetic algorithm to optimize surface flux transport models using National Solar Observatory (NSO) magnetogram data for Solar Cycle 23. This is applied to both a 1D model that inserts new magnetic flux in the form of idealized bipolar magnetic regions, and also to a 2D model that assimilates specific shapes of real active regions. The genetic algorithm searches for parameter sets (meridional flow speed and profile, supergranular diffusivity, initial magnetic field, and radial decay time) that produce the best fit between observed and simulated butterfly diagrams, weighted by a latitude-dependent error structure which reflects uncertainty in observations. Due to the easily adaptable nature of the 2D model, the optimization process is repeated for Cycles 21, 22, and 24 in order to analyse cycle-to-cycle variation of the optimal solution. We find that the ranges and optimal solutions for the various regimes are in reasonable agreement with results from the literature, both theoretical and observational. The optimal meridional flow profiles for each regime are almost entirely within observational bounds determined by magnetic feature tracking, with the 2D model being able to accommodate the mean observed profile more successfully. Differences between models appear to be important in deciding values for the diffusive and decay terms. In like fashion, differences in the behaviours of different solar cycles lead to contrasts in parameters defining the meridional flow and initial field strength.

Chaos minimization in DC-DC boost converter using circuit parameter optimization

NASA Astrophysics Data System (ADS)

Sudhakar, N.; Natarajan, Rajasekar; Gourav, Kumar; Padmavathi, P.

2017-11-01

DC-DC converters are prone to several types of nonlinear phenomena including bifurcation, quasi periodicity, intermittency and chaos. These undesirable effects must be controlled for periodic operation of the converter to ensure the stability. In this paper an effective solution to control of chaos in solar fed DC-DC boost converter is proposed. Controlling of chaos is significantly achieved using optimal circuit parameters obtained through Bacterial Foraging Optimization Algorithm. The optimization renders the suitable parameters in minimum computational time. The obtained results are compared with the operation of traditional boost converter. Further the obtained results with BFA optimized parameter ensures the operations of the converter are within the controllable region. To elaborate the study of bifurcation analysis with optimized and unoptimized parameters are also presented.

Optimization of Eisenia fetida stocking density for the bioconversion of rock phosphate enriched cow dung-waste paper mixtures.

PubMed

Unuofin, F O; Mnkeni, P N S

2014-11-01

Vermitechnology is gaining recognition as an environmental friendly waste management strategy. Its successful implementation requires that the key operational parameters like earthworm stocking density be established for each target waste/waste mixture. One target waste mixture in South Africa is waste paper mixed with cow dung and rock phosphate (RP) for P enrichment. This study sought to establish optimal Eisenia fetida stocking density for maximum P release and rapid bioconversion of RP enriched cow dung-paper waste mixtures. E. fetida stocking densities of 0, 7.5, 12.5, 17.5 and 22.5 g-worms kg(-1) dry weight of cow dung-waste paper mixtures were evaluated. The stocking density of 12.5 g-worms kg(-1) resulted in the highest earthworm growth rate and humification of the RP enriched waste mixture as reflected by a C:N ratio of <12 and a humic acid/fulvic acid ratio of >1.9 in final vermicomposts. A germination test revealed that the resultant vermicompost had no inhibitory effect on the germination of tomato, carrot, and radish. Extractable P increased with stocking density up to 22.5 g-worm kg(-1) feedstock suggesting that for maximum P release from RP enriched wastes a high stocking density should be considered. Copyright © 2014. Published by Elsevier Ltd.

Optimization of laccase production by Trametes versicolor cultivated on industrial waste.

PubMed

Tišma, Marina; Znidaršič-Plazl, Polona; Vasić-Rački, Durđa; Zelić, Bruno

2012-01-01

Laccases are very interesting biocatalysts for several industrial applications. Its production by different white-rot fungi can be stimulated by a variety of inducing substrates, and the use of lignocellulosic wastes or industrial by-products is one of the possible approaches to reduce production costs. In this work, various industrial wastes were tested for laccase production by Trametes versicolor MZKI G-99. Solid waste from chemomechanical treatment facility of a paper manufacturing plant showed the highest potential for laccase production. Enzyme production during submerged cultivation of T. versicolor on the chosen industrial waste has been further improved by medium optimization using genetic algorithm. Concentrations of five components in the medium were optimized within 60 shake-flasks experiments, where the highest laccase activity of 2,378 U dm(-3) was achieved. Waste from the paper industry containing microparticles of CaCO(3) was found to stimulate the formation of freely dispersed mycelium and laccase production during submerged cultivation of T. versicolor. It was proven to be a safe and inexpensive substrate for commercial production of laccase and might be more widely applicable for metabolite production by filamentous fungi.

Determination of the optimal mesh parameters for Iguassu centrifuge flow and separation calculations

NASA Astrophysics Data System (ADS)

Romanihin, S. M.; Tronin, I. V.

2016-09-01

We present the method and the results of the determination for optimal computational mesh parameters for axisymmetric modeling of flow and separation in the Iguasu gas centrifuge. The aim of this work was to determine the mesh parameters which provide relatively low computational cost whithout loss of accuracy. We use direct search optimization algorithm to calculate optimal mesh parameters. Obtained parameters were tested by the calculation of the optimal working regime of the Iguasu GC. Separative power calculated using the optimal mesh parameters differs less than 0.5% from the result obtained on the detailed mesh. Presented method can be used to determine optimal mesh parameters of the Iguasu GC with different rotor speeds.

Optimization of the anaerobic treatment of a waste stream from an enhanced oil recovery process.

PubMed

Alimahmoodi, Mahmood; Mulligan, Catherine N

2011-01-01

The aim of this work was to optimize the anaerobic treatment of a waste stream from an enhanced oil recovery (EOR) process. The treatment of a simulated waste water containing about 150 mg chemical oxygen demand (COD)/L of total petroleum hydrocarbons (TPH) and the saturation level of CO2 was evaluated. A two-step anaerobic system was undertaken in the mesophilic temperature range (30-40°C). The method of evolutionary operation EVOP factorial design was used to optimize pH, temperature and organic loading rate with the target parameters of CO2 reduction and CH4 production in the first reactor and TPH removal in the second reactor. The results showed 98% methanogenic removal of CO2 and CH4 yield of 0.38 L/gCOD in the first reactor and 83% TPH removal in the second reactor. In addition to enhancing CO2 and TPH removal and CH4 production, application of this method showed the degree of importance of the operational variables and their interactive effects for the two reactors in series. Copyright © 2010 Elsevier Ltd. All rights reserved.

SPECT System Optimization Against A Discrete Parameter Space

PubMed Central

Meng, L. J.; Li, N.

2013-01-01

In this paper, we present an analytical approach for optimizing the design of a static SPECT system or optimizing the sampling strategy with a variable/adaptive SPECT imaging hardware against an arbitrarily given set of system parameters. This approach has three key aspects. First, it is designed to operate over a discretized system parameter space. Second, we have introduced an artificial concept of virtual detector as the basic building block of an imaging system. With a SPECT system described as a collection of the virtual detectors, one can convert the task of system optimization into a process of finding the optimum imaging time distribution (ITD) across all virtual detectors. Thirdly, the optimization problem (finding the optimum ITD) could be solved with a block-iterative approach or other non-linear optimization algorithms. In essence, the resultant optimum ITD could provide a quantitative measure of the relative importance (or effectiveness) of the virtual detectors and help to identify the system configuration or sampling strategy that leads to an optimum imaging performance. Although we are using SPECT imaging as a platform to demonstrate the system optimization strategy, this development also provides a useful framework for system optimization problems in other modalities, such as positron emission tomography (PET) and X-ray computed tomography (CT) [1, 2]. PMID:23587609

Enhanced Bio-Ethanol Production from Industrial Potato Waste by Statistical Medium Optimization

PubMed Central

Izmirlioglu, Gulten; Demirci, Ali

2015-01-01

Industrial wastes are of great interest as a substrate in production of value-added products to reduce cost, while managing the waste economically and environmentally. Bio-ethanol production from industrial wastes has gained attention because of its abundance, availability, and rich carbon and nitrogen content. In this study, industrial potato waste was used as a carbon source and a medium was optimized for ethanol production by using statistical designs. The effect of various medium components on ethanol production was evaluated. Yeast extract, malt extract, and MgSO4·7H2O showed significantly positive effects, whereas KH2PO4 and CaCl2·2H2O had a significantly negative effect (p-value < 0.05). Using response surface methodology, a medium consisting of 40.4 g/L (dry basis) industrial waste potato, 50 g/L malt extract, and 4.84 g/L MgSO4·7H2O was found optimal and yielded 24.6 g/L ethanol at 30 °C, 150 rpm, and 48 h of fermentation. In conclusion, this study demonstrated that industrial potato waste can be used effectively to enhance bioethanol production. PMID:26501261

Enhanced Bio-Ethanol Production from Industrial Potato Waste by Statistical Medium Optimization.

PubMed

Izmirlioglu, Gulten; Demirci, Ali

2015-10-15

Industrial wastes are of great interest as a substrate in production of value-added products to reduce cost, while managing the waste economically and environmentally. Bio-ethanol production from industrial wastes has gained attention because of its abundance, availability, and rich carbon and nitrogen content. In this study, industrial potato waste was used as a carbon source and a medium was optimized for ethanol production by using statistical designs. The effect of various medium components on ethanol production was evaluated. Yeast extract, malt extract, and MgSO₄·7H₂O showed significantly positive effects, whereas KH₂PO₄ and CaCl₂·2H₂O had a significantly negative effect (p-value<0.05). Using response surface methodology, a medium consisting of 40.4 g/L (dry basis) industrial waste potato, 50 g/L malt extract, and 4.84 g/L MgSO₄·7H₂O was found optimal and yielded 24.6 g/L ethanol at 30 °C, 150 rpm, and 48 h of fermentation. In conclusion, this study demonstrated that industrial potato waste can be used effectively to enhance bioethanol production.

Multi-objective optimization in quantum parameter estimation

NASA Astrophysics Data System (ADS)

Gong, BeiLi; Cui, Wei

2018-04-01

We investigate quantum parameter estimation based on linear and Kerr-type nonlinear controls in an open quantum system, and consider the dissipation rate as an unknown parameter. We show that while the precision of parameter estimation is improved, it usually introduces a significant deformation to the system state. Moreover, we propose a multi-objective model to optimize the two conflicting objectives: (1) maximizing the Fisher information, improving the parameter estimation precision, and (2) minimizing the deformation of the system state, which maintains its fidelity. Finally, simulations of a simplified ɛ-constrained model demonstrate the feasibility of the Hamiltonian control in improving the precision of the quantum parameter estimation.

Optimization of hydraulic turbine governor parameters based on WPA

NASA Astrophysics Data System (ADS)

Gao, Chunyang; Yu, Xiangyang; Zhu, Yong; Feng, Baohao

2018-01-01

The parameters of hydraulic turbine governor directly affect the dynamic characteristics of the hydraulic unit, thus affecting the regulation capacity and the power quality of power grid. The governor of conventional hydropower unit is mainly PID governor with three adjustable parameters, which are difficult to set up. In order to optimize the hydraulic turbine governor, this paper proposes wolf pack algorithm (WPA) for intelligent tuning since the good global optimization capability of WPA. Compared with the traditional optimization method and PSO algorithm, the results show that the PID controller designed by WPA achieves a dynamic quality of hydraulic system and inhibits overshoot.

Estimating cellular parameters through optimization procedures: elementary principles and applications.

PubMed

Kimura, Akatsuki; Celani, Antonio; Nagao, Hiromichi; Stasevich, Timothy; Nakamura, Kazuyuki

2015-01-01

Construction of quantitative models is a primary goal of quantitative biology, which aims to understand cellular and organismal phenomena in a quantitative manner. In this article, we introduce optimization procedures to search for parameters in a quantitative model that can reproduce experimental data. The aim of optimization is to minimize the sum of squared errors (SSE) in a prediction or to maximize likelihood. A (local) maximum of likelihood or (local) minimum of the SSE can efficiently be identified using gradient approaches. Addition of a stochastic process enables us to identify the global maximum/minimum without becoming trapped in local maxima/minima. Sampling approaches take advantage of increasing computational power to test numerous sets of parameters in order to determine the optimum set. By combining Bayesian inference with gradient or sampling approaches, we can estimate both the optimum parameters and the form of the likelihood function related to the parameters. Finally, we introduce four examples of research that utilize parameter optimization to obtain biological insights from quantified data: transcriptional regulation, bacterial chemotaxis, morphogenesis, and cell cycle regulation. With practical knowledge of parameter optimization, cell and developmental biologists can develop realistic models that reproduce their observations and thus, obtain mechanistic insights into phenomena of interest.

Finding optimal vaccination strategies under parameter uncertainty using stochastic programming.

PubMed

Tanner, Matthew W; Sattenspiel, Lisa; Ntaimo, Lewis

2008-10-01

We present a stochastic programming framework for finding the optimal vaccination policy for controlling infectious disease epidemics under parameter uncertainty. Stochastic programming is a popular framework for including the effects of parameter uncertainty in a mathematical optimization model. The problem is initially formulated to find the minimum cost vaccination policy under a chance-constraint. The chance-constraint requires that the probability that R(*) parameter alpha, where R(*) is the post-vaccination reproduction number. We also show how to formulate the problem in two additional cases: (a) finding the optimal vaccination policy when vaccine supply is limited and (b) a cost-benefit scenario. The class of epidemic models for which this method can be used is described and we present an example formulation for which the resulting problem is a mixed-integer program. A short numerical example based on plausible parameter values and distributions is given to illustrate how including parameter uncertainty improves the robustness of the optimal strategy at the cost of higher coverage of the population. Results derived from a stochastic programming analysis can also help to guide decisions about how much effort and resources to focus on collecting data needed to provide better estimates of key parameters.

Statistical optimization for lipase production from solid waste of vegetable oil industry.

PubMed

Sahoo, Rajesh Kumar; Kumar, Mohit; Mohanty, Swati; Sawyer, Matthew; Rahman, Pattanathu K S M; Sukla, Lala Behari; Subudhi, Enketeswara

2018-04-21

The production of biofuel using thermostable bacterial lipase from hot spring bacteria out of low-cost agricultural residue olive oil cake is reported in the present paper. Using a lipase enzyme from Bacillus licheniformis, a 66.5% yield of methyl esters was obtained. Optimum parameters were determined, with maximum production of lipase at a pH of 8.2, temperature 50.8°C, moisture content of 55.7%, and biosurfactant content of 1.693 mg. The contour plots and 3D surface responses depict the significant interaction of pH and moisture content with biosurfactant during lipase production. Chromatographic analysis of the lipase transesterification product was methyl esters, from kitchen waste oil under optimized conditions, generated methyl palmitate, methyl stearate, methyl oleate, and methyl linoleate.

Low-dose cone-beam CT via raw counts domain low-signal correction schemes: Performance assessment and task-based parameter optimization (Part II. Task-based parameter optimization).

PubMed

Gomez-Cardona, Daniel; Hayes, John W; Zhang, Ran; Li, Ke; Cruz-Bastida, Juan Pablo; Chen, Guang-Hong

2018-05-01

Different low-signal correction (LSC) methods have been shown to efficiently reduce noise streaks and noise level in CT to provide acceptable images at low-radiation dose levels. These methods usually result in CT images with highly shift-variant and anisotropic spatial resolution and noise, which makes the parameter optimization process highly nontrivial. The purpose of this work was to develop a local task-based parameter optimization framework for LSC methods. Two well-known LSC methods, the adaptive trimmed mean (ATM) filter and the anisotropic diffusion (AD) filter, were used as examples to demonstrate how to use the task-based framework to optimize filter parameter selection. Two parameters, denoted by the set P, for each LSC method were included in the optimization problem. For the ATM filter, these parameters are the low- and high-signal threshold levels p l and p h ; for the AD filter, the parameters are the exponents δ and γ in the brightness gradient function. The detectability index d' under the non-prewhitening (NPW) mathematical observer model was selected as the metric for parameter optimization. The optimization problem was formulated as an unconstrained optimization problem that consisted of maximizing an objective function d'(P), where i and j correspond to the i-th imaging task and j-th spatial location, respectively. Since there is no explicit mathematical function to describe the dependence of d' on the set of parameters P for each LSC method, the optimization problem was solved via an experimentally measured d' map over a densely sampled parameter space. In this work, three high-contrast-high-frequency discrimination imaging tasks were defined to explore the parameter space of each of the LSC methods: a vertical bar pattern (task I), a horizontal bar pattern (task II), and a multidirectional feature (task III). Two spatial locations were considered for the analysis, a posterior region-of-interest (ROI) located within the noise streaks region

Selective waste collection optimization in Romania and its impact to urban climate

NASA Astrophysics Data System (ADS)

Mihai, Šercǎianu; Iacoboaea, Cristina; Petrescu, Florian; Aldea, Mihaela; Luca, Oana; Gaman, Florian; Parlow, Eberhard

2016-08-01

According to European Directives, transposed in national legislation, the Member States should organize separate collection systems at least for paper, metal, plastic, and glass until 2015. In Romania, since 2011 only 12% of municipal collected waste was recovered, the rest being stored in landfills, although storage is considered the last option in the waste hierarchy. At the same time there was selectively collected only 4% of the municipal waste. Surveys have shown that the Romanian people do not have selective collection bins close to their residencies. The article aims to analyze the current situation in Romania in the field of waste collection and management and to make a proposal for selective collection containers layout, using geographic information systems tools, for a case study in Romania. Route optimization is used based on remote sensing technologies and network analyst protocols. Optimizing selective collection system the greenhouse gases, particles and dust emissions can be reduced.

Optimal parameters uncoupling vibration modes of oscillators

NASA Astrophysics Data System (ADS)

Le, K. C.; Pieper, A.

2017-07-01

This paper proposes a novel optimization concept for an oscillator with two degrees of freedom. By using specially defined motion ratios, we control the action of springs to each degree of freedom of the oscillator. We aim at showing that, if the potential action of the springs in one period of vibration, used as the payoff function for the conservative oscillator, is maximized among all admissible parameters and motions satisfying Lagrange's equations, then the optimal motion ratios uncouple vibration modes. A similar result holds true for the dissipative oscillator having dampers. The application to optimal design of vehicle suspension is discussed.

Electrostatic separation for recycling waste printed circuit board: a study on external factor and a robust design for optimization.

PubMed

Hou, Shibing; Wu, Jiang; Qin, Yufei; Xu, Zhenming

2010-07-01

Electrostatic separation is an effective and environmentally friendly method for recycling waste printed circuit board (PCB) by several kinds of electrostatic separators. However, some notable problems have been detected in its applications and cannot be efficiently resolved by optimizing the separation process. Instead of the separator itself, these problems are mainly caused by some external factors such as the nonconductive powder (NP) and the superficial moisture of feeding granule mixture. These problems finally lead to an inefficient separation. In the present research, the impacts of these external factors were investigated and a robust design was built to optimize the process and to weaken the adverse impact. A most robust parameter setting (25 kv, 80 rpm) was concluded from the experimental design. In addition, some theoretical methods, including cyclone separation, were presented to eliminate these problems substantially. This will contribute to efficient electrostatic separation of waste PCB and make remarkable progress for industrial applications.

Parameter Optimization for Turbulent Reacting Flows Using Adjoints

NASA Astrophysics Data System (ADS)

Lapointe, Caelan; Hamlington, Peter E.

2017-11-01

The formulation of a new adjoint solver for topology optimization of turbulent reacting flows is presented. This solver provides novel configurations (e.g., geometries and operating conditions) based on desired system outcomes (i.e., objective functions) for complex reacting flow problems of practical interest. For many such problems, it would be desirable to know optimal values of design parameters (e.g., physical dimensions, fuel-oxidizer ratios, and inflow-outflow conditions) prior to real-world manufacture and testing, which can be expensive, time-consuming, and dangerous. However, computational optimization of these problems is made difficult by the complexity of most reacting flows, necessitating the use of gradient-based optimization techniques in order to explore a wide design space at manageable computational cost. The adjoint method is an attractive way to obtain the required gradients, because the cost of the method is determined by the dimension of the objective function rather than the size of the design space. Here, the formulation of a novel solver is outlined that enables gradient-based parameter optimization of turbulent reacting flows using the discrete adjoint method. Initial results and an outlook for future research directions are provided.

Development of engineering parameters for the design of metal biosorption waste treatment systems

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

Graham, W.S.

1991-12-03

Untreated landfill leachates and wastes from metal plating and mining operations are sources of environmental contamination by heavy metals. Because of their toxicity and potential for accumulation, the discharge of heavy metals must be controlled. Standard physical and chemical treatments used to remove metals from wastes such as concentration by electro-precipitation, ion exchange, solvent extraction, evaporative recovery, and conventional precipitation, are usually expensive and produce high quantities of sludge. Biosorption is the removal of metals from aqueous solutions by microorganisms. It is called biosorption rather than bioadsorption or bioaccumulation because the mechanisms of removal are not restricted to adsorption ormore » metabolic uptake and so the more general term is preferable and has come to be accepted. In this thesis the focus is one two microorganisms and two metals. However, the possible combinations of conditions such as pH, relative metal molarities, time of contact, and organism are numerous. These experiments are designed to provide optimized parameters to facilitate the design of a functioning biosorption system. The two metals chosen for study are copper and lead in aqueous solution. The two types of microorganisms chosen for testing include an actinomycete and a fungus. The purpose of this research is to identify the significant engineering parameters to be evaluated include reaction rates, equilibrium partitioning of metal ions between those in solution and those removed to the cells, optimum pH for achieving the removal or recovery goal, and biosorption selectivity for one metal over another.« less

Optimizing Spectral CT Parameters for Material Classification Tasks

PubMed Central

Rigie, D. S.; La Rivière, P. J.

2017-01-01

In this work, we propose a framework for optimizing spectral CT imaging parameters and hardware design with regard to material classification tasks. Compared with conventional CT, many more parameters must be considered when designing spectral CT systems and protocols. These choices will impact material classification performance in a non-obvious, task-dependent way with direct implications for radiation dose reduction. In light of this, we adapt Hotelling Observer formalisms typically applied to signal detection tasks to the spectral CT, material-classification problem. The result is a rapidly computable metric that makes it possible to sweep out many system configurations, generating parameter optimization curves (POC’s) that can be used to select optimal settings. The proposed model avoids restrictive assumptions about the basis-material decomposition (e.g. linearity) and incorporates signal uncertainty with a stochastic object model. This technique is demonstrated on dual-kVp and photon-counting systems for two different, clinically motivated material classification tasks (kidney stone classification and plaque removal). We show that the POC’s predicted with the proposed analytic model agree well with those derived from computationally intensive numerical simulation studies. PMID:27227430

Optimizing spectral CT parameters for material classification tasks

NASA Astrophysics Data System (ADS)

Rigie, D. S.; La Rivière, P. J.

2016-06-01

In this work, we propose a framework for optimizing spectral CT imaging parameters and hardware design with regard to material classification tasks. Compared with conventional CT, many more parameters must be considered when designing spectral CT systems and protocols. These choices will impact material classification performance in a non-obvious, task-dependent way with direct implications for radiation dose reduction. In light of this, we adapt Hotelling Observer formalisms typically applied to signal detection tasks to the spectral CT, material-classification problem. The result is a rapidly computable metric that makes it possible to sweep out many system configurations, generating parameter optimization curves (POC’s) that can be used to select optimal settings. The proposed model avoids restrictive assumptions about the basis-material decomposition (e.g. linearity) and incorporates signal uncertainty with a stochastic object model. This technique is demonstrated on dual-kVp and photon-counting systems for two different, clinically motivated material classification tasks (kidney stone classification and plaque removal). We show that the POC’s predicted with the proposed analytic model agree well with those derived from computationally intensive numerical simulation studies.

Search Parameter Optimization for Discrete, Bayesian, and Continuous Search Algorithms

DTIC Science & Technology

2017-09-01

NAVAL POSTGRADUATE SCHOOL MONTEREY, CALIFORNIA THESIS SEARCH PARAMETER OPTIMIZATION FOR DISCRETE , BAYESIAN, AND CONTINUOUS SEARCH ALGORITHMS by...to 09-22-2017 4. TITLE AND SUBTITLE SEARCH PARAMETER OPTIMIZATION FOR DISCRETE , BAYESIAN, AND CON- TINUOUS SEARCH ALGORITHMS 5. FUNDING NUMBERS 6...simple search and rescue acts to prosecuting aerial/surface/submersible targets on mission. This research looks at varying the known discrete and

Real-time parameter optimization based on neural network for smart injection molding

NASA Astrophysics Data System (ADS)

Lee, H.; Liau, Y.; Ryu, K.

2018-03-01

The manufacturing industry has been facing several challenges, including sustainability, performance and quality of production. Manufacturers attempt to enhance the competitiveness of companies by implementing CPS (Cyber-Physical Systems) through the convergence of IoT(Internet of Things) and ICT(Information & Communication Technology) in the manufacturing process level. Injection molding process has a short cycle time and high productivity. This features have been making it suitable for mass production. In addition, this process is used to produce precise parts in various industry fields such as automobiles, optics and medical devices. Injection molding process has a mixture of discrete and continuous variables. In order to optimized the quality, variables that is generated in the injection molding process must be considered. Furthermore, Optimal parameter setting is time-consuming work to predict the optimum quality of the product. Since the process parameter cannot be easily corrected during the process execution. In this research, we propose a neural network based real-time process parameter optimization methodology that sets optimal process parameters by using mold data, molding machine data, and response data. This paper is expected to have academic contribution as a novel study of parameter optimization during production compare with pre - production parameter optimization in typical studies.

An Integrated Framework for Parameter-based Optimization of Scientific Workflows.

PubMed

Kumar, Vijay S; Sadayappan, P; Mehta, Gaurang; Vahi, Karan; Deelman, Ewa; Ratnakar, Varun; Kim, Jihie; Gil, Yolanda; Hall, Mary; Kurc, Tahsin; Saltz, Joel

2009-01-01

Data analysis processes in scientific applications can be expressed as coarse-grain workflows of complex data processing operations with data flow dependencies between them. Performance optimization of these workflows can be viewed as a search for a set of optimal values in a multi-dimensional parameter space. While some performance parameters such as grouping of workflow components and their mapping to machines do not a ect the accuracy of the output, others may dictate trading the output quality of individual components (and of the whole workflow) for performance. This paper describes an integrated framework which is capable of supporting performance optimizations along multiple dimensions of the parameter space. Using two real-world applications in the spatial data analysis domain, we present an experimental evaluation of the proposed framework.

Parameters optimization for magnetic resonance coupling wireless power transmission.

PubMed

Li, Changsheng; Zhang, He; Jiang, Xiaohua

2014-01-01

Taking maximum power transmission and power stable transmission as research objectives, optimal design for the wireless power transmission system based on magnetic resonance coupling is carried out in this paper. Firstly, based on the mutual coupling model, mathematical expressions of optimal coupling coefficients for the maximum power transmission target are deduced. Whereafter, methods of enhancing power transmission stability based on parameters optimal design are investigated. It is found that the sensitivity of the load power to the transmission parameters can be reduced and the power transmission stability can be enhanced by improving the system resonance frequency or coupling coefficient between the driving/pick-up coil and the transmission/receiving coil. Experiment results are well conformed to the theoretical analysis conclusions.

Cosmological parameter estimation using Particle Swarm Optimization

NASA Astrophysics Data System (ADS)

Prasad, J.; Souradeep, T.

2014-03-01

Constraining parameters of a theoretical model from observational data is an important exercise in cosmology. There are many theoretically motivated models, which demand greater number of cosmological parameters than the standard model of cosmology uses, and make the problem of parameter estimation challenging. It is a common practice to employ Bayesian formalism for parameter estimation for which, in general, likelihood surface is probed. For the standard cosmological model with six parameters, likelihood surface is quite smooth and does not have local maxima, and sampling based methods like Markov Chain Monte Carlo (MCMC) method are quite successful. However, when there are a large number of parameters or the likelihood surface is not smooth, other methods may be more effective. In this paper, we have demonstrated application of another method inspired from artificial intelligence, called Particle Swarm Optimization (PSO) for estimating cosmological parameters from Cosmic Microwave Background (CMB) data taken from the WMAP satellite.

Research on Optimization of GLCM Parameter in Cell Classification

NASA Astrophysics Data System (ADS)

Zhang, Xi-Kun; Hou, Jie; Hu, Xin-Hua

2016-05-01

Real-time classification of biological cells according to their 3D morphology is highly desired in a flow cytometer setting. Gray level co-occurrence matrix (GLCM) algorithm has been developed to extract feature parameters from measured diffraction images ,which are too complicated to coordinate with the real-time system for a large amount of calculation. An optimization of GLCM algorithm is provided based on correlation analysis of GLCM parameters. The results of GLCM analysis and subsequent classification demonstrate optimized method can lower the time complexity significantly without loss of classification accuracy.

Parameter meta-optimization of metaheuristics of solving specific NP-hard facility location problem

NASA Astrophysics Data System (ADS)

Skakov, E. S.; Malysh, V. N.

2018-03-01

The aim of the work is to create an evolutionary method for optimizing the values of the control parameters of metaheuristics of solving the NP-hard facility location problem. A system analysis of the tuning process of optimization algorithms parameters is carried out. The problem of finding the parameters of a metaheuristic algorithm is formulated as a meta-optimization problem. Evolutionary metaheuristic has been chosen to perform the task of meta-optimization. Thus, the approach proposed in this work can be called “meta-metaheuristic”. Computational experiment proving the effectiveness of the procedure of tuning the control parameters of metaheuristics has been performed.

Multiresponse Optimization of Process Parameters in Turning of GFRP Using TOPSIS Method

PubMed Central

Parida, Arun Kumar; Routara, Bharat Chandra

2014-01-01

Taguchi's design of experiment is utilized to optimize the process parameters in turning operation with dry environment. Three parameters, cutting speed (v), feed (f), and depth of cut (d), with three different levels are taken for the responses like material removal rate (MRR) and surface roughness (R a). The machining is conducted with Taguchi L9 orthogonal array, and based on the S/N analysis, the optimal process parameters for surface roughness and MRR are calculated separately. Considering the larger-the-better approach, optimal process parameters for material removal rate are cutting speed at level 3, feed at level 2, and depth of cut at level 3, that is, v 3-f 2-d 3. Similarly for surface roughness, considering smaller-the-better approach, the optimal process parameters are cutting speed at level 1, feed at level 1, and depth of cut at level 3, that is, v 1-f 1-d 3. Results of the main effects plot indicate that depth of cut is the most influencing parameter for MRR but cutting speed is the most influencing parameter for surface roughness and feed is found to be the least influencing parameter for both the responses. The confirmation test is conducted for both MRR and surface roughness separately. Finally, an attempt has been made to optimize the multiresponses using technique for order preference by similarity to ideal solution (TOPSIS) with Taguchi approach. PMID:27437503

Optimizing Resource and Energy Recovery for Municipal Solid Waste Management

EPA Science Inventory

Significant reductions of carbon emissions and air quality impacts can be achieved by optimizing municipal solid waste (MSW) as a resource. Materials and discards management were found to contribute ~40% of overall U.S. GHG emissions as a result of materials extraction, transpo...

Standardless quantification by parameter optimization in electron probe microanalysis

NASA Astrophysics Data System (ADS)

Limandri, Silvina P.; Bonetto, Rita D.; Josa, Víctor Galván; Carreras, Alejo C.; Trincavelli, Jorge C.

2012-11-01

A method for standardless quantification by parameter optimization in electron probe microanalysis is presented. The method consists in minimizing the quadratic differences between an experimental spectrum and an analytical function proposed to describe it, by optimizing the parameters involved in the analytical prediction. This algorithm, implemented in the software POEMA (Parameter Optimization in Electron Probe Microanalysis), allows the determination of the elemental concentrations, along with their uncertainties. The method was tested in a set of 159 elemental constituents corresponding to 36 spectra of standards (mostly minerals) that include trace elements. The results were compared with those obtained with the commercial software GENESIS Spectrum® for standardless quantification. The quantifications performed with the method proposed here are better in the 74% of the cases studied. In addition, the performance of the method proposed is compared with the first principles standardless analysis procedure DTSA for a different data set, which excludes trace elements. The relative deviations with respect to the nominal concentrations are lower than 0.04, 0.08 and 0.35 for the 66% of the cases for POEMA, GENESIS and DTSA, respectively.

Optimization of Eisenia fetida stocking density for the bioconversion of rock phosphate enriched cow dung–waste paper mixtures

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

Unuofin, F.O., E-mail: funmifrank2009@gmail.com; Mnkeni, P.N.S., E-mail: pmnkeni@ufh.ac.za

2014-11-15

Highlights: • Vermidegradation of RP-enriched waste mixtures is dependent on E. fetida stocking density. • A stocking density of 12.5 g-worms kg{sup -1} resulted in highly humified vermicomposts. • P release from RP-enriched waste vermicomposts increases with E. fetida stocking density. • RP-enriched waste vermicomposts had no inhibitory effect on seed germination. - Abstract: Vermitechnology is gaining recognition as an environmental friendly waste management strategy. Its successful implementation requires that the key operational parameters like earthworm stocking density be established for each target waste/waste mixture. One target waste mixture in South Africa is waste paper mixed with cow dung andmore » rock phosphate (RP) for P enrichment. This study sought to establish optimal Eisenia fetida stocking density for maximum P release and rapid bioconversion of RP enriched cow dung–paper waste mixtures. E. fetida stocking densities of 0, 7.5, 12.5, 17.5 and 22.5 g-worms kg{sup −1} dry weight of cow dung–waste paper mixtures were evaluated. The stocking density of 12.5 g-worms kg{sup −1} resulted in the highest earthworm growth rate and humification of the RP enriched waste mixture as reflected by a C:N ratio of <12 and a humic acid/fulvic acid ratio of >1.9 in final vermicomposts. A germination test revealed that the resultant vermicompost had no inhibitory effect on the germination of tomato, carrot, and radish. Extractable P increased with stocking density up to 22.5 g-worm kg{sup −1} feedstock suggesting that for maximum P release from RP enriched wastes a high stocking density should be considered.« less

Optimal line drop compensation parameters under multi-operating conditions

NASA Astrophysics Data System (ADS)

Wan, Yuan; Li, Hang; Wang, Kai; He, Zhe

2017-01-01

Line Drop Compensation (LDC) is a main function of Reactive Current Compensation (RCC) which is developed to improve voltage stability. While LDC has benefit to voltage, it may deteriorate the small-disturbance rotor angle stability of power system. In present paper, an intelligent algorithm which is combined by Genetic Algorithm (GA) and Backpropagation Neural Network (BPNN) is proposed to optimize parameters of LDC. The objective function proposed in present paper takes consideration of voltage deviation and power system oscillation minimal damping ratio under multi-operating conditions. A simulation based on middle area of Jiangxi province power system is used to demonstrate the intelligent algorithm. The optimization result shows that coordinate optimized parameters can meet the multioperating conditions requirement and improve voltage stability as much as possible while guaranteeing enough damping ratio.

Optimal Design of Material and Process Parameters in Powder Injection Molding

NASA Astrophysics Data System (ADS)

Ayad, G.; Barriere, T.; Gelin, J. C.; Song, J.; Liu, B.

2007-04-01

The paper is concerned with optimization and parametric identification for the different stages in Powder Injection Molding process that consists first in injection of powder mixture with polymer binder and then to the sintering of the resulting powders part by solid state diffusion. In the first part, one describes an original methodology to optimize the process and geometry parameters in injection stage based on the combination of design of experiments and an adaptive Response Surface Modeling. Then the second part of the paper describes the identification strategy that one proposes for the sintering stage, using the identification of sintering parameters from dilatometeric curves followed by the optimization of the sintering process. The proposed approaches are applied to the optimization of material and process parameters for manufacturing a ceramic femoral implant. One demonstrates that the proposed approach give satisfactory results.

Investigation of thermodynamic parameters in the thermal decomposition of plastic waste-waste lube oil compounds.

PubMed

Kim, Yong Sang; Kim, Young Seok; Kim, Sung Hyun

2010-07-01

Thermal decomposition properties of plastic waste-waste lube oil compounds were investigated under nonisothermal conditions. Polyethylene (PE), polypropylene (PP), polystyrene (PS), and polyethylene terephthalate (PET) were selected as representative household plastic wastes. A plastic waste mixture (PWM) and waste lube oil (WLO) were mixed with mixing ratios of 33, 50, and 67 (w/w) % on a PWM weight basis, and thermogravimetric (TG) experiments were performed from 25 to 600 degrees C. The Flynn-Wall method and the Ozawa-Flynn-Wall method were used for analyses of thermodynamic parameters. In this study, activation energies of PWM/WLO compounds ranged from 73.4 to 229.6 kJ/mol between 0.2 and 0.8 of normalized mass conversions, and the 50% PWM/WLO compound had lower activation energies and enthalpies among the PWM/WLO samples at each mass conversion. At the point of maximum differential mass conversion, the analyzed activation energies, enthalpies, entropies, and Gibbs free energies indicated that mixing PWM and WLO has advantages in reducing energy to decrease the degree of disorder. However, no difference in overall energy that would require overcoming both thermal decomposition reactions and degree of disorder was observed among PWM/WLO compounds under these experimental conditions.

Estimating parameters with pre-specified accuracies in distributed parameter systems using optimal experiment design

NASA Astrophysics Data System (ADS)

Potters, M. G.; Bombois, X.; Mansoori, M.; Hof, Paul M. J. Van den

2016-08-01

Estimation of physical parameters in dynamical systems driven by linear partial differential equations is an important problem. In this paper, we introduce the least costly experiment design framework for these systems. It enables parameter estimation with an accuracy that is specified by the experimenter prior to the identification experiment, while at the same time minimising the cost of the experiment. We show how to adapt the classical framework for these systems and take into account scaling and stability issues. We also introduce a progressive subdivision algorithm that further generalises the experiment design framework in the sense that it returns the lowest cost by finding the optimal input signal, and optimal sensor and actuator locations. Our methodology is then applied to a relevant problem in heat transfer studies: estimation of conductivity and diffusivity parameters in front-face experiments. We find good correspondence between numerical and theoretical results.

Optimization of a centrifugal compressor impeller using CFD: the choice of simulation model parameters

NASA Astrophysics Data System (ADS)

Neverov, V. V.; Kozhukhov, Y. V.; Yablokov, A. M.; Lebedev, A. A.

2017-08-01

Nowadays the optimization using computational fluid dynamics (CFD) plays an important role in the design process of turbomachines. However, for the successful and productive optimization it is necessary to define a simulation model correctly and rationally. The article deals with the choice of a grid and computational domain parameters for optimization of centrifugal compressor impellers using computational fluid dynamics. Searching and applying optimal parameters of the grid model, the computational domain and solver settings allows engineers to carry out a high-accuracy modelling and to use computational capability effectively. The presented research was conducted using Numeca Fine/Turbo package with Spalart-Allmaras and Shear Stress Transport turbulence models. Two radial impellers was investigated: the high-pressure at ψT=0.71 and the low-pressure at ψT=0.43. The following parameters of the computational model were considered: the location of inlet and outlet boundaries, type of mesh topology, size of mesh and mesh parameter y+. Results of the investigation demonstrate that the choice of optimal parameters leads to the significant reduction of the computational time. Optimal parameters in comparison with non-optimal but visually similar parameters can reduce the calculation time up to 4 times. Besides, it is established that some parameters have a major impact on the result of modelling.

Optimal routing for efficient municipal solid waste transportation by using ArcGIS application in Chennai, India.

PubMed

Sanjeevi, V; Shahabudeen, P

2016-01-01

Worldwide, about US$410 billion is spent every year to manage four billion tonnes of municipal solid wastes (MSW). Transport cost alone constitutes more than 50% of the total expenditure on solid waste management (SWM) in major cities of the developed world and the collection and transport cost is about 85% in the developing world. There is a need to improve the ability of the city administrators to manage the municipal solid wastes with least cost. Since 2000, new technologies such as geographical information system (GIS) and related optimization software have been used to optimize the haul route distances. The city limits of Chennai were extended from 175 to 426 km(2) in 2011, leading to sub-optimum levels in solid waste transportation of 4840 tonnes per day. After developing a spatial database for the whole of Chennai with 200 wards, the route optimization procedures have been run for the transport of solid wastes from 13 wards (generating nodes) to one transfer station (intermediary before landfill), using ArcGIS. The optimization process reduced the distances travelled by 9.93%. The annual total cost incurred for this segment alone is Indian Rupees (INR) 226.1 million. Savings in terms of time taken for both the current and shortest paths have also been computed, considering traffic conditions. The overall savings are thus very meaningful and call for optimization of the haul routes for the entire Chennai. © The Author(s) 2015.

DOUBLE SHELL TANK (DST) INTEGRITY PROJECT HIGH LEVEL WASTE CHEMISTRY OPTIMIZATION

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

WASHENFELDER DJ

2008-01-22

The U.S. Department of Energy's Office (DOE) of River Protection (ORP) has a continuing program for chemical optimization to better characterize corrosion behavior of High-Level Waste (HLW). The DOE controls the chemistry in its HLW to minimize the propensity of localized corrosion, such as pitting, and stress corrosion cracking (SCC) in nitrate-containing solutions. By improving the control of localized corrosion and SCC, the ORP can increase the life of the Double-Shell Tank (DST) carbon steel structural components and reduce overall mission costs. The carbon steel tanks at the Hanford Site are critical to the mission of safely managing stored HLWmore » until it can be treated for disposal. The DOE has historically used additions of sodium hydroxide to retard corrosion processes in HLW tanks. This also increases the amount of waste to be treated. The reactions with carbon dioxide from the air and solid chemical species in the tank continually deplete the hydroxide ion concentration, which then requires continued additions. The DOE can reduce overall costs for caustic addition and treatment of waste, and more effectively utilize waste storage capacity by minimizing these chemical additions. Hydroxide addition is a means to control localized and stress corrosion cracking in carbon steel by providing a passive environment. The exact mechanism that causes nitrate to drive the corrosion process is not yet clear. The SCC is less of a concern in the newer stress relieved double shell tanks due to reduced residual stress. The optimization of waste chemistry will further reduce the propensity for SCC. The corrosion testing performed to optimize waste chemistry included cyclic potentiodynamic volarization studies. slow strain rate tests. and stress intensity factor/crack growth rate determinations. Laboratory experimental evidence suggests that nitrite is a highly effective:inhibitor for pitting and SCC in alkaline nitrate environments. Revision of the corrosion control

A systematic review on the composting of green waste: Feedstock quality and optimization strategies.

PubMed

Reyes-Torres, M; Oviedo-Ocaña, E R; Dominguez, I; Komilis, D; Sánchez, A

2018-04-27

Green waste (GW) is an important fraction of municipal solid waste (MSW). The composting of lignocellulosic GW is challenging due to its low decomposition rate. Recently, an increasing number of studies that include strategies to optimize GW composting appeared in the literature. This literature review focuses on the physicochemical quality of GW and on the effect of strategies used to improve the process and product quality. A systematic search was carried out, using keywords, and 447 papers published between 2002 and 2018 were identified. After a screening process, 41 papers addressing feedstock quality and 32 papers on optimization strategies were selected to be reviewed and analyzed in detail. The GW composition is highly variable due to the diversity of the source materials, the type of vegetation, and climatic conditions. This variability limits a strict categorization of the GW physicochemical characteristics. However, this research established that the predominant features of GW are a C/N ratio higher than 25, a deficit in important nutrients, namely nitrogen (0.5-1.5% db), phosphorous (0.1-0.2% db) and potassium (0.4-0.8% db) and a high content of recalcitrant organic compounds (e.g. lignin). The promising strategies to improve composting of GW were: i) GW particle size reduction (e.g. shredding and separation of GW fractions); ii) addition of energy amendments (e.g. non-refined sugar, phosphate rock, food waste, volatile ashes), bulking materials (e.g. biocarbon, wood chips), or microbial inoculum (e.g. fungal consortia); and iii) variations in operating parameters (aeration, temperature, and two-phase composting). These alternatives have successfully led to the reduction of process length and have managed to transform recalcitrant substances to a high-quality end-product. Copyright © 2018 Elsevier Ltd. All rights reserved.

Parameter optimization of electrochemical machining process using black hole algorithm

NASA Astrophysics Data System (ADS)

Singh, Dinesh; Shukla, Rajkamal

2017-12-01

Advanced machining processes are significant as higher accuracy in machined component is required in the manufacturing industries. Parameter optimization of machining processes gives optimum control to achieve the desired goals. In this paper, electrochemical machining (ECM) process is considered to evaluate the performance of the considered process using black hole algorithm (BHA). BHA considers the fundamental idea of a black hole theory and it has less operating parameters to tune. The two performance parameters, material removal rate (MRR) and overcut (OC) are considered separately to get optimum machining parameter settings using BHA. The variations of process parameters with respect to the performance parameters are reported for better and effective understanding of the considered process using single objective at a time. The results obtained using BHA are found better while compared with results of other metaheuristic algorithms, such as, genetic algorithm (GA), artificial bee colony (ABC) and bio-geography based optimization (BBO) attempted by previous researchers.

Human-in-the-loop Bayesian optimization of wearable device parameters

PubMed Central

Malcolm, Philippe; Speeckaert, Jozefien; Siviy, Christoper J.; Walsh, Conor J.; Kuindersma, Scott

2017-01-01

The increasing capabilities of exoskeletons and powered prosthetics for walking assistance have paved the way for more sophisticated and individualized control strategies. In response to this opportunity, recent work on human-in-the-loop optimization has considered the problem of automatically tuning control parameters based on realtime physiological measurements. However, the common use of metabolic cost as a performance metric creates significant experimental challenges due to its long measurement times and low signal-to-noise ratio. We evaluate the use of Bayesian optimization—a family of sample-efficient, noise-tolerant, and global optimization methods—for quickly identifying near-optimal control parameters. To manage experimental complexity and provide comparisons against related work, we consider the task of minimizing metabolic cost by optimizing walking step frequencies in unaided human subjects. Compared to an existing approach based on gradient descent, Bayesian optimization identified a near-optimal step frequency with a faster time to convergence (12 minutes, p < 0.01), smaller inter-subject variability in convergence time (± 2 minutes, p < 0.01), and lower overall energy expenditure (p < 0.01). PMID:28926613

Optimal structure and parameter learning of Ising models

DOE PAGES

Lokhov, Andrey; Vuffray, Marc Denis; Misra, Sidhant; ...

2018-03-16

Reconstruction of the structure and parameters of an Ising model from binary samples is a problem of practical importance in a variety of disciplines, ranging from statistical physics and computational biology to image processing and machine learning. The focus of the research community shifted toward developing universal reconstruction algorithms that are both computationally efficient and require the minimal amount of expensive data. Here, we introduce a new method, interaction screening, which accurately estimates model parameters using local optimization problems. The algorithm provably achieves perfect graph structure recovery with an information-theoretically optimal number of samples, notably in the low-temperature regime, whichmore » is known to be the hardest for learning. Here, the efficacy of interaction screening is assessed through extensive numerical tests on synthetic Ising models of various topologies with different types of interactions, as well as on real data produced by a D-Wave quantum computer. Finally, this study shows that the interaction screening method is an exact, tractable, and optimal technique that universally solves the inverse Ising problem.« less

Optimal structure and parameter learning of Ising models

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

Lokhov, Andrey; Vuffray, Marc Denis; Misra, Sidhant

Reconstruction of the structure and parameters of an Ising model from binary samples is a problem of practical importance in a variety of disciplines, ranging from statistical physics and computational biology to image processing and machine learning. The focus of the research community shifted toward developing universal reconstruction algorithms that are both computationally efficient and require the minimal amount of expensive data. Here, we introduce a new method, interaction screening, which accurately estimates model parameters using local optimization problems. The algorithm provably achieves perfect graph structure recovery with an information-theoretically optimal number of samples, notably in the low-temperature regime, whichmore » is known to be the hardest for learning. Here, the efficacy of interaction screening is assessed through extensive numerical tests on synthetic Ising models of various topologies with different types of interactions, as well as on real data produced by a D-Wave quantum computer. Finally, this study shows that the interaction screening method is an exact, tractable, and optimal technique that universally solves the inverse Ising problem.« less

Multi-Response Parameter Interval Sensitivity and Optimization for the Composite Tape Winding Process.

PubMed

Deng, Bo; Shi, Yaoyao; Yu, Tao; Kang, Chao; Zhao, Pan

2018-01-31

The composite tape winding process, which utilizes a tape winding machine and prepreg tapes, provides a promising way to improve the quality of composite products. Nevertheless, the process parameters of composite tape winding have crucial effects on the tensile strength and void content, which are closely related to the performances of the winding products. In this article, two different object values of winding products, including mechanical performance (tensile strength) and a physical property (void content), were respectively calculated. Thereafter, the paper presents an integrated methodology by combining multi-parameter relative sensitivity analysis and single-parameter sensitivity analysis to obtain the optimal intervals of the composite tape winding process. First, the global multi-parameter sensitivity analysis method was applied to investigate the sensitivity of each parameter in the tape winding processing. Then, the local single-parameter sensitivity analysis method was employed to calculate the sensitivity of a single parameter within the corresponding range. Finally, the stability and instability ranges of each parameter were distinguished. Meanwhile, the authors optimized the process parameter ranges and provided comprehensive optimized intervals of the winding parameters. The verification test validated that the optimized intervals of the process parameters were reliable and stable for winding products manufacturing.

Multi-Response Parameter Interval Sensitivity and Optimization for the Composite Tape Winding Process

PubMed Central

Yu, Tao; Kang, Chao; Zhao, Pan

2018-01-01

The composite tape winding process, which utilizes a tape winding machine and prepreg tapes, provides a promising way to improve the quality of composite products. Nevertheless, the process parameters of composite tape winding have crucial effects on the tensile strength and void content, which are closely related to the performances of the winding products. In this article, two different object values of winding products, including mechanical performance (tensile strength) and a physical property (void content), were respectively calculated. Thereafter, the paper presents an integrated methodology by combining multi-parameter relative sensitivity analysis and single-parameter sensitivity analysis to obtain the optimal intervals of the composite tape winding process. First, the global multi-parameter sensitivity analysis method was applied to investigate the sensitivity of each parameter in the tape winding processing. Then, the local single-parameter sensitivity analysis method was employed to calculate the sensitivity of a single parameter within the corresponding range. Finally, the stability and instability ranges of each parameter were distinguished. Meanwhile, the authors optimized the process parameter ranges and provided comprehensive optimized intervals of the winding parameters. The verification test validated that the optimized intervals of the process parameters were reliable and stable for winding products manufacturing. PMID:29385048

Case study: Optimizing fault model input parameters using bio-inspired algorithms

NASA Astrophysics Data System (ADS)

Plucar, Jan; Grunt, Onřej; Zelinka, Ivan

2017-07-01

We present a case study that demonstrates a bio-inspired approach in the process of finding optimal parameters for GSM fault model. This model is constructed using Petri Nets approach it represents dynamic model of GSM network environment in the suburban areas of Ostrava city (Czech Republic). We have been faced with a task of finding optimal parameters for an application that requires high amount of data transfers between the application itself and secure servers located in datacenter. In order to find the optimal set of parameters we employ bio-inspired algorithms such as Differential Evolution (DE) or Self Organizing Migrating Algorithm (SOMA). In this paper we present use of these algorithms, compare results and judge their performance in fault probability mitigation.

Optimization of municipal solid waste transportation by integrating GIS analysis, equation-based, and agent-based model.

PubMed

Nguyen-Trong, Khanh; Nguyen-Thi-Ngoc, Anh; Nguyen-Ngoc, Doanh; Dinh-Thi-Hai, Van

2017-01-01

The amount of municipal solid waste (MSW) has been increasing steadily over the last decade by reason of population rising and waste generation rate. In most of the urban areas, disposal sites are usually located outside of the urban areas due to the scarcity of land. There is no fixed route map for transportation. The current waste collection and transportation are already overloaded arising from the lack of facilities and insufficient resources. In this paper, a model for optimizing municipal solid waste collection will be proposed. Firstly, the optimized plan is developed in a static context, and then it is integrated into a dynamic context using multi-agent based modelling and simulation. A case study related to Hagiang City, Vietnam, is presented to show the efficiency of the proposed model. From the optimized results, it has been found that the cost of the MSW collection is reduced by 11.3%. Copyright © 2016 Elsevier Ltd. All rights reserved.

Parameter assessment for virtual Stackelberg game in aerodynamic shape optimization

NASA Astrophysics Data System (ADS)

Wang, Jing; Xie, Fangfang; Zheng, Yao; Zhang, Jifa

2018-05-01

In this paper, parametric studies of virtual Stackelberg game (VSG) are conducted to assess the impact of critical parameters on aerodynamic shape optimization, including design cycle, split of design variables and role assignment. Typical numerical cases, including the inverse design and drag reduction design of airfoil, have been carried out. The numerical results confirm the effectiveness and efficiency of VSG. Furthermore, the most significant parameters are identified, e.g. the increase of design cycle can improve the optimization results but it will also add computational burden. These studies will maximize the productivity of the effort in aerodynamic optimization for more complicated engineering problems, such as the multi-element airfoil and wing-body configurations.

Egg shell waste as heterogeneous nanocatalyst for biodiesel production: Optimized by response surface methodology.

PubMed

Pandit, Priti R; Fulekar, M H

2017-08-01

Worldwide consumption of hen eggs results in availability of large amount of discarded egg waste particularly egg shells. In the present study, the waste shells were utilized for the synthesis of highly active heterogeneous calcium oxide (CaO) nanocatalyst to transesterify dry biomass into methyl esters (biodiesel). The CaO nanocatalyst was synthesied by calcination-hydration-dehydration technique and fully characterized by infrared spectroscopy, X-ray powder diffraction (XRD), scanning electron microscope (SEM), transmission electron microscope (TEM), brunauer-emmett-teller (BET) elemental and thermogravimetric analysis. TEM image showed that the nano catalyst had spherical shape with average particle size of 75 nm. BET analysis indicated that the catalyst specific surface area was 16.4 m 2  g -1 with average pore diameter of 5.07 nm. The effect of nano CaO catalyst was investigated by direct transesterification of dry biomass into biodiesel along with other reaction parameters such as catalyst ratio, reaction time and stirring rate. The impact of the transesterification reaction parameters and microalgal biodiesel yield were analyzed by response surface methodology based on a full factorial, central composite design. The significance of the predicted mode was verified and 86.41% microalgal biodiesel yield was reported at optimal parameter conditions 1.7% (w/w), catalyst ratio, 3.6 h reaction time and stirring rate of 140.6 rpm. The biodiesel conversion was determined by 1 H nuclear magnetic resonance spectroscopy (NMR). The fuel properties of prepared biodiesel were found to be highly comply with the biodiesel standard ASTMD6751 and EN14214. Copyright © 2017 Elsevier Ltd. All rights reserved.

Hybrid Disease Diagnosis Using Multiobjective Optimization with Evolutionary Parameter Optimization

PubMed Central

Nalluri, MadhuSudana Rao; K., Kannan; M., Manisha

2017-01-01

With the widespread adoption of e-Healthcare and telemedicine applications, accurate, intelligent disease diagnosis systems have been profoundly coveted. In recent years, numerous individual machine learning-based classifiers have been proposed and tested, and the fact that a single classifier cannot effectively classify and diagnose all diseases has been almost accorded with. This has seen a number of recent research attempts to arrive at a consensus using ensemble classification techniques. In this paper, a hybrid system is proposed to diagnose ailments using optimizing individual classifier parameters for two classifier techniques, namely, support vector machine (SVM) and multilayer perceptron (MLP) technique. We employ three recent evolutionary algorithms to optimize the parameters of the classifiers above, leading to six alternative hybrid disease diagnosis systems, also referred to as hybrid intelligent systems (HISs). Multiple objectives, namely, prediction accuracy, sensitivity, and specificity, have been considered to assess the efficacy of the proposed hybrid systems with existing ones. The proposed model is evaluated on 11 benchmark datasets, and the obtained results demonstrate that our proposed hybrid diagnosis systems perform better in terms of disease prediction accuracy, sensitivity, and specificity. Pertinent statistical tests were carried out to substantiate the efficacy of the obtained results. PMID:29065626

Improving flood forecasting capability of physically based distributed hydrological model by parameter optimization

NASA Astrophysics Data System (ADS)

Chen, Y.; Li, J.; Xu, H.

2015-10-01

Physically based distributed hydrological models discrete the terrain of the whole catchment into a number of grid cells at fine resolution, and assimilate different terrain data and precipitation to different cells, and are regarded to have the potential to improve the catchment hydrological processes simulation and prediction capability. In the early stage, physically based distributed hydrological models are assumed to derive model parameters from the terrain properties directly, so there is no need to calibrate model parameters, but unfortunately, the uncertanties associated with this model parameter deriving is very high, which impacted their application in flood forecasting, so parameter optimization may also be necessary. There are two main purposes for this study, the first is to propose a parameter optimization method for physically based distributed hydrological models in catchment flood forecasting by using PSO algorithm and to test its competence and to improve its performances, the second is to explore the possibility of improving physically based distributed hydrological models capability in cathcment flood forecasting by parameter optimization. In this paper, based on the scalar concept, a general framework for parameter optimization of the PBDHMs for catchment flood forecasting is first proposed that could be used for all PBDHMs. Then, with Liuxihe model as the study model, which is a physically based distributed hydrological model proposed for catchment flood forecasting, the improverd Particle Swarm Optimization (PSO) algorithm is developed for the parameter optimization of Liuxihe model in catchment flood forecasting, the improvements include to adopt the linear decreasing inertia weight strategy to change the inertia weight, and the arccosine function strategy to adjust the acceleration coefficients. This method has been tested in two catchments in southern China with different sizes, and the results show that the improved PSO algorithm could be

Use of multilevel modeling for determining optimal parameters of heat supply systems

NASA Astrophysics Data System (ADS)

Stennikov, V. A.; Barakhtenko, E. A.; Sokolov, D. V.

2017-07-01

The problem of finding optimal parameters of a heat-supply system (HSS) is in ensuring the required throughput capacity of a heat network by determining pipeline diameters and characteristics and location of pumping stations. Effective methods for solving this problem, i.e., the method of stepwise optimization based on the concept of dynamic programming and the method of multicircuit optimization, were proposed in the context of the hydraulic circuit theory developed at Melentiev Energy Systems Institute (Siberian Branch, Russian Academy of Sciences). These methods enable us to determine optimal parameters of various types of piping systems due to flexible adaptability of the calculation procedure to intricate nonlinear mathematical models describing features of used equipment items and methods of their construction and operation. The new and most significant results achieved in developing methodological support and software for finding optimal parameters of complex heat supply systems are presented: a new procedure for solving the problem based on multilevel decomposition of a heat network model that makes it possible to proceed from the initial problem to a set of interrelated, less cumbersome subproblems with reduced dimensionality; a new algorithm implementing the method of multicircuit optimization and focused on the calculation of a hierarchical model of a heat supply system; the SOSNA software system for determining optimum parameters of intricate heat-supply systems and implementing the developed methodological foundation. The proposed procedure and algorithm enable us to solve engineering problems of finding the optimal parameters of multicircuit heat supply systems having large (real) dimensionality, and are applied in solving urgent problems related to the optimal development and reconstruction of these systems. The developed methodological foundation and software can be used for designing heat supply systems in the Central and the Admiralty regions in

Multi-parameter optimization of piezoelectric actuators for multi-mode active vibration control of cylindrical shells

NASA Astrophysics Data System (ADS)

Hu, K. M.; Li, Hua

2018-07-01

A novel technique for the multi-parameter optimization of distributed piezoelectric actuators is presented in this paper. The proposed method is designed to improve the performance of multi-mode vibration control in cylindrical shells. The optimization parameters of actuator patch configuration include position, size, and tilt angle. The modal control force of tilted orthotropic piezoelectric actuators is derived and the multi-parameter cylindrical shell optimization model is established. The linear quadratic energy index is employed as the optimization criterion. A geometric constraint is proposed to prevent overlap between tilted actuators, which is plugged into a genetic algorithm to search the optimal configuration parameters. A simply-supported closed cylindrical shell with two actuators serves as a case study. The vibration control efficiencies of various parameter sets are evaluated via frequency response and transient response simulations. The results show that the linear quadratic energy indexes of position and size optimization decreased by 14.0% compared to position optimization; those of position and tilt angle optimization decreased by 16.8%; and those of position, size, and tilt angle optimization decreased by 25.9%. It indicates that, adding configuration optimization parameters is an efficient approach to improving the vibration control performance of piezoelectric actuators on shells.

Optimizing supercritical carbon dioxide in the inactivation of bacteria in clinical solid waste by using response surface methodology

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

Hossain, Md. Sohrab; Nik Ab Rahman, Nik Norulaini; Balakrishnan, Venugopal

2015-04-15

Highlights: • Supercritical carbon dioxide sterilization of clinical solid waste. • Inactivation of bacteria in clinical solid waste using supercritical carbon dioxide. • Reduction of the hazardous exposure of clinical solid waste. • Optimization of the supercritical carbon dioxide experimental conditions. - Abstract: Clinical solid waste (CSW) poses a challenge to health care facilities because of the presence of pathogenic microorganisms, leading to concerns in the effective sterilization of the CSW for safe handling and elimination of infectious disease transmission. In the present study, supercritical carbon dioxide (SC-CO{sub 2}) was applied to inactivate gram-positive Staphylococcus aureus, Enterococcus faecalis, Bacillus subtilis,more » and gram-negative Escherichia coli in CSW. The effects of SC-CO{sub 2} sterilization parameters such as pressure, temperature, and time were investigated and optimized by response surface methodology (RSM). Results showed that the data were adequately fitted into the second-order polynomial model. The linear quadratic terms and interaction between pressure and temperature had significant effects on the inactivation of S. aureus, E. coli, E. faecalis, and B. subtilis in CSW. Optimum conditions for the complete inactivation of bacteria within the experimental range of the studied variables were 20 MPa, 60 °C, and 60 min. The SC-CO{sub 2}-treated bacterial cells, observed under a scanning electron microscope, showed morphological changes, including cell breakage and dislodged cell walls, which could have caused the inactivation. This espouses the inference that SC-CO{sub 2} exerts strong inactivating effects on the bacteria present in CSW, and has the potential to be used in CSW management for the safe handling and recycling-reuse of CSW materials.« less

Optimization of squalene produced from crude palm oil waste

NASA Astrophysics Data System (ADS)

Wandira, Irda; Legowo, Evita H.; Widiputri, Diah I.

2017-01-01

Squalene is a hydrocarbon originally and still mostly extracted from shark liver oil. Due to environmental issues over shark hunting, there have been efforts to extract squalene from alternative sources, such as Palm Fatty Acid Distillate (PFAD), one of crude palm oil (CPO) wastes. Previous researches have shown that squalene can be extracted from PFAD using saponification process followed with liquid-liquid extraction process although the method had yet to be optimized in order to optimize the amount of squalene extracted from PFAD. The optimization was done by optimizing both processes of squalene extraction method: saponification and liquid-liquid extraction. The factors utilized in the saponification process optimization were KOH concentration and saponification duration while during the liquid-liquid extraction (LLE) process optimization, the factors used were the volumes of distilled water and dichloromethane. The optimum percentage of squalene content in the extract (24.08%) was achieved by saponifying the PFAD with 50%w/v KOH for 60 minutes and subjecting the saponified PFAD to LLE, utilizing 100 ml of distilled water along with 3 times addition of fresh dichloromethane, 75 ml each; those factors would be utilized in the optimum squalene extraction method.

The solution of private problems for optimization heat exchangers parameters

NASA Astrophysics Data System (ADS)

Melekhin, A.

2017-11-01

The relevance of the topic due to the decision of problems of the economy of resources in heating systems of buildings. To solve this problem we have developed an integrated method of research which allows solving tasks on optimization of parameters of heat exchangers. This method decides multicriteria optimization problem with the program nonlinear optimization on the basis of software with the introduction of an array of temperatures obtained using thermography. The author have developed a mathematical model of process of heat exchange in heat exchange surfaces of apparatuses with the solution of multicriteria optimization problem and check its adequacy to the experimental stand in the visualization of thermal fields, an optimal range of managed parameters influencing the process of heat exchange with minimal metal consumption and the maximum heat output fin heat exchanger, the regularities of heat exchange process with getting generalizing dependencies distribution of temperature on the heat-release surface of the heat exchanger vehicles, defined convergence of the results of research in the calculation on the basis of theoretical dependencies and solving mathematical model.

Global Parameter Optimization of CLM4.5 Using Sparse-Grid Based Surrogates

NASA Astrophysics Data System (ADS)

Lu, D.; Ricciuto, D. M.; Gu, L.

2016-12-01

Calibration of the Community Land Model (CLM) is challenging because of its model complexity, large parameter sets, and significant computational requirements. Therefore, only a limited number of simulations can be allowed in any attempt to find a near-optimal solution within an affordable time. The goal of this study is to calibrate some of the CLM parameters in order to improve model projection of carbon fluxes. To this end, we propose a computationally efficient global optimization procedure using sparse-grid based surrogates. We first use advanced sparse grid (SG) interpolation to construct a surrogate system of the actual CLM model, and then we calibrate the surrogate model in the optimization process. As the surrogate model is a polynomial whose evaluation is fast, it can be efficiently evaluated with sufficiently large number of times in the optimization, which facilitates the global search. We calibrate five parameters against 12 months of GPP, NEP, and TLAI data from the U.S. Missouri Ozark (US-MOz) tower. The results indicate that an accurate surrogate model can be created for the CLM4.5 with a relatively small number of SG points (i.e., CLM4.5 simulations), and the application of the optimized parameters leads to a higher predictive capacity than the default parameter values in the CLM4.5 for the US-MOz site.

An effective parameter optimization with radiation balance constraints in the CAM5

NASA Astrophysics Data System (ADS)

Wu, L.; Zhang, T.; Qin, Y.; Lin, Y.; Xue, W.; Zhang, M.

2017-12-01

Uncertain parameters in physical parameterizations of General Circulation Models (GCMs) greatly impact model performance. Traditional parameter tuning methods are mostly unconstrained optimization, leading to the simulation results with optimal parameters may not meet the conditions that models have to keep. In this study, the radiation balance constraint is taken as an example, which is involved in the automatic parameter optimization procedure. The Lagrangian multiplier method is used to solve this optimization problem with constrains. In our experiment, we use CAM5 atmosphere model under 5-yr AMIP simulation with prescribed seasonal climatology of SST and sea ice. We consider the synthesized metrics using global means of radiation, precipitation, relative humidity, and temperature as the goal of optimization, and simultaneously consider the conditions that FLUT and FSNTOA should satisfy as constraints. The global average of the output variables FLUT and FSNTOA are set to be approximately equal to 240 Wm-2 in CAM5. Experiment results show that the synthesized metrics is 13.6% better than the control run. At the same time, both FLUT and FSNTOA are close to the constrained conditions. The FLUT condition is well satisfied, which is obviously better than the average annual FLUT obtained with the default parameters. The FSNTOA has a slight deviation from the observed value, but the relative error is less than 7.7‰.

Assessing the applicability of WRF optimal parameters under the different precipitation simulations in the Greater Beijing Area

NASA Astrophysics Data System (ADS)

Di, Zhenhua; Duan, Qingyun; Wang, Chen; Ye, Aizhong; Miao, Chiyuan; Gong, Wei

2018-03-01

Forecasting skills of the complex weather and climate models have been improved by tuning the sensitive parameters that exert the greatest impact on simulated results based on more effective optimization methods. However, whether the optimal parameter values are still work when the model simulation conditions vary, which is a scientific problem deserving of study. In this study, a highly-effective optimization method, adaptive surrogate model-based optimization (ASMO), was firstly used to tune nine sensitive parameters from four physical parameterization schemes of the Weather Research and Forecasting (WRF) model to obtain better summer precipitation forecasting over the Greater Beijing Area in China. Then, to assess the applicability of the optimal parameter values, simulation results from the WRF model with default and optimal parameter values were compared across precipitation events, boundary conditions, spatial scales, and physical processes in the Greater Beijing Area. The summer precipitation events from 6 years were used to calibrate and evaluate the optimal parameter values of WRF model. Three boundary data and two spatial resolutions were adopted to evaluate the superiority of the calibrated optimal parameters to default parameters under the WRF simulations with different boundary conditions and spatial resolutions, respectively. Physical interpretations of the optimal parameters indicating how to improve precipitation simulation results were also examined. All the results showed that the optimal parameters obtained by ASMO are superior to the default parameters for WRF simulations for predicting summer precipitation in the Greater Beijing Area because the optimal parameters are not constrained by specific precipitation events, boundary conditions, and spatial resolutions. The optimal values of the nine parameters were determined from 127 parameter samples using the ASMO method, which showed that the ASMO method is very highly-efficient for optimizing WRF

Improving flood forecasting capability of physically based distributed hydrological models by parameter optimization

NASA Astrophysics Data System (ADS)

Chen, Y.; Li, J.; Xu, H.

2016-01-01

Physically based distributed hydrological models (hereafter referred to as PBDHMs) divide the terrain of the whole catchment into a number of grid cells at fine resolution and assimilate different terrain data and precipitation to different cells. They are regarded to have the potential to improve the catchment hydrological process simulation and prediction capability. In the early stage, physically based distributed hydrological models are assumed to derive model parameters from the terrain properties directly, so there is no need to calibrate model parameters. However, unfortunately the uncertainties associated with this model derivation are very high, which impacted their application in flood forecasting, so parameter optimization may also be necessary. There are two main purposes for this study: the first is to propose a parameter optimization method for physically based distributed hydrological models in catchment flood forecasting by using particle swarm optimization (PSO) algorithm and to test its competence and to improve its performances; the second is to explore the possibility of improving physically based distributed hydrological model capability in catchment flood forecasting by parameter optimization. In this paper, based on the scalar concept, a general framework for parameter optimization of the PBDHMs for catchment flood forecasting is first proposed that could be used for all PBDHMs. Then, with the Liuxihe model as the study model, which is a physically based distributed hydrological model proposed for catchment flood forecasting, the improved PSO algorithm is developed for the parameter optimization of the Liuxihe model in catchment flood forecasting. The improvements include adoption of the linearly decreasing inertia weight strategy to change the inertia weight and the arccosine function strategy to adjust the acceleration coefficients. This method has been tested in two catchments in southern China with different sizes, and the results show

Using string invariants for prediction searching for optimal parameters

NASA Astrophysics Data System (ADS)

Bundzel, Marek; Kasanický, Tomáš; Pinčák, Richard

2016-02-01

We have developed a novel prediction method based on string invariants. The method does not require learning but a small set of parameters must be set to achieve optimal performance. We have implemented an evolutionary algorithm for the parametric optimization. We have tested the performance of the method on artificial and real world data and compared the performance to statistical methods and to a number of artificial intelligence methods. We have used data and the results of a prediction competition as a benchmark. The results show that the method performs well in single step prediction but the method's performance for multiple step prediction needs to be improved. The method works well for a wide range of parameters.

Parameter Selection and Performance Comparison of Particle Swarm Optimization in Sensor Networks Localization.

PubMed

Cui, Huanqing; Shu, Minglei; Song, Min; Wang, Yinglong

2017-03-01

Localization is a key technology in wireless sensor networks. Faced with the challenges of the sensors' memory, computational constraints, and limited energy, particle swarm optimization has been widely applied in the localization of wireless sensor networks, demonstrating better performance than other optimization methods. In particle swarm optimization-based localization algorithms, the variants and parameters should be chosen elaborately to achieve the best performance. However, there is a lack of guidance on how to choose these variants and parameters. Further, there is no comprehensive performance comparison among particle swarm optimization algorithms. The main contribution of this paper is three-fold. First, it surveys the popular particle swarm optimization variants and particle swarm optimization-based localization algorithms for wireless sensor networks. Secondly, it presents parameter selection of nine particle swarm optimization variants and six types of swarm topologies by extensive simulations. Thirdly, it comprehensively compares the performance of these algorithms. The results show that the particle swarm optimization with constriction coefficient using ring topology outperforms other variants and swarm topologies, and it performs better than the second-order cone programming algorithm.

Parameter Selection and Performance Comparison of Particle Swarm Optimization in Sensor Networks Localization

PubMed Central

Cui, Huanqing; Shu, Minglei; Song, Min; Wang, Yinglong

2017-01-01

Localization is a key technology in wireless sensor networks. Faced with the challenges of the sensors’ memory, computational constraints, and limited energy, particle swarm optimization has been widely applied in the localization of wireless sensor networks, demonstrating better performance than other optimization methods. In particle swarm optimization-based localization algorithms, the variants and parameters should be chosen elaborately to achieve the best performance. However, there is a lack of guidance on how to choose these variants and parameters. Further, there is no comprehensive performance comparison among particle swarm optimization algorithms. The main contribution of this paper is three-fold. First, it surveys the popular particle swarm optimization variants and particle swarm optimization-based localization algorithms for wireless sensor networks. Secondly, it presents parameter selection of nine particle swarm optimization variants and six types of swarm topologies by extensive simulations. Thirdly, it comprehensively compares the performance of these algorithms. The results show that the particle swarm optimization with constriction coefficient using ring topology outperforms other variants and swarm topologies, and it performs better than the second-order cone programming algorithm. PMID:28257060

Optimization of municipal solid waste management in Port Said - Egypt

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

Badran, M.F.; El-Haggar, S.M.

2006-07-01

Optimization of solid waste management systems using operational research methodologies has not yet been applied in any Egyptian governorate. In this paper, a proposed model for a municipal solid waste management system in Port Said, Egypt is presented. It includes the use of the concept of collection stations, which have not yet been used in Egypt. Mixed integer programming is used to model the proposed system and its solution is performed using MPL software V4.2. The results show that the best model would include 27 collection stations of 15-ton daily capacity and 2 collection stations of 10 ton daily capacity.more » Any transfer of waste between the collection station and the landfill should not occur. Moreover, the flow of the district waste should not be confined to the district collection stations. The cost of the objective function for this solution is 10,122 LE/day (equivalent to US$1716). After further calculations, the profit generated by the proposed model is 49,655.8 LE/day (equivalent to US$8418.23)« less

Aerodynamic optimization by simultaneously updating flow variables and design parameters

NASA Technical Reports Server (NTRS)

Rizk, M. H.

1990-01-01

The application of conventional optimization schemes to aerodynamic design problems leads to inner-outer iterative procedures that are very costly. An alternative approach is presented based on the idea of updating the flow variable iterative solutions and the design parameter iterative solutions simultaneously. Two schemes based on this idea are applied to problems of correcting wind tunnel wall interference and optimizing advanced propeller designs. The first of these schemes is applicable to a limited class of two-design-parameter problems with an equality constraint. It requires the computation of a single flow solution. The second scheme is suitable for application to general aerodynamic problems. It requires the computation of several flow solutions in parallel. In both schemes, the design parameters are updated as the iterative flow solutions evolve. Computations are performed to test the schemes' efficiency, accuracy, and sensitivity to variations in the computational parameters.

Optimization of laser butt welding parameters with multiple performance characteristics

NASA Astrophysics Data System (ADS)

Sathiya, P.; Abdul Jaleel, M. Y.; Katherasan, D.; Shanmugarajan, B.

2011-04-01

This paper presents a study carried out on 3.5 kW cooled slab laser welding of 904 L super austenitic stainless steel. The joints have butts welded with different shielding gases, namely argon, helium and nitrogen, at a constant flow rate. Super austenitic stainless steel (SASS) normally contains high amount of Mo, Cr, Ni, N and Mn. The mechanical properties are controlled to obtain good welded joints. The quality of the joint is evaluated by studying the features of weld bead geometry, such as bead width (BW) and depth of penetration (DOP). In this paper, the tensile strength and bead profiles (BW and DOP) of laser welded butt joints made of AISI 904 L SASS are investigated. The Taguchi approach is used as a statistical design of experiment (DOE) technique for optimizing the selected welding parameters. Grey relational analysis and the desirability approach are applied to optimize the input parameters by considering multiple output variables simultaneously. Confirmation experiments have also been conducted for both of the analyses to validate the optimized parameters.

Application of Differential Evolutionary Optimization Methodology for Parameter Structure Identification in Groundwater Modeling

NASA Astrophysics Data System (ADS)

Chiu, Y.; Nishikawa, T.

2013-12-01

With the increasing complexity of parameter-structure identification (PSI) in groundwater modeling, there is a need for robust, fast, and accurate optimizers in the groundwater-hydrology field. For this work, PSI is defined as identifying parameter dimension, structure, and value. In this study, Voronoi tessellation and differential evolution (DE) are used to solve the optimal PSI problem. Voronoi tessellation is used for automatic parameterization, whereby stepwise regression and the error covariance matrix are used to determine the optimal parameter dimension. DE is a novel global optimizer that can be used to solve nonlinear, nondifferentiable, and multimodal optimization problems. It can be viewed as an improved version of genetic algorithms and employs a simple cycle of mutation, crossover, and selection operations. DE is used to estimate the optimal parameter structure and its associated values. A synthetic numerical experiment of continuous hydraulic conductivity distribution was conducted to demonstrate the proposed methodology. The results indicate that DE can identify the global optimum effectively and efficiently. A sensitivity analysis of the control parameters (i.e., the population size, mutation scaling factor, crossover rate, and mutation schemes) was performed to examine their influence on the objective function. The proposed DE was then applied to solve a complex parameter-estimation problem for a small desert groundwater basin in Southern California. Hydraulic conductivity, specific yield, specific storage, fault conductance, and recharge components were estimated simultaneously. Comparison of DE and a traditional gradient-based approach (PEST) shows DE to be more robust and efficient. The results of this work not only provide an alternative for PSI in groundwater models, but also extend DE applications towards solving complex, regional-scale water management optimization problems.

Geographic information system-based healthcare waste management planning for treatment site location and optimal transportation routeing.

PubMed

Shanmugasundaram, Jothiganesh; Soulalay, Vongdeuane; Chettiyappan, Visvanathan

2012-06-01

In Lao People's Democratic Republic (Lao PDR), a growth of healthcare centres, and the environmental hazards and public health risks typically accompanying them, increased the need for healthcare waste (HCW) management planning. An effective planning of an HCW management system including components such as the treatment plant siting and an optimized routeing system for collection and transportation of waste is deemed important. National government offices at developing countries often lack the proper tools and methodologies because of the high costs usually associated with them. However, this study attempts to demonstrate the use of an inexpensive GIS modelling tool for healthcare waste management in the country. Two areas were designed for this study on HCW management, including: (a) locating centralized treatment plants and designing optimum travel routes for waste collection from nearby healthcare facilities; and (b) utilizing existing hospital incinerators and designing optimum routes for collecting waste from nearby healthcare facilities. Spatial analysis paved the way to understand the spatial distribution of healthcare wastes and to identify hotspots of higher waste generating locations. Optimal route models were designed for collecting and transporting HCW to treatment plants, which also highlights constraints in collecting and transporting waste for treatment and disposal. The proposed model can be used as a decision support tool for the efficient management of hospital wastes by government healthcare waste management authorities and hospitals.

Optimal Number of Thermoelectric Couples in a Heat Pipe Assisted Thermoelectric Generator for Waste Heat Recovery

NASA Astrophysics Data System (ADS)

Liu, Tongjun; Wang, Tongcai; Luan, Weiling; Cao, Qimin

2017-05-01

Waste heat recovery through thermoelectric generators is a promising way to improve energy conversion efficiency. This paper proposes a type of heat pipe assisted thermoelectric generator (HP-TEG) system. The expandable evaporator and condenser surface of the heat pipe facilitates the intensive assembly of thermoelectric (TE) modules to compose a compact device. Compared with a conventional layer structure thermoelectric generator, this system is feasible for the installment of more TE couples, thus increasing power output. To investigate the performance of the HP-TEG and the optimal number of TE couples, a theoretical model was presented and verified by experiment results. Further theoretical analysis results showed the performance of the HP-TEG could be further improved by optimizing the parameters, including the inlet air temperature, the thermal resistance of the heating section, and thermal resistance of the cooling structure. Moreover, applying a proper number of TE couples is important to acquire the best power output performance.

Error propagation of partial least squares for parameters optimization in NIR modeling.

PubMed

Du, Chenzhao; Dai, Shengyun; Qiao, Yanjiang; Wu, Zhisheng

2018-03-05

A novel methodology is proposed to determine the error propagation of partial least-square (PLS) for parameters optimization in near-infrared (NIR) modeling. The parameters include spectral pretreatment, latent variables and variable selection. In this paper, an open source dataset (corn) and a complicated dataset (Gardenia) were used to establish PLS models under different modeling parameters. And error propagation of modeling parameters for water quantity in corn and geniposide quantity in Gardenia were presented by both type І and type II error. For example, when variable importance in the projection (VIP), interval partial least square (iPLS) and backward interval partial least square (BiPLS) variable selection algorithms were used for geniposide in Gardenia, compared with synergy interval partial least squares (SiPLS), the error weight varied from 5% to 65%, 55% and 15%. The results demonstrated how and what extent the different modeling parameters affect error propagation of PLS for parameters optimization in NIR modeling. The larger the error weight, the worse the model. Finally, our trials finished a powerful process in developing robust PLS models for corn and Gardenia under the optimal modeling parameters. Furthermore, it could provide a significant guidance for the selection of modeling parameters of other multivariate calibration models. Copyright © 2017. Published by Elsevier B.V.

Error propagation of partial least squares for parameters optimization in NIR modeling

NASA Astrophysics Data System (ADS)

Du, Chenzhao; Dai, Shengyun; Qiao, Yanjiang; Wu, Zhisheng

2018-03-01

A novel methodology is proposed to determine the error propagation of partial least-square (PLS) for parameters optimization in near-infrared (NIR) modeling. The parameters include spectral pretreatment, latent variables and variable selection. In this paper, an open source dataset (corn) and a complicated dataset (Gardenia) were used to establish PLS models under different modeling parameters. And error propagation of modeling parameters for water quantity in corn and geniposide quantity in Gardenia were presented by both type І and type II error. For example, when variable importance in the projection (VIP), interval partial least square (iPLS) and backward interval partial least square (BiPLS) variable selection algorithms were used for geniposide in Gardenia, compared with synergy interval partial least squares (SiPLS), the error weight varied from 5% to 65%, 55% and 15%. The results demonstrated how and what extent the different modeling parameters affect error propagation of PLS for parameters optimization in NIR modeling. The larger the error weight, the worse the model. Finally, our trials finished a powerful process in developing robust PLS models for corn and Gardenia under the optimal modeling parameters. Furthermore, it could provide a significant guidance for the selection of modeling parameters of other multivariate calibration models.

Web-GIS oriented systems viability for municipal solid waste selective collection optimization in developed and transient economies.

PubMed

Rada, E C; Ragazzi, M; Fedrizzi, P

2013-04-01

Municipal solid waste management is a multidisciplinary activity that includes generation, source separation, storage, collection, transfer and transport, processing and recovery, and, last but not least, disposal. The optimization of waste collection, through source separation, is compulsory where a landfill based management must be overcome. In this paper, a few aspects related to the implementation of a Web-GIS based system are analyzed. This approach is critically analyzed referring to the experience of two Italian case studies and two additional extra-European case studies. The first case is one of the best examples of selective collection optimization in Italy. The obtained efficiency is very high: 80% of waste is source separated for recycling purposes. In the second reference case, the local administration is going to be faced with the optimization of waste collection through Web-GIS oriented technologies for the first time. The starting scenario is far from an optimized management of municipal solid waste. The last two case studies concern pilot experiences in China and Malaysia. Each step of the Web-GIS oriented strategy is comparatively discussed referring to typical scenarios of developed and transient economies. The main result is that transient economies are ready to move toward Web oriented tools for MSW management, but this opportunity is not yet well exploited in the sector. Copyright © 2013 Elsevier Ltd. All rights reserved.

Determining optimal parameters in magnetic spacecraft stabilization via attitude feedback

NASA Astrophysics Data System (ADS)

Bruni, Renato; Celani, Fabio

2016-10-01

The attitude control of a spacecraft using magnetorquers can be achieved by a feedback control law which has four design parameters. However, the practical determination of appropriate values for these parameters is a critical open issue. We propose here an innovative systematic approach for finding these values: they should be those that minimize the convergence time to the desired attitude. This a particularly diffcult optimization problem, for several reasons: 1) such time cannot be expressed in analytical form as a function of parameters and initial conditions; 2) design parameters may range over very wide intervals; 3) convergence time depends also on the initial conditions of the spacecraft, which are not known in advance. To overcome these diffculties, we present a solution approach based on derivative-free optimization. These algorithms do not need to write analytically the objective function: they only need to compute it in a number of points. We also propose a fast probing technique to identify which regions of the search space have to be explored densely. Finally, we formulate a min-max model to find robust parameters, namely design parameters that minimize convergence time under the worst initial conditions. Results are very promising.

Parameter Optimization for Selected Correlation Analysis of Intracranial Pathophysiology.

PubMed

Faltermeier, Rupert; Proescholdt, Martin A; Bele, Sylvia; Brawanski, Alexander

2015-01-01

Recently we proposed a mathematical tool set, called selected correlation analysis, that reliably detects positive and negative correlations between arterial blood pressure (ABP) and intracranial pressure (ICP). Such correlations are associated with severe impairment of the cerebral autoregulation and intracranial compliance, as predicted by a mathematical model. The time resolved selected correlation analysis is based on a windowing technique combined with Fourier-based coherence calculations and therefore depends on several parameters. For real time application of this method at an ICU it is inevitable to adjust this mathematical tool for high sensitivity and distinct reliability. In this study, we will introduce a method to optimize the parameters of the selected correlation analysis by correlating an index, called selected correlation positive (SCP), with the outcome of the patients represented by the Glasgow Outcome Scale (GOS). For that purpose, the data of twenty-five patients were used to calculate the SCP value for each patient and multitude of feasible parameter sets of the selected correlation analysis. It could be shown that an optimized set of parameters is able to improve the sensitivity of the method by a factor greater than four in comparison to our first analyses.

Parameter Optimization for Selected Correlation Analysis of Intracranial Pathophysiology

PubMed Central

Faltermeier, Rupert; Proescholdt, Martin A.; Bele, Sylvia; Brawanski, Alexander

2015-01-01

Recently we proposed a mathematical tool set, called selected correlation analysis, that reliably detects positive and negative correlations between arterial blood pressure (ABP) and intracranial pressure (ICP). Such correlations are associated with severe impairment of the cerebral autoregulation and intracranial compliance, as predicted by a mathematical model. The time resolved selected correlation analysis is based on a windowing technique combined with Fourier-based coherence calculations and therefore depends on several parameters. For real time application of this method at an ICU it is inevitable to adjust this mathematical tool for high sensitivity and distinct reliability. In this study, we will introduce a method to optimize the parameters of the selected correlation analysis by correlating an index, called selected correlation positive (SCP), with the outcome of the patients represented by the Glasgow Outcome Scale (GOS). For that purpose, the data of twenty-five patients were used to calculate the SCP value for each patient and multitude of feasible parameter sets of the selected correlation analysis. It could be shown that an optimized set of parameters is able to improve the sensitivity of the method by a factor greater than four in comparison to our first analyses. PMID:26693250

Differential-Evolution Control Parameter Optimization for Unmanned Aerial Vehicle Path Planning

PubMed Central

Kok, Kai Yit; Rajendran, Parvathy

2016-01-01

The differential evolution algorithm has been widely applied on unmanned aerial vehicle (UAV) path planning. At present, four random tuning parameters exist for differential evolution algorithm, namely, population size, differential weight, crossover, and generation number. These tuning parameters are required, together with user setting on path and computational cost weightage. However, the optimum settings of these tuning parameters vary according to application. Instead of trial and error, this paper presents an optimization method of differential evolution algorithm for tuning the parameters of UAV path planning. The parameters that this research focuses on are population size, differential weight, crossover, and generation number. The developed algorithm enables the user to simply define the weightage desired between the path and computational cost to converge with the minimum generation required based on user requirement. In conclusion, the proposed optimization of tuning parameters in differential evolution algorithm for UAV path planning expedites and improves the final output path and computational cost. PMID:26943630

Teaching-learning-based Optimization Algorithm for Parameter Identification in the Design of IIR Filters

NASA Astrophysics Data System (ADS)

Singh, R.; Verma, H. K.

2013-12-01

This paper presents a teaching-learning-based optimization (TLBO) algorithm to solve parameter identification problems in the designing of digital infinite impulse response (IIR) filter. TLBO based filter modelling is applied to calculate the parameters of unknown plant in simulations. Unlike other heuristic search algorithms, TLBO algorithm is an algorithm-specific parameter-less algorithm. In this paper big bang-big crunch (BB-BC) optimization and PSO algorithms are also applied to filter design for comparison. Unknown filter parameters are considered as a vector to be optimized by these algorithms. MATLAB programming is used for implementation of proposed algorithms. Experimental results show that the TLBO is more accurate to estimate the filter parameters than the BB-BC optimization algorithm and has faster convergence rate when compared to PSO algorithm. TLBO is used where accuracy is more essential than the convergence speed.

Parameter Optimization and Electrode Improvement of Rotary Stepper Micromotor

NASA Astrophysics Data System (ADS)

Sone, Junji; Mizuma, Toshinari; Mochizuki, Shunsuke; Sarajlic, Edin; Yamahata, Christophe; Fujita, Hiroyuki

We developed a three-phase electrostatic stepper micromotor and performed a numerical simulation to improve its performance for practical use and to optimize its design. We conducted its circuit simulation by simplifying its structure, and the effect of springback force generated by supported mechanism using flexures was considered. And we considered new improvement method for electrodes. This improvement and other parameter optimizations achieved the low voltage drive of micromotor.

Minimal residual method provides optimal regularization parameter for diffuse optical tomography

NASA Astrophysics Data System (ADS)

Jagannath, Ravi Prasad K.; Yalavarthy, Phaneendra K.

2012-10-01

The inverse problem in the diffuse optical tomography is known to be nonlinear, ill-posed, and sometimes under-determined, requiring regularization to obtain meaningful results, with Tikhonov-type regularization being the most popular one. The choice of this regularization parameter dictates the reconstructed optical image quality and is typically chosen empirically or based on prior experience. An automated method for optimal selection of regularization parameter that is based on regularized minimal residual method (MRM) is proposed and is compared with the traditional generalized cross-validation method. The results obtained using numerical and gelatin phantom data indicate that the MRM-based method is capable of providing the optimal regularization parameter.

Minimal residual method provides optimal regularization parameter for diffuse optical tomography.

PubMed

Jagannath, Ravi Prasad K; Yalavarthy, Phaneendra K

2012-10-01

The inverse problem in the diffuse optical tomography is known to be nonlinear, ill-posed, and sometimes under-determined, requiring regularization to obtain meaningful results, with Tikhonov-type regularization being the most popular one. The choice of this regularization parameter dictates the reconstructed optical image quality and is typically chosen empirically or based on prior experience. An automated method for optimal selection of regularization parameter that is based on regularized minimal residual method (MRM) is proposed and is compared with the traditional generalized cross-validation method. The results obtained using numerical and gelatin phantom data indicate that the MRM-based method is capable of providing the optimal regularization parameter.

Research on the drying kinetics of household food waste for the development and optimization of domestic waste drying technique.

PubMed

Sotiropoulos, A; Malamis, D; Michailidis, P; Krokida, M; Loizidou, M

2016-01-01

Domestic food waste drying foresees the significant reduction of household food waste mass through the hygienic removal of its moisture content at source. In this manuscript, a new approach for the development and optimization of an innovative household waste dryer for the effective dehydration of food waste at source is presented. Food waste samples were dehydrated with the use of the heated air-drying technique under different air-drying conditions, namely air temperature and air velocity, in order to investigate their drying kinetics. Different thin-layer drying models have been applied, in which the drying constant is a function of the process variables. The Midilli model demonstrated the best performance in fitting the experimental data in all tested samples, whereas it was found that food waste drying is greatly affected by temperature and to a smaller scale by air velocity. Due to the increased moisture content of food waste, an appropriate configuration of the drying process variables can lead to a total reduction of its mass by 87% w/w, thus achieving a sustainable residence time and energy consumption level. Thus, the development of a domestic waste dryer can be proved to be economically and environmentally viable in the future.

Life-cycle cost as basis to optimize waste collection in space and time: A methodology for obtaining a detailed cost breakdown structure.

PubMed

Sousa, Vitor; Dias-Ferreira, Celia; Vaz, João M; Meireles, Inês

2018-05-01

Extensive research has been carried out on waste collection costs mainly to differentiate costs of distinct waste streams and spatial optimization of waste collection services (e.g. routes, number, and location of waste facilities). However, waste collection managers also face the challenge of optimizing assets in time, for instance deciding when to replace and how to maintain, or which technological solution to adopt. These issues require a more detailed knowledge about the waste collection services' cost breakdown structure. The present research adjusts the methodology for buildings' life-cycle cost (LCC) analysis, detailed in the ISO 15686-5:2008, to the waste collection assets. The proposed methodology is then applied to the waste collection assets owned and operated by a real municipality in Portugal (Cascais Ambiente - EMAC). The goal is to highlight the potential of the LCC tool in providing a baseline for time optimization of the waste collection service and assets, namely assisting on decisions regarding equipment operation and replacement.

A web-based Decision Support System for the optimal management of construction and demolition waste.

PubMed

Banias, G; Achillas, Ch; Vlachokostas, Ch; Moussiopoulos, N; Papaioannou, I

2011-12-01

Wastes from construction activities constitute nowadays the largest by quantity fraction of solid wastes in urban areas. In addition, it is widely accepted that the particular waste stream contains hazardous materials, such as insulating materials, plastic frames of doors, windows, etc. Their uncontrolled disposal result to long-term pollution costs, resource overuse and wasted energy. Within the framework of the DEWAM project, a web-based Decision Support System (DSS) application - namely DeconRCM - has been developed, aiming towards the identification of the optimal construction and demolition waste (CDW) management strategy that minimises end-of-life costs and maximises the recovery of salvaged building materials. This paper addresses both technical and functional structure of the developed web-based application. The web-based DSS provides an accurate estimation of the generated CDW quantities of twenty-one different waste streams (e.g. concrete, bricks, glass, etc.) for four different types of buildings (residential, office, commercial and industrial). With the use of mathematical programming, the DeconRCM provides also the user with the optimal end-of-life management alternative, taking into consideration both economic and environmental criteria. The DSS's capabilities are illustrated through a real world case study of a typical five floor apartment building in Thessaloniki, Greece. Copyright © 2011 Elsevier Ltd. All rights reserved.

An improved swarm optimization for parameter estimation and biological model selection.

PubMed

Abdullah, Afnizanfaizal; Deris, Safaai; Mohamad, Mohd Saberi; Anwar, Sohail

2013-01-01

One of the key aspects of computational systems biology is the investigation on the dynamic biological processes within cells. Computational models are often required to elucidate the mechanisms and principles driving the processes because of the nonlinearity and complexity. The models usually incorporate a set of parameters that signify the physical properties of the actual biological systems. In most cases, these parameters are estimated by fitting the model outputs with the corresponding experimental data. However, this is a challenging task because the available experimental data are frequently noisy and incomplete. In this paper, a new hybrid optimization method is proposed to estimate these parameters from the noisy and incomplete experimental data. The proposed method, called Swarm-based Chemical Reaction Optimization, integrates the evolutionary searching strategy employed by the Chemical Reaction Optimization, into the neighbouring searching strategy of the Firefly Algorithm method. The effectiveness of the method was evaluated using a simulated nonlinear model and two biological models: synthetic transcriptional oscillators, and extracellular protease production models. The results showed that the accuracy and computational speed of the proposed method were better than the existing Differential Evolution, Firefly Algorithm and Chemical Reaction Optimization methods. The reliability of the estimated parameters was statistically validated, which suggests that the model outputs produced by these parameters were valid even when noisy and incomplete experimental data were used. Additionally, Akaike Information Criterion was employed to evaluate the model selection, which highlighted the capability of the proposed method in choosing a plausible model based on the experimental data. In conclusion, this paper presents the effectiveness of the proposed method for parameter estimation and model selection problems using noisy and incomplete experimental data. This

An Improved Swarm Optimization for Parameter Estimation and Biological Model Selection

PubMed Central

Abdullah, Afnizanfaizal; Deris, Safaai; Mohamad, Mohd Saberi; Anwar, Sohail

2013-01-01

One of the key aspects of computational systems biology is the investigation on the dynamic biological processes within cells. Computational models are often required to elucidate the mechanisms and principles driving the processes because of the nonlinearity and complexity. The models usually incorporate a set of parameters that signify the physical properties of the actual biological systems. In most cases, these parameters are estimated by fitting the model outputs with the corresponding experimental data. However, this is a challenging task because the available experimental data are frequently noisy and incomplete. In this paper, a new hybrid optimization method is proposed to estimate these parameters from the noisy and incomplete experimental data. The proposed method, called Swarm-based Chemical Reaction Optimization, integrates the evolutionary searching strategy employed by the Chemical Reaction Optimization, into the neighbouring searching strategy of the Firefly Algorithm method. The effectiveness of the method was evaluated using a simulated nonlinear model and two biological models: synthetic transcriptional oscillators, and extracellular protease production models. The results showed that the accuracy and computational speed of the proposed method were better than the existing Differential Evolution, Firefly Algorithm and Chemical Reaction Optimization methods. The reliability of the estimated parameters was statistically validated, which suggests that the model outputs produced by these parameters were valid even when noisy and incomplete experimental data were used. Additionally, Akaike Information Criterion was employed to evaluate the model selection, which highlighted the capability of the proposed method in choosing a plausible model based on the experimental data. In conclusion, this paper presents the effectiveness of the proposed method for parameter estimation and model selection problems using noisy and incomplete experimental data. This

Numerically accurate computational techniques for optimal estimator analyses of multi-parameter models

NASA Astrophysics Data System (ADS)

Berger, Lukas; Kleinheinz, Konstantin; Attili, Antonio; Bisetti, Fabrizio; Pitsch, Heinz; Mueller, Michael E.

2018-05-01

Modelling unclosed terms in partial differential equations typically involves two steps: First, a set of known quantities needs to be specified as input parameters for a model, and second, a specific functional form needs to be defined to model the unclosed terms by the input parameters. Both steps involve a certain modelling error, with the former known as the irreducible error and the latter referred to as the functional error. Typically, only the total modelling error, which is the sum of functional and irreducible error, is assessed, but the concept of the optimal estimator enables the separate analysis of the total and the irreducible errors, yielding a systematic modelling error decomposition. In this work, attention is paid to the techniques themselves required for the practical computation of irreducible errors. Typically, histograms are used for optimal estimator analyses, but this technique is found to add a non-negligible spurious contribution to the irreducible error if models with multiple input parameters are assessed. Thus, the error decomposition of an optimal estimator analysis becomes inaccurate, and misleading conclusions concerning modelling errors may be drawn. In this work, numerically accurate techniques for optimal estimator analyses are identified and a suitable evaluation of irreducible errors is presented. Four different computational techniques are considered: a histogram technique, artificial neural networks, multivariate adaptive regression splines, and an additive model based on a kernel method. For multiple input parameter models, only artificial neural networks and multivariate adaptive regression splines are found to yield satisfactorily accurate results. Beyond a certain number of input parameters, the assessment of models in an optimal estimator analysis even becomes practically infeasible if histograms are used. The optimal estimator analysis in this paper is applied to modelling the filtered soot intermittency in large eddy

On the effect of response transformations in sequential parameter optimization.

PubMed

Wagner, Tobias; Wessing, Simon

2012-01-01

Parameter tuning of evolutionary algorithms (EAs) is attracting more and more interest. In particular, the sequential parameter optimization (SPO) framework for the model-assisted tuning of stochastic optimizers has resulted in established parameter tuning algorithms. In this paper, we enhance the SPO framework by introducing transformation steps before the response aggregation and before the actual modeling. Based on design-of-experiments techniques, we empirically analyze the effect of integrating different transformations. We show that in particular, a rank transformation of the responses provides significant improvements. A deeper analysis of the resulting models and additional experiments with adaptive procedures indicates that the rank and the Box-Cox transformation are able to improve the properties of the resultant distributions with respect to symmetry and normality of the residuals. Moreover, model-based effect plots document a higher discriminatory power obtained by the rank transformation.

Hybrid artificial bee colony algorithm for parameter optimization of five-parameter bidirectional reflectance distribution function model.

PubMed

Wang, Qianqian; Zhao, Jing; Gong, Yong; Hao, Qun; Peng, Zhong

2017-11-20

A hybrid artificial bee colony (ABC) algorithm inspired by the best-so-far solution and bacterial chemotaxis was introduced to optimize the parameters of the five-parameter bidirectional reflectance distribution function (BRDF) model. To verify the performance of the hybrid ABC algorithm, we measured BRDF of three kinds of samples and simulated the undetermined parameters of the five-parameter BRDF model using the hybrid ABC algorithm and the genetic algorithm, respectively. The experimental results demonstrate that the hybrid ABC algorithm outperforms the genetic algorithm in convergence speed, accuracy, and time efficiency under the same conditions.

Optimal feedback scheme and universal time scaling for Hamiltonian parameter estimation.

PubMed

Yuan, Haidong; Fung, Chi-Hang Fred

2015-09-11

Time is a valuable resource and it is expected that a longer time period should lead to better precision in Hamiltonian parameter estimation. However, recent studies in quantum metrology have shown that in certain cases more time may even lead to worse estimations, which puts this intuition into question. In this Letter we show that by including feedback controls this intuition can be restored. By deriving asymptotically optimal feedback controls we quantify the maximal improvement feedback controls can provide in Hamiltonian parameter estimation and show a universal time scaling for the precision limit under the optimal feedback scheme. Our study reveals an intriguing connection between noncommutativity in the dynamics and the gain of feedback controls in Hamiltonian parameter estimation.

Microbiological parameters and maturity degree during composting of Posidonia oceanica residues mixed with vegetable wastes in semi-arid pedo-climatic condition.

PubMed

Saidi, Neyla; Kouki, Soulwene; M'hiri, Fadhel; Jedidi, Naceur; Mahrouk, Meriam; Hassen, Abdennaceur; Ouzari, Hadda

2009-01-01

The aim of this study was to characterize the biological stability and maturity degree of compost during a controlled pile-composting trial of mixed vegetable residues (VR) collected from markets of Tunis City with residues of Posidonia oceanica (PoR), collected from Tunis beaches. The accumulation in beaches (as well as their removal) constitutes a serious environmental problem in all Mediterranean countries particularly in Tunisia. Aerobic-thermophilic composting is the most reasonable way to profit highly-valuable content of organic matter in these wastes for agricultural purposes. The physical, chemical, and biological parameters were monitored during composting over 150 d. The most appropriate parameters were selected to establish the maturity degree. The main result of this research was the deduction of the following maturity criterion: (a) C/N ratio < 15; (b) NH4+-N < 400 mg/kg; (c) CO2-C < 2000 mg CO2-C/kg; (d) dehydrogenase activity < 1 mg TPF/g dry matter; (e) germination index (GI) > 80%. These five parameters, considered jointly are indicative of a high maturity degree and thus of a high-quality organic amendment which employed in a rational way, may improve soil fertility and soil quality. The mature compost was relatively rich in N (13.0 g/kg), P (4.74 g/kg) and MgO (15.80 g/kg). Thus composting definitively constitutes the most optimal option to exploit these wastes.

Estimation of Saxophone Control Parameters by Convex Optimization.

PubMed

Wang, Cheng-I; Smyth, Tamara; Lipton, Zachary C

2014-12-01

In this work, an approach to jointly estimating the tone hole configuration (fingering) and reed model parameters of a saxophone is presented. The problem isn't one of merely estimating pitch as one applied fingering can be used to produce several different pitches by bugling or overblowing. Nor can a fingering be estimated solely by the spectral envelope of the produced sound (as it might for estimation of vocal tract shape in speech) since one fingering can produce markedly different spectral envelopes depending on the player's embouchure and control of the reed. The problem is therefore addressed by jointly estimating both the reed (source) parameters and the fingering (filter) of a saxophone model using convex optimization and 1) a bank of filter frequency responses derived from measurement of the saxophone configured with all possible fingerings and 2) sample recordings of notes produced using all possible fingerings, played with different overblowing, dynamics and timbre. The saxophone model couples one of several possible frequency response pairs (corresponding to the applied fingering), and a quasi-static reed model generating input pressure at the mouthpiece, with control parameters being blowing pressure and reed stiffness. Applied fingering and reed parameters are estimated for a given recording by formalizing a minimization problem, where the cost function is the error between the recording and the synthesized sound produced by the model having incremental parameter values for blowing pressure and reed stiffness. The minimization problem is nonlinear and not differentiable and is made solvable using convex optimization. The performance of the fingering identification is evaluated with better accuracy than previous reported value.

Evolution of process control parameters during extended co-composting of green waste and solid fraction of cattle slurry to obtain growing media.

PubMed

Cáceres, Rafaela; Coromina, Narcís; Malińska, Krystyna; Marfà, Oriol

2015-03-01

This study aimed to monitor process parameters when two by-products (green waste - GW, and the solid fraction of cattle slurry - SFCS) were composted to obtain growing media. Using compost in growing medium mixtures involves prolonged composting processes that can last at least half a year. It is therefore crucial to study the parameters that affect compost stability as measured in the field in order to shorten the composting process at composting facilities. Two mixtures were prepared: GW25 (25% GW and 75% SFCS, v/v) and GW75 (75% GW and 25% SFCS, v/v). The different raw mixtures resulted in the production of two different growing media, and the evolution of process management parameters was different. A new parameter has been proposed to deal with attaining the thermophilic temperature range and maintaining it during composting, not only it would be useful to optimize composting processes, but also to assess the hygienization degree. Copyright © 2014 Elsevier Ltd. All rights reserved.

Optimal experimental design for parameter estimation of a cell signaling model.

PubMed

Bandara, Samuel; Schlöder, Johannes P; Eils, Roland; Bock, Hans Georg; Meyer, Tobias

2009-11-01

Differential equation models that describe the dynamic changes of biochemical signaling states are important tools to understand cellular behavior. An essential task in building such representations is to infer the affinities, rate constants, and other parameters of a model from actual measurement data. However, intuitive measurement protocols often fail to generate data that restrict the range of possible parameter values. Here we utilized a numerical method to iteratively design optimal live-cell fluorescence microscopy experiments in order to reveal pharmacological and kinetic parameters of a phosphatidylinositol 3,4,5-trisphosphate (PIP(3)) second messenger signaling process that is deregulated in many tumors. The experimental approach included the activation of endogenous phosphoinositide 3-kinase (PI3K) by chemically induced recruitment of a regulatory peptide, reversible inhibition of PI3K using a kinase inhibitor, and monitoring of the PI3K-mediated production of PIP(3) lipids using the pleckstrin homology (PH) domain of Akt. We found that an intuitively planned and established experimental protocol did not yield data from which relevant parameters could be inferred. Starting from a set of poorly defined model parameters derived from the intuitively planned experiment, we calculated concentration-time profiles for both the inducing and the inhibitory compound that would minimize the predicted uncertainty of parameter estimates. Two cycles of optimization and experimentation were sufficient to narrowly confine the model parameters, with the mean variance of estimates dropping more than sixty-fold. Thus, optimal experimental design proved to be a powerful strategy to minimize the number of experiments needed to infer biological parameters from a cell signaling assay.

A local segmentation parameter optimization approach for mapping heterogeneous urban environments using VHR imagery

NASA Astrophysics Data System (ADS)

Grippa, Tais; Georganos, Stefanos; Lennert, Moritz; Vanhuysse, Sabine; Wolff, Eléonore

2017-10-01

Mapping large heterogeneous urban areas using object-based image analysis (OBIA) remains challenging, especially with respect to the segmentation process. This could be explained both by the complex arrangement of heterogeneous land-cover classes and by the high diversity of urban patterns which can be encountered throughout the scene. In this context, using a single segmentation parameter to obtain satisfying segmentation results for the whole scene can be impossible. Nonetheless, it is possible to subdivide the whole city into smaller local zones, rather homogeneous according to their urban pattern. These zones can then be used to optimize the segmentation parameter locally, instead of using the whole image or a single representative spatial subset. This paper assesses the contribution of a local approach for the optimization of segmentation parameter compared to a global approach. Ouagadougou, located in sub-Saharan Africa, is used as case studies. First, the whole scene is segmented using a single globally optimized segmentation parameter. Second, the city is subdivided into 283 local zones, homogeneous in terms of building size and building density. Each local zone is then segmented using a locally optimized segmentation parameter. Unsupervised segmentation parameter optimization (USPO), relying on an optimization function which tends to maximize both intra-object homogeneity and inter-object heterogeneity, is used to select the segmentation parameter automatically for both approaches. Finally, a land-use/land-cover classification is performed using the Random Forest (RF) classifier. The results reveal that the local approach outperforms the global one, especially by limiting confusions between buildings and their bare-soil neighbors.

A Taguchi approach on optimal process control parameters for HDPE pipe extrusion process

NASA Astrophysics Data System (ADS)

Sharma, G. V. S. S.; Rao, R. Umamaheswara; Rao, P. Srinivasa

2017-06-01

High-density polyethylene (HDPE) pipes find versatile applicability for transportation of water, sewage and slurry from one place to another. Hence, these pipes undergo tremendous pressure by the fluid carried. The present work entails the optimization of the withstanding pressure of the HDPE pipes using Taguchi technique. The traditional heuristic methodology stresses on a trial and error approach and relies heavily upon the accumulated experience of the process engineers for determining the optimal process control parameters. This results in setting up of less-than-optimal values. Hence, there arouse a necessity to determine optimal process control parameters for the pipe extrusion process, which can ensure robust pipe quality and process reliability. In the proposed optimization strategy, the design of experiments (DoE) are conducted wherein different control parameter combinations are analyzed by considering multiple setting levels of each control parameter. The concept of signal-to-noise ratio ( S/ N ratio) is applied and ultimately optimum values of process control parameters are obtained as: pushing zone temperature of 166 °C, Dimmer speed at 08 rpm, and Die head temperature to be 192 °C. Confirmation experimental run is also conducted to verify the analysis and research result and values proved to be in synchronization with the main experimental findings and the withstanding pressure showed a significant improvement from 0.60 to 1.004 Mpa.

Sensitivity Analysis of Genetic Algorithm Parameters for Optimal Groundwater Monitoring Network Design

NASA Astrophysics Data System (ADS)

Abdeh-Kolahchi, A.; Satish, M.; Datta, B.

2004-05-01

A state art groundwater monitoring network design is introduced. The method combines groundwater flow and transport results with optimization Genetic Algorithm (GA) to identify optimal monitoring well locations. Optimization theory uses different techniques to find a set of parameter values that minimize or maximize objective functions. The suggested groundwater optimal monitoring network design is based on the objective of maximizing the probability of tracking a transient contamination plume by determining sequential monitoring locations. The MODFLOW and MT3DMS models included as separate modules within the Groundwater Modeling System (GMS) are used to develop three dimensional groundwater flow and contamination transport simulation. The groundwater flow and contamination simulation results are introduced as input to the optimization model, using Genetic Algorithm (GA) to identify the groundwater optimal monitoring network design, based on several candidate monitoring locations. The groundwater monitoring network design model is used Genetic Algorithms with binary variables representing potential monitoring location. As the number of decision variables and constraints increase, the non-linearity of the objective function also increases which make difficulty to obtain optimal solutions. The genetic algorithm is an evolutionary global optimization technique, which is capable of finding the optimal solution for many complex problems. In this study, the GA approach capable of finding the global optimal solution to a groundwater monitoring network design problem involving 18.4X 1018 feasible solutions will be discussed. However, to ensure the efficiency of the solution process and global optimality of the solution obtained using GA, it is necessary that appropriate GA parameter values be specified. The sensitivity analysis of genetic algorithms parameters such as random number, crossover probability, mutation probability, and elitism are discussed for solution of

Advanced rotorcraft control using parameter optimization

NASA Technical Reports Server (NTRS)

Vansteenwyk, Brett; Ly, Uy-Loi

1991-01-01

A reliable algorithm for the evaluation of a quadratic performance index and its gradients with respect to the controller design parameters is presented. The algorithm is part of a design algorithm for an optimal linear dynamic output feedback controller that minimizes a finite time quadratic performance index. The numerical scheme is particularly robust when it is applied to the control law synthesis for systems with densely packed modes and where there is a high likelihood of encountering degeneracies in the closed loop eigensystem. This approach through the use of a accurate Pade series approximation does not require the closed loop system matrix to be diagonalizable. The algorithm has been included in a control design package for optimal robust low order controllers. Usefulness of the proposed numerical algorithm has been demonstrated using numerous practical design cases where degeneracies occur frequently in the closed loop system under an arbitrary controller design initialization and during the numerical search.

[Optimization of end-tool parameters based on robot hand-eye calibration].

PubMed

Zhang, Lilong; Cao, Tong; Liu, Da

2017-04-01

A new one-time registration method was developed in this research for hand-eye calibration of a surgical robot to simplify the operation process and reduce the preparation time. And a new and practical method is introduced in this research to optimize the end-tool parameters of the surgical robot based on analysis of the error sources in this registration method. In the process with one-time registration method, firstly a marker on the end-tool of the robot was recognized by a fixed binocular camera, and then the orientation and position of the marker were calculated based on the joint parameters of the robot. Secondly the relationship between the camera coordinate system and the robot base coordinate system could be established to complete the hand-eye calibration. Because of manufacturing and assembly errors of robot end-tool, an error equation was established with the transformation matrix between the robot end coordinate system and the robot end-tool coordinate system as the variable. Numerical optimization was employed to optimize end-tool parameters of the robot. The experimental results showed that the one-time registration method could significantly improve the efficiency of the robot hand-eye calibration compared with the existing methods. The parameter optimization method could significantly improve the absolute positioning accuracy of the one-time registration method. The absolute positioning accuracy of the one-time registration method can meet the requirements of the clinical surgery.

Parameter Estimation for Simultaneous Saccharification and Fermentation of Food Waste Into Ethanol Using Matlab Simulink

NASA Astrophysics Data System (ADS)

Davis, Rebecca Anne

The increase in waste disposal and energy costs has provided an incentive to convert carbohydrate-rich food waste streams into fuel. For example, dining halls and restaurants discard foods that require tipping fees for removal. An effective use of food waste may be the enzymatic hydrolysis of the waste to simple sugars and fermentation of the sugars to ethanol. As these wastes have complex compositions which may change day-to-day, experiments were carried out to test fermentability of two different types of food waste at 27° C using Saccharomyces cerevisiae yeast (ATCC4124) and Genencor's STARGEN™ enzyme in batch simultaneous saccharification and fermentation (SSF) experiments. A mathematical model of SSF based on experimentally matched rate equations for enzyme hydrolysis and yeast fermentation was developed in Matlab Simulink®. Using Simulink® parameter estimation 1.1.3, parameters for hydrolysis and fermentation were estimated through modified Michaelis-Menten and Monod-type equations with the aim of predicting changes in the levels of ethanol and glycerol from different initial concentrations of glucose, fructose, maltose, and starch. The model predictions and experimental observations agree reasonably well for the two food waste streams and a third validation dataset. The approach of using Simulink® as a dynamic visual model for SSF represents a simple method which can be applied to a variety of biological pathways and may be very useful for systems approaches in metabolic engineering in the future.

Definitive screening design enables optimization of LC-ESI-MS/MS parameters in proteomics.

PubMed

Aburaya, Shunsuke; Aoki, Wataru; Minakuchi, Hiroyoshi; Ueda, Mitsuyoshi

2017-12-01

In proteomics, more than 100,000 peptides are generated from the digestion of human cell lysates. Proteome samples have a broad dynamic range in protein abundance; therefore, it is critical to optimize various parameters of LC-ESI-MS/MS to comprehensively identify these peptides. However, there are many parameters for LC-ESI-MS/MS analysis. In this study, we applied definitive screening design to simultaneously optimize 14 parameters in the operation of monolithic capillary LC-ESI-MS/MS to increase the number of identified proteins and/or the average peak area of MS1. The simultaneous optimization enabled the determination of two-factor interactions between LC and MS. Finally, we found two parameter sets of monolithic capillary LC-ESI-MS/MS that increased the number of identified proteins by 8.1% or the average peak area of MS1 by 67%. The definitive screening design would be highly useful for high-throughput analysis of the best parameter set in LC-ESI-MS/MS systems.

Prioritizing and optimizing sustainable measures for food waste prevention and management.

PubMed

Cristóbal, Jorge; Castellani, Valentina; Manfredi, Simone; Sala, Serenella

2018-02-01

Food waste has gained prominence in the European political debate thanks to the recent Circular Economy package. Currently the waste hierarchy, introduced by the Waste Framework Directive, has been the rule followed to prioritize food waste prevention and management measures according to the environmental criteria. But when considering other criteria along with the environmental one, such as the economic, other tools are needed for the prioritization and optimization. This paper addresses the situation in which a decision-maker has to design a food waste prevention programme considering the limited economic resources in order to achieve the highest environmental impact prevention along the whole food life cycle. A methodology using Life Cycle Assessment and mathematical programing is proposed and its capabilities are shown through a case study. Results show that the order established in the waste hierarchy is generally followed. The proposed methodology revealed to be especially helpful in identifying "quick wins" - measures that should be always prioritized since they avoid a high environmental impact at a low cost. Besides, in order to aggregate the environmental scores related to a variety of impact categories, different weighting sets were proposed. In general, results show that the relevance of the weighting set in the prioritization of the measures appears to be limited. Finally, the correlation between reducing food waste generation and reducing environmental impact along the Food Supply Chain has been studied. Results highlight that when planning food waste prevention strategies, it is important to set the targets at the level of environmental impact instead of setting the targets at the level of avoided food waste generation (in mass). Copyright © 2017 The Author(s). Published by Elsevier Ltd.. All rights reserved.

Parameter optimization for reproducible cardiac 1 H-MR spectroscopy at 3 Tesla.

PubMed

de Heer, Paul; Bizino, Maurice B; Lamb, Hildo J; Webb, Andrew G

2016-11-01

To optimize data acquisition parameters in cardiac proton MR spectroscopy, and to evaluate the intra- and intersession variability in myocardial triglyceride content. Data acquisition parameters at 3 Tesla (T) were optimized and reproducibility measured using, in total, 49 healthy subjects. The signal-to-noise-ratio (SNR) and the variance in metabolite amplitude between averages were measured for: (i) global versus local power optimization; (ii) static magnetic field (B 0 ) shimming performed during free-breathing or within breathholds; (iii) post R-wave peak measurement times between 50 and 900 ms; (iv) without respiratory compensation, with breathholds and with navigator triggering; and (v) frequency selective excitation, Chemical Shift Selective (CHESS) and Multiply Optimized Insensitive Suppression Train (MOIST) water suppression techniques. Using the optimized parameters intra- and intersession myocardial triglyceride content reproducibility was measured. Two cardiac proton spectra were acquired with the same parameters and compared (intrasession reproducibility) after which the subject was removed from the scanner and placed back in the scanner and a third spectrum was acquired which was compared with the first measurement (intersession reproducibility). Local power optimization increased SNR on average by 22% compared with global power optimization (P = 0.0002). The average linewidth was not significantly different for pencil beam B 0 shimming using free-breathing or breathholds (19.1 Hz versus 17.5 Hz; P = 0.15). The highest signal stability occurred at a cardiac trigger delay around 240 ms. The mean amplitude variation was significantly lower for breathholds versus free-breathing (P = 0.03) and for navigator triggering versus free-breathing (P = 0.03) as well as for navigator triggering versus breathhold (P = 0.02). The mean residual water signal using CHESS (1.1%, P = 0.01) or MOIST (0.7%, P = 0.01) water suppression was significantly lower than using

Polishing parameter optimization for end-surface of chalcogenide glass fiber connector

NASA Astrophysics Data System (ADS)

Guo, Fangxia; Dai, Shixun; Tang, Junzhou; Wang, Xunsi; Li, Xing; Xu, Yinsheng; Wu, Yuehao; Liu, Zijun

2017-11-01

We have investigated the optimization parameters for polishing end-surface of chalcogenide glass fiber connector in the paper. Six SiC abrasive particles of different sizes were used to polish the fiber in order of size from large to small. We analyzed the effects of polishing parameters such as particle sizes, grinding speeds and polishing durations on the quality of the fiber end surface and determined the optimized polishing parameters. We found that, high-quality fiber end surface can be achieved using only three different SiC abrasives. The surface roughness of the final ChG fiber end surface is about 48 nm without any scratches, spots and cracks. Such polishing processes could reduce the average insertion loss of the connector to about 3.4 dB.

Optimal error functional for parameter identification in anisotropic finite strain elasto-plasticity

NASA Astrophysics Data System (ADS)

Shutov, A. V.; Kaygorodtseva, A. A.; Dranishnikov, N. S.

2017-10-01

A problem of parameter identification for a model of finite strain elasto-plasticity is discussed. The utilized phenomenological material model accounts for nonlinear isotropic and kinematic hardening; the model kinematics is described by a nested multiplicative split of the deformation gradient. A hierarchy of optimization problems is considered. First, following the standard procedure, the material parameters are identified through minimization of a certain least square error functional. Next, the focus is placed on finding optimal weighting coefficients which enter the error functional. Toward that end, a stochastic noise with systematic and non-systematic components is introduced to the available measurement results; a superordinate optimization problem seeks to minimize the sensitivity of the resulting material parameters to the introduced noise. The advantage of this approach is that no additional experiments are required; it also provides an insight into the robustness of the identification procedure. As an example, experimental data for the steel 42CrMo4 are considered and a set of weighting coefficients is found, which is optimal in a certain class.

Optimizing chirped laser pulse parameters for electron acceleration in vacuum

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

Akhyani, Mina; Jahangiri, Fazel; Niknam, Ali Reza

2015-11-14

Electron dynamics in the field of a chirped linearly polarized laser pulse is investigated. Variations of electron energy gain versus chirp parameter, time duration, and initial phase of laser pulse are studied. Based on maximizing laser pulse asymmetry, a numerical optimization procedure is presented, which leads to the elimination of rapid fluctuations of gain versus the chirp parameter. Instead, a smooth variation is observed that considerably reduces the accuracy required for experimentally adjusting the chirp parameter.

Development and Application of a Tool for Optimizing Composite Matrix Viscoplastic Material Parameters

NASA Technical Reports Server (NTRS)

Murthy, Pappu L. N.; Naghipour Ghezeljeh, Paria; Bednarcyk, Brett A.

2018-01-01

This document describes a recently developed analysis tool that enhances the resident capabilities of the Micromechanics Analysis Code with the Generalized Method of Cells (MAC/GMC) and its application. MAC/GMC is a composite material and laminate analysis software package developed at NASA Glenn Research Center. The primary focus of the current effort is to provide a graphical user interface (GUI) capability that helps users optimize highly nonlinear viscoplastic constitutive law parameters by fitting experimentally observed/measured stress-strain responses under various thermo-mechanical conditions for braided composites. The tool has been developed utilizing the MATrix LABoratory (MATLAB) (The Mathworks, Inc., Natick, MA) programming language. Illustrative examples shown are for a specific braided composite system wherein the matrix viscoplastic behavior is represented by a constitutive law described by seven parameters. The tool is general enough to fit any number of experimentally observed stress-strain responses of the material. The number of parameters to be optimized, as well as the importance given to each stress-strain response, are user choice. Three different optimization algorithms are included: (1) Optimization based on gradient method, (2) Genetic algorithm (GA) based optimization and (3) Particle Swarm Optimization (PSO). The user can mix and match the three algorithms. For example, one can start optimization with either 2 or 3 and then use the optimized solution to further fine tune with approach 1. The secondary focus of this paper is to demonstrate the application of this tool to optimize/calibrate parameters for a nonlinear viscoplastic matrix to predict stress-strain curves (for constituent and composite levels) at different rates, temperatures and/or loading conditions utilizing the Generalized Method of Cells. After preliminary validation of the tool through comparison with experimental results, a detailed virtual parametric study is

A methodology for optimal MSW management, with an application in the waste transportation of Attica Region, Greece.

PubMed

Economopoulou, M A; Economopoulou, A A; Economopoulos, A P

2013-11-01

The paper describes a software system capable of formulating alternative optimal Municipal Solid Wastes (MSWs) management plans, each of which meets a set of constraints that may reflect selected objections and/or wishes of local communities. The objective function to be minimized in each plan is the sum of the annualized capital investment and annual operating cost of all transportation, treatment and final disposal operations involved, taking into consideration the possible income from the sale of products and any other financial incentives or disincentives that may exist. For each plan formulated, the system generates several reports that define the plan, analyze its cost elements and yield an indicative profile of selected types of installations, as well as data files that facilitate the geographic representation of the optimal solution in maps through the use of GIS. A number of these reports compare the technical and economic data from all scenarios considered at the study area, municipality and installation level constituting in effect sensitivity analysis. The generation of alternative plans offers local authorities the opportunity of choice and the results of the sensitivity analysis allow them to choose wisely and with consensus. The paper presents also an application of this software system in the capital Region of Attica in Greece, for the purpose of developing an optimal waste transportation system in line with its approved waste management plan. The formulated plan was able to: (a) serve 113 Municipalities and Communities that generate nearly 2 milliont/y of comingled MSW with distinctly different waste collection patterns, (b) take into consideration several existing waste transfer stations (WTS) and optimize their use within the overall plan, (c) select the most appropriate sites among the potentially suitable (new and in use) ones, (d) generate the optimal profile of each WTS proposed, and (e) perform sensitivity analysis so as to define the impact

Optimal Parameters for Intervertebral Disk Resection Using Aqua-Plasma Beams.

PubMed

Yoon, Sung-Young; Kim, Gon-Ho; Kim, Yushin; Kim, Nack Hwan; Lee, Sangheon; Kawai, Christina; Hong, Youngki

2018-06-14

 A minimally invasive procedure for intervertebral disk resection using plasma beams has been developed. Conventional parameters for the plasma procedure such as voltage and tip speed mainly rely on the surgeon's personal experience, without adequate evidence from experiments. Our objective was to determine the optimal parameters for plasma disk resection.  Rate of ablation was measured at different procedural tip speeds and voltages using porcine nucleus pulposi. The amount of heat formation during experimental conditions was also measured to evaluate the thermal safety of the plasma procedure.  The ablation rate increased at slower procedural speeds and higher voltages. However, for thermal safety, the optimal parameters for plasma procedures with minimal tissue damage were an electrical output of 280 volts root-mean-square (V rms ) and a procedural tip speed of 2.5 mm/s.  Our findings provide useful information for an effective and safe plasma procedure for disk resection in a clinical setting. Georg Thieme Verlag KG Stuttgart · New York.

Optimized Production of Biodiesel from Waste Cooking Oil by Lipase Immobilized on Magnetic Nanoparticles

PubMed Central

Yu, Chi-Yang; Huang, Liang-Yu; Kuan, I-Ching; Lee, Shiow-Ling

2013-01-01

Biodiesel, a non-toxic and biodegradable fuel, has recently become a major source of renewable alternative fuels. Utilization of lipase as a biocatalyst to produce biodiesel has advantages over common alkaline catalysts such as mild reaction conditions, easy product separation, and use of waste cooking oil as raw material. In this study, Pseudomonas cepacia lipase immobilized onto magnetic nanoparticles (MNP) was used for biodiesel production from waste cooking oil. The optimal dosage of lipase-bound MNP was 40% (w/w of oil) and there was little difference between stepwise addition of methanol at 12 h- and 24 h-intervals. Reaction temperature, substrate molar ratio (methanol/oil), and water content (w/w of oil) were optimized using response surface methodology (RSM). The optimal reaction conditions were 44.2 °C, substrate molar ratio of 5.2, and water content of 12.5%. The predicted and experimental molar conversions of fatty acid methyl esters (FAME) were 80% and 79%, respectively. PMID:24336109

A Parameter Communication Optimization Strategy for Distributed Machine Learning in Sensors.

PubMed

Zhang, Jilin; Tu, Hangdi; Ren, Yongjian; Wan, Jian; Zhou, Li; Li, Mingwei; Wang, Jue; Yu, Lifeng; Zhao, Chang; Zhang, Lei

2017-09-21

In order to utilize the distributed characteristic of sensors, distributed machine learning has become the mainstream approach, but the different computing capability of sensors and network delays greatly influence the accuracy and the convergence rate of the machine learning model. Our paper describes a reasonable parameter communication optimization strategy to balance the training overhead and the communication overhead. We extend the fault tolerance of iterative-convergent machine learning algorithms and propose the Dynamic Finite Fault Tolerance (DFFT). Based on the DFFT, we implement a parameter communication optimization strategy for distributed machine learning, named Dynamic Synchronous Parallel Strategy (DSP), which uses the performance monitoring model to dynamically adjust the parameter synchronization strategy between worker nodes and the Parameter Server (PS). This strategy makes full use of the computing power of each sensor, ensures the accuracy of the machine learning model, and avoids the situation that the model training is disturbed by any tasks unrelated to the sensors.

A Parameter Communication Optimization Strategy for Distributed Machine Learning in Sensors

PubMed Central

Zhang, Jilin; Tu, Hangdi; Ren, Yongjian; Wan, Jian; Zhou, Li; Li, Mingwei; Wang, Jue; Yu, Lifeng; Zhao, Chang; Zhang, Lei

2017-01-01

In order to utilize the distributed characteristic of sensors, distributed machine learning has become the mainstream approach, but the different computing capability of sensors and network delays greatly influence the accuracy and the convergence rate of the machine learning model. Our paper describes a reasonable parameter communication optimization strategy to balance the training overhead and the communication overhead. We extend the fault tolerance of iterative-convergent machine learning algorithms and propose the Dynamic Finite Fault Tolerance (DFFT). Based on the DFFT, we implement a parameter communication optimization strategy for distributed machine learning, named Dynamic Synchronous Parallel Strategy (DSP), which uses the performance monitoring model to dynamically adjust the parameter synchronization strategy between worker nodes and the Parameter Server (PS). This strategy makes full use of the computing power of each sensor, ensures the accuracy of the machine learning model, and avoids the situation that the model training is disturbed by any tasks unrelated to the sensors. PMID:28934163

Optimization of Parameter Ranges for Composite Tape Winding Process Based on Sensitivity Analysis

NASA Astrophysics Data System (ADS)

Yu, Tao; Shi, Yaoyao; He, Xiaodong; Kang, Chao; Deng, Bo; Song, Shibo

2017-08-01

This study is focus on the parameters sensitivity of winding process for composite prepreg tape. The methods of multi-parameter relative sensitivity analysis and single-parameter sensitivity analysis are proposed. The polynomial empirical model of interlaminar shear strength is established by response surface experimental method. Using this model, the relative sensitivity of key process parameters including temperature, tension, pressure and velocity is calculated, while the single-parameter sensitivity curves are obtained. According to the analysis of sensitivity curves, the stability and instability range of each parameter are recognized. Finally, the optimization method of winding process parameters is developed. The analysis results show that the optimized ranges of the process parameters for interlaminar shear strength are: temperature within [100 °C, 150 °C], tension within [275 N, 387 N], pressure within [800 N, 1500 N], and velocity within [0.2 m/s, 0.4 m/s], respectively.

A methodology for optimal MSW management, with an application in the waste transportation of Attica Region, Greece

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

Economopoulou, M.A.; Economopoulou, A.A.; Economopoulos, A.P., E-mail: eco@otenet.gr

2013-11-15

Highlights: • A two-step (strategic and detailed optimal planning) methodology is used for solving complex MSW management problems. • A software package is outlined, which can be used for generating detailed optimal plans. • Sensitivity analysis compares alternative scenarios that address objections and/or wishes of local communities. • A case study shows the application of the above procedure in practice and demonstrates the results and benefits obtained. - Abstract: The paper describes a software system capable of formulating alternative optimal Municipal Solid Wastes (MSWs) management plans, each of which meets a set of constraints that may reflect selected objections and/ormore » wishes of local communities. The objective function to be minimized in each plan is the sum of the annualized capital investment and annual operating cost of all transportation, treatment and final disposal operations involved, taking into consideration the possible income from the sale of products and any other financial incentives or disincentives that may exist. For each plan formulated, the system generates several reports that define the plan, analyze its cost elements and yield an indicative profile of selected types of installations, as well as data files that facilitate the geographic representation of the optimal solution in maps through the use of GIS. A number of these reports compare the technical and economic data from all scenarios considered at the study area, municipality and installation level constituting in effect sensitivity analysis. The generation of alternative plans offers local authorities the opportunity of choice and the results of the sensitivity analysis allow them to choose wisely and with consensus. The paper presents also an application of this software system in the capital Region of Attica in Greece, for the purpose of developing an optimal waste transportation system in line with its approved waste management plan. The formulated plan was

Enhancing anaerobic digestion of high-pressure extruded food waste by inoculum optimization.

PubMed

Kong, Xin; Xu, Shuang; Liu, Jianguo; Li, Huan; Zhao, Ke; He, Liang

2016-01-15

The inoculation for extruded food waste anaerobic digestion (AD) was optimized to improve methane (CH4) yield. The inoculum of acclimated anaerobic sludge resulted in high biodegradability, producing CH4 yields from 580 mLCH4 g(-1)·VSadded to 605 mLCH4 g(-1)·VSadded, with corresponding BDCH4 ranging from 90% to 94%. We also investigated inoculum to substrate ratios (ISRs). With regards to digested slurry as inoculum, we found that a decrease in ISR improved CH4 yield, while a lower ISR prolonged the lag time of the initial AD stage due to lipid inhibition caused by excessive food waste. These results demonstrate that minimal inocula are required to start the AD system for high-pressure extruded food waste because it is easily biodegraded. High ammonia concentration had a negative effect on CH4 production (i.e., when free ammonia nitrogen [FAN] increased from 20 to 30 mg L(-1) to 120-140 mg L(-1), the CH4 yield decreased by 25%), suggesting that FAN was a significant inhibitor in CH4 yield reduction. In terms of CH4 yield and lag time of the AD process, the optimal inoculation of digested slurry for the extruded food waste had an ISR of 0.33 with CH4 yield of 505 mLCH4 g(-1)VSadded, which was 20% higher than what was found for higher ISR controls of 2, 1 and 0.5. Copyright © 2015 Elsevier Ltd. All rights reserved.

Parameter optimization for the visco-hyperelastic constitutive model of tendon using FEM.

PubMed

Tang, C Y; Ng, G Y F; Wang, Z W; Tsui, C P; Zhang, G

2011-01-01

Numerous constitutive models describing the mechanical properties of tendons have been proposed during the past few decades. However, few were widely used owing to the lack of implementation in the general finite element (FE) software, and very few systematic studies have been done on selecting the most appropriate parameters for these constitutive laws. In this work, the visco-hyperelastic constitutive model of the tendon implemented through the use of three-parameter Mooney-Rivlin form and sixty-four-parameter Prony series were firstly analyzed using ANSYS FE software. Afterwards, an integrated optimization scheme was developed by coupling two optimization toolboxes (OPTs) of ANSYS and MATLAB for estimating these unknown constitutive parameters of the tendon. Finally, a group of Sprague-Dawley rat tendons was used to execute experimental and numerical simulation investigation. The simulated results showed good agreement with the experimental data. An important finding revealed that too many Maxwell elements was not necessary for assuring accuracy of the model, which is often neglected in most open literatures. Thus, all these proved that the constitutive parameter optimization scheme was reliable and highly efficient. Furthermore, the approach can be extended to study other tendons or ligaments, as well as any visco-hyperelastic solid materials.

Optimization of injection molding process parameters for a plastic cell phone housing component

NASA Astrophysics Data System (ADS)

Rajalingam, Sokkalingam; Vasant, Pandian; Khe, Cheng Seong; Merican, Zulkifli; Oo, Zeya

2016-11-01

To produce thin-walled plastic items, injection molding process is one of the most widely used application tools. However, to set optimal process parameters is difficult as it may cause to produce faulty items on injected mold like shrinkage. This study aims at to determine such an optimum injection molding process parameters which can reduce the fault of shrinkage on a plastic cell phone cover items. Currently used setting of machines process produced shrinkage and mis-specified length and with dimensions below the limit. Thus, for identification of optimum process parameters, maintaining closer targeted length and width setting magnitudes with minimal variations, more experiments are needed. The mold temperature, injection pressure and screw rotation speed are used as process parameters in this research. For optimal molding process parameters the Response Surface Methods (RSM) is applied. The major contributing factors influencing the responses were identified from analysis of variance (ANOVA) technique. Through verification runs it was found that the shrinkage defect can be minimized with the optimal setting found by RSM.

Automated optimization of water-water interaction parameters for a coarse-grained model.

PubMed

Fogarty, Joseph C; Chiu, See-Wing; Kirby, Peter; Jakobsson, Eric; Pandit, Sagar A

2014-02-13

We have developed an automated parameter optimization software framework (ParOpt) that implements the Nelder-Mead simplex algorithm and applied it to a coarse-grained polarizable water model. The model employs a tabulated, modified Morse potential with decoupled short- and long-range interactions incorporating four water molecules per interaction site. Polarizability is introduced by the addition of a harmonic angle term defined among three charged points within each bead. The target function for parameter optimization was based on the experimental density, surface tension, electric field permittivity, and diffusion coefficient. The model was validated by comparison of statistical quantities with experimental observation. We found very good performance of the optimization procedure and good agreement of the model with experiment.

An automated analysis workflow for optimization of force-field parameters using neutron scattering data

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

Lynch, Vickie E.; Borreguero, Jose M.; Bhowmik, Debsindhu

Graphical abstract: - Highlights: • An automated workflow to optimize force-field parameters. • Used the workflow to optimize force-field parameter for a system containing nanodiamond and tRNA. • The mechanism relies on molecular dynamics simulation and neutron scattering experimental data. • The workflow can be generalized to any other experimental and simulation techniques. - Abstract: Large-scale simulations and data analysis are often required to explain neutron scattering experiments to establish a connection between the fundamental physics at the nanoscale and data probed by neutrons. However, to perform simulations at experimental conditions it is critical to use correct force-field (FF) parametersmore » which are unfortunately not available for most complex experimental systems. In this work, we have developed a workflow optimization technique to provide optimized FF parameters by comparing molecular dynamics (MD) to neutron scattering data. We describe the workflow in detail by using an example system consisting of tRNA and hydrophilic nanodiamonds in a deuterated water (D{sub 2}O) environment. Quasi-elastic neutron scattering (QENS) data show a faster motion of the tRNA in the presence of nanodiamond than without the ND. To compare the QENS and MD results quantitatively, a proper choice of FF parameters is necessary. We use an efficient workflow to optimize the FF parameters between the hydrophilic nanodiamond and water by comparing to the QENS data. Our results show that we can obtain accurate FF parameters by using this technique. The workflow can be generalized to other types of neutron data for FF optimization, such as vibrational spectroscopy and spin echo.« less

Solar collector parameter identification from unsteady data by a discrete-gradient optimization algorithm

NASA Technical Reports Server (NTRS)

Hotchkiss, G. B.; Burmeister, L. C.; Bishop, K. A.

1980-01-01

A discrete-gradient optimization algorithm is used to identify the parameters in a one-node and a two-node capacitance model of a flat-plate collector. Collector parameters are first obtained by a linear-least-squares fit to steady state data. These parameters, together with the collector heat capacitances, are then determined from unsteady data by use of the discrete-gradient optimization algorithm with less than 10 percent deviation from the steady state determination. All data were obtained in the indoor solar simulator at the NASA Lewis Research Center.

Parametric Optimization of Thermoelectric Generators for Waste Heat Recovery

NASA Astrophysics Data System (ADS)

Huang, Shouyuan; Xu, Xianfan

2016-10-01

This paper presents a methodology for design optimization of thermoelectric-based waste heat recovery systems called thermoelectric generators (TEGs). The aim is to maximize the power output from thermoelectrics which are used as add-on modules to an existing gas-phase heat exchanger, without negative impacts, e.g., maintaining a minimum heat dissipation rate from the hot side. A numerical model is proposed for TEG coupled heat transfer and electrical power output. This finite-volume-based model simulates different types of heat exchangers, i.e., counter-flow and cross-flow, for TEGs. Multiple-filled skutterudites and bismuth-telluride-based thermoelectric modules (TEMs) are applied, respectively, in higher and lower temperature regions. The response surface methodology is implemented to determine the optimized TEG size along and across the flow direction and the height of thermoelectric couple legs, and to analyze their covariance and relative sensitivity. A genetic algorithm is employed to verify the globality of the optimum. The presented method will be generally useful for optimizing heat-exchanger-based TEG performance.

Optimization of PID Parameters Utilizing Variable Weight Grey-Taguchi Method and Particle Swarm Optimization

NASA Astrophysics Data System (ADS)

Azmi, Nur Iffah Mohamed; Arifin Mat Piah, Kamal; Yusoff, Wan Azhar Wan; Romlay, Fadhlur Rahman Mohd

2018-03-01

Controller that uses PID parameters requires a good tuning method in order to improve the control system performance. Tuning PID control method is divided into two namely the classical methods and the methods of artificial intelligence. Particle swarm optimization algorithm (PSO) is one of the artificial intelligence methods. Previously, researchers had integrated PSO algorithms in the PID parameter tuning process. This research aims to improve the PSO-PID tuning algorithms by integrating the tuning process with the Variable Weight Grey- Taguchi Design of Experiment (DOE) method. This is done by conducting the DOE on the two PSO optimizing parameters: the particle velocity limit and the weight distribution factor. Computer simulations and physical experiments were conducted by using the proposed PSO- PID with the Variable Weight Grey-Taguchi DOE and the classical Ziegler-Nichols methods. They are implemented on the hydraulic positioning system. Simulation results show that the proposed PSO-PID with the Variable Weight Grey-Taguchi DOE has reduced the rise time by 48.13% and settling time by 48.57% compared to the Ziegler-Nichols method. Furthermore, the physical experiment results also show that the proposed PSO-PID with the Variable Weight Grey-Taguchi DOE tuning method responds better than Ziegler-Nichols tuning. In conclusion, this research has improved the PSO-PID parameter by applying the PSO-PID algorithm together with the Variable Weight Grey-Taguchi DOE method as a tuning method in the hydraulic positioning system.

SU-E-T-295: Simultaneous Beam Sampling and Aperture Shape Optimization for Station Parameter Optimized Radiation Therapy (SPORT)

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

Zarepisheh, M; Li, R; Xing, L

Purpose: Station Parameter Optimized Radiation Therapy (SPORT) was recently proposed to fully utilize the technical capability of emerging digital LINACs, in which the station parameters of a delivery system, (such as aperture shape and weight, couch position/angle, gantry/collimator angle) are optimized altogether. SPORT promises to deliver unprecedented radiation dose distributions efficiently, yet there does not exist any optimization algorithm to implement it. The purpose of this work is to propose an optimization algorithm to simultaneously optimize the beam sampling and aperture shapes. Methods: We build a mathematical model whose variables are beam angles (including non-coplanar and/or even nonisocentric beams) andmore » aperture shapes. To solve the resulting large scale optimization problem, we devise an exact, convergent and fast optimization algorithm by integrating three advanced optimization techniques named column generation, gradient method, and pattern search. Column generation is used to find a good set of aperture shapes as an initial solution by adding apertures sequentially. Then we apply the gradient method to iteratively improve the current solution by reshaping the aperture shapes and updating the beam angles toward the gradient. Algorithm continues by pattern search method to explore the part of the search space that cannot be reached by the gradient method. Results: The proposed technique is applied to a series of patient cases and significantly improves the plan quality. In a head-and-neck case, for example, the left parotid gland mean-dose, brainstem max-dose, spinal cord max-dose, and mandible mean-dose are reduced by 10%, 7%, 24% and 12% respectively, compared to the conventional VMAT plan while maintaining the same PTV coverage. Conclusion: Combined use of column generation, gradient search and pattern search algorithms provide an effective way to optimize simultaneously the large collection of station parameters and significantly

Web-GIS oriented systems viability for municipal solid waste selective collection optimization in developed and transient economies

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

Rada, E.C., E-mail: Elena.Rada@ing.unitn.it; Ragazzi, M.; Fedrizzi, P.

Highlights: ► As an appropriate solution for MSW management in developed and transient countries. ► As an option to increase the efficiency of MSW selective collection. ► As an opportunity to integrate MSW management needs and services inventories. ► As a tool to develop Urban Mining actions. - Abstract: Municipal solid waste management is a multidisciplinary activity that includes generation, source separation, storage, collection, transfer and transport, processing and recovery, and, last but not least, disposal. The optimization of waste collection, through source separation, is compulsory where a landfill based management must be overcome. In this paper, a few aspectsmore » related to the implementation of a Web-GIS based system are analyzed. This approach is critically analyzed referring to the experience of two Italian case studies and two additional extra-European case studies. The first case is one of the best examples of selective collection optimization in Italy. The obtained efficiency is very high: 80% of waste is source separated for recycling purposes. In the second reference case, the local administration is going to be faced with the optimization of waste collection through Web-GIS oriented technologies for the first time. The starting scenario is far from an optimized management of municipal solid waste. The last two case studies concern pilot experiences in China and Malaysia. Each step of the Web-GIS oriented strategy is comparatively discussed referring to typical scenarios of developed and transient economies. The main result is that transient economies are ready to move toward Web oriented tools for MSW management, but this opportunity is not yet well exploited in the sector.« less

Pyrolysis of low density polyethylene waste in subcritical water optimized by response surface methodology.

PubMed

Wong, S L; Ngadi, N; Amin, N A S; Abdullah, T A T; Inuwa, I M

2016-01-01

Pyrolysis of low density polyethylene (LDPE) waste from local waste separation company in subcritical water was conducted to investigate the effect of reaction time, temperature, as well as the mass ratio of water to polymer on the liquid yield. The data obtained from the study were used to optimize the liquid yield using response surface methodology. The range of reaction temperature used was 162-338°C, while the reaction time ranged from 37 min to 143 min, and the ratio of water to polymer ranged from 1.9 to 7.1. It was found that pyrolysis of LDPE waste in subcritical water produced hydrogen, methane, carbon monoxide and carbon dioxide, while the liquid product contained alkanes and alkenes with 10-50 carbons atoms, as well as heptadecanone, dichloroacetic acid and heptadecyl ester. The optimized conditions were 152.3°C, reaction time of 1.2 min and ratio of water solution to polymer of 32.7, with the optimum liquid yield of 13.6 wt% and gases yield of 2.6 wt%.

Spectral gap optimization of order parameters for sampling complex molecular systems

PubMed Central

Tiwary, Pratyush; Berne, B. J.

2016-01-01

In modern-day simulations of many-body systems, much of the computational complexity is shifted to the identification of slowly changing molecular order parameters called collective variables (CVs) or reaction coordinates. A vast array of enhanced-sampling methods are based on the identification and biasing of these low-dimensional order parameters, whose fluctuations are important in driving rare events of interest. Here, we describe a new algorithm for finding optimal low-dimensional CVs for use in enhanced-sampling biasing methods like umbrella sampling, metadynamics, and related methods, when limited prior static and dynamic information is known about the system, and a much larger set of candidate CVs is specified. The algorithm involves estimating the best combination of these candidate CVs, as quantified by a maximum path entropy estimate of the spectral gap for dynamics viewed as a function of that CV. The algorithm is called spectral gap optimization of order parameters (SGOOP). Through multiple practical examples, we show how this postprocessing procedure can lead to optimization of CV and several orders of magnitude improvement in the convergence of the free energy calculated through metadynamics, essentially giving the ability to extract useful information even from unsuccessful metadynamics runs. PMID:26929365

Aggregation Pheromone System: A Real-parameter Optimization Algorithm using Aggregation Pheromones as the Base Metaphor

NASA Astrophysics Data System (ADS)

Tsutsui, Shigeyosi

This paper proposes an aggregation pheromone system (APS) for solving real-parameter optimization problems using the collective behavior of individuals which communicate using aggregation pheromones. APS was tested on several test functions used in evolutionary computation. The results showed APS could solve real-parameter optimization problems fairly well. The sensitivity analysis of control parameters of APS is also studied.

Optimal evaluation of infectious medical waste disposal companies using the fuzzy analytic hierarchy process

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

Ho, Chao Chung, E-mail: ho919@pchome.com.tw

Ever since Taiwan's National Health Insurance implemented the diagnosis-related groups payment system in January 2010, hospital income has declined. Therefore, to meet their medical waste disposal needs, hospitals seek suppliers that provide high-quality services at a low cost. The enactment of the Waste Disposal Act in 1974 had facilitated some improvement in the management of waste disposal. However, since the implementation of the National Health Insurance program, the amount of medical waste from disposable medical products has been increasing. Further, of all the hazardous waste types, the amount of infectious medical waste has increased at the fastest rate. This ismore » because of the increase in the number of items considered as infectious waste by the Environmental Protection Administration. The present study used two important findings from previous studies to determine the critical evaluation criteria for selecting infectious medical waste disposal firms. It employed the fuzzy analytic hierarchy process to set the objective weights of the evaluation criteria and select the optimal infectious medical waste disposal firm through calculation and sorting. The aim was to propose a method of evaluation with which medical and health care institutions could objectively and systematically choose appropriate infectious medical waste disposal firms.« less

Optimal evaluation of infectious medical waste disposal companies using the fuzzy analytic hierarchy process.

PubMed

Ho, Chao Chung

2011-07-01

Ever since Taiwan's National Health Insurance implemented the diagnosis-related groups payment system in January 2010, hospital income has declined. Therefore, to meet their medical waste disposal needs, hospitals seek suppliers that provide high-quality services at a low cost. The enactment of the Waste Disposal Act in 1974 had facilitated some improvement in the management of waste disposal. However, since the implementation of the National Health Insurance program, the amount of medical waste from disposable medical products has been increasing. Further, of all the hazardous waste types, the amount of infectious medical waste has increased at the fastest rate. This is because of the increase in the number of items considered as infectious waste by the Environmental Protection Administration. The present study used two important findings from previous studies to determine the critical evaluation criteria for selecting infectious medical waste disposal firms. It employed the fuzzy analytic hierarchy process to set the objective weights of the evaluation criteria and select the optimal infectious medical waste disposal firm through calculation and sorting. The aim was to propose a method of evaluation with which medical and health care institutions could objectively and systematically choose appropriate infectious medical waste disposal firms. Copyright © 2011 Elsevier Ltd. All rights reserved.

Process Parameters Optimization in Single Point Incremental Forming

NASA Astrophysics Data System (ADS)

Gulati, Vishal; Aryal, Ashmin; Katyal, Puneet; Goswami, Amitesh

2016-04-01

This work aims to optimize the formability and surface roughness of parts formed by the single-point incremental forming process for an Aluminium-6063 alloy. The tests are based on Taguchi's L18 orthogonal array selected on the basis of DOF. The tests have been carried out on vertical machining center (DMC70V); using CAD/CAM software (SolidWorks V5/MasterCAM). Two levels of tool radius, three levels of sheet thickness, step size, tool rotational speed, feed rate and lubrication have been considered as the input process parameters. Wall angle and surface roughness have been considered process responses. The influential process parameters for the formability and surface roughness have been identified with the help of statistical tool (response table, main effect plot and ANOVA). The parameter that has the utmost influence on formability and surface roughness is lubrication. In the case of formability, lubrication followed by the tool rotational speed, feed rate, sheet thickness, step size and tool radius have the influence in descending order. Whereas in surface roughness, lubrication followed by feed rate, step size, tool radius, sheet thickness and tool rotational speed have the influence in descending order. The predicted optimal values for the wall angle and surface roughness are found to be 88.29° and 1.03225 µm. The confirmation experiments were conducted thrice and the value of wall angle and surface roughness were found to be 85.76° and 1.15 µm respectively.

Optimizing Muscle Parameters in Musculoskeletal Modeling Using Monte Carlo Simulations

NASA Technical Reports Server (NTRS)

Hanson, Andrea; Reed, Erik; Cavanagh, Peter

2011-01-01

Astronauts assigned to long-duration missions experience bone and muscle atrophy in the lower limbs. The use of musculoskeletal simulation software has become a useful tool for modeling joint and muscle forces during human activity in reduced gravity as access to direct experimentation is limited. Knowledge of muscle and joint loads can better inform the design of exercise protocols and exercise countermeasure equipment. In this study, the LifeModeler(TM) (San Clemente, CA) biomechanics simulation software was used to model a squat exercise. The initial model using default parameters yielded physiologically reasonable hip-joint forces. However, no activation was predicted in some large muscles such as rectus femoris, which have been shown to be active in 1-g performance of the activity. Parametric testing was conducted using Monte Carlo methods and combinatorial reduction to find a muscle parameter set that more closely matched physiologically observed activation patterns during the squat exercise. Peak hip joint force using the default parameters was 2.96 times body weight (BW) and increased to 3.21 BW in an optimized, feature-selected test case. The rectus femoris was predicted to peak at 60.1% activation following muscle recruitment optimization, compared to 19.2% activation with default parameters. These results indicate the critical role that muscle parameters play in joint force estimation and the need for exploration of the solution space to achieve physiologically realistic muscle activation.

Optimization of geometric parameters of heat exchange pipes pin finning

NASA Astrophysics Data System (ADS)

Akulov, K. A.; Golik, V. V.; Voronin, K. S.; Zakirzakov, A. G.

2018-05-01

The work is devoted to optimization of geometric parameters of the pin finning of heat-exchanging pipes. Pin fins were considered from the point of view of mechanics of a deformed solid body as overhang beams with a uniformly distributed load. It was found out under what geometric parameters of the nib (diameter and length); the stresses in it from the influence of the washer fluid will not exceed the yield strength of the material (aluminum). Optimal values of the geometric parameters of nibs were obtained for different velocities of the medium washed by them. As a flow medium, water and air were chosen, and the cross section of the nibs was round and square. Pin finning turned out to be more than 3 times more compact than circumferential finning, so its use makes it possible to increase the number of fins per meter of the heat-exchanging pipe. And it is well-known that this is the main method for increasing the heat transfer of a convective surface, giving them an indisputable advantage.

Vehicle-Routing Optimization for Municipal Solid Waste Collection Using Genetic Algorithm: The Case of Southern Nablus City

NASA Astrophysics Data System (ADS)

Assaf, Ramiz; Saleh, Yahya

2017-09-01

Municipalities are responsible for solid waste collectiont for environmental, social and economic purposes. Practices of municipalities should be effective and efficient, with the objectives of reducing the total incurred costs in the solid waste collection network concurrently achieving the highest service level. This study aims at finding the best routes of solid waste collection network in Nablus city-Palestine. More specifically, the study seeks the optimal route that minimizes the total travelled distance by the trucks and hence the resulted costs. The current situation is evaluated and the problem is modelled as a Vehicle Routing Problem (VRP). The VRP is then optimized via a genetic algorithm. Specifically, compared to the current situation, the trucks total travelled distance was reduced by 66%, whereas the collection time was reduced from 7 hours per truck-trip to 2.3 hours. The findings of this study is useful for all municipality policy makers that are responsible for solid waste collection.

Study of optimal laser parameters for cutting QFN packages by Taguchi's matrix method

NASA Astrophysics Data System (ADS)

Li, Chen-Hao; Tsai, Ming-Jong; Yang, Ciann-Dong

2007-06-01

This paper reports the study of optimal laser parameters for cutting QFN (Quad Flat No-lead) packages by using a diode pumped solid-state laser system (DPSSL). The QFN cutting path includes two different materials, which are the encapsulated epoxy and a copper lead frame substrate. The Taguchi's experimental method with orthogonal array of L 9(3 4) is employed to obtain optimal combinatorial parameters. A quantified mechanism was proposed for examining the laser cutting quality of a QFN package. The influences of the various factors such as laser current, laser frequency, and cutting speed on the laser cutting quality is also examined. From the experimental results, the factors on the cutting quality in the order of decreasing significance are found to be (a) laser frequency, (b) cutting speed, and (c) laser driving current. The optimal parameters were obtained at the laser frequency of 2 kHz, the cutting speed of 2 mm/s, and the driving current of 29 A. Besides identifying this sequence of dominance, matrix experiment also determines the best level for each control factor. The verification experiment confirms that the application of laser cutting technology to QFN is very successfully by using the optimal laser parameters predicted from matrix experiments.

Optimization of design parameters for bulk micromachined silicon membranes for piezoresistive pressure sensing application

NASA Astrophysics Data System (ADS)

Belwanshi, Vinod; Topkar, Anita

2016-05-01

Finite element analysis study has been carried out to optimize the design parameters for bulk micro-machined silicon membranes for piezoresistive pressure sensing applications. The design is targeted for measurement of pressure up to 200 bar for nuclear reactor applications. The mechanical behavior of bulk micro-machined silicon membranes in terms of deflection and stress generation has been simulated. Based on the simulation results, optimization of the membrane design parameters in terms of length, width and thickness has been carried out. Subsequent to optimization of membrane geometrical parameters, the dimensions and location of the high stress concentration region for implantation of piezoresistors have been obtained for sensing of pressure using piezoresistive sensing technique.

Coastal aquifer management under parameter uncertainty: Ensemble surrogate modeling based simulation-optimization

NASA Astrophysics Data System (ADS)

Janardhanan, S.; Datta, B.

2011-12-01

Surrogate models are widely used to develop computationally efficient simulation-optimization models to solve complex groundwater management problems. Artificial intelligence based models are most often used for this purpose where they are trained using predictor-predictand data obtained from a numerical simulation model. Most often this is implemented with the assumption that the parameters and boundary conditions used in the numerical simulation model are perfectly known. However, in most practical situations these values are uncertain. Under these circumstances the application of such approximation surrogates becomes limited. In our study we develop a surrogate model based coupled simulation optimization methodology for determining optimal pumping strategies for coastal aquifers considering parameter uncertainty. An ensemble surrogate modeling approach is used along with multiple realization optimization. The methodology is used to solve a multi-objective coastal aquifer management problem considering two conflicting objectives. Hydraulic conductivity and the aquifer recharge are considered as uncertain values. Three dimensional coupled flow and transport simulation model FEMWATER is used to simulate the aquifer responses for a number of scenarios corresponding to Latin hypercube samples of pumping and uncertain parameters to generate input-output patterns for training the surrogate models. Non-parametric bootstrap sampling of this original data set is used to generate multiple data sets which belong to different regions in the multi-dimensional decision and parameter space. These data sets are used to train and test multiple surrogate models based on genetic programming. The ensemble of surrogate models is then linked to a multi-objective genetic algorithm to solve the pumping optimization problem. Two conflicting objectives, viz, maximizing total pumping from beneficial wells and minimizing the total pumping from barrier wells for hydraulic control of

A Method for Optimizing Lightweight-Gypsum Design Based on Sequential Measurements of Physical Parameters

NASA Astrophysics Data System (ADS)

Vimmrová, Alena; Kočí, Václav; Krejsová, Jitka; Černý, Robert

2016-06-01

A method for lightweight-gypsum material design using waste stone dust as the foaming agent is described. The main objective is to reach several physical properties which are inversely related in a certain way. Therefore, a linear optimization method is applied to handle this task systematically. The optimization process is based on sequential measurement of physical properties. The results are subsequently point-awarded according to a complex point criterion and new composition is proposed. After 17 trials the final mixture is obtained, having the bulk density equal to (586 ± 19) kg/m3 and compressive strength (1.10 ± 0.07) MPa. According to a detailed comparative analysis with reference gypsum, the newly developed material can be used as excellent thermally insulating interior plaster with the thermal conductivity of (0.082 ± 0.005) W/(m·K). In addition, its practical application can bring substantial economic and environmental benefits as the material contains 25 % of waste stone dust.

Accuracy Analysis and Parameters Optimization in Urban Flood Simulation by PEST Model

NASA Astrophysics Data System (ADS)

Keum, H.; Han, K.; Kim, H.; Ha, C.

2017-12-01

The risk of urban flooding has been increasing due to heavy rainfall, flash flooding and rapid urbanization. Rainwater pumping stations, underground reservoirs are used to actively take measures against flooding, however, flood damage from lowlands continues to occur. Inundation in urban areas has resulted in overflow of sewer. Therefore, it is important to implement a network system that is intricately entangled within a city, similar to the actual physical situation and accurate terrain due to the effects on buildings and roads for accurate two-dimensional flood analysis. The purpose of this study is to propose an optimal scenario construction procedure watershed partitioning and parameterization for urban runoff analysis and pipe network analysis, and to increase the accuracy of flooded area prediction through coupled model. The establishment of optimal scenario procedure was verified by applying it to actual drainage in Seoul. In this study, optimization was performed by using four parameters such as Manning's roughness coefficient for conduits, watershed width, Manning's roughness coefficient for impervious area, Manning's roughness coefficient for pervious area. The calibration range of the parameters was determined using the SWMM manual and the ranges used in the previous studies, and the parameters were estimated using the automatic calibration method PEST. The correlation coefficient showed a high correlation coefficient for the scenarios using PEST. The RPE and RMSE also showed high accuracy for the scenarios using PEST. In the case of RPE, error was in the range of 13.9-28.9% in the no-parameter estimation scenarios, but in the scenario using the PEST, the error range was reduced to 6.8-25.7%. Based on the results of this study, it can be concluded that more accurate flood analysis is possible when the optimum scenario is selected by determining the appropriate reference conduit for future urban flooding analysis and if the results is applied to various

Development of optimization model for sputtering process parameter based on gravitational search algorithm

NASA Astrophysics Data System (ADS)

Norlina, M. S.; Diyana, M. S. Nor; Mazidah, P.; Rusop, M.

2016-07-01

In the RF magnetron sputtering process, the desirable layer properties are largely influenced by the process parameters and conditions. If the quality of the thin film has not reached up to its intended level, the experiments have to be repeated until the desirable quality has been met. This research is proposing Gravitational Search Algorithm (GSA) as the optimization model to reduce the time and cost to be spent in the thin film fabrication. The optimization model's engine has been developed using Java. The model is developed based on GSA concept, which is inspired by the Newtonian laws of gravity and motion. In this research, the model is expected to optimize four deposition parameters which are RF power, deposition time, oxygen flow rate and substrate temperature. The results have turned out to be promising and it could be concluded that the performance of the model is satisfying in this parameter optimization problem. Future work could compare GSA with other nature based algorithms and test them with various set of data.

A parameter optimization approach to controller partitioning for integrated flight/propulsion control application

NASA Technical Reports Server (NTRS)

Schmidt, Phillip; Garg, Sanjay; Holowecky, Brian

1992-01-01

A parameter optimization framework is presented to solve the problem of partitioning a centralized controller into a decentralized hierarchical structure suitable for integrated flight/propulsion control implementation. The controller partitioning problem is briefly discussed and a cost function to be minimized is formulated, such that the resulting 'optimal' partitioned subsystem controllers will closely match the performance (including robustness) properties of the closed-loop system with the centralized controller while maintaining the desired controller partitioning structure. The cost function is written in terms of parameters in a state-space representation of the partitioned sub-controllers. Analytical expressions are obtained for the gradient of this cost function with respect to parameters, and an optimization algorithm is developed using modern computer-aided control design and analysis software. The capabilities of the algorithm are demonstrated by application to partitioned integrated flight/propulsion control design for a modern fighter aircraft in the short approach to landing task. The partitioning optimization is shown to lead to reduced-order subcontrollers that match the closed-loop command tracking and decoupling performance achieved by a high-order centralized controller.

A parameter optimization approach to controller partitioning for integrated flight/propulsion control application

NASA Technical Reports Server (NTRS)

Schmidt, Phillip H.; Garg, Sanjay; Holowecky, Brian R.

1993-01-01

A parameter optimization framework is presented to solve the problem of partitioning a centralized controller into a decentralized hierarchical structure suitable for integrated flight/propulsion control implementation. The controller partitioning problem is briefly discussed and a cost function to be minimized is formulated, such that the resulting 'optimal' partitioned subsystem controllers will closely match the performance (including robustness) properties of the closed-loop system with the centralized controller while maintaining the desired controller partitioning structure. The cost function is written in terms of parameters in a state-space representation of the partitioned sub-controllers. Analytical expressions are obtained for the gradient of this cost function with respect to parameters, and an optimization algorithm is developed using modern computer-aided control design and analysis software. The capabilities of the algorithm are demonstrated by application to partitioned integrated flight/propulsion control design for a modern fighter aircraft in the short approach to landing task. The partitioning optimization is shown to lead to reduced-order subcontrollers that match the closed-loop command tracking and decoupling performance achieved by a high-order centralized controller.

Optimization of the blade trailing edge geometric parameters for a small scale ORC turbine

NASA Astrophysics Data System (ADS)

Zhang, L.; Zhuge, W. L.; Peng, J.; Liu, S. J.; Zhang, Y. J.

2013-12-01

In general, the method proposed by Whitfield and Baines is adopted for the turbine preliminary design. In this design procedure for the turbine blade trailing edge geometry, two assumptions (ideal gas and zero discharge swirl) and two experience values (WR and γ) are used to get the three blade trailing edge geometric parameters: relative exit flow angle β6, the exit tip radius R6t and hub radius R6h for the purpose of maximizing the rotor total-to-static isentropic efficiency. The method above is established based on the experience and results of testing using air as working fluid, so it does not provide a mathematical optimal solution to instruct the optimization of geometry parameters and consider the real gas effects of the organic, working fluid which must be taken into consideration for the ORC turbine design procedure. In this paper, a new preliminary design and optimization method is established for the purpose of reducing the exit kinetic energy loss to improve the turbine efficiency ηts, and the blade trailing edge geometric parameters for a small scale ORC turbine with working fluid R123 are optimized based on this method. The mathematical optimal solution to minimize the exit kinetic energy is deduced, which can be used to design and optimize the exit shroud/hub radius and exit blade angle. And then, the influence of blade trailing edge geometric parameters on turbine efficiency ηts are analysed and the optimal working ranges of these parameters for the equations are recommended in consideration of working fluid R123. This method is used to modify an existing ORC turbine exit kinetic energy loss from 11.7% to 7%, which indicates the effectiveness of the method. However, the internal passage loss increases from 7.9% to 9.4%, so the only way to consider the influence of geometric parameters on internal passage loss is to give the empirical ranges of these parameters, such as the recommended ranges that the value of γ is at 0.3 to 0.4, and the value

Density-based penalty parameter optimization on C-SVM.

PubMed

Liu, Yun; Lian, Jie; Bartolacci, Michael R; Zeng, Qing-An

2014-01-01

The support vector machine (SVM) is one of the most widely used approaches for data classification and regression. SVM achieves the largest distance between the positive and negative support vectors, which neglects the remote instances away from the SVM interface. In order to avoid a position change of the SVM interface as the result of an error system outlier, C-SVM was implemented to decrease the influences of the system's outliers. Traditional C-SVM holds a uniform parameter C for both positive and negative instances; however, according to the different number proportions and the data distribution, positive and negative instances should be set with different weights for the penalty parameter of the error terms. Therefore, in this paper, we propose density-based penalty parameter optimization of C-SVM. The experiential results indicated that our proposed algorithm has outstanding performance with respect to both precision and recall.

[Optimization of the parameters of microcirculatory structural adaptation model based on improved quantum-behaved particle swarm optimization algorithm].

PubMed

Pan, Qing; Yao, Jialiang; Wang, Ruofan; Cao, Ping; Ning, Gangmin; Fang, Luping

2017-08-01

The vessels in the microcirculation keep adjusting their structure to meet the functional requirements of the different tissues. A previously developed theoretical model can reproduce the process of vascular structural adaptation to help the study of the microcirculatory physiology. However, until now, such model lacks the appropriate methods for its parameter settings with subsequent limitation of further applications. This study proposed an improved quantum-behaved particle swarm optimization (QPSO) algorithm for setting the parameter values in this model. The optimization was performed on a real mesenteric microvascular network of rat. The results showed that the improved QPSO was superior to the standard particle swarm optimization, the standard QPSO and the previously reported Downhill algorithm. We conclude that the improved QPSO leads to a better agreement between mathematical simulation and animal experiment, rendering the model more reliable in future physiological studies.

Parameter optimization of differential evolution algorithm for automatic playlist generation problem

NASA Astrophysics Data System (ADS)

Alamag, Kaye Melina Natividad B.; Addawe, Joel M.

2017-11-01

With the digitalization of music, the number of collection of music increased largely and there is a need to create lists of music that filter the collection according to user preferences, thus giving rise to the Automatic Playlist Generation Problem (APGP). Previous attempts to solve this problem include the use of search and optimization algorithms. If a music database is very large, the algorithm to be used must be able to search the lists thoroughly taking into account the quality of the playlist given a set of user constraints. In this paper we perform an evolutionary meta-heuristic optimization algorithm, Differential Evolution (DE) using different combination of parameter values and select the best performing set when used to solve four standard test functions. Performance of the proposed algorithm is then compared with normal Genetic Algorithm (GA) and a hybrid GA with Tabu Search. Numerical simulations are carried out to show better results from Differential Evolution approach with the optimized parameter values.

Multidimensional Optimization of Signal Space Distance Parameters in WLAN Positioning

PubMed Central

Brković, Milenko; Simić, Mirjana

2014-01-01

Accurate indoor localization of mobile users is one of the challenging problems of the last decade. Besides delivering high speed Internet, Wireless Local Area Network (WLAN) can be used as an effective indoor positioning system, being competitive both in terms of accuracy and cost. Among the localization algorithms, nearest neighbor fingerprinting algorithms based on Received Signal Strength (RSS) parameter have been extensively studied as an inexpensive solution for delivering indoor Location Based Services (LBS). In this paper, we propose the optimization of the signal space distance parameters in order to improve precision of WLAN indoor positioning, based on nearest neighbor fingerprinting algorithms. Experiments in a real WLAN environment indicate that proposed optimization leads to substantial improvements of the localization accuracy. Our approach is conceptually simple, is easy to implement, and does not require any additional hardware. PMID:24757443

A Particle Swarm Optimization Algorithm for Optimal Operating Parameters of VMI Systems in a Two-Echelon Supply Chain

NASA Astrophysics Data System (ADS)

Sue-Ann, Goh; Ponnambalam, S. G.

This paper focuses on the operational issues of a Two-echelon Single-Vendor-Multiple-Buyers Supply chain (TSVMBSC) under vendor managed inventory (VMI) mode of operation. To determine the optimal sales quantity for each buyer in TSVMBC, a mathematical model is formulated. Based on the optimal sales quantity can be obtained and the optimal sales price that will determine the optimal channel profit and contract price between the vendor and buyer. All this parameters depends upon the understanding of the revenue sharing between the vendor and buyers. A Particle Swarm Optimization (PSO) is proposed for this problem. Solutions obtained from PSO is compared with the best known results reported in literature.

Flash Cracking Reactor for Waste Plastic Processing

NASA Technical Reports Server (NTRS)

Timko, Michael T.; Wong, Hsi-Wu; Gonzalez, Lino A.; Broadbelt, Linda; Raviknishan, Vinu

2013-01-01

Conversion of waste plastic to energy is a growing problem that is especially acute in space exploration applications. Moreover, utilization of heavy hydrocarbon resources (wastes, waxes, etc.) as fuels and chemicals will be a growing need in the future. Existing technologies require a trade-off between product selectivity and feedstock conversion. The objective of this work was to maintain high plastic-to-fuel conversion without sacrificing the liquid yield. The developed technology accomplishes this goal with a combined understanding of thermodynamics, reaction rates, and mass transport to achieve high feed conversion without sacrificing product selectivity. The innovation requires a reaction vessel, hydrocarbon feed, gas feed, and pressure and temperature control equipment. Depending on the feedstock and desired product distribution, catalyst can be added. The reactor is heated to the desired tempera ture, pressurized to the desired pressure, and subject to a sweep flow at the optimized superficial velocity. Software developed under this project can be used to determine optimal values for these parameters. Product is vaporized, transferred to a receiver, and cooled to a liquid - a form suitable for long-term storage as a fuel or chemical. An important NASA application is the use of solar energy to convert waste plastic into a form that can be utilized during periods of low solar energy flux. Unlike previous work in this field, this innovation uses thermodynamic, mass transport, and reaction parameters to tune product distribution of pyrolysis cracking. Previous work in this field has used some of these variables, but never all in conjunction for process optimization. This method is useful for municipal waste incinerator operators and gas-to-liquids companies.

Effect of biochars produced from solid organic municipal waste on soil quality parameters.

PubMed

Randolph, P; Bansode, R R; Hassan, O A; Rehrah, Dj; Ravella, R; Reddy, M R; Watts, D W; Novak, J M; Ahmedna, M

2017-05-01

New value-added uses for solid municipal waste are needed for environmental and economic sustainability. Fortunately, value-added biochars can be produced from mixed solid waste, thereby addressing solid waste management issues, and enabling long-term carbon sequestration. We hypothesize that soil deficiencies can be remedied by the application of municipal waste-based biochars. Select municipal organic wastes (newspaper, cardboard, woodchips and landscaping residues) individually or in a 25% blend of all four waste streams were used as feedstocks of biochars. Three sets of pyrolysis temperatures (350, 500, and 750 °C) and 3 sets of pyrolysis residence time (2, 4 and 6 h) were used for biochar preparation. The biochar yield was in the range of 21-62% across all feedstocks and pyrolysis conditions. We observed variations in key biochar properties such as pH, electrical conductivity, bulk density and surface area depending on the feedstocks and production conditions. Biochar increased soil pH and improved its electrical conductivity, aggregate stability, water retention and micronutrient contents. Similarly, leachate from the soil amended with biochar showed increased pH and electrical conductivity. Some elements such as Ca and Mg decreased while NO 3 -N increased in the leachates of soils incubated with biochars. Overall, solid waste-based biochar produced significant improvements to soil fertility parameters indicating that solid municipal wastes hold promising potential as feedstocks for manufacturing value-added biochars with varied physicochemical characteristics, allowing them to not only serve the needs for solid waste management and greenhouse gas mitigation, but also as a resource for improving the quality of depleted soils. Copyright © 2017 Elsevier Ltd. All rights reserved.

Optimizing Support Vector Machine Parameters with Genetic Algorithm for Credit Risk Assessment

NASA Astrophysics Data System (ADS)

Manurung, Jonson; Mawengkang, Herman; Zamzami, Elviawaty

2017-12-01

Support vector machine (SVM) is a popular classification method known to have strong generalization capabilities. SVM can solve the problem of classification and linear regression or nonlinear kernel which can be a learning algorithm for the ability of classification and regression. However, SVM also has a weakness that is difficult to determine the optimal parameter value. SVM calculates the best linear separator on the input feature space according to the training data. To classify data which are non-linearly separable, SVM uses kernel tricks to transform the data into a linearly separable data on a higher dimension feature space. The kernel trick using various kinds of kernel functions, such as : linear kernel, polynomial, radial base function (RBF) and sigmoid. Each function has parameters which affect the accuracy of SVM classification. To solve the problem genetic algorithms are proposed to be applied as the optimal parameter value search algorithm thus increasing the best classification accuracy on SVM. Data taken from UCI repository of machine learning database: Australian Credit Approval. The results show that the combination of SVM and genetic algorithms is effective in improving classification accuracy. Genetic algorithms has been shown to be effective in systematically finding optimal kernel parameters for SVM, instead of randomly selected kernel parameters. The best accuracy for data has been upgraded from kernel Linear: 85.12%, polynomial: 81.76%, RBF: 77.22% Sigmoid: 78.70%. However, for bigger data sizes, this method is not practical because it takes a lot of time.

Aerobic composting of waste activated sludge: Kinetic analysis for microbiological reaction and oxygen consumption

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

Yamada, Y.; Kawase, Y.

2006-07-01

In order to examine the optimal design and operating parameters, kinetics for microbiological reaction and oxygen consumption in composting of waste activated sludge were quantitatively examined. A series of experiments was conducted to discuss the optimal operating parameters for aerobic composting of waste activated sludge obtained from Kawagoe City Wastewater Treatment Plant (Saitama, Japan) using 4 and 20 L laboratory scale bioreactors. Aeration rate, compositions of compost mixture and height of compost pile were investigated as main design and operating parameters. The optimal aerobic composting of waste activated sludge was found at the aeration rate of 2.0 L/min/kg (initial compostingmore » mixture dry weight). A compost pile up to 0.5 m could be operated effectively. A simple model for composting of waste activated sludge in a composting reactor was developed by assuming that a solid phase of compost mixture is well mixed and the kinetics for microbiological reaction is represented by a Monod-type equation. The model predictions could fit the experimental data for decomposition of waste activated sludge with an average deviation of 2.14%. Oxygen consumption during composting was also examined using a simplified model in which the oxygen consumption was represented by a Monod-type equation and the axial distribution of oxygen concentration in the composting pile was described by a plug-flow model. The predictions could satisfactorily simulate the experiment results for the average maximum oxygen consumption rate during aerobic composting with an average deviation of 7.4%.« less

An automatic and effective parameter optimization method for model tuning

NASA Astrophysics Data System (ADS)

Zhang, T.; Li, L.; Lin, Y.; Xue, W.; Xie, F.; Xu, H.; Huang, X.

2015-05-01

Physical parameterizations in General Circulation Models (GCMs), having various uncertain parameters, greatly impact model performance and model climate sensitivity. Traditional manual and empirical tuning of these parameters is time consuming and ineffective. In this study, a "three-step" methodology is proposed to automatically and effectively obtain the optimum combination of some key parameters in cloud and convective parameterizations according to a comprehensive objective evaluation metrics. Different from the traditional optimization methods, two extra steps, one determines parameter sensitivity and the other chooses the optimum initial value of sensitive parameters, are introduced before the downhill simplex method to reduce the computational cost and improve the tuning performance. Atmospheric GCM simulation results show that the optimum combination of these parameters determined using this method is able to improve the model's overall performance by 9%. The proposed methodology and software framework can be easily applied to other GCMs to speed up the model development process, especially regarding unavoidable comprehensive parameters tuning during the model development stage.

Parameters-tuning of PID controller for automatic voltage regulators using the African buffalo optimization.

PubMed

Odili, Julius Beneoluchi; Mohmad Kahar, Mohd Nizam; Noraziah, A

2017-01-01

In this paper, an attempt is made to apply the African Buffalo Optimization (ABO) to tune the parameters of a PID controller for an effective Automatic Voltage Regulator (AVR). Existing metaheuristic tuning methods have been proven to be quite successful but there were observable areas that need improvements especially in terms of the system's gain overshoot and steady steady state errors. Using the ABO algorithm where each buffalo location in the herd is a candidate solution to the Proportional-Integral-Derivative parameters was very helpful in addressing these two areas of concern. The encouraging results obtained from the simulation of the PID Controller parameters-tuning using the ABO when compared with the performance of Genetic Algorithm PID (GA-PID), Particle-Swarm Optimization PID (PSO-PID), Ant Colony Optimization PID (ACO-PID), PID, Bacteria-Foraging Optimization PID (BFO-PID) etc makes ABO-PID a good addition to solving PID Controller tuning problems using metaheuristics.

Using a 4D-Variational Method to Optimize Model Parameters in an Intermediate Coupled Model of ENSO

NASA Astrophysics Data System (ADS)

Gao, C.; Zhang, R. H.

2017-12-01

Large biases exist in real-time ENSO prediction, which is attributed to uncertainties in initial conditions and model parameters. Previously, a four dimentional variational (4D-Var) data assimilation system was developed for an intermediate coupled model (ICM) and used to improve ENSO modeling through optimized initial conditions. In this paper, this system is further applied to optimize model parameters. In the ICM used, one important process for ENSO is related to the anomalous temperature of subsurface water entrained into the mixed layer (Te), which is empirically and explicitly related to sea level (SL) variation, written as Te=αTe×FTe (SL). The introduced parameter, αTe, represents the strength of the thermocline effect on sea surface temperature (SST; referred as the thermocline effect). A numerical procedure is developed to optimize this model parameter through the 4D-Var assimilation of SST data in a twin experiment context with an idealized setting. Experiments having initial condition optimized only and having initial condition plus this additional model parameter optimized both are compared. It is shown that ENSO evolution can be more effectively recovered by including the additional optimization of this parameter in ENSO modeling. The demonstrated feasibility of optimizing model parameter and initial condition together through the 4D-Var method provides a modeling platform for ENSO studies. Further applications of the 4D-Var data assimilation system implemented in the ICM are also discussed.

Optimization of operating parameters for gas-phase photocatalytic splitting of H2S by novel vermiculate packed tubular reactor.

PubMed

Preethi, V; Kanmani, S

2016-10-01

Hydrogen production by gas-phase photocatalytic splitting of Hydrogen Sulphide (H2S) was investigated on four semiconductor photocatalysts including CuGa1.6Fe0.4O2, ZnFe2O3, (CdS + ZnS)/Fe2O3 and Ce/TiO2. The CdS and ZnS coated core shell particles (CdS + ZnS)/Fe2O3 shows the highest rate of hydrogen (H2) production under optimized conditions. Packed bed tubular reactor was used to study the performance of prepared photocatalysts. Selection of the best packing material is a key for maximum removal efficiency. Cheap, lightweight and easily adsorbing vermiculate materials were used as a novel packing material and were found to be effective in splitting H2S. Effect of various operating parameters like flow rate, sulphide concentration, catalyst dosage, light irradiation were tested and optimized for maximum H2 conversion of 92% from industrial waste H2S. Copyright © 2016 Elsevier Ltd. All rights reserved.

Optimizing the vermicomposting of organic wastes amended with inorganic materials for production of nutrient-rich organic fertilizers: a review.

PubMed

Mupambwa, Hupenyu Allan; Mnkeni, Pearson Nyari Stephano

2018-04-01

Vermicomposting is a bio-oxidative process that involves the action of mainly epigeic earthworm species and different micro-organisms to accelerate the biodegradation and stabilization of organic materials. There has been a growing realization that the process of vermicomposting can be used to greatly improve the fertilizer value of different organic materials, thus, creating an opportunity for their enhanced use as organic fertilizers in agriculture. The link between earthworms and micro-organisms creates a window of opportunity to optimize the vermi-degradation process for effective waste biodegradation, stabilization, and nutrient mineralization. In this review, we look at up-to-date research work that has been done on vermicomposting with the intention of highlighting research gaps on how further research can optimize vermi-degradation. Though several researchers have studied the vermicomposting process, critical parameters that drive this earthworm-microbe-driven process which are C/N and C/P ratios; substrate biodegradation fraction, earthworm species, and stocking density have yet to be adequately optimized. This review highlights that optimizing the vermicomposting process of composts amended with nutrient-rich inorganic materials such as fly ash and rock phosphate and inoculated with microbial inoculants can enable the development of commercially acceptable organic fertilizers, thus, improving their utilization in agriculture.

Concurrently adjusting interrelated control parameters to achieve optimal engine performance

DOEpatents

Jiang, Li; Lee, Donghoon; Yilmaz, Hakan; Stefanopoulou, Anna

2015-12-01

Methods and systems for real-time engine control optimization are provided. A value of an engine performance variable is determined, a value of a first operating condition and a value of a second operating condition of a vehicle engine are detected, and initial values for a first engine control parameter and a second engine control parameter are determined based on the detected first operating condition and the detected second operating condition. The initial values for the first engine control parameter and the second engine control parameter are adjusted based on the determined value of the engine performance variable to cause the engine performance variable to approach a target engine performance variable. In order to cause the engine performance variable to approach the target engine performance variable, adjusting the initial value for the first engine control parameter necessitates a corresponding adjustment of the initial value for the second engine control parameter.

Dynamic imaging model and parameter optimization for a star tracker.

PubMed

Yan, Jinyun; Jiang, Jie; Zhang, Guangjun

2016-03-21

Under dynamic conditions, star spots move across the image plane of a star tracker and form a smeared star image. This smearing effect increases errors in star position estimation and degrades attitude accuracy. First, an analytical energy distribution model of a smeared star spot is established based on a line segment spread function because the dynamic imaging process of a star tracker is equivalent to the static imaging process of linear light sources. The proposed model, which has a clear physical meaning, explicitly reflects the key parameters of the imaging process, including incident flux, exposure time, velocity of a star spot in an image plane, and Gaussian radius. Furthermore, an analytical expression of the centroiding error of the smeared star spot is derived using the proposed model. An accurate and comprehensive evaluation of centroiding accuracy is obtained based on the expression. Moreover, analytical solutions of the optimal parameters are derived to achieve the best performance in centroid estimation. Finally, we perform numerical simulations and a night sky experiment to validate the correctness of the dynamic imaging model, the centroiding error expression, and the optimal parameters.

An inexact reverse logistics model for municipal solid waste management systems.

PubMed

Zhang, Yi Mei; Huang, Guo He; He, Li

2011-03-01

This paper proposed an inexact reverse logistics model for municipal solid waste management systems (IRWM). Waste managers, suppliers, industries and distributors were involved in strategic planning and operational execution through reverse logistics management. All the parameters were assumed to be intervals to quantify the uncertainties in the optimization process and solutions in IRWM. To solve this model, a piecewise interval programming was developed to deal with Min-Min functions in both objectives and constraints. The application of the model was illustrated through a classical municipal solid waste management case. With different cost parameters for landfill and the WTE, two scenarios were analyzed. The IRWM could reflect the dynamic and uncertain characteristics of MSW management systems, and could facilitate the generation of desired management plans. The model could be further advanced through incorporating methods of stochastic or fuzzy parameters into its framework. Design of multi-waste, multi-echelon, multi-uncertainty reverse logistics model for waste management network would also be preferred. Copyright © 2010 Elsevier Ltd. All rights reserved.

Selecting and optimizing eco-physiological parameters of Biome-BGC to reproduce observed woody and leaf biomass growth of Eucommia ulmoides plantation in China using Dakota optimizer

NASA Astrophysics Data System (ADS)

Miyauchi, T.; Machimura, T.

2013-12-01

In the simulation using an ecosystem process model, the adjustment of parameters is indispensable for improving the accuracy of prediction. This procedure, however, requires much time and effort for approaching the simulation results to the measurements on models consisting of various ecosystem processes. In this study, we tried to apply a general purpose optimization tool in the parameter optimization of an ecosystem model, and examined its validity by comparing the simulated and measured biomass growth of a woody plantation. A biometric survey of tree biomass growth was performed in 2009 in an 11-year old Eucommia ulmoides plantation in Henan Province, China. Climate of the site was dry temperate. Leaf, above- and below-ground woody biomass were measured from three cut trees and converted into carbon mass per area by measured carbon contents and stem density. Yearly woody biomass growth of the plantation was calculated according to allometric relationships determined by tree ring analysis of seven cut trees. We used Biome-BGC (Thornton, 2002) to reproduce biomass growth of the plantation. Air temperature and humidity from 1981 to 2010 was used as input climate condition. The plant functional type was deciduous broadleaf, and non-optimizing parameters were left default. 11-year long normal simulations were performed following a spin-up run. In order to select optimizing parameters, we analyzed the sensitivity of leaf, above- and below-ground woody biomass to eco-physiological parameters. Following the selection, optimization of parameters was performed by using the Dakota optimizer. Dakota is an optimizer developed by Sandia National Laboratories for providing a systematic and rapid means to obtain optimal designs using simulation based models. As the object function, we calculated the sum of relative errors between simulated and measured leaf, above- and below-ground woody carbon at each of eleven years. In an alternative run, errors at the last year (at the

General equations for optimal selection of diagnostic image acquisition parameters in clinical X-ray imaging.

PubMed

Zheng, Xiaoming

2017-12-01

The purpose of this work was to examine the effects of relationship functions between diagnostic image quality and radiation dose on the governing equations for image acquisition parameter variations in X-ray imaging. Various equations were derived for the optimal selection of peak kilovoltage (kVp) and exposure parameter (milliAmpere second, mAs) in computed tomography (CT), computed radiography (CR), and direct digital radiography. Logistic, logarithmic, and linear functions were employed to establish the relationship between radiation dose and diagnostic image quality. The radiation dose to the patient, as a function of image acquisition parameters (kVp, mAs) and patient size (d), was used in radiation dose and image quality optimization. Both logistic and logarithmic functions resulted in the same governing equation for optimal selection of image acquisition parameters using a dose efficiency index. For image quality as a linear function of radiation dose, the same governing equation was derived from the linear relationship. The general equations should be used in guiding clinical X-ray imaging through optimal selection of image acquisition parameters. The radiation dose to the patient could be reduced from current levels in medical X-ray imaging.

Optimization of the dressing parameters in cylindrical grinding based on a generalized utility function

NASA Astrophysics Data System (ADS)

Aleksandrova, Irina

2016-01-01

The existing studies, concerning the dressing process, focus on the major influence of the dressing conditions on the grinding response variables. However, the choice of the dressing conditions is often made, based on the experience of the qualified staff or using data from reference books. The optimal dressing parameters, which are only valid for the particular methods and dressing and grinding conditions, are also used. The paper presents a methodology for optimization of the dressing parameters in cylindrical grinding. The generalized utility function has been chosen as an optimization parameter. It is a complex indicator determining the economic, dynamic and manufacturing characteristics of the grinding process. The developed methodology is implemented for the dressing of aluminium oxide grinding wheels by using experimental diamond roller dressers with different grit sizes made of medium- and high-strength synthetic diamonds type ??32 and ??80. To solve the optimization problem, a model of the generalized utility function is created which reflects the complex impact of dressing parameters. The model is built based on the results from the conducted complex study and modeling of the grinding wheel lifetime, cutting ability, production rate and cutting forces during grinding. They are closely related to the dressing conditions (dressing speed ratio, radial in-feed of the diamond roller dresser and dress-out time), the diamond roller dresser grit size/grinding wheel grit size ratio, the type of synthetic diamonds and the direction of dressing. Some dressing parameters are determined for which the generalized utility function has a maximum and which guarantee an optimum combination of the following: the lifetime and cutting ability of the abrasive wheels, the tangential cutting force magnitude and the production rate of the grinding process. The results obtained prove the possibility of control and optimization of grinding by selecting particular dressing

Parameter estimation with bio-inspired meta-heuristic optimization: modeling the dynamics of endocytosis.

PubMed

Tashkova, Katerina; Korošec, Peter; Silc, Jurij; Todorovski, Ljupčo; Džeroski, Sašo

2011-10-11

We address the task of parameter estimation in models of the dynamics of biological systems based on ordinary differential equations (ODEs) from measured data, where the models are typically non-linear and have many parameters, the measurements are imperfect due to noise, and the studied system can often be only partially observed. A representative task is to estimate the parameters in a model of the dynamics of endocytosis, i.e., endosome maturation, reflected in a cut-out switch transition between the Rab5 and Rab7 domain protein concentrations, from experimental measurements of these concentrations. The general parameter estimation task and the specific instance considered here are challenging optimization problems, calling for the use of advanced meta-heuristic optimization methods, such as evolutionary or swarm-based methods. We apply three global-search meta-heuristic algorithms for numerical optimization, i.e., differential ant-stigmergy algorithm (DASA), particle-swarm optimization (PSO), and differential evolution (DE), as well as a local-search derivative-based algorithm 717 (A717) to the task of estimating parameters in ODEs. We evaluate their performance on the considered representative task along a number of metrics, including the quality of reconstructing the system output and the complete dynamics, as well as the speed of convergence, both on real-experimental data and on artificial pseudo-experimental data with varying amounts of noise. We compare the four optimization methods under a range of observation scenarios, where data of different completeness and accuracy of interpretation are given as input. Overall, the global meta-heuristic methods (DASA, PSO, and DE) clearly and significantly outperform the local derivative-based method (A717). Among the three meta-heuristics, differential evolution (DE) performs best in terms of the objective function, i.e., reconstructing the output, and in terms of convergence. These results hold for both real and

Parameter estimation with bio-inspired meta-heuristic optimization: modeling the dynamics of endocytosis

PubMed Central

2011-01-01

Background We address the task of parameter estimation in models of the dynamics of biological systems based on ordinary differential equations (ODEs) from measured data, where the models are typically non-linear and have many parameters, the measurements are imperfect due to noise, and the studied system can often be only partially observed. A representative task is to estimate the parameters in a model of the dynamics of endocytosis, i.e., endosome maturation, reflected in a cut-out switch transition between the Rab5 and Rab7 domain protein concentrations, from experimental measurements of these concentrations. The general parameter estimation task and the specific instance considered here are challenging optimization problems, calling for the use of advanced meta-heuristic optimization methods, such as evolutionary or swarm-based methods. Results We apply three global-search meta-heuristic algorithms for numerical optimization, i.e., differential ant-stigmergy algorithm (DASA), particle-swarm optimization (PSO), and differential evolution (DE), as well as a local-search derivative-based algorithm 717 (A717) to the task of estimating parameters in ODEs. We evaluate their performance on the considered representative task along a number of metrics, including the quality of reconstructing the system output and the complete dynamics, as well as the speed of convergence, both on real-experimental data and on artificial pseudo-experimental data with varying amounts of noise. We compare the four optimization methods under a range of observation scenarios, where data of different completeness and accuracy of interpretation are given as input. Conclusions Overall, the global meta-heuristic methods (DASA, PSO, and DE) clearly and significantly outperform the local derivative-based method (A717). Among the three meta-heuristics, differential evolution (DE) performs best in terms of the objective function, i.e., reconstructing the output, and in terms of convergence. These

An automatic and effective parameter optimization method for model tuning

NASA Astrophysics Data System (ADS)

Zhang, T.; Li, L.; Lin, Y.; Xue, W.; Xie, F.; Xu, H.; Huang, X.

2015-11-01

Physical parameterizations in general circulation models (GCMs), having various uncertain parameters, greatly impact model performance and model climate sensitivity. Traditional manual and empirical tuning of these parameters is time-consuming and ineffective. In this study, a "three-step" methodology is proposed to automatically and effectively obtain the optimum combination of some key parameters in cloud and convective parameterizations according to a comprehensive objective evaluation metrics. Different from the traditional optimization methods, two extra steps, one determining the model's sensitivity to the parameters and the other choosing the optimum initial value for those sensitive parameters, are introduced before the downhill simplex method. This new method reduces the number of parameters to be tuned and accelerates the convergence of the downhill simplex method. Atmospheric GCM simulation results show that the optimum combination of these parameters determined using this method is able to improve the model's overall performance by 9 %. The proposed methodology and software framework can be easily applied to other GCMs to speed up the model development process, especially regarding unavoidable comprehensive parameter tuning during the model development stage.

Optimization of processing parameters of UAV integral structural components based on yield response

NASA Astrophysics Data System (ADS)

Chen, Yunsheng

2018-05-01

In order to improve the overall strength of unmanned aerial vehicle (UAV), it is necessary to optimize the processing parameters of UAV structural components, which is affected by initial residual stress in the process of UAV structural components processing. Because machining errors are easy to occur, an optimization model for machining parameters of UAV integral structural components based on yield response is proposed. The finite element method is used to simulate the machining parameters of UAV integral structural components. The prediction model of workpiece surface machining error is established, and the influence of the path of walking knife on residual stress of UAV integral structure is studied, according to the stress of UAV integral component. The yield response of the time-varying stiffness is analyzed, and the yield response and the stress evolution mechanism of the UAV integral structure are analyzed. The simulation results show that this method is used to optimize the machining parameters of UAV integral structural components and improve the precision of UAV milling processing. The machining error is reduced, and the deformation prediction and error compensation of UAV integral structural parts are realized, thus improving the quality of machining.

Assessing FPAR Source and Parameter Optimization Scheme in Application of a Diagnostic Carbon Flux Model

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

Turner, D P; Ritts, W D; Wharton, S

2009-02-26

The combination of satellite remote sensing and carbon cycle models provides an opportunity for regional to global scale monitoring of terrestrial gross primary production, ecosystem respiration, and net ecosystem production. FPAR (the fraction of photosynthetically active radiation absorbed by the plant canopy) is a critical input to diagnostic models, however little is known about the relative effectiveness of FPAR products from different satellite sensors nor about the sensitivity of flux estimates to different parameterization approaches. In this study, we used multiyear observations of carbon flux at four eddy covariance flux tower sites within the conifer biome to evaluate these factors.more » FPAR products from the MODIS and SeaWiFS sensors, and the effects of single site vs. cross-site parameter optimization were tested with the CFLUX model. The SeaWiFs FPAR product showed greater dynamic range across sites and resulted in slightly reduced flux estimation errors relative to the MODIS product when using cross-site optimization. With site-specific parameter optimization, the flux model was effective in capturing seasonal and interannual variation in the carbon fluxes at these sites. The cross-site prediction errors were lower when using parameters from a cross-site optimization compared to parameter sets from optimization at single sites. These results support the practice of multisite optimization within a biome for parameterization of diagnostic carbon flux models.« less

Engineering-Scale Demonstration of DuraLith and Ceramicrete Waste Forms

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

Josephson, Gary B.; Westsik, Joseph H.; Pires, Richard P.

2011-09-23

To support the selection of a waste form for the liquid secondary wastes from the Hanford Waste Immobilization and Treatment Plant, Washington River Protection Solutions (WRPS) has initiated secondary waste form testing on four candidate waste forms. Two of the candidate waste forms have not been developed to scale as the more mature waste forms. This work describes engineering-scale demonstrations conducted on Ceramicrete and DuraLith candidate waste forms. Both candidate waste forms were successfully demonstrated at an engineering scale. A preliminary conceptual design could be prepared for full-scale production of the candidate waste forms. However, both waste forms are stillmore » too immature to support a detailed design. Formulations for each candidate waste form need to be developed so that the material has a longer working time after mixing the liquid and solid constituents together. Formulations optimized based on previous lab studies did not have sufficient working time to support large-scale testing. The engineering-scale testing was successfully completed using modified formulations. Further lab development and parametric studies are needed to optimize formulations with adequate working time and assess the effects of changes in raw materials and process parameters on the final product performance. Studies on effects of mixing intensity on the initial set time of the waste forms are also needed.« less

Optimization-Based Inverse Identification of the Parameters of a Concrete Cap Material Model

NASA Astrophysics Data System (ADS)

Král, Petr; Hokeš, Filip; Hušek, Martin; Kala, Jiří; Hradil, Petr

2017-10-01

Issues concerning the advanced numerical analysis of concrete building structures in sophisticated computing systems currently require the involvement of nonlinear mechanics tools. The efforts to design safer, more durable and mainly more economically efficient concrete structures are supported via the use of advanced nonlinear concrete material models and the geometrically nonlinear approach. The application of nonlinear mechanics tools undoubtedly presents another step towards the approximation of the real behaviour of concrete building structures within the framework of computer numerical simulations. However, the success rate of this application depends on having a perfect understanding of the behaviour of the concrete material models used and having a perfect understanding of the used material model parameters meaning. The effective application of nonlinear concrete material models within computer simulations often becomes very problematic because these material models very often contain parameters (material constants) whose values are difficult to obtain. However, getting of the correct values of material parameters is very important to ensure proper function of a concrete material model used. Today, one possibility, which permits successful solution of the mentioned problem, is the use of optimization algorithms for the purpose of the optimization-based inverse material parameter identification. Parameter identification goes hand in hand with experimental investigation while it trying to find parameter values of the used material model so that the resulting data obtained from the computer simulation will best approximate the experimental data. This paper is focused on the optimization-based inverse identification of the parameters of a concrete cap material model which is known under the name the Continuous Surface Cap Model. Within this paper, material parameters of the model are identified on the basis of interaction between nonlinear computer simulations

Evaluation of Externality Costs in Life-Cycle Optimization of Municipal Solid Waste Management Systems.

PubMed

Martinez-Sanchez, Veronica; Levis, James W; Damgaard, Anders; DeCarolis, Joseph F; Barlaz, Morton A; Astrup, Thomas F

2017-03-21

The development of sustainable solid waste management (SWM) systems requires consideration of both economic and environmental impacts. Societal life-cycle costing (S-LCC) provides a quantitative framework to estimate both economic and environmental impacts, by including "budget costs" and "externality costs". Budget costs include market goods and services (economic impact), whereas externality costs include effects outside the economic system (e.g., environmental impact). This study demonstrates the applicability of S-LCC to SWM life-cycle optimization through a case study based on an average suburban U.S. county of 500 000 people generating 320 000 Mg of waste annually. Estimated externality costs are based on emissions of CO 2 , CH 4 , N 2 O, PM 2.5 , PM 10 , NO x , SO 2 , VOC, CO, NH 3 , Hg, Pb, Cd, Cr (VI), Ni, As, and dioxins. The results indicate that incorporating S-LCC into optimized SWM strategy development encourages the use of a mixed waste material recovery facility with residues going to incineration, and separated organics to anaerobic digestion. Results are sensitive to waste composition, energy mix and recycling rates. Most of the externality costs stem from SO 2 , NO x , PM 2.5 , CH 4 , fossil CO 2 , and NH 3 emissions. S-LCC proved to be a valuable tool for policy analysis, but additional data on key externality costs such as organic compounds emissions to water would improve future analyses.

Co-composting of organic fraction of municipal solid waste mixed with different bulking waste: characterization of physicochemical parameters and microbial enzymatic dynamic.

PubMed

Awasthi, Mukesh Kumar; Pandey, Akhilesh Kumar; Bundela, Pushpendra Singh; Khan, Jamaluddin

2015-04-01

The effect of various bulking waste such as wood shaving, agricultural and yard trimming waste combined with organic fraction of municipal solid waste (OFMSW) composting was investigated through assessing their influence on microbial enzymatic activities and quality of finished compost. All three piles of OFMSW with different bulking waste were inoculated with microbial consortium. The results revealed that OFMSW combined with wood shaving and microbial consortium (Phanerochaete chrysosporium, Trichoderma viride and Pseudomonas aeruginosa) were helpful tool to facilitate the enzymatic activity and shortened composting period within 4 weeks. Maximum enzymatic activity were observed in pile 1 and 3 during the first 3 weeks, while in pile 2 relatively very low. But phosphatase activity was relatively higher in all piles until the end of the process. Maturity parameters of compost quality also favored the pile 1 as the best formulation for OFMSW composting. Copyright © 2015 Elsevier Ltd. All rights reserved.

Optimization of processing parameters of amaranth grits before grinding into flour

NASA Astrophysics Data System (ADS)

Zharkova, I. M.; Safonova, Yu A.; Slepokurova, Yu I.

2018-05-01

There are the results of experimental studies about the influence of infrared treatment (IR processing) parameters of the amaranth grits before their grinding into flour on the composition and properties of the received product. Using the method called as regressionfactor analysis, the optimal conditions of the thermal processing to the amaranth grits were obtained: the belt speed of the conveyor – 0.049 m/s; temperature of amaranth grits in the tempering silo – 65.4 °C the thickness of the layer of amaranth grits on the belt is 3 - 5 mm and the lamp power is 69.2 kW/m2. The conducted researches confirmed that thermal effect to the amaranth grains in the IR setting allows getting flour with a smaller size of starch grains, with the increased water-holding ability, and with a changed value of its glycemic index. Mathematical processing of experimental data allowed establishing the dependence of the structural and technological characteristics of the amaranth flour on the IR processing parameters of amaranth grits. The obtained results are quite consistent with the experimental ones that proves the effectiveness of optimization based on mathematical planning of the experiment to determine the influence of heat treatment optimal parameters of the amaranth grits on the functional and technological properties of the flour received from it.

Statistical Evaluation and Optimization of Factors Affecting the Leaching Performance of Copper Flotation Waste

PubMed Central

Çoruh, Semra; Elevli, Sermin; Geyikçi, Feza

2012-01-01

Copper flotation waste is an industrial by-product material produced from the process of manufacturing copper. The main concern with respect to landfilling of copper flotation waste is the release of elements (e.g., salts and heavy metals) when in contact with water, that is, leaching. Copper flotation waste generally contains a significant amount of Cu together with trace elements of other toxic metals, such as Zn, Co, and Pb. The release of heavy metals into the environment has resulted in a number of environmental problems. The aim of this study is to investigate the leaching characteristics of copper flotation waste by use of the Box-Behnken experimental design approach. In order to obtain the optimized condition of leachability, a second-order model was examined. The best leaching conditions achieved were as follows: pH = 9, stirring time = 5 min, and temperature = 41.5°C. PMID:22629194

Statistical evaluation and optimization of factors affecting the leaching performance of copper flotation waste.

PubMed

Coruh, Semra; Elevli, Sermin; Geyikçi, Feza

2012-01-01

Copper flotation waste is an industrial by-product material produced from the process of manufacturing copper. The main concern with respect to landfilling of copper flotation waste is the release of elements (e.g., salts and heavy metals) when in contact with water, that is, leaching. Copper flotation waste generally contains a significant amount of Cu together with trace elements of other toxic metals, such as Zn, Co, and Pb. The release of heavy metals into the environment has resulted in a number of environmental problems. The aim of this study is to investigate the leaching characteristics of copper flotation waste by use of the Box-Behnken experimental design approach. In order to obtain the optimized condition of leachability, a second-order model was examined. The best leaching conditions achieved were as follows: pH = 9, stirring time = 5 min, and temperature = 41.5 °C.

Optimization of bone drilling parameters using Taguchi method based on finite element analysis

NASA Astrophysics Data System (ADS)

Rosidi, Ayip; Lenggo Ginta, Turnad; Rani, Ahmad Majdi Bin Abdul

2017-05-01

Thermal necrosis results fracture problems and implant failure if temperature exceeds 47 °C for one minute during bone drilling. To solve this problem, this work studied a new thermal model by using three drilling parameters: drill diameter, feed rate and spindle speed. Effects of those parameters to heat generation were studied. The drill diameters were 4 mm, 6 mm and 6 mm; the feed rates were 80 mm/min, 100 mm/min and 120 mm/min whereas the spindle speeds were 400 rpm, 500 rpm and 600 rpm then an optimization was done by Taguchi method to which combination parameter can be used to prevent thermal necrosis during bone drilling. The results showed that all the combination of parameters produce confidence results which were below 47 °C and finite element analysis combined with Taguchi method can be used for predicting temperature generation and optimizing bone drilling parameters prior to clinical bone drilling. All of the combination parameters can be used for surgeon to achieve sustainable orthopaedic surgery.

Optimization of electro-optical parameters of LCD for advertising systems

NASA Astrophysics Data System (ADS)

Olifierczuk, Marek; Zielinski, Jerzy; Klosowicz, Stanislaw J.

1998-02-01

The analysis of the optimization of negative image twisted nematic LCD is presented. Theoretical considerations are confirmed by experimental results. The effect of material parameters and technology on the contrast ratio and display dynamics is given. The effect in TN display with black dye is presented.

Determination of full piezoelectric complex parameters using gradient-based optimization algorithm

NASA Astrophysics Data System (ADS)

Kiyono, C. Y.; Pérez, N.; Silva, E. C. N.

2016-02-01

At present, numerical techniques allow the precise simulation of mechanical structures, but the results are limited by the knowledge of the material properties. In the case of piezoelectric ceramics, the full model determination in the linear range involves five elastic, three piezoelectric, and two dielectric complex parameters. A successful solution to obtaining piezoceramic properties consists of comparing the experimental measurement of the impedance curve and the results of a numerical model by using the finite element method (FEM). In the present work, a new systematic optimization method is proposed to adjust the full piezoelectric complex parameters in the FEM model. Once implemented, the method only requires the experimental data (impedance modulus and phase data acquired by an impedometer), material density, geometry, and initial values for the properties. This method combines a FEM routine implemented using an 8-noded axisymmetric element with a gradient-based optimization routine based on the method of moving asymptotes (MMA). The main objective of the optimization procedure is minimizing the quadratic difference between the experimental and numerical electrical conductance and resistance curves (to consider resonance and antiresonance frequencies). To assure the convergence of the optimization procedure, this work proposes restarting the optimization loop whenever the procedure ends in an undesired or an unfeasible solution. Two experimental examples using PZ27 and APC850 samples are presented to test the precision of the method and to check the dependency of the frequency range used, respectively.

Response surface optimization of substrates for thermophilic anaerobic codigestion of sewage sludge and food waste.

PubMed

Kim, Hyun-Woo; Shin, Hang-Sik; Han, Sun-Kee; Oh, Sae-Eun

2007-03-01

This study investigated the effects of food waste constituents on thermophilic (55 degrees C) anaerobic codigestion of sewage sludge and food waste by using statistical techniques based on biochemical methane potential tests. Various combinations of grain, vegetable, and meat as cosubstrate were tested, and then the data of methane potential (MP), methane production rate (MPR), and first-order kinetic constant of hydrolysis (kH) were collected for further analyses. Response surface methodology by the Box-Behnken design can verify the effects and their interactions of three variables on responses efficiently. MP was mainly affected by grain, whereas MPR and kH were affected by both vegetable and meat. Estimated polynomial regression models can properly explain the variability of experimental data with a high-adjusted R2 of 0.727, 0.836, and 0.915, respectively. By applying a series of optimization techniques, it was possible to find the proper criteria of cosubstrate. The optimal cosubstrate region was suggested based on overlay contours of overall mean responses. With the desirability contour plots, it was found that optimal conditions of cosubstrate for the maximum MPR (56.6 mL of CH4/g of chemical oxygen demand [COD]/day) were 0.71 g of COD/L of grain, 0.18 g of COD/L of vegetable, and 0.38 g of COD/L of meat by the simultaneous consideration of MP, MPR, and kH. Within the range of each factor examined, the corresponding optimal ratio of sewage sludge to cosubstrate was 71:29 as the COD basis. Elaborate discussions could yield practical operational strategies for the enhanced thermophilic anaerobic codigestion of sewage sludge and food waste.

Strain selection and medium optimization for glucoamylase production from industrial potato waste by Aspergillus niger.

PubMed

Izmirlioglu, Gulten; Demirci, Ali

2016-06-01

Glucoamylase is one of the most common enzymes used in the food industry to break down starch into its monomers. Glucoamylase production and its activity are highly dependent on medium composition. Starch is well known as a glucoamylase inducer, and utilization of industrial starchy potato waste is an inexpensive way of improving glucoamylase production. Since glucoamylase production is highly dependent on medium composition, in this study medium optimization for glucoamylase production was considered to enhance glucoamylase activity. Among the evaluated microbial species, Aspergillus niger van Tieghem was found to be the best glucoamylase-producing fungus. The Plackett-Burman design was used to screen various medium ingredients, and malt extract, FeSO4 .7H2 O and CaCl2 ·2H2 O were found to have significant effects on glucoamylase production. Finally, malt extract, FeSO4 .7H2 O and CaCl2 .2H2 O were optimized by using a central composite design of response surface methodology. The results showed that the optimal medium composition for A. niger van Tieghem was 50 g L(-1) industrial waste potato mash supplemented with 51.82 g L(-1) malt extract, 9.27 g L(-1) CaCl2 ·2H2 O and 0.50 g L(-1) FeSO4 .7H2 O. At the end of optimization, glucoamylase activity and glucose production were improved 126% and 98% compared to only industrial waste potato mash basal medium; 274.4 U mL(-1) glucoamylase activity and 41.7 g L(-1) glucose levels were achieved, respectively. © 2015 Society of Chemical Industry. © 2015 Society of Chemical Industry.

Physical and chemical evaluation of furniture waste briquettes.

PubMed

Moreno, Ana Isabel; Font, Rafael; Conesa, Juan A

2016-03-01

Furniture waste is mainly composed of wood and upholstery foam (mostly polyurethane foam). Both of these have a high calorific value, therefore, energy recovery would be an appropriate process to manage these wastes. Nevertheless, the drawback is that the energy content of these wastes is limited due to their low density mainly that of upholstery foam. Densification of separate foam presents difficulties due to its elastic character. The significance of this work lies in obtaining densified material by co-densification of furniture wood waste and polyurethane foam waste. Densification of furniture wood and the co-densification of furniture wood waste with polyurethane foam have been studied. On the one hand, the parameters that have an effect on the quality of the furniture waste briquettes have been analysed, i.e., moisture content, compaction pressure, presence of lignin, etc. The maximum weight percentage of polyurethane foam that can be added with furniture wood waste to obtain durable briquettes and the optimal moisture were determined. On the other hand, some parameters were analysed in order to evaluate the possible effect on the combustion. The chemical composition of waste wood was compared with untreated wood biomass; the higher nitrogen content and the concentration of some metals were the most important differences, with a significant difference of Ti content. Copyright © 2016 Elsevier Ltd. All rights reserved.

Optimal parameter estimation with a fixed rate of abstention

NASA Astrophysics Data System (ADS)

Gendra, B.; Ronco-Bonvehi, E.; Calsamiglia, J.; Muñoz-Tapia, R.; Bagan, E.

2013-07-01

The problems of optimally estimating a phase, a direction, and the orientation of a Cartesian frame (or trihedron) with general pure states are addressed. Special emphasis is put on estimation schemes that allow for inconclusive answers or abstention. It is shown that such schemes enable drastic improvements, up to the extent of attaining the Heisenberg limit in some cases, and the required amount of abstention is quantified. A general mathematical framework to deal with the asymptotic limit of many qubits or large angular momentum is introduced and used to obtain analytical results for all the relevant cases under consideration. Parameter estimation with abstention is also formulated as a semidefinite programming problem, for which very efficient numerical optimization techniques exist.

Application of dragonfly algorithm for optimal performance analysis of process parameters in turn-mill operations- A case study

NASA Astrophysics Data System (ADS)

Vikram, K. Arun; Ratnam, Ch; Lakshmi, VVK; Kumar, A. Sunny; Ramakanth, RT

2018-02-01

Meta-heuristic multi-response optimization methods are widely in use to solve multi-objective problems to obtain Pareto optimal solutions during optimization. This work focuses on optimal multi-response evaluation of process parameters in generating responses like surface roughness (Ra), surface hardness (H) and tool vibration displacement amplitude (Vib) while performing operations like tangential and orthogonal turn-mill processes on A-axis Computer Numerical Control vertical milling center. Process parameters like tool speed, feed rate and depth of cut are considered as process parameters machined over brass material under dry condition with high speed steel end milling cutters using Taguchi design of experiments (DOE). Meta-heuristic like Dragonfly algorithm is used to optimize the multi-objectives like ‘Ra’, ‘H’ and ‘Vib’ to identify the optimal multi-response process parameters combination. Later, the results thus obtained from multi-objective dragonfly algorithm (MODA) are compared with another multi-response optimization technique Viz. Grey relational analysis (GRA).

Analysis of parameter estimation and optimization application of ant colony algorithm in vehicle routing problem

NASA Astrophysics Data System (ADS)

Xu, Quan-Li; Cao, Yu-Wei; Yang, Kun

2018-03-01

Ant Colony Optimization (ACO) is the most widely used artificial intelligence algorithm at present. This study introduced the principle and mathematical model of ACO algorithm in solving Vehicle Routing Problem (VRP), and designed a vehicle routing optimization model based on ACO, then the vehicle routing optimization simulation system was developed by using c ++ programming language, and the sensitivity analyses, estimations and improvements of the three key parameters of ACO were carried out. The results indicated that the ACO algorithm designed in this paper can efficiently solve rational planning and optimization of VRP, and the different values of the key parameters have significant influence on the performance and optimization effects of the algorithm, and the improved algorithm is not easy to locally converge prematurely and has good robustness.

Estimating soil hydraulic parameters from transient flow experiments in a centrifuge using parameter optimization technique

USGS Publications Warehouse

Šimůnek, Jirka; Nimmo, John R.

2005-01-01

A modified version of the Hydrus software package that can directly or inversely simulate water flow in a transient centrifugal field is presented. The inverse solver for parameter estimation of the soil hydraulic parameters is then applied to multirotation transient flow experiments in a centrifuge. Using time‐variable water contents measured at a sequence of several rotation speeds, soil hydraulic properties were successfully estimated by numerical inversion of transient experiments. The inverse method was then evaluated by comparing estimated soil hydraulic properties with those determined independently using an equilibrium analysis. The optimized soil hydraulic properties compared well with those determined using equilibrium analysis and steady state experiment. Multirotation experiments in a centrifuge not only offer significant time savings by accelerating time but also provide significantly more information for the parameter estimation procedure compared to multistep outflow experiments in a gravitational field.

Optimization of process parameters in drilling of fibre hybrid composite using Taguchi and grey relational analysis

NASA Astrophysics Data System (ADS)

Vijaya Ramnath, B.; Sharavanan, S.; Jeykrishnan, J.

2017-03-01

Nowadays quality plays a vital role in all the products. Hence, the development in manufacturing process focuses on the fabrication of composite with high dimensional accuracy and also incurring low manufacturing cost. In this work, an investigation on machining parameters has been performed on jute-flax hybrid composite. Here, the two important responses characteristics like surface roughness and material removal rate are optimized by employing 3 machining input parameters. The input variables considered are drill bit diameter, spindle speed and feed rate. Machining is done on CNC vertical drilling machine at different levels of drilling parameters. Taguchi’s L16 orthogonal array is used for optimizing individual tool parameters. Analysis Of Variance is used to find the significance of individual parameters. The simultaneous optimization of the process parameters is done by grey relational analysis. The results of this investigation shows that, spindle speed and drill bit diameter have most effect on material removal rate and surface roughness followed by feed rate.

Optimization and characterization of gelatin and chitosan extracted from fish and shrimp waste

NASA Astrophysics Data System (ADS)

Ait Boulahsen, M.; Chairi, H.; Laglaoui, A.; Arakrak, A.; Zantar, S.; Bakkali, M.; Hassani, M.

2018-05-01

Fish and seafood processing industries generate large quantities of waste which are at the origin of several environmental, economic and social problems. However fish waste could contain high value-added substances such as biopolymers. This work focuses on optimizing the gelatin and chitosan extraction from tilapia fish skins and shrimp shells respectively. The gelatin extraction process was optimized using alkali acid treatment prior to thermal hydrolysis. Three different acids were tested at different concentrations. Chitosan was obtained after acid demineralization followed by simultaneous hydrothermal deproteinization and deacetylation by an alkali treatment with different concentrations of HCl and NaOH. The extracted gelatin and chitosan with the highest yield were characterized by determining their main physicochemical properties (Degree of deacetylation, viscosity, pH, moisture and ash content). Results show a significant influence of the acid type and concentration on the extraction yield of gelatin and chitosan, with an average yield of 12.24% and 3.85% respectively. Furthermore, the obtained physicochemical properties of both extracted gelatin and chitosan were within the recommended standard values of the commercial ones used in the industry.

Optimization of extraction of high purity all-trans-lycopene from tomato pulp waste.

PubMed

Poojary, Mahesha M; Passamonti, Paolo

2015-12-01

The aim of this work was to optimize the extraction of pure all-trans-lycopene from the pulp fractions of tomato processing waste. A full factorial design (FFD) consisting of four independent variables including extraction temperature (30-50 °C), time (1-60 min), percentage of acetone in n-hexane (25-75%, v/v) and solvent volume (10-30 ml) was used to investigate the effects of process variables on the extraction. The absolute amount of lycopene present in the pulp waste was found to be 0.038 mg/g. The optimal conditions for extraction were as follows: extraction temperature 20 °C, time 40 min, a solvent composition of 25% acetone in n-hexane (v/v) and solvent volume 40 ml. Under these conditions, the maximal recovery of lycopene was 94.7%. The HPLC-DAD analysis demonstrated that, lycopene was obtained in the all-trans-configuration at a very high purity grade of 98.3% while the amount of cis-isomers and other carotenoids were limited. Copyright © 2015 Elsevier Ltd. All rights reserved.

Practical input optimization for aircraft parameter estimation experiments. Ph.D. Thesis, 1990

NASA Technical Reports Server (NTRS)

Morelli, Eugene A.

1993-01-01

The object of this research was to develop an algorithm for the design of practical, optimal flight test inputs for aircraft parameter estimation experiments. A general, single pass technique was developed which allows global optimization of the flight test input design for parameter estimation using the principles of dynamic programming with the input forms limited to square waves only. Provision was made for practical constraints on the input, including amplitude constraints, control system dynamics, and selected input frequency range exclusions. In addition, the input design was accomplished while imposing output amplitude constraints required by model validity and considerations of safety during the flight test. The algorithm has multiple input design capability, with optional inclusion of a constraint that only one control move at a time, so that a human pilot can implement the inputs. It is shown that the technique can be used to design experiments for estimation of open loop model parameters from closed loop flight test data. The report includes a new formulation of the optimal input design problem, a description of a new approach to the solution, and a summary of the characteristics of the algorithm, followed by three example applications of the new technique which demonstrate the quality and expanded capabilities of the input designs produced by the new technique. In all cases, the new input design approach showed significant improvement over previous input design methods in terms of achievable parameter accuracies.

Zener Diode Compact Model Parameter Extraction Using Xyce-Dakota Optimization.

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

Buchheit, Thomas E.; Wilcox, Ian Zachary; Sandoval, Andrew J

This report presents a detailed process for compact model parameter extraction for DC circuit Zener diodes. Following the traditional approach of Zener diode parameter extraction, circuit model representation is defined and then used to capture the different operational regions of a real diode's electrical behavior. The circuit model contains 9 parameters represented by resistors and characteristic diodes as circuit model elements. The process of initial parameter extraction, the identification of parameter values for the circuit model elements, is presented in a way that isolates the dependencies between certain electrical parameters and highlights both the empirical nature of the extraction andmore » portions of the real diode physical behavior which of the parameters are intended to represent. Optimization of the parameters, a necessary part of a robost parameter extraction process, is demonstrated using a 'Xyce-Dakota' workflow, discussed in more detail in the report. Among other realizations during this systematic approach of electrical model parameter extraction, non-physical solutions are possible and can be difficult to avoid because of the interdependencies between the different parameters. The process steps described are fairly general and can be leveraged for other types of semiconductor device model extractions. Also included in the report are recommendations for experiment setups for generating optimum dataset for model extraction and the Parameter Identification and Ranking Table (PIRT) for Zener diodes.« less

Tailored parameter optimization methods for ordinary differential equation models with steady-state constraints.

PubMed

Fiedler, Anna; Raeth, Sebastian; Theis, Fabian J; Hausser, Angelika; Hasenauer, Jan

2016-08-22

Ordinary differential equation (ODE) models are widely used to describe (bio-)chemical and biological processes. To enhance the predictive power of these models, their unknown parameters are estimated from experimental data. These experimental data are mostly collected in perturbation experiments, in which the processes are pushed out of steady state by applying a stimulus. The information that the initial condition is a steady state of the unperturbed process provides valuable information, as it restricts the dynamics of the process and thereby the parameters. However, implementing steady-state constraints in the optimization often results in convergence problems. In this manuscript, we propose two new methods for solving optimization problems with steady-state constraints. The first method exploits ideas from optimization algorithms on manifolds and introduces a retraction operator, essentially reducing the dimension of the optimization problem. The second method is based on the continuous analogue of the optimization problem. This continuous analogue is an ODE whose equilibrium points are the optima of the constrained optimization problem. This equivalence enables the use of adaptive numerical methods for solving optimization problems with steady-state constraints. Both methods are tailored to the problem structure and exploit the local geometry of the steady-state manifold and its stability properties. A parameterization of the steady-state manifold is not required. The efficiency and reliability of the proposed methods is evaluated using one toy example and two applications. The first application example uses published data while the second uses a novel dataset for Raf/MEK/ERK signaling. The proposed methods demonstrated better convergence properties than state-of-the-art methods employed in systems and computational biology. Furthermore, the average computation time per converged start is significantly lower. In addition to the theoretical results, the

Information Extraction of High Resolution Remote Sensing Images Based on the Calculation of Optimal Segmentation Parameters

PubMed Central

Zhu, Hongchun; Cai, Lijie; Liu, Haiying; Huang, Wei

2016-01-01

Multi-scale image segmentation and the selection of optimal segmentation parameters are the key processes in the object-oriented information extraction of high-resolution remote sensing images. The accuracy of remote sensing special subject information depends on this extraction. On the basis of WorldView-2 high-resolution data, the optimal segmentation parameters methodof object-oriented image segmentation and high-resolution image information extraction, the following processes were conducted in this study. Firstly, the best combination of the bands and weights was determined for the information extraction of high-resolution remote sensing image. An improved weighted mean-variance method was proposed andused to calculatethe optimal segmentation scale. Thereafter, the best shape factor parameter and compact factor parameters were computed with the use of the control variables and the combination of the heterogeneity and homogeneity indexes. Different types of image segmentation parameters were obtained according to the surface features. The high-resolution remote sensing images were multi-scale segmented with the optimal segmentation parameters. Ahierarchical network structure was established by setting the information extraction rules to achieve object-oriented information extraction. This study presents an effective and practical method that can explain expert input judgment by reproducible quantitative measurements. Furthermore the results of this procedure may be incorporated into a classification scheme. PMID:27362762

Information Extraction of High Resolution Remote Sensing Images Based on the Calculation of Optimal Segmentation Parameters.

PubMed

Zhu, Hongchun; Cai, Lijie; Liu, Haiying; Huang, Wei

2016-01-01

Multi-scale image segmentation and the selection of optimal segmentation parameters are the key processes in the object-oriented information extraction of high-resolution remote sensing images. The accuracy of remote sensing special subject information depends on this extraction. On the basis of WorldView-2 high-resolution data, the optimal segmentation parameters methodof object-oriented image segmentation and high-resolution image information extraction, the following processes were conducted in this study. Firstly, the best combination of the bands and weights was determined for the information extraction of high-resolution remote sensing image. An improved weighted mean-variance method was proposed andused to calculatethe optimal segmentation scale. Thereafter, the best shape factor parameter and compact factor parameters were computed with the use of the control variables and the combination of the heterogeneity and homogeneity indexes. Different types of image segmentation parameters were obtained according to the surface features. The high-resolution remote sensing images were multi-scale segmented with the optimal segmentation parameters. Ahierarchical network structure was established by setting the information extraction rules to achieve object-oriented information extraction. This study presents an effective and practical method that can explain expert input judgment by reproducible quantitative measurements. Furthermore the results of this procedure may be incorporated into a classification scheme.

Parameter extraction using global particle swarm optimization approach and the influence of polymer processing temperature on the solar cell parameters

NASA Astrophysics Data System (ADS)

Kumar, S.; Singh, A.; Dhar, A.

2017-08-01

The accurate estimation of the photovoltaic parameters is fundamental to gain an insight of the physical processes occurring inside a photovoltaic device and thereby to optimize its design, fabrication processes, and quality. A simulative approach of accurately determining the device parameters is crucial for cell array and module simulation when applied in practical on-field applications. In this work, we have developed a global particle swarm optimization (GPSO) approach to estimate the different solar cell parameters viz., ideality factor (η), short circuit current (Isc), open circuit voltage (Voc), shunt resistant (Rsh), and series resistance (Rs) with wide a search range of over ±100 % for each model parameter. After validating the accurateness and global search power of the proposed approach with synthetic and noisy data, we applied the technique to the extract the PV parameters of ZnO/PCDTBT based hybrid solar cells (HSCs) prepared under different annealing conditions. Further, we examine the variation of extracted model parameters to unveil the physical processes occurring when different annealing temperatures are employed during the device fabrication and establish the role of improved charge transport in polymer films from independent FET measurements. The evolution of surface morphology, optical absorption, and chemical compositional behaviour of PCDTBT co-polymer films as a function of processing temperature has also been captured in the study and correlated with the findings from the PV parameters extracted using GPSO approach.

Multi-parameter geometrical scaledown study for energy optimization of MTJ and related spintronics nanodevices

NASA Astrophysics Data System (ADS)

Farhat, I. A. H.; Alpha, C.; Gale, E.; Atia, D. Y.; Stein, A.; Isakovic, A. F.

The scaledown of magnetic tunnel junctions (MTJ) and related nanoscale spintronics devices poses unique challenges for energy optimization of their performance. We demonstrate the dependence of the switching current on the scaledown variable, while considering the influence of geometric parameters of MTJ, such as the free layer thickness, tfree, lateral size of the MTJ, w, and the anisotropy parameter of the MTJ. At the same time, we point out which values of the saturation magnetization, Ms, and anisotropy field, Hk, can lead to lowering the switching current and overall decrease of the energy needed to operate an MTJ. It is demonstrated that scaledown via decreasing the lateral size of the MTJ, while allowing some other parameters to be unconstrained, can improve energy performance by a measurable factor, shown to be the function of both geometric and physical parameters above. Given the complex interdependencies among both families of parameters, we developed a particle swarm optimization (PSO) algorithm that can simultaneously lower energy of operation and the switching current density. Results we obtained in scaledown study and via PSO optimization are compared to experimental results. Support by Mubadala-SRC 2012-VJ-2335 is acknowledged, as are staff at Cornell-CNF and BNL-CFN.

NWP model forecast skill optimization via closure parameter variations

NASA Astrophysics Data System (ADS)

Järvinen, H.; Ollinaho, P.; Laine, M.; Solonen, A.; Haario, H.

2012-04-01

We present results of a novel approach to tune predictive skill of numerical weather prediction (NWP) models. These models contain tunable parameters which appear in parameterizations schemes of sub-grid scale physical processes. The current practice is to specify manually the numerical parameter values, based on expert knowledge. We developed recently a concept and method (QJRMS 2011) for on-line estimation of the NWP model parameters via closure parameter variations. The method called EPPES ("Ensemble prediction and parameter estimation system") utilizes ensemble prediction infra-structure for parameter estimation in a very cost-effective way: practically no new computations are introduced. The approach provides an algorithmic decision making tool for model parameter optimization in operational NWP. In EPPES, statistical inference about the NWP model tunable parameters is made by (i) generating an ensemble of predictions so that each member uses different model parameter values, drawn from a proposal distribution, and (ii) feeding-back the relative merits of the parameter values to the proposal distribution, based on evaluation of a suitable likelihood function against verifying observations. In this presentation, the method is first illustrated in low-order numerical tests using a stochastic version of the Lorenz-95 model which effectively emulates the principal features of ensemble prediction systems. The EPPES method correctly detects the unknown and wrongly specified parameters values, and leads to an improved forecast skill. Second, results with an ensemble prediction system emulator, based on the ECHAM5 atmospheric GCM show that the model tuning capability of EPPES scales up to realistic models and ensemble prediction systems. Finally, preliminary results of EPPES in the context of ECMWF forecasting system are presented.

Parameter optimization of fusion splicing of photonic crystal fibers and conventional fibers to increase strength

NASA Astrophysics Data System (ADS)

Zhang, Chunxi; Zhang, Zuchen; Song, Jingming; Wu, Chunxiao; Song, Ningfang

2015-03-01

A splicing parameter optimization method to increase the tensile strength of splicing joint between photonic crystal fiber (PCF) and conventional fiber is demonstrated. Based on the splicing recipes provided by splicer or fiber manufacturers, the optimal values of some major splicing parameters are obtained in sequence, and a conspicuous improvement in the mechanical strength of splicing joints between PCFs and conventional fibers is validated through experiments.

Computer-Assisted Optimization of Electrodeposited Hydroxyapatite Coating Parameters on Medical Alloys

NASA Astrophysics Data System (ADS)

Coşkun, M. İbrahim; Karahan, İsmail H.; Yücel, Yasin; Golden, Teresa D.

2016-04-01

CoCrMo bio-metallic alloys were coated with a hydroxyapatite (HA) film by electrodeposition using various electrochemical parameters. Response surface methodology and central composite design were used to optimize deposition parameters such as electrolyte pH, deposition potential, and deposition time. The effects of the coating parameters were evaluated within the limits of solution pH (3.66 to 5.34), deposition potential (-1.13 to -1.97 V), and deposition time (6.36 to 73.64 minutes). A 5-level-3-factor experimental plan was used to determine ideal deposition parameters. Optimum conditions for the deposition parameters of the HA coating with high in vitro corrosion performance were determined as electrolyte pH of 5.00, deposition potential of -1.8 V, and deposition time of 20 minutes.

An efficient framework for optimization and parameter sensitivity analysis in arterial growth and remodeling computations

PubMed Central

Sankaran, Sethuraman; Humphrey, Jay D.; Marsden, Alison L.

2013-01-01

Computational models for vascular growth and remodeling (G&R) are used to predict the long-term response of vessels to changes in pressure, flow, and other mechanical loading conditions. Accurate predictions of these responses are essential for understanding numerous disease processes. Such models require reliable inputs of numerous parameters, including material properties and growth rates, which are often experimentally derived, and inherently uncertain. While earlier methods have used a brute force approach, systematic uncertainty quantification in G&R models promises to provide much better information. In this work, we introduce an efficient framework for uncertainty quantification and optimal parameter selection, and illustrate it via several examples. First, an adaptive sparse grid stochastic collocation scheme is implemented in an established G&R solver to quantify parameter sensitivities, and near-linear scaling with the number of parameters is demonstrated. This non-intrusive and parallelizable algorithm is compared with standard sampling algorithms such as Monte-Carlo. Second, we determine optimal arterial wall material properties by applying robust optimization. We couple the G&R simulator with an adaptive sparse grid collocation approach and a derivative-free optimization algorithm. We show that an artery can achieve optimal homeostatic conditions over a range of alterations in pressure and flow; robustness of the solution is enforced by including uncertainty in loading conditions in the objective function. We then show that homeostatic intramural and wall shear stress is maintained for a wide range of material properties, though the time it takes to achieve this state varies. We also show that the intramural stress is robust and lies within 5% of its mean value for realistic variability of the material parameters. We observe that prestretch of elastin and collagen are most critical to maintaining homeostasis, while values of the material properties are

Development of a parameter optimization technique for the design of automatic control systems

NASA Technical Reports Server (NTRS)

Whitaker, P. H.

1977-01-01

Parameter optimization techniques for the design of linear automatic control systems that are applicable to both continuous and digital systems are described. The model performance index is used as the optimization criterion because of the physical insight that can be attached to it. The design emphasis is to start with the simplest system configuration that experience indicates would be practical. Design parameters are specified, and a digital computer program is used to select that set of parameter values which minimizes the performance index. The resulting design is examined, and complexity, through the use of more complex information processing or more feedback paths, is added only if performance fails to meet operational specifications. System performance specifications are assumed to be such that the desired step function time response of the system can be inferred.

Evaluation of a new parallel numerical parameter optimization algorithm for a dynamical system

NASA Astrophysics Data System (ADS)

Duran, Ahmet; Tuncel, Mehmet

2016-10-01

It is important to have a scalable parallel numerical parameter optimization algorithm for a dynamical system used in financial applications where time limitation is crucial. We use Message Passing Interface parallel programming and present such a new parallel algorithm for parameter estimation. For example, we apply the algorithm to the asset flow differential equations that have been developed and analyzed since 1989 (see [3-6] and references contained therein). We achieved speed-up for some time series to run up to 512 cores (see [10]). Unlike [10], we consider more extensive financial market situations, for example, in presence of low volatility, high volatility and stock market price at a discount/premium to its net asset value with varying magnitude, in this work. Moreover, we evaluated the convergence of the model parameter vector, the nonlinear least squares error and maximum improvement factor to quantify the success of the optimization process depending on the number of initial parameter vectors.

Systematic exploration of efficient strategies to manage solid waste in U.S. municipalities: perspectives from the solid waste optimization life-cycle framework (SWOLF).

PubMed

Levis, James W; Barlaz, Morton A; Decarolis, Joseph F; Ranjithan, S Ranji

2014-04-01

Solid waste management (SWM) systems must proactively adapt to changing policy requirements, waste composition, and an evolving energy system to sustainably manage future solid waste. This study represents the first application of an optimizable dynamic life-cycle assessment framework capable of considering these future changes. The framework was used to draw insights by analyzing the SWM system of a hypothetical suburban U.S. city of 100 000 people over 30 years while considering changes to population, waste generation, and energy mix and costs. The SWM system included 3 waste generation sectors, 30 types of waste materials, and 9 processes for waste separation, treatment, and disposal. A business-as-usual scenario (BAU) was compared to three optimization scenarios that (1) minimized cost (Min Cost), (2) maximized diversion (Max Diversion), and (3) minimized greenhouse gas (GHG) emissions (Min GHG) from the system. The Min Cost scenario saved $7.2 million (12%) and reduced GHG emissions (3%) relative to the BAU scenario. Compared to the Max Diversion scenario, the Min GHG scenario cost approximately 27% less and more than doubled the net reduction in GHG emissions. The results illustrate how the timed-deployment of technologies in response to changes in waste composition and the energy system results in more efficient SWM system performance compared to what is possible from static analyses.

Multi-objective optimization of solid waste flows: environmentally sustainable strategies for municipalities.

PubMed

Minciardi, Riccardo; Paolucci, Massimo; Robba, Michela; Sacile, Roberto

2008-11-01

An approach to sustainable municipal solid waste (MSW) management is presented, with the aim of supporting the decision on the optimal flows of solid waste sent to landfill, recycling and different types of treatment plants, whose sizes are also decision variables. This problem is modeled with a non-linear, multi-objective formulation. Specifically, four objectives to be minimized have been taken into account, which are related to economic costs, unrecycled waste, sanitary landfill disposal and environmental impact (incinerator emissions). An interactive reference point procedure has been developed to support decision making; these methods are considered appropriate for multi-objective decision problems in environmental applications. In addition, interactive methods are generally preferred by decision makers as they can be directly involved in the various steps of the decision process. Some results deriving from the application of the proposed procedure are presented. The application of the procedure is exemplified by considering the interaction with two different decision makers who are assumed to be in charge of planning the MSW system in the municipality of Genova (Italy).

Determination of the optimal area of waste incineration in a rotary kiln using a simulation model.

PubMed

Bujak, J

2015-08-01

The article presents a mathematical model to determine the flux of incinerated waste in terms of its calorific values. The model is applicable in waste incineration systems equipped with rotary kilns. It is based on the known and proven energy flux balances and equations that describe the specific losses of energy flux while considering the specificity of waste incineration systems. The model is universal as it can be used both for the analysis and testing of systems burning different types of waste (municipal, medical, animal, etc.) and for allowing the use of any kind of additional fuel. Types of waste incinerated and additional fuel are identified by a determination of their elemental composition. The computational model has been verified in three existing industrial-scale plants. Each system incinerated a different type of waste. Each waste type was selected in terms of a different calorific value. This allowed the full verification of the model. Therefore the model can be used to optimize the operation of waste incineration system both at the design stage and during its lifetime. Copyright © 2015 Elsevier Ltd. All rights reserved.

Numerical Parameter Optimization of the Ignition and Growth Model for HMX Based Plastic Bonded Explosives

NASA Astrophysics Data System (ADS)

Gambino, James; Tarver, Craig; Springer, H. Keo; White, Bradley; Fried, Laurence

2017-06-01

We present a novel method for optimizing parameters of the Ignition and Growth reactive flow (I&G) model for high explosives. The I&G model can yield accurate predictions of experimental observations. However, calibrating the model is a time-consuming task especially with multiple experiments. In this study, we couple the differential evolution global optimization algorithm to simulations of shock initiation experiments in the multi-physics code ALE3D. We develop parameter sets for HMX based explosives LX-07 and LX-10. The optimization finds the I&G model parameters that globally minimize the difference between calculated and experimental shock time of arrival at embedded pressure gauges. This work was performed under the auspices of the U.S. DOE by LLNL under contract DE-AC52-07NA27344. LLNS, LLC LLNL-ABS- 724898.

Optimization of process parameters for a quasi-continuous tablet coating system using design of experiments.

PubMed

Cahyadi, Christine; Heng, Paul Wan Sia; Chan, Lai Wah

2011-03-01

The aim of this study was to identify and optimize the critical process parameters of the newly developed Supercell quasi-continuous coater for optimal tablet coat quality. Design of experiments, aided by multivariate analysis techniques, was used to quantify the effects of various coating process conditions and their interactions on the quality of film-coated tablets. The process parameters varied included batch size, inlet temperature, atomizing pressure, plenum pressure, spray rate and coating level. An initial screening stage was carried out using a 2(6-1(IV)) fractional factorial design. Following these preliminary experiments, optimization study was carried out using the Box-Behnken design. Main response variables measured included drug-loading efficiency, coat thickness variation, and the extent of tablet damage. Apparent optimum conditions were determined by using response surface plots. The process parameters exerted various effects on the different response variables. Hence, trade-offs between individual optima were necessary to obtain the best compromised set of conditions. The adequacy of the optimized process conditions in meeting the combined goals for all responses was indicated by the composite desirability value. By using response surface methodology and optimization, coating conditions which produced coated tablets of high drug-loading efficiency, low incidences of tablet damage and low coat thickness variation were defined. Optimal conditions were found to vary over a large spectrum when different responses were considered. Changes in processing parameters across the design space did not result in drastic changes to coat quality, thereby demonstrating robustness in the Supercell coating process. © 2010 American Association of Pharmaceutical Scientists

Idealized Experiments for Optimizing Model Parameters Using a 4D-Variational Method in an Intermediate Coupled Model of ENSO

NASA Astrophysics Data System (ADS)

Gao, Chuan; Zhang, Rong-Hua; Wu, Xinrong; Sun, Jichang

2018-04-01

Large biases exist in real-time ENSO prediction, which can be attributed to uncertainties in initial conditions and model parameters. Previously, a 4D variational (4D-Var) data assimilation system was developed for an intermediate coupled model (ICM) and used to improve ENSO modeling through optimized initial conditions. In this paper, this system is further applied to optimize model parameters. In the ICM used, one important process for ENSO is related to the anomalous temperature of subsurface water entrained into the mixed layer ( T e), which is empirically and explicitly related to sea level (SL) variation. The strength of the thermocline effect on SST (referred to simply as "the thermocline effect") is represented by an introduced parameter, α Te. A numerical procedure is developed to optimize this model parameter through the 4D-Var assimilation of SST data in a twin experiment context with an idealized setting. Experiments having their initial condition optimized only, and having their initial condition plus this additional model parameter optimized, are compared. It is shown that ENSO evolution can be more effectively recovered by including the additional optimization of this parameter in ENSO modeling. The demonstrated feasibility of optimizing model parameters and initial conditions together through the 4D-Var method provides a modeling platform for ENSO studies. Further applications of the 4D-Var data assimilation system implemented in the ICM are also discussed.

Simultaneous versus sequential optimal experiment design for the identification of multi-parameter microbial growth kinetics as a function of temperature.

PubMed

Van Derlinden, E; Bernaerts, K; Van Impe, J F

2010-05-21

Optimal experiment design for parameter estimation (OED/PE) has become a popular tool for efficient and accurate estimation of kinetic model parameters. When the kinetic model under study encloses multiple parameters, different optimization strategies can be constructed. The most straightforward approach is to estimate all parameters simultaneously from one optimal experiment (single OED/PE strategy). However, due to the complexity of the optimization problem or the stringent limitations on the system's dynamics, the experimental information can be limited and parameter estimation convergence problems can arise. As an alternative, we propose to reduce the optimization problem to a series of two-parameter estimation problems, i.e., an optimal experiment is designed for a combination of two parameters while presuming the other parameters known. Two different approaches can be followed: (i) all two-parameter optimal experiments are designed based on identical initial parameter estimates and parameters are estimated simultaneously from all resulting experimental data (global OED/PE strategy), and (ii) optimal experiments are calculated and implemented sequentially whereby the parameter values are updated intermediately (sequential OED/PE strategy). This work exploits OED/PE for the identification of the Cardinal Temperature Model with Inflection (CTMI) (Rosso et al., 1993). This kinetic model describes the effect of temperature on the microbial growth rate and encloses four parameters. The three OED/PE strategies are considered and the impact of the OED/PE design strategy on the accuracy of the CTMI parameter estimation is evaluated. Based on a simulation study, it is observed that the parameter values derived from the sequential approach deviate more from the true parameters than the single and global strategy estimates. The single and global OED/PE strategies are further compared based on experimental data obtained from design implementation in a bioreactor

Optimization of IBF parameters based on adaptive tool-path algorithm

NASA Astrophysics Data System (ADS)

Deng, Wen Hui; Chen, Xian Hua; Jin, Hui Liang; Zhong, Bo; Hou, Jin; Li, An Qi

2018-03-01

As a kind of Computer Controlled Optical Surfacing(CCOS) technology. Ion Beam Figuring(IBF) has obvious advantages in the control of surface accuracy, surface roughness and subsurface damage. The superiority and characteristics of IBF in optical component processing are analyzed from the point of view of removal mechanism. For getting more effective and automatic tool path with the information of dwell time, a novel algorithm is proposed in this thesis. Based on the removal functions made through our IBF equipment and the adaptive tool-path, optimized parameters are obtained through analysis the residual error that would be created in the polishing process. A Φ600 mm plane reflector element was used to be a simulation instance. The simulation result shows that after four combinations of processing, the surface accuracy of PV (Peak Valley) value and the RMS (Root Mean Square) value was reduced to 4.81 nm and 0.495 nm from 110.22 nm and 13.998 nm respectively in the 98% aperture. The result shows that the algorithm and optimized parameters provide a good theoretical for high precision processing of IBF.

A parameters optimization method for planar joint clearance model and its application for dynamics simulation of reciprocating compressor

NASA Astrophysics Data System (ADS)

Hai-yang, Zhao; Min-qiang, Xu; Jin-dong, Wang; Yong-bo, Li

2015-05-01

In order to improve the accuracy of dynamics response simulation for mechanism with joint clearance, a parameter optimization method for planar joint clearance contact force model was presented in this paper, and the optimized parameters were applied to the dynamics response simulation for mechanism with oversized joint clearance fault. By studying the effect of increased clearance on the parameters of joint clearance contact force model, the relation of model parameters between different clearances was concluded. Then the dynamic equation of a two-stage reciprocating compressor with four joint clearances was developed using Lagrange method, and a multi-body dynamic model built in ADAMS software was used to solve this equation. To obtain a simulated dynamic response much closer to that of experimental tests, the parameters of joint clearance model, instead of using the designed values, were optimized by genetic algorithms approach. Finally, the optimized parameters were applied to simulate the dynamics response of model with oversized joint clearance fault according to the concluded parameter relation. The dynamics response of experimental test verified the effectiveness of this application.

Computation of physiological human vocal fold parameters by mathematical optimization of a biomechanical model

PubMed Central

Yang, Anxiong; Stingl, Michael; Berry, David A.; Lohscheller, Jörg; Voigt, Daniel; Eysholdt, Ulrich; Döllinger, Michael

2011-01-01

With the use of an endoscopic, high-speed camera, vocal fold dynamics may be observed clinically during phonation. However, observation and subjective judgment alone may be insufficient for clinical diagnosis and documentation of improved vocal function, especially when the laryngeal disease lacks any clear morphological presentation. In this study, biomechanical parameters of the vocal folds are computed by adjusting the corresponding parameters of a three-dimensional model until the dynamics of both systems are similar. First, a mathematical optimization method is presented. Next, model parameters (such as pressure, tension and masses) are adjusted to reproduce vocal fold dynamics, and the deduced parameters are physiologically interpreted. Various combinations of global and local optimization techniques are attempted. Evaluation of the optimization procedure is performed using 50 synthetically generated data sets. The results show sufficient reliability, including 0.07 normalized error, 96% correlation, and 91% accuracy. The technique is also demonstrated on data from human hemilarynx experiments, in which a low normalized error (0.16) and high correlation (84%) values were achieved. In the future, this technique may be applied to clinical high-speed images, yielding objective measures with which to document improved vocal function of patients with voice disorders. PMID:21877808

An Improved Cuckoo Search Optimization Algorithm for the Problem of Chaotic Systems Parameter Estimation

PubMed Central

Wang, Jun; Zhou, Bihua; Zhou, Shudao

2016-01-01

This paper proposes an improved cuckoo search (ICS) algorithm to establish the parameters of chaotic systems. In order to improve the optimization capability of the basic cuckoo search (CS) algorithm, the orthogonal design and simulated annealing operation are incorporated in the CS algorithm to enhance the exploitation search ability. Then the proposed algorithm is used to establish parameters of the Lorenz chaotic system and Chen chaotic system under the noiseless and noise condition, respectively. The numerical results demonstrate that the algorithm can estimate parameters with high accuracy and reliability. Finally, the results are compared with the CS algorithm, genetic algorithm, and particle swarm optimization algorithm, and the compared results demonstrate the method is energy-efficient and superior. PMID:26880874

Parameter Identification of Static Friction Based on An Optimal Exciting Trajectory

NASA Astrophysics Data System (ADS)

Tu, X.; Zhao, P.; Zhou, Y. F.

2017-12-01

In this paper, we focus on how to improve the identification efficiency of friction parameters in a robot joint. First, the static friction model that has only linear dependencies with respect to their parameters is adopted so that the servomotor dynamics can be linearized. In this case, the traditional exciting trajectory based on Fourier series is modified by replacing the constant term with quintic polynomial to ensure the boundary continuity of speed and acceleration. Then, the Fourier-related parameters are optimized by genetic algorithm(GA) in which the condition number of regression matrix is set as the fitness function. At last, compared with the constant-velocity tracking experiment, the friction parameters from the exciting trajectory experiment has the similar result with the advantage of time reduction.

Parameter optimization of a hydrologic model in a snow-dominated basin using a modular Python framework

NASA Astrophysics Data System (ADS)

Volk, J. M.; Turner, M. A.; Huntington, J. L.; Gardner, M.; Tyler, S.; Sheneman, L.

2016-12-01

Many distributed models that simulate watershed hydrologic processes require a collection of multi-dimensional parameters as input, some of which need to be calibrated before the model can be applied. The Precipitation Runoff Modeling System (PRMS) is a physically-based and spatially distributed hydrologic model that contains a considerable number of parameters that often need to be calibrated. Modelers can also benefit from uncertainty analysis of these parameters. To meet these needs, we developed a modular framework in Python to conduct PRMS parameter optimization, uncertainty analysis, interactive visual inspection of parameters and outputs, and other common modeling tasks. Here we present results for multi-step calibration of sensitive parameters controlling solar radiation, potential evapo-transpiration, and streamflow in a PRMS model that we applied to the snow-dominated Dry Creek watershed in Idaho. We also demonstrate how our modular approach enables the user to use a variety of parameter optimization and uncertainty methods or easily define their own, such as Monte Carlo random sampling, uniform sampling, or even optimization methods such as the downhill simplex method or its commonly used, more robust counterpart, shuffled complex evolution.

Optimization of rotational arc station parameter optimized radiation therapy.

PubMed

Dong, P; Ungun, B; Boyd, S; Xing, L

2016-09-01

To develop a fast optimization method for station parameter optimized radiation therapy (SPORT) and show that SPORT is capable of matching VMAT in both plan quality and delivery efficiency by using three clinical cases of different disease sites. The angular space from 0° to 360° was divided into 180 station points (SPs). A candidate aperture was assigned to each of the SPs based on the calculation results using a column generation algorithm. The weights of the apertures were then obtained by optimizing the objective function using a state-of-the-art GPU based proximal operator graph solver. To avoid being trapped in a local minimum in beamlet-based aperture selection using the gradient descent algorithm, a stochastic gradient descent was employed here. Apertures with zero or low weight were thrown out. To find out whether there was room to further improve the plan by adding more apertures or SPs, the authors repeated the above procedure with consideration of the existing dose distribution from the last iteration. At the end of the second iteration, the weights of all the apertures were reoptimized, including those of the first iteration. The above procedure was repeated until the plan could not be improved any further. The optimization technique was assessed by using three clinical cases (prostate, head and neck, and brain) with the results compared to that obtained using conventional VMAT in terms of dosimetric properties, treatment time, and total MU. Marked dosimetric quality improvement was demonstrated in the SPORT plans for all three studied cases. For the prostate case, the volume of the 50% prescription dose was decreased by 22% for the rectum and 6% for the bladder. For the head and neck case, SPORT improved the mean dose for the left and right parotids by 15% each. The maximum dose was lowered from 72.7 to 71.7 Gy for the mandible, and from 30.7 to 27.3 Gy for the spinal cord. The mean dose for the pharynx and larynx was reduced by 8% and 6

Optimization of rotational arc station parameter optimized radiation therapy

PubMed Central

Dong, P.; Ungun, B.; Boyd, S.; Xing, L.

2016-01-01

Purpose: To develop a fast optimization method for station parameter optimized radiation therapy (SPORT) and show that SPORT is capable of matching VMAT in both plan quality and delivery efficiency by using three clinical cases of different disease sites. Methods: The angular space from 0° to 360° was divided into 180 station points (SPs). A candidate aperture was assigned to each of the SPs based on the calculation results using a column generation algorithm. The weights of the apertures were then obtained by optimizing the objective function using a state-of-the-art GPU based proximal operator graph solver. To avoid being trapped in a local minimum in beamlet-based aperture selection using the gradient descent algorithm, a stochastic gradient descent was employed here. Apertures with zero or low weight were thrown out. To find out whether there was room to further improve the plan by adding more apertures or SPs, the authors repeated the above procedure with consideration of the existing dose distribution from the last iteration. At the end of the second iteration, the weights of all the apertures were reoptimized, including those of the first iteration. The above procedure was repeated until the plan could not be improved any further. The optimization technique was assessed by using three clinical cases (prostate, head and neck, and brain) with the results compared to that obtained using conventional VMAT in terms of dosimetric properties, treatment time, and total MU. Results: Marked dosimetric quality improvement was demonstrated in the SPORT plans for all three studied cases. For the prostate case, the volume of the 50% prescription dose was decreased by 22% for the rectum and 6% for the bladder. For the head and neck case, SPORT improved the mean dose for the left and right parotids by 15% each. The maximum dose was lowered from 72.7 to 71.7 Gy for the mandible, and from 30.7 to 27.3 Gy for the spinal cord. The mean dose for the pharynx and larynx was

Optimization of rotational arc station parameter optimized radiation therapy

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

Dong, P.; Ungun, B.

Purpose: To develop a fast optimization method for station parameter optimized radiation therapy (SPORT) and show that SPORT is capable of matching VMAT in both plan quality and delivery efficiency by using three clinical cases of different disease sites. Methods: The angular space from 0° to 360° was divided into 180 station points (SPs). A candidate aperture was assigned to each of the SPs based on the calculation results using a column generation algorithm. The weights of the apertures were then obtained by optimizing the objective function using a state-of-the-art GPU based proximal operator graph solver. To avoid being trappedmore » in a local minimum in beamlet-based aperture selection using the gradient descent algorithm, a stochastic gradient descent was employed here. Apertures with zero or low weight were thrown out. To find out whether there was room to further improve the plan by adding more apertures or SPs, the authors repeated the above procedure with consideration of the existing dose distribution from the last iteration. At the end of the second iteration, the weights of all the apertures were reoptimized, including those of the first iteration. The above procedure was repeated until the plan could not be improved any further. The optimization technique was assessed by using three clinical cases (prostate, head and neck, and brain) with the results compared to that obtained using conventional VMAT in terms of dosimetric properties, treatment time, and total MU. Results: Marked dosimetric quality improvement was demonstrated in the SPORT plans for all three studied cases. For the prostate case, the volume of the 50% prescription dose was decreased by 22% for the rectum and 6% for the bladder. For the head and neck case, SPORT improved the mean dose for the left and right parotids by 15% each. The maximum dose was lowered from 72.7 to 71.7 Gy for the mandible, and from 30.7 to 27.3 Gy for the spinal cord. The mean dose for the pharynx and larynx

Optimal Parameter Design of Coarse Alignment for Fiber Optic Gyro Inertial Navigation System.

PubMed

Lu, Baofeng; Wang, Qiuying; Yu, Chunmei; Gao, Wei

2015-06-25

Two different coarse alignment algorithms for Fiber Optic Gyro (FOG) Inertial Navigation System (INS) based on inertial reference frame are discussed in this paper. Both of them are based on gravity vector integration, therefore, the performance of these algorithms is determined by integration time. In previous works, integration time is selected by experience. In order to give a criterion for the selection process, and make the selection of the integration time more accurate, optimal parameter design of these algorithms for FOG INS is performed in this paper. The design process is accomplished based on the analysis of the error characteristics of these two coarse alignment algorithms. Moreover, this analysis and optimal parameter design allow us to make an adequate selection of the most accurate algorithm for FOG INS according to the actual operational conditions. The analysis and simulation results show that the parameter provided by this work is the optimal value, and indicate that in different operational conditions, the coarse alignment algorithms adopted for FOG INS are different in order to achieve better performance. Lastly, the experiment results validate the effectiveness of the proposed algorithm.

Parameter estimation of a pulp digester model with derivative-free optimization strategies

NASA Astrophysics Data System (ADS)

Seiça, João C.; Romanenko, Andrey; Fernandes, Florbela P.; Santos, Lino O.; Fernandes, Natércia C. P.

2017-07-01

The work concerns the parameter estimation in the context of the mechanistic modelling of a pulp digester. The problem is cast as a box bounded nonlinear global optimization problem in order to minimize the mismatch between the model outputs with the experimental data observed at a real pulp and paper plant. MCSFilter and Simulated Annealing global optimization methods were used to solve the optimization problem. While the former took longer to converge to the global minimum, the latter terminated faster at a significantly higher value of the objective function and, thus, failed to find the global solution.

Key parameters for behaviour related to source separation of household organic waste: A case study in Hanoi, Vietnam.

PubMed

Kawai, Kosuke; Huong, Luong Thi Mai

2017-03-01

Proper management of food waste, a major component of municipal solid waste (MSW), is needed, especially in developing Asian countries where most MSW is disposed of in landfill sites without any pretreatment. Source separation can contribute to solving problems derived from the disposal of food waste. An organic waste source separation and collection programme has been operated in model areas in Hanoi, Vietnam, since 2007. This study proposed three key parameters (participation rate, proper separation rate and proper discharge rate) for behaviour related to source separation of household organic waste, and monitored the progress of the programme based on the physical composition of household waste sampled from 558 households in model programme areas of Hanoi. The results showed that 13.8% of 558 households separated organic waste, and 33.0% discharged mixed (unseparated) waste improperly. About 41.5% (by weight) of the waste collected as organic waste was contaminated by inorganic waste, and one-third of the waste disposed of as organic waste by separators was inorganic waste. We proposed six hypothetical future household behaviour scenarios to help local officials identify a final or midterm goal for the programme. We also suggested that the city government take further actions to increase the number of people participating in separating organic waste, improve the accuracy of separation and prevent non-separators from discharging mixed waste improperly.

Capacity planning for waste management systems: an interval fuzzy robust dynamic programming approach.

PubMed

Nie, Xianghui; Huang, Guo H; Li, Yongping

2009-11-01

This study integrates the concepts of interval numbers and fuzzy sets into optimization analysis by dynamic programming as a means of accounting for system uncertainty. The developed interval fuzzy robust dynamic programming (IFRDP) model improves upon previous interval dynamic programming methods. It allows highly uncertain information to be effectively communicated into the optimization process through introducing the concept of fuzzy boundary interval and providing an interval-parameter fuzzy robust programming method for an embedded linear programming problem. Consequently, robustness of the optimization process and solution can be enhanced. The modeling approach is applied to a hypothetical problem for the planning of waste-flow allocation and treatment/disposal facility expansion within a municipal solid waste (MSW) management system. Interval solutions for capacity expansion of waste management facilities and relevant waste-flow allocation are generated and interpreted to provide useful decision alternatives. The results indicate that robust and useful solutions can be obtained, and the proposed IFRDP approach is applicable to practical problems that are associated with highly complex and uncertain information.

Optimization and influence of parameter affecting the compressive strength of geopolymer concrete containing recycled concrete aggregate: using full factorial design approach

NASA Astrophysics Data System (ADS)

Krishnan, Thulasirajan; Purushothaman, Revathi

2017-07-01

There are several parameters that influence the properties of geopolymer concrete, which contains recycled concrete aggregate as the coarse aggregate. In the present study, the vital parameters affecting the compressive strength of geopolymer concrete containing recycled concrete aggregate are analyzedby varying four parameters with two levels using full factorial design in statistical software Minitab® 17. The objective of the present work is to gain an idea on the optimization, main parameter effects, their interactions and the predicted response of the model generated using factorial design. The parameters such as molarity of sodium hydroxide (8M and 12M), curing time (6hrs and 24 hrs), curing temperature (60°C and 90°C) and percentage of recycled concrete aggregate (0% and 100%) are considered. The results show that the curing time, molarity of sodium hydroxide and curing temperature were the orderly significant parameters and the percentage of Recycled concrete aggregate (RCA) was statistically insignificant in the production of geopolymer concrete. Thus, it may be noticeable that the RCA content had negligible effect on the compressive strength of geopolymer concrete. The expected responses from the generated model showed a satisfactory and rational agreement to the experimental data with the R2 value of 97.70%. Thus, geopolymer concrete comprising recycled concrete aggregate can solve the major social and environmental concerns such as the depletion of the naturally available aggregate sources and disposal of construction and demolition waste into the landfill.

Optimization of Empirical Force Fields by Parameter Space Mapping: A Single-Step Perturbation Approach.

PubMed

Stroet, Martin; Koziara, Katarzyna B; Malde, Alpeshkumar K; Mark, Alan E

2017-12-12

A general method for parametrizing atomic interaction functions is presented. The method is based on an analysis of surfaces corresponding to the difference between calculated and target data as a function of alternative combinations of parameters (parameter space mapping). The consideration of surfaces in parameter space as opposed to local values or gradients leads to a better understanding of the relationships between the parameters being optimized and a given set of target data. This in turn enables for a range of target data from multiple molecules to be combined in a robust manner and for the optimal region of parameter space to be trivially identified. The effectiveness of the approach is illustrated by using the method to refine the chlorine 6-12 Lennard-Jones parameters against experimental solvation free enthalpies in water and hexane as well as the density and heat of vaporization of the liquid at atmospheric pressure for a set of 10 aromatic-chloro compounds simultaneously. Single-step perturbation is used to efficiently calculate solvation free enthalpies for a wide range of parameter combinations. The capacity of this approach to parametrize accurate and transferrable force fields is discussed.

Effect on Ca(OH)2 pretreatment to enhance biogas production of organic food waste

NASA Astrophysics Data System (ADS)

Junoh, H.; Yip, CH; Kumaran, P.

2016-03-01

This study investigated the effect of calcium hydroxide, Ca(OH)2 pretreatment in optimizing COD solubilisation and methane production through anaerobic digestion process. Two different parameters, chemical concentration (40-190 mEq/L) and pretreatment time (1-6 hours) were used to pretreat food waste. A central composite design and response surface methodology (RSM) was applied in obtaining the optimized condition for COD solubilisation. Result showed COD solubilisation was optimized at 166.98 mEq/L (equivalent to 6.1 g Ca(OH)2/L) for 1 hour. These conditions were applied through biomethane potential test with methane production of 864.19 mL/g VSdestructed and an increase of 20.0% as compared to untreated food waste.

A stochastic optimization model under modeling uncertainty and parameter certainty for groundwater remediation design--part I. Model development.

PubMed

He, L; Huang, G H; Lu, H W

2010-04-15

Solving groundwater remediation optimization problems based on proxy simulators can usually yield optimal solutions differing from the "true" ones of the problem. This study presents a new stochastic optimization model under modeling uncertainty and parameter certainty (SOMUM) and the associated solution method for simultaneously addressing modeling uncertainty associated with simulator residuals and optimizing groundwater remediation processes. This is a new attempt different from the previous modeling efforts. The previous ones focused on addressing uncertainty in physical parameters (i.e. soil porosity) while this one aims to deal with uncertainty in mathematical simulator (arising from model residuals). Compared to the existing modeling approaches (i.e. only parameter uncertainty is considered), the model has the advantages of providing mean-variance analysis for contaminant concentrations, mitigating the effects of modeling uncertainties on optimal remediation strategies, offering confidence level of optimal remediation strategies to system designers, and reducing computational cost in optimization processes. 2009 Elsevier B.V. All rights reserved.

A Short-Term and High-Resolution System Load Forecasting Approach Using Support Vector Regression with Hybrid Parameters Optimization

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

Jiang, Huaiguang

This work proposes an approach for distribution system load forecasting, which aims to provide highly accurate short-term load forecasting with high resolution utilizing a support vector regression (SVR) based forecaster and a two-step hybrid parameters optimization method. Specifically, because the load profiles in distribution systems contain abrupt deviations, a data normalization is designed as the pretreatment for the collected historical load data. Then an SVR model is trained by the load data to forecast the future load. For better performance of SVR, a two-step hybrid optimization algorithm is proposed to determine the best parameters. In the first step of themore » hybrid optimization algorithm, a designed grid traverse algorithm (GTA) is used to narrow the parameters searching area from a global to local space. In the second step, based on the result of the GTA, particle swarm optimization (PSO) is used to determine the best parameters in the local parameter space. After the best parameters are determined, the SVR model is used to forecast the short-term load deviation in the distribution system.« less

Optimizing and developing a continuous separation system for the wet process separation of aluminum and polyethylene in aseptic composite packaging waste.

PubMed

Yan, Dahai; Peng, Zheng; Liu, Yuqiang; Li, Li; Huang, Qifei; Xie, Minghui; Wang, Qi

2015-01-01

The consumption of milk in China is increasing as living standards rapidly improve, and huge amounts of aseptic composite milk packaging waste are being generated. Aseptic composite packaging is composed of paper, polyethylene, and aluminum. It is difficult to separate the polyethylene and aluminum, so most of the waste is currently sent to landfill or incinerated with other municipal solid waste, meaning that enormous amounts of resources are wasted. A wet process technique for separating the aluminum and polyethylene from the composite materials after the paper had been removed from the original packaging waste was studied. The separation efficiency achieved using different separation reagents was compared, different separation mechanisms were explored, and the impacts of a range of parameters, such as the reagent concentration, temperature, and liquid-solid ratio, on the separation time and aluminum loss ratio were studied. Methanoic acid was found to be the optimal separation reagent, and the suitable conditions were a reagent concentration of 2-4 mol/L, a temperature of 60-80°C, and a liquid-solid ratio of 30 L/kg. These conditions allowed aluminum and polyethylene to be separated in less than 30 min, with an aluminum loss ratio of less than 3%. A mass balance was produced for the aluminum-polyethylene separation system, and control technique was developed to keep the ion concentrations in the reaction system stable. This allowed a continuous industrial-scale process for separating aluminum and polyethylene to be developed, and a demonstration facility with a capacity of 50t/d was built. The demonstration facility gave polyethylene and aluminum recovery rates of more than 98% and more than 72%, respectively. Separating 1t of aluminum-polyethylene composite packaging material gave a profit of 1769 Yuan, meaning that an effective method for recycling aseptic composite packaging waste was achieved. Copyright © 2014 Elsevier Ltd. All rights reserved.

Parameter Estimation of Fractional-Order Chaotic Systems by Using Quantum Parallel Particle Swarm Optimization Algorithm

PubMed Central

Huang, Yu; Guo, Feng; Li, Yongling; Liu, Yufeng

2015-01-01

Parameter estimation for fractional-order chaotic systems is an important issue in fractional-order chaotic control and synchronization and could be essentially formulated as a multidimensional optimization problem. A novel algorithm called quantum parallel particle swarm optimization (QPPSO) is proposed to solve the parameter estimation for fractional-order chaotic systems. The parallel characteristic of quantum computing is used in QPPSO. This characteristic increases the calculation of each generation exponentially. The behavior of particles in quantum space is restrained by the quantum evolution equation, which consists of the current rotation angle, individual optimal quantum rotation angle, and global optimal quantum rotation angle. Numerical simulation based on several typical fractional-order systems and comparisons with some typical existing algorithms show the effectiveness and efficiency of the proposed algorithm. PMID:25603158

Optimization of metals and plastics recovery from electric cable wastes using a plate-type electrostatic separator.

PubMed

Richard, Gontran; Touhami, Seddik; Zeghloul, Thami; Dascalescu, Lucien

2017-02-01

Plate-type electrostatic separators are commonly employed for the selective sorting of conductive and non-conductive granular materials. The aim of this work is to identify the optimal operating conditions of such equipment, when employed for separating copper and plastics from either flexible or rigid electric wire wastes. The experiments are performed according to the response surface methodology, on samples composed of either "calibrated" particles, obtained by manually cutting of electric wires at a predefined length (4mm), or actual machine-grinded scraps, characterized by a relatively-wide size distribution (1-4mm). The results point out the effect of particle size and shape on the effectiveness of the electrostatic separation. Different optimal operating conditions are found for flexible and rigid wires. A separate processing of the two classes of wire wastes is recommended. Copyright © 2016 Elsevier Ltd. All rights reserved.

Factorization and reduction methods for optimal control of distributed parameter systems

NASA Technical Reports Server (NTRS)

Burns, J. A.; Powers, R. K.

1985-01-01

A Chandrasekhar-type factorization method is applied to the linear-quadratic optimal control problem for distributed parameter systems. An aeroelastic control problem is used as a model example to demonstrate that if computationally efficient algorithms, such as those of Chandrasekhar-type, are combined with the special structure often available to a particular problem, then an abstract approximation theory developed for distributed parameter control theory becomes a viable method of solution. A numerical scheme based on averaging approximations is applied to hereditary control problems. Numerical examples are given.

An optimal autonomous microgrid cluster based on distributed generation droop parameter optimization and renewable energy sources using an improved grey wolf optimizer

NASA Astrophysics Data System (ADS)

Moazami Goodarzi, Hamed; Kazemi, Mohammad Hosein

2018-05-01

Microgrid (MG) clustering is regarded as an important driver in improving the robustness of MGs. However, little research has been conducted on providing appropriate MG clustering. This article addresses this shortfall. It proposes a novel multi-objective optimization approach for finding optimal clustering of autonomous MGs by focusing on variables such as distributed generation (DG) droop parameters, the location and capacity of DG units, renewable energy sources, capacitors and powerline transmission. Power losses are minimized and voltage stability is improved while virtual cut-set lines with minimum power transmission for clustering MGs are obtained. A novel chaotic grey wolf optimizer (CGWO) algorithm is applied to solve the proposed multi-objective problem. The performance of the approach is evaluated by utilizing a 69-bus MG in several scenarios.

Theoretic aspects of the identification of the parameters in the optimal control model

NASA Technical Reports Server (NTRS)

Vanwijk, R. A.; Kok, J. J.

1977-01-01

The identification of the parameters of the optimal control model from input-output data of the human operator is considered. Accepting the basic structure of the model as a cascade of a full-order observer and a feedback law, and suppressing the inherent optimality of the human controller, the parameters to be identified are the feedback matrix, the observer gain matrix, and the intensity matrices of the observation noise and the motor noise. The identification of the parameters is a statistical problem, because the system and output are corrupted by noise, and therefore the solution must be based on the statistics (probability density function) of the input and output data of the human operator. However, based on the statistics of the input-output data of the human operator, no distinction can be made between the observation and the motor noise, which shows that the model suffers from overparameterization.

Optimization of process parameters in welding of dissimilar steels using robot TIG welding

NASA Astrophysics Data System (ADS)

Navaneeswar Reddy, G.; VenkataRamana, M.

2018-03-01

Robot TIG welding is a modern technique used for joining two work pieces with high precision. Design of Experiments is used to conduct experiments by varying weld parameters like current, wire feed and travelling speed. The welding parameters play important role in joining of dissimilar stainless steel SS 304L and SS430. In this work, influences of welding parameter on Robot TIG Welded specimens are investigated using Response Surface Methodology. The Micro Vickers hardness tests of the weldments are measured. The process parameters are optimized to maximize the hardness of the weldments.

Optimization of cutting parameters for machining time in turning process

NASA Astrophysics Data System (ADS)

Mavliutov, A. R.; Zlotnikov, E. G.

2018-03-01

This paper describes the most effective methods for nonlinear constraint optimization of cutting parameters in the turning process. Among them are Linearization Programming Method with Dual-Simplex algorithm, Interior Point method, and Augmented Lagrangian Genetic Algorithm (ALGA). Every each of them is tested on an actual example – the minimization of production rate in turning process. The computation was conducted in the MATLAB environment. The comparative results obtained from the application of these methods show: The optimal value of the linearized objective and the original function are the same. ALGA gives sufficiently accurate values, however, when the algorithm uses the Hybrid function with Interior Point algorithm, the resulted values have the maximal accuracy.

Evolutionary algorithm optimization of biological learning parameters in a biomimetic neuroprosthesis

PubMed Central

Dura-Bernal, S.; Neymotin, S. A.; Kerr, C. C.; Sivagnanam, S.; Majumdar, A.; Francis, J. T.; Lytton, W. W.

2017-01-01

Biomimetic simulation permits neuroscientists to better understand the complex neuronal dynamics of the brain. Embedding a biomimetic simulation in a closed-loop neuroprosthesis, which can read and write signals from the brain, will permit applications for amelioration of motor, psychiatric, and memory-related brain disorders. Biomimetic neuroprostheses require real-time adaptation to changes in the external environment, thus constituting an example of a dynamic data-driven application system. As model fidelity increases, so does the number of parameters and the complexity of finding appropriate parameter configurations. Instead of adapting synaptic weights via machine learning, we employed major biological learning methods: spike-timing dependent plasticity and reinforcement learning. We optimized the learning metaparameters using evolutionary algorithms, which were implemented in parallel and which used an island model approach to obtain sufficient speed. We employed these methods to train a cortical spiking model to utilize macaque brain activity, indicating a selected target, to drive a virtual musculoskeletal arm with realistic anatomical and biomechanical properties to reach to that target. The optimized system was able to reproduce macaque data from a comparable experimental motor task. These techniques can be used to efficiently tune the parameters of multiscale systems, linking realistic neuronal dynamics to behavior, and thus providing a useful tool for neuroscience and neuroprosthetics. PMID:29200477

Anaerobic co-digestion of commercial food waste and dairy manure: Characterizing biochemical parameters and synergistic effects.

PubMed

Ebner, Jacqueline H; Labatut, Rodrigo A; Lodge, Jeffrey S; Williamson, Anahita A; Trabold, Thomas A

2016-06-01

Anaerobic digestion of commercial food waste is a promising alternative to landfilling commercial food waste. This study characterized 11 types of commercial food wastes and 12 co-digestion blends. Bio-methane potential, biodegradable fraction, and apparent first-order hydrolysis rate coefficients were reported based upon biochemical methane potential (BMP) assays. Food waste bio-methane potentials ranged from 165 to 496 mL CH4/g VS. Substrates high in lipids or readily degradable carbohydrates showed the highest methane production. Average bio-methane potential observed for co-digested substrates was -5% to +20% that of the bio-methane potential of the individual substrates weighted by VS content. Apparent hydrolysis rate coefficients ranged from 0.19d(-1) to 0.65d(-1). Co-digested substrates showed an accelerated apparent hydrolysis rate relative to the weighted average of individual substrate rates. These results provide a database of key bio-digestion parameters to advance modeling and utilization of commercial food waste in anaerobic digestion. Copyright © 2016 Elsevier Ltd. All rights reserved.

TRU Waste Management Program. Cost/schedule optimization analysis

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

Detamore, J.A.; Raudenbush, M.H.; Wolaver, R.W.

This Current Year Work Plan presents in detail a description of the activities to be performed by the Joint Integration Office Rockwell International (JIO/RI) during FY86. It breaks down the activities into two major work areas: Program Management and Program Analysis. Program Management is performed by the JIO/RI by providing technical planning and guidance for the development of advanced TRU waste management capabilities. This includes equipment/facility design, engineering, construction, and operations. These functions are integrated to allow transition from interim storage to final disposition. JIO/RI tasks include program requirements identification, long-range technical planning, budget development, program planning document preparation, taskmore » guidance development, task monitoring, task progress information gathering and reporting to DOE, interfacing with other agencies and DOE lead programs, integrating public involvement with program efforts, and preparation of reports for DOE detailing program status. Program Analysis is performed by the JIO/RI to support identification and assessment of alternatives, and development of long-term TRU waste program capabilities. These analyses include short-term analyses in response to DOE information requests, along with performing an RH Cost/Schedule Optimization report. Systems models will be developed, updated, and upgraded as needed to enhance JIO/RI's capability to evaluate the adequacy of program efforts in various fields. A TRU program data base will be maintained and updated to provide DOE with timely responses to inventory related questions.« less

Multi-Objective Optimization of Friction Stir Welding Process Parameters of AA6061-T6 and AA7075-T6 Using a Biogeography Based Optimization Algorithm

PubMed Central

Tamjidy, Mehran; Baharudin, B. T. Hang Tuah; Paslar, Shahla; Matori, Khamirul Amin; Sulaiman, Shamsuddin; Fadaeifard, Firouz

2017-01-01

The development of Friction Stir Welding (FSW) has provided an alternative approach for producing high-quality welds, in a fast and reliable manner. This study focuses on the mechanical properties of the dissimilar friction stir welding of AA6061-T6 and AA7075-T6 aluminum alloys. The FSW process parameters such as tool rotational speed, tool traverse speed, tilt angle, and tool offset influence the mechanical properties of the friction stir welded joints significantly. A mathematical regression model is developed to determine the empirical relationship between the FSW process parameters and mechanical properties, and the results are validated. In order to obtain the optimal values of process parameters that simultaneously optimize the ultimate tensile strength, elongation, and minimum hardness in the heat affected zone (HAZ), a metaheuristic, multi objective algorithm based on biogeography based optimization is proposed. The Pareto optimal frontiers for triple and dual objective functions are obtained and the best optimal solution is selected through using two different decision making techniques, technique for order of preference by similarity to ideal solution (TOPSIS) and Shannon’s entropy. PMID:28772893

Multi-Objective Optimization of Friction Stir Welding Process Parameters of AA6061-T6 and AA7075-T6 Using a Biogeography Based Optimization Algorithm.

PubMed

Tamjidy, Mehran; Baharudin, B T Hang Tuah; Paslar, Shahla; Matori, Khamirul Amin; Sulaiman, Shamsuddin; Fadaeifard, Firouz

2017-05-15

The development of Friction Stir Welding (FSW) has provided an alternative approach for producing high-quality welds, in a fast and reliable manner. This study focuses on the mechanical properties of the dissimilar friction stir welding of AA6061-T6 and AA7075-T6 aluminum alloys. The FSW process parameters such as tool rotational speed, tool traverse speed, tilt angle, and tool offset influence the mechanical properties of the friction stir welded joints significantly. A mathematical regression model is developed to determine the empirical relationship between the FSW process parameters and mechanical properties, and the results are validated. In order to obtain the optimal values of process parameters that simultaneously optimize the ultimate tensile strength, elongation, and minimum hardness in the heat affected zone (HAZ), a metaheuristic, multi objective algorithm based on biogeography based optimization is proposed. The Pareto optimal frontiers for triple and dual objective functions are obtained and the best optimal solution is selected through using two different decision making techniques, technique for order of preference by similarity to ideal solution (TOPSIS) and Shannon's entropy.

Optimization of the fiber laser parameters for local high-temperature impact on metal

NASA Astrophysics Data System (ADS)

Yatsko, Dmitrii S.; Polonik, Marina V.; Dudko, Olga V.

2016-11-01

This paper presents the local laser heating process of surface layer of the metal sample. The aim is to create the molten pool with the required depth by laser thermal treatment. During the heating the metal temperature at any point of the molten zone should not reach the boiling point of the main material. The laser power, exposure time and the spot size of a laser beam are selected as the variable parameters. The mathematical model for heat transfer in a semi-infinite body, applicable to finite slab, is used for preliminary theoretical estimation of acceptable parameters values of the laser thermal treatment. The optimization problem is solved by using an algorithm based on the scanning method of the search space (the zero-order method of conditional optimization). The calculated values of the parameters (the optimal set of "laser radiation power - exposure time - spot radius") are used to conduct a series of natural experiments to obtain a molten pool with the required depth. A two-stage experiment consists of: a local laser treatment of metal plate (steel) and then the examination of the microsection of the laser irradiated region. According to the experimental results, we can judge the adequacy of the ongoing calculations within the selected models.

Error reduction and parameter optimization of the TAPIR method for fast T1 mapping.

PubMed

Zaitsev, M; Steinhoff, S; Shah, N J

2003-06-01

A methodology is presented for the reduction of both systematic and random errors in T(1) determination using TAPIR, a Look-Locker-based fast T(1) mapping technique. The relations between various sequence parameters were carefully investigated in order to develop recipes for choosing optimal sequence parameters. Theoretical predictions for the optimal flip angle were verified experimentally. Inversion pulse imperfections were identified as the main source of systematic errors in T(1) determination with TAPIR. An effective remedy is demonstrated which includes extension of the measurement protocol to include a special sequence for mapping the inversion efficiency itself. Copyright 2003 Wiley-Liss, Inc.

Investigation of the Influence of Acoustic Oscillation Parameters on the Mechanism of Waste Rubber Products Combustion

NASA Astrophysics Data System (ADS)

Shakurov, R. F.; Sitnikov, O. R.; Galimova, A. I.; Sabitova, A. F.

2018-03-01

The article presents an analysis of the used methods of recycling of waste rubber products. The worn out tires are exposed to natural decomposition only after 50 - 100 years, and toxic organic compounds used in the manufacture constitute a danger to the environment. It contemplates a method of recycling waste rubber products in devices where pulsating combustion is realized. The dependence of the influence of acoustic pulsation parameters on the combustion mechanism of waste rubber products and on the composition of combustion products was experimentally investigated and established. For this purpose, the setup scheme based on the Rijke effect is optimized. The resonance pipe is coaxially embedded in the shaft. The known mathematical model of finding the combustion zones in the Rijke pipe, corresponding to the gas flow oscillations with the maximum amplitude, is applied to the chosen scheme. Investigations were carried out for three positions of the grate relative to the lower section of the experimental pipe, in which 1st, 2nd, 3rd modes of oscillation are formed. There are favorable conditions arise for the secondary combustion of mechanical particles entrained in the gas flow in the tube. The favorable conditions for afterburning also include the fact that through the upper section of the resonant pipe, the ambient air, caused by the features of the standing wave, is mixed into the gas stream. A comparative analysis of the change of gas concentration composition along the length of the resonance tube is carried out. It is established that the basic mode of oscillations contributes to the reduction of nitrogen oxides, in comparison with the oscillations occurring simultaneously at several harmonics, considering the main one. The results of research for the three positions of the grate in relation to the lower section of the installation are presented in tabular form, in which 1, 2, 3 modes of oscillation are formed. The analysis of experimental results confirms

Optimal Inversion Parameters for Full Waveform Inversion using OBS Data Set

NASA Astrophysics Data System (ADS)

Kim, S.; Chung, W.; Shin, S.; Kim, D.; Lee, D.

2017-12-01

In recent years, full Waveform Inversion (FWI) has been the most researched technique in seismic data processing. It uses the residuals between observed and modeled data as an objective function; thereafter, the final subsurface velocity model is generated through a series of iterations meant to minimize the residuals.Research on FWI has expanded from acoustic media to elastic media. In acoustic media, the subsurface property is defined by P-velocity; however, in elastic media, properties are defined by multiple parameters, such as P-velocity, S-velocity, and density. Further, the elastic media can also be defined by Lamé constants, density or impedance PI, SI; consequently, research is being carried out to ascertain the optimal parameters.From results of advanced exploration equipment and Ocean Bottom Seismic (OBS) survey, it is now possible to obtain multi-component seismic data. However, to perform FWI on these data and generate an accurate subsurface model, it is important to determine optimal inversion parameters among (Vp, Vs, ρ), (λ, μ, ρ), and (PI, SI) in elastic media. In this study, staggered grid finite difference method was applied to simulate OBS survey. As in inversion, l2-norm was set as objective function. Further, the accurate computation of gradient direction was performed using the back-propagation technique and its scaling was done using the Pseudo-hessian matrix.In acoustic media, only Vp is used as the inversion parameter. In contrast, various sets of parameters, such as (Vp, Vs, ρ) and (λ, μ, ρ) can be used to define inversion in elastic media. Therefore, it is important to ascertain the parameter that gives the most accurate result for inversion with OBS data set.In this study, we generated Vp and Vs subsurface models by using (λ, μ, ρ) and (Vp, Vs, ρ) as inversion parameters in every iteration, and compared the final two FWI results.This research was supported by the Basic Research Project(17-3312) of the Korea Institute of

Pharmacokinetic design optimization in children and estimation of maturation parameters: example of cytochrome P450 3A4.

PubMed

Bouillon-Pichault, Marion; Jullien, Vincent; Bazzoli, Caroline; Pons, Gérard; Tod, Michel

2011-02-01

The aim of this work was to determine whether optimizing the study design in terms of ages and sampling times for a drug eliminated solely via cytochrome P450 3A4 (CYP3A4) would allow us to accurately estimate the pharmacokinetic parameters throughout the entire childhood timespan, while taking into account age- and weight-related changes. A linear monocompartmental model with first-order absorption was used successively with three different residual error models and previously published pharmacokinetic parameters ("true values"). The optimal ages were established by D-optimization using the CYP3A4 maturation function to create "optimized demographic databases." The post-dose times for each previously selected age were determined by D-optimization using the pharmacokinetic model to create "optimized sparse sampling databases." We simulated concentrations by applying the population pharmacokinetic model to the optimized sparse sampling databases to create optimized concentration databases. The latter were modeled to estimate population pharmacokinetic parameters. We then compared true and estimated parameter values. The established optimal design comprised four age ranges: 0.008 years old (i.e., around 3 days), 0.192 years old (i.e., around 2 months), 1.325 years old, and adults, with the same number of subjects per group and three or four samples per subject, in accordance with the error model. The population pharmacokinetic parameters that we estimated with this design were precise and unbiased (root mean square error [RMSE] and mean prediction error [MPE] less than 11% for clearance and distribution volume and less than 18% for k(a)), whereas the maturation parameters were unbiased but less precise (MPE < 6% and RMSE < 37%). Based on our results, taking growth and maturation into account a priori in a pediatric pharmacokinetic study is theoretically feasible. However, it requires that very early ages be included in studies, which may present an obstacle to the

Parameter estimation techniques based on optimizing goodness-of-fit statistics for structural reliability

NASA Technical Reports Server (NTRS)

Starlinger, Alois; Duffy, Stephen F.; Palko, Joseph L.

1993-01-01

New methods are presented that utilize the optimization of goodness-of-fit statistics in order to estimate Weibull parameters from failure data. It is assumed that the underlying population is characterized by a three-parameter Weibull distribution. Goodness-of-fit tests are based on the empirical distribution function (EDF). The EDF is a step function, calculated using failure data, and represents an approximation of the cumulative distribution function for the underlying population. Statistics (such as the Kolmogorov-Smirnov statistic and the Anderson-Darling statistic) measure the discrepancy between the EDF and the cumulative distribution function (CDF). These statistics are minimized with respect to the three Weibull parameters. Due to nonlinearities encountered in the minimization process, Powell's numerical optimization procedure is applied to obtain the optimum value of the EDF. Numerical examples show the applicability of these new estimation methods. The results are compared to the estimates obtained with Cooper's nonlinear regression algorithm.

Effect of Data Assimilation Parameters on The Optimized Surface CO2 Flux in Asia

NASA Astrophysics Data System (ADS)

Kim, Hyunjung; Kim, Hyun Mee; Kim, Jinwoong; Cho, Chun-Ho

2018-02-01

In this study, CarbonTracker, an inverse modeling system based on the ensemble Kalman filter, was used to evaluate the effects of data assimilation parameters (assimilation window length and ensemble size) on the estimation of surface CO2 fluxes in Asia. Several experiments with different parameters were conducted, and the results were verified using CO2 concentration observations. The assimilation window lengths tested were 3, 5, 7, and 10 weeks, and the ensemble sizes were 100, 150, and 300. Therefore, a total of 12 experiments using combinations of these parameters were conducted. The experimental period was from January 2006 to December 2009. Differences between the optimized surface CO2 fluxes of the experiments were largest in the Eurasian Boreal (EB) area, followed by Eurasian Temperate (ET) and Tropical Asia (TA), and were larger in boreal summer than in boreal winter. The effect of ensemble size on the optimized biosphere flux is larger than the effect of the assimilation window length in Asia, but the importance of them varies in specific regions in Asia. The optimized biosphere flux was more sensitive to the assimilation window length in EB, whereas it was sensitive to the ensemble size as well as the assimilation window length in ET. The larger the ensemble size and the shorter the assimilation window length, the larger the uncertainty (i.e., spread of ensemble) of optimized surface CO2 fluxes. The 10-week assimilation window and 300 ensemble size were the optimal configuration for CarbonTracker in the Asian region based on several verifications using CO2 concentration measurements.

[Optimization of application parameters of soil seed bank in vegetation recovery via response surface methodology].

PubMed

He, Meng-Xuan; Li, Hong-Yuan; Mo, Xun-Qiang; Meng, Wei-Qing; Yang, Jia-Nan

2014-08-01

The thickness of surface soil, the covering thickness and the number of adding arbor seeds are all important factors to be considered in the application of soil seed bank (SSB) for vegetation recovery. To determine the optimal conditions, the Box-Behnken central composite design with three parameters and three levels was conducted and Design-Expert was used for response surface optimization. Finally, the optimal model and optimal level of each parameter were selected. The quadratic model was more suitable for response surface optimization (P < 0.0001), indicating the model had good statistical significance which could express ideal relations between all the independent variable and dependent variable. For the optimum condition, the thickness of surface soil was 4.3 cm, the covering thickness was 2 cm, and the number of adding arbor seeds was 224 ind x m(-2), under which the number of germinated seedlings could be reached up to 6222 plants x m(-2). During the process of seed germination, significant interactions between the thickness of surface soil and the covering thickness, as well as the thickness of surface soil and the number of adding arbor seeds were found, but the relationship between the covering thickness and the number of adding arbor seeds was relatively unremarkable. Among all the parameters, the thickness of surface soil was the most important one, which had the steepest curve and the largest standardized coefficient.

A Minimum (Delta)V Orbit Maintenance Strategy for Low-Altitude Missions Using Burn Parameter Optimization

NASA Technical Reports Server (NTRS)

Brown, Aaron J.

2011-01-01

Orbit maintenance is the series of burns performed during a mission to ensure the orbit satisfies mission constraints. Low-altitude missions often require non-trivial orbit maintenance (Delta)V due to sizable orbital perturbations and minimum altitude thresholds. A strategy is presented for minimizing this (Delta)V using impulsive burn parameter optimization. An initial estimate for the burn parameters is generated by considering a feasible solution to the orbit maintenance problem. An example demonstrates the dV savings from the feasible solution to the optimal solution.

Improving hot region prediction by parameter optimization of density clustering in PPI.

PubMed

Hu, Jing; Zhang, Xiaolong

2016-11-01

This paper proposed an optimized algorithm which combines density clustering of parameter selection with feature-based classification for hot region prediction. First, all the residues are classified by SVM to remove non-hot spot residues, then density clustering of parameter selection is used to find hot regions. In the density clustering, this paper studies how to select input parameters. There are two parameters radius and density in density-based incremental clustering. We firstly fix density and enumerate radius to find a pair of parameters which leads to maximum number of clusters, and then we fix radius and enumerate density to find another pair of parameters which leads to maximum number of clusters. Experiment results show that the proposed method using both two pairs of parameters provides better prediction performance than the other method, and compare these two predictive results, the result by fixing radius and enumerating density have slightly higher prediction accuracy than that by fixing density and enumerating radius. Copyright © 2016. Published by Elsevier Inc.

Multi-objective optimization of combustion, performance and emission parameters in a jatropha biodiesel engine using Non-dominated sorting genetic algorithm-II

NASA Astrophysics Data System (ADS)

Dhingra, Sunil; Bhushan, Gian; Dubey, Kashyap Kumar

2014-03-01

The present work studies and identifies the different variables that affect the output parameters involved in a single cylinder direct injection compression ignition (CI) engine using jatropha biodiesel. Response surface methodology based on Central composite design (CCD) is used to design the experiments. Mathematical models are developed for combustion parameters (Brake specific fuel consumption (BSFC) and peak cylinder pressure (Pmax)), performance parameter brake thermal efficiency (BTE) and emission parameters (CO, NO x , unburnt HC and smoke) using regression techniques. These regression equations are further utilized for simultaneous optimization of combustion (BSFC, Pmax), performance (BTE) and emission (CO, NO x , HC, smoke) parameters. As the objective is to maximize BTE and minimize BSFC, Pmax, CO, NO x , HC, smoke, a multiobjective optimization problem is formulated. Nondominated sorting genetic algorithm-II is used in predicting the Pareto optimal sets of solution. Experiments are performed at suitable optimal solutions for predicting the combustion, performance and emission parameters to check the adequacy of the proposed model. The Pareto optimal sets of solution can be used as guidelines for the end users to select optimal combination of engine output and emission parameters depending upon their own requirements.

Optimizing Parameters of Axial Pressure-Compounded Ultra-Low Power Impulse Turbines at Preliminary Design

NASA Astrophysics Data System (ADS)

Kalabukhov, D. S.; Radko, V. M.; Grigoriev, V. A.

2018-01-01

Ultra-low power turbine drives are used as energy sources in auxiliary power systems, energy units, terrestrial, marine, air and space transport within the confines of shaft power N td = 0.01…10 kW. In this paper we propose a new approach to the development of surrogate models for evaluating the integrated efficiency of multistage ultra-low power impulse turbine with pressure stages. This method is based on the use of existing mathematical models of ultra-low power turbine stage efficiency and mass. It has been used in a method for selecting the rational parameters of two-stage axial ultra-low power turbine. The article describes the basic features of an algorithm for two-stage turbine parameters optimization and for efficiency criteria evaluating. Pledged mathematical models are intended for use at the preliminary design of turbine drive. The optimization method was tested at preliminary design of an air starter turbine. Validation was carried out by comparing the results of optimization calculations and numerical gas-dynamic simulation in the Ansys CFX package. The results indicate a sufficient accuracy of used surrogate models for axial two-stage turbine parameters selection

Assessment of municipal solid waste settlement models based on field-scale data analysis.

PubMed

Bareither, Christopher A; Kwak, Seungbok

2015-08-01

An evaluation of municipal solid waste (MSW) settlement model performance and applicability was conducted based on analysis of two field-scale datasets: (1) Yolo and (2) Deer Track Bioreactor Experiment (DTBE). Twelve MSW settlement models were considered that included a range of compression behavior (i.e., immediate compression, mechanical creep, and biocompression) and range of total (2-22) and optimized (2-7) model parameters. A multi-layer immediate settlement analysis developed for Yolo provides a framework to estimate initial waste thickness and waste thickness at the end-of-immediate compression. Model application to the Yolo test cells (conventional and bioreactor landfills) via least squares optimization yielded high coefficient of determinations for all settlement models (R(2)>0.83). However, empirical models (i.e., power creep, logarithmic, and hyperbolic models) are not recommended for use in MSW settlement modeling due to potential non-representative long-term MSW behavior, limited physical significance of model parameters, and required settlement data for model parameterization. Settlement models that combine mechanical creep and biocompression into a single mathematical function constrain time-dependent settlement to a single process with finite magnitude, which limits model applicability. Overall, all models evaluated that couple multiple compression processes (immediate, creep, and biocompression) provided accurate representations of both Yolo and DTBE datasets. A model presented in Gourc et al. (2010) included the lowest number of total and optimized model parameters and yielded high statistical performance for all model applications (R(2)⩾0.97). Copyright © 2015 Elsevier Ltd. All rights reserved.

Evaluation and Parameter Analysis of Burn up Calculations for the Assessment of Radioactive Waste - 13187

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

Fast, Ivan; Aksyutina, Yuliya; Tietze-Jaensch, Holger

2013-07-01

Burn up calculations facilitate a determination of the composition and nuclear inventory of spent nuclear fuel, if operational history is known. In case this information is not available, the total nuclear inventory can be determined by means of destructive or, even on industrial scale, nondestructive measurement methods. For non-destructive measurements however only a few easy-to-measure, so-called key nuclides, are determined due to their characteristic gamma lines or neutron emission. From these measured activities the fuel burn up and cooling time are derived to facilitate the numerical inventory determination of spent fuel elements. Most regulatory bodies require an independent assessment ofmore » nuclear waste properties and their documentation. Prominent part of this assessment is a consistency check of inventory declaration. The waste packages often contain wastes from different types of spent fuels of different history and information about the secondary reactor parameters may not be available. In this case the so-called characteristic fuel burn up and cooling time are determined. These values are obtained from a correlations involving key-nuclides with a certain bandwidth, thus with upper and lower limits. The bandwidth is strongly dependent on secondary reactor parameter such as initial enrichment, temperature and density of the fuel and moderator, hence the reactor type, fuel element geometry and plant operation history. The purpose of our investigation is to look into the scaling and correlation limitations, to define and verify the range of validity and to scrutinize the dependencies and propagation of uncertainties that affect the waste inventory declarations and their independent verification. This is accomplished by numerical assessment and simulation of waste production using well accepted codes SCALE 6.0 and 6.1 to simulate the cooling time and burn up of a spent fuel element. The simulations are benchmarked against spent fuel from the real

Exo-pectinase production by Bacillus pumilus using different agricultural wastes and optimizing of medium components using response surface methodology.

PubMed

Tepe, Ozlem; Dursun, Arzu Y

2014-01-01

In this research, the production of exo-pectinase by Bacillus pumilus using different agricultural wastes was studied. Agricultural wastes containing pectin such as wheat bran, sugar beet pulp, sunflower plate, orange peel, banana peel, apple pomace and grape pomace were tested as substrates, and activity of exo-pectinase was determined only in the mediums containing sugar beet pulp and wheat bran. Then, effects of parameters such as concentrations of solid substrate (wheat bran and sugar beet pulp) (A), ammonium sulphate (B) and yeast extract (C) on the production of exo-pectinase were investigated by response surface methodology. First, wheat bran was used as solid substrate, and it was determined that exo-pectinase activity increased when relatively low concentrations of ammonium sulphate (0.12-0.21% w/v) and yeast extract (0.12-0.3% w/v) and relatively high wheat bran (~5-6% w/v) were used. Then, exo-pectinase production was optimized by response surface methodology using sugar beet pulp as a solid substrate. In comparison to P values of the coefficients, values of not greater than 0.05 of A and B (2) showed that the effect of these process variables in exo-pectinase production was important and that changes done in these variables will alter the enzyme activity.

An approach to design controllers for MIMO fractional-order plants based on parameter optimization algorithm.

PubMed

Xue, Dingyü; Li, Tingxue

2017-04-27

The parameter optimization method for multivariable systems is extended to the controller design problems for multiple input multiple output (MIMO) square fractional-order plants. The algorithm can be applied to search for the optimal parameters of integer-order controllers for fractional-order plants with or without time delays. Two examples are given to present the controller design procedures for MIMO fractional-order systems. Simulation studies show that the integer-order controllers designed are robust to plant gain variations. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

USING WASTE TO CLEAN UP THE ENVIRONMENT: CELLULOSIC ETHANOL, THE FUTURE OF FUELS

EPA Science Inventory

In the process of converting municipal solid waste (MSW) into ethanol we optimized the first two major steps of pretreatment and enzymatic hydrolysis stages to enhance the sugar yield and to reduce the cost. For the pretreatment process, we tested different parameters of react...

A LSQR-type method provides a computationally efficient automated optimal choice of regularization parameter in diffuse optical tomography.

PubMed

Prakash, Jaya; Yalavarthy, Phaneendra K

2013-03-01

Developing a computationally efficient automated method for the optimal choice of regularization parameter in diffuse optical tomography. The least-squares QR (LSQR)-type method that uses Lanczos bidiagonalization is known to be computationally efficient in performing the reconstruction procedure in diffuse optical tomography. The same is effectively deployed via an optimization procedure that uses the simplex method to find the optimal regularization parameter. The proposed LSQR-type method is compared with the traditional methods such as L-curve, generalized cross-validation (GCV), and recently proposed minimal residual method (MRM)-based choice of regularization parameter using numerical and experimental phantom data. The results indicate that the proposed LSQR-type and MRM-based methods performance in terms of reconstructed image quality is similar and superior compared to L-curve and GCV-based methods. The proposed method computational complexity is at least five times lower compared to MRM-based method, making it an optimal technique. The LSQR-type method was able to overcome the inherent limitation of computationally expensive nature of MRM-based automated way finding the optimal regularization parameter in diffuse optical tomographic imaging, making this method more suitable to be deployed in real-time.

Optimized batch fermentation of cheese whey. Supplemented feedlot waste filtrate to produce a nitrogen-rich feed supplement for ruminants

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

Erdman, M.D.; Reddy, C.A.

1986-03-01

An optimized batch fermentation process for the conversion of cattle feedlot waste filtrate, supplemented with cheese whey, into a nitrogenous feed supplement for ruminants is described. Feedlot waste filtrate supplemented with cheese whey (5 g of whey per 100 ml) was fermented by the indigenous microbial flora in the feedlot waste filtrate. Ammonium hydroxide was added to the fermentation not only to maintain a constant pH but also to produce ammonium salts of organic acids, which have been shown to be valuable as nitrogenous feed supplements for ruminants. The utilization of substrate carbohydrate at pH 7.0 and 43 degrees Cmore » was greater than 94% within 8 h, and the crude protein (total N X 6.25) content of the product was 70 to 78% (dry weight basis). About 66 to 69% of the crude protein was in the form of ammonia nitrogen. Lactate and acetate were the predominant acids during the first 6 to 8 hours of fermentation, but after 24 hours, appreciable levels of propionate and butyrate were also present. The rate of fermentation and the crude protein content of the product were optimal at pH 7.0 and decreased at a lower pH. For example, fermentation did not go to completion even after 24 hours at pH 4.5. Fermentation proceeded optimally at 43 degrees C, less so at 37 degrees C, and considerably more slowly at 23 and 50 degrees C. Concentrations of up to 15 g of cheese whey per 100 ml of feedlot waste filtrate were fermented efficiently. Fermentation of feedlot waste filtrate obtained from animals fed low silage-high grain, high silage-low grain, or dairy rations resulted in similar products in terms of total nitrogen and organic acid composition.« less

Identification of Important Parameter from Leachate Solid Waste Landfill on Water Quality, Case Study of Pesanggrahan River

NASA Astrophysics Data System (ADS)

Yanidar, R.; Hartono, D. M.; Moersidik, S. S.

2018-03-01

Cipayung Landfill takes waste generation from Depok City approximately ± 750 tons/day of solid waste. The south and west boundaries of the landfill is Pesanggarahan River which 200m faraway. The objectives of this study are to indicate an important parameter which greatly affects the water quality of Pesanggrahan River and purpose the dynamic model for improving our understanding of the dynamic behavior that captures the interactions and feedbacks important parameter in river in order to identify and assess the effects of the treated leachate from final solid waste disposal activity as it responds to changes over time in the river. The high concentrations of BOD and COD are not the only cause significantly affect the quality of the pesanggrahan water, it also because the river has been contaminated in the upstream area. It need the water quality model to support the effectiveness calculation of activities for preventing a selected the pollutant sources the model should be developed for simulating and predicting the trend of water quality performance in Pesanggrahan River which can potentially be used by policy makers in strategic management to sustain river water quality as raw drinking water.

Parameter Estimation of Computationally Expensive Watershed Models Through Efficient Multi-objective Optimization and Interactive Decision Analytics

NASA Astrophysics Data System (ADS)

Akhtar, Taimoor; Shoemaker, Christine

2016-04-01

Watershed model calibration is inherently a multi-criteria problem. Conflicting trade-offs exist between different quantifiable calibration criterions indicating the non-existence of a single optimal parameterization. Hence, many experts prefer a manual approach to calibration where the inherent multi-objective nature of the calibration problem is addressed through an interactive, subjective, time-intensive and complex decision making process. Multi-objective optimization can be used to efficiently identify multiple plausible calibration alternatives and assist calibration experts during the parameter estimation process. However, there are key challenges to the use of multi objective optimization in the parameter estimation process which include: 1) multi-objective optimization usually requires many model simulations, which is difficult for complex simulation models that are computationally expensive; and 2) selection of one from numerous calibration alternatives provided by multi-objective optimization is non-trivial. This study proposes a "Hybrid Automatic Manual Strategy" (HAMS) for watershed model calibration to specifically address the above-mentioned challenges. HAMS employs a 3-stage framework for parameter estimation. Stage 1 incorporates the use of an efficient surrogate multi-objective algorithm, GOMORS, for identification of numerous calibration alternatives within a limited simulation evaluation budget. The novelty of HAMS is embedded in Stages 2 and 3 where an interactive visual and metric based analytics framework is available as a decision support tool to choose a single calibration from the numerous alternatives identified in Stage 1. Stage 2 of HAMS provides a goodness-of-fit measure / metric based interactive framework for identification of a small subset (typically less than 10) of meaningful and diverse set of calibration alternatives from the numerous alternatives obtained in Stage 1. Stage 3 incorporates the use of an interactive visual

Optimization of waste transportation route at waste transfers point in Lowokwaru District, Malang City

NASA Astrophysics Data System (ADS)

Hariyani, S.; Meidiana, C.

2018-04-01

Increasing population led to the emergence of the urban infrastructure services issue including waste problems especially waste transportation system. Data in 2016 shows that the amount of waste in Malang was 659.21 tons / day. The amount of waste transported to landfill only reached 464.74 tons / day. This indicates that not all waste can be transported to the landfill Supiturang because Level of Service (LoS) reached 70.49%. This study aims to determine the effectiveness of waste transportation system and determine the fastest route from waste transfers point in Lowokwaru district to the landfill Supiturang. The data collection method in this research were 1) primary survey by interview officials from the Sanitation and Gardening Agency which questions related to the condition of the waste transportation system in waste transfer point, 2) Secondary survey related to data of waste transportation system in Malang City i.e the amount of waste generation in waste transfer point, number of garbage trucks and other data related to the garbage transportation system. To determine the fastest route analyzed by network analyst using ArcGIS software. The results of network analyst show that not all routes are already using the fastest route to the landfill Supiturang.

Automatic x-ray image contrast enhancement based on parameter auto-optimization.

PubMed

Qiu, Jianfeng; Harold Li, H; Zhang, Tiezhi; Ma, Fangfang; Yang, Deshan

2017-11-01

Insufficient image contrast associated with radiation therapy daily setup x-ray images could negatively affect accurate patient treatment setup. We developed a method to perform automatic and user-independent contrast enhancement on 2D kilo voltage (kV) and megavoltage (MV) x-ray images. The goal was to provide tissue contrast optimized for each treatment site in order to support accurate patient daily treatment setup and the subsequent offline review. The proposed method processes the 2D x-ray images with an optimized image processing filter chain, which consists of a noise reduction filter and a high-pass filter followed by a contrast limited adaptive histogram equalization (CLAHE) filter. The most important innovation is to optimize the image processing parameters automatically to determine the required image contrast settings per disease site and imaging modality. Three major parameters controlling the image processing chain, i.e., the Gaussian smoothing weighting factor for the high-pass filter, the block size, and the clip limiting parameter for the CLAHE filter, were determined automatically using an interior-point constrained optimization algorithm. Fifty-two kV and MV x-ray images were included in this study. The results were manually evaluated and ranked with scores from 1 (worst, unacceptable) to 5 (significantly better than adequate and visually praise worthy) by physicians and physicists. The average scores for the images processed by the proposed method, the CLAHE, and the best window-level adjustment were 3.92, 2.83, and 2.27, respectively. The percentage of the processed images received a score of 5 were 48, 29, and 18%, respectively. The proposed method is able to outperform the standard image contrast adjustment procedures that are currently used in the commercial clinical systems. When the proposed method is implemented in the clinical systems as an automatic image processing filter, it could be useful for allowing quicker and potentially more

A continuous optimization approach for inferring parameters in mathematical models of regulatory networks.

PubMed

Deng, Zhimin; Tian, Tianhai

2014-07-29

The advances of systems biology have raised a large number of sophisticated mathematical models for describing the dynamic property of complex biological systems. One of the major steps in developing mathematical models is to estimate unknown parameters of the model based on experimentally measured quantities. However, experimental conditions limit the amount of data that is available for mathematical modelling. The number of unknown parameters in mathematical models may be larger than the number of observation data. The imbalance between the number of experimental data and number of unknown parameters makes reverse-engineering problems particularly challenging. To address the issue of inadequate experimental data, we propose a continuous optimization approach for making reliable inference of model parameters. This approach first uses a spline interpolation to generate continuous functions of system dynamics as well as the first and second order derivatives of continuous functions. The expanded dataset is the basis to infer unknown model parameters using various continuous optimization criteria, including the error of simulation only, error of both simulation and the first derivative, or error of simulation as well as the first and second derivatives. We use three case studies to demonstrate the accuracy and reliability of the proposed new approach. Compared with the corresponding discrete criteria using experimental data at the measurement time points only, numerical results of the ERK kinase activation module show that the continuous absolute-error criteria using both function and high order derivatives generate estimates with better accuracy. This result is also supported by the second and third case studies for the G1/S transition network and the MAP kinase pathway, respectively. This suggests that the continuous absolute-error criteria lead to more accurate estimates than the corresponding discrete criteria. We also study the robustness property of these three

Determining Optimal Waste Volume From an Intravenous Catheter

PubMed Central

Baker, Rachel B.; Summer, Suzanne S.; Lawrence, Michelle; Shova, Amy; McGraw, Catherine A.; Khoury, Jane

2013-01-01

Waste is blood drawn from an intravenous (IV) catheter to remove saline before obtaining a blood sample. This study examines the minimum waste volume resulting in an undiluted sample. A repeated measures design was used. Investigators placed an IV catheter in 60 healthy adults and obtained samples at baseline and following waste volume ranging from 0.5 mL to 3 mL. A random effects mixed model was used to determine the stabilizing point. For sodium and glucose measurements, this stabilizing point was 1 mL of waste. Knowing that only 1 mL of waste is needed will prevent clinicians from obtaining extra waste and discarding blood needlessly. PMID:23455970

Forecasting the Amount of Waste-Sewage Water Discharged into the Yangtze River Basin Based on the Optimal Fractional Order Grey Model.

PubMed

Li, Shuliang; Meng, Wei; Xie, Yufeng

2017-12-23

With the rapid development of the Yangtze River economic belt, the amount of waste-sewage water discharged into the Yangtze River basin increases sharply year by year, which has impeded the sustainable development of the Yangtze River basin. The water security along the Yangtze River basin is very important for China, It is something aboutwater security of roughly one-third of China's population and the sustainable development of the 19 provinces, municipalities and autonomous regions among the Yangtze River basin. Therefore, a scientific prediction of the amount of waste-sewage water discharged into Yangtze River basin has a positive significance on sustainable development of industry belt along with Yangtze River basin. This paper builds the fractional DWSGM(1,1)(DWSGM(1,1) model is short for Discharge amount of Waste Sewage Grey Model for one order equation and one variable) model based on the fractional accumulating generation operator and fractional reducing operator, and calculates the optimal order of "r" by using particle swarm optimization(PSO)algorithm for solving the minimum average relative simulation error. Meanwhile, the simulation performance of DWSGM(1,1)model with the optimal fractional order is tested by comparing the simulation results of grey prediction models with different orders. Finally, the optimal fractional order DWSGM(1,1)grey model is applied to predict the amount of waste-sewage water discharged into the Yangtze River basin, and corresponding countermeasures and suggestions are put forward through analyzing and comparing the prediction results. This paper has positive significance on enriching the fractional order modeling method of the grey system.

Optimization of food waste compost with the use of biochar.

PubMed

Waqas, M; Nizami, A S; Aburiazaiza, A S; Barakat, M A; Ismail, I M I; Rashid, M I

2018-06-15

This paper aims to examine the influence of biochar produced from lawn waste in accelerating the degradation and mineralization rates of food waste compost. Biochar produced at two different temperatures (350 and 450 °C) was applied at the rates 10 and 15% (w/w) of the total waste to an in-vessel compost bioreactor for evaluating its effects on food waste compost. The quality of compost was assessed against stabilization indices such as moisture contents (MC), electrical conductivity (EC), organic matters (OM) degradation, change in total carbon (TC) and mineral nitrogen contents such as ammonium (NH 4 + ) and nitrate (NO 3 - ). The use of biochar significantly improved the composting process and physiochemical properties of the final compost. Results showed that in comparison to control trial, biochar amended compost mixtures rapidly achieved the thermophilic temperature, increased the OM degradation by 14.4-15.3%, concentration of NH 4 + by 37.8-45.6% and NO 3 - by 50-62%. The most prominent effects in term of achieving rapid thermophilic temperature and a higher concentration of NH 4 + and NO 3 - were observed at 15% (w/w) biochar. According to compost quality standard of United States (US), California, Germany, and Austria, the compost stability as a result of biochar addition was achieved in 50-60 days. Nonetheless, the biochar produced at 450 °C had similar effects as to biochar produced at 350 °C for most of the compost parameters. Therefore, it is recommended to produce biochar at 350 °C to reduce the energy requirements for resource recovery of biomass and should be added at a concentration of 15% (w/w) to the compost bioreactor for achieving a stable compost. Copyright © 2017 Elsevier Ltd. All rights reserved.

Multi-criteria optimization of chassis parameters of Nissan 200 SX for drifting competitions

NASA Astrophysics Data System (ADS)

Maniowski, M.

2016-09-01

The work objective is to increase performance of Nissan 200sx S13 prepared for a quasi-static state of drifting on a circular path with given constant radius (R=15 m) and tyre-road friction coefficient (μ = 0.9). First, a high fidelity “miMA” multibody model of the vehicle is formulated. Then, a multicriteria optimization problem is solved with one of the goals to maximize a stable drift angle (β) of the vehicle. The decision variables contain 11 parameters of the vehicle chassis (describing the wheel suspension stiffness and geometry) and 2 parameters responsible for a driver steering and accelerator actions, that control this extreme closed-loop manoeuvre. The optimized chassis setup results in the drift angle increase by 14% from 35 to 40 deg.

A two-step parameter optimization algorithm for improving estimation of optical properties using spatial frequency domain imaging

NASA Astrophysics Data System (ADS)

Hu, Dong; Lu, Renfu; Ying, Yibin

2018-03-01

This research was aimed at optimizing the inverse algorithm for estimating the optical absorption (μa) and reduced scattering (μs‧) coefficients from spatial frequency domain diffuse reflectance. Studies were first conducted to determine the optimal frequency resolution and start and end frequencies in terms of the reciprocal of mean free path (1/mfp‧). The results showed that the optimal frequency resolution increased with μs‧ and remained stable when μs‧ was larger than 2 mm-1. The optimal end frequency decreased from 0.3/mfp‧ to 0.16/mfp‧ with μs‧ ranging from 0.4 mm-1 to 3 mm-1, while the optimal start frequency remained at 0 mm-1. A two-step parameter estimation method was proposed based on the optimized frequency parameters, which improved estimation accuracies by 37.5% and 9.8% for μa and μs‧, respectively, compared with the conventional one-step method. Experimental validations with seven liquid optical phantoms showed that the optimized algorithm resulted in the mean absolute errors of 15.4%, 7.6%, 5.0% for μa and 16.4%, 18.0%, 18.3% for μs‧ at the wavelengths of 675 nm, 700 nm, and 715 nm, respectively. Hence, implementation of the optimized parameter estimation method should be considered in order to improve the measurement of optical properties of biological materials when using spatial frequency domain imaging technique.

Design of robust systems by means of the numerical optimization with harmonic changing of the model parameters

NASA Astrophysics Data System (ADS)

Zhmud, V. A.; Reva, I. L.; Dimitrov, L. V.

2017-01-01

The design of robust feedback systems by means of the numerical optimization method is mostly accomplished with modeling of the several systems simultaneously. In each such system, regulators are similar. But the object models are different. It includes all edge values from the possible variants of the object model parameters. With all this, not all possible sets of model parameters are taken into account. Hence, the regulator can be not robust, i. e. it can not provide system stability in some cases, which were not tested during the optimization procedure. The paper proposes an alternative method. It consists in sequent changing of all parameters according to harmonic low. The frequencies of changing of each parameter are aliquant. It provides full covering of the parameters space.

Model Predictive Optimal Control of a Time-Delay Distributed-Parameter Systems

NASA Technical Reports Server (NTRS)

Nguyen, Nhan

2006-01-01

This paper presents an optimal control method for a class of distributed-parameter systems governed by first order, quasilinear hyperbolic partial differential equations that arise in many physical systems. Such systems are characterized by time delays since information is transported from one state to another by wave propagation. A general closed-loop hyperbolic transport model is controlled by a boundary control embedded in a periodic boundary condition. The boundary control is subject to a nonlinear differential equation constraint that models actuator dynamics of the system. The hyperbolic equation is thus coupled with the ordinary differential equation via the boundary condition. Optimality of this coupled system is investigated using variational principles to seek an adjoint formulation of the optimal control problem. The results are then applied to implement a model predictive control design for a wind tunnel to eliminate a transport delay effect that causes a poor Mach number regulation.

Up-cycling waste glass to minimal water adsorption/absorption lightweight aggregate by rapid low temperature sintering: optimization by dual process-mixture response surface methodology.

PubMed

Velis, Costas A; Franco-Salinas, Claudia; O'Sullivan, Catherine; Najorka, Jens; Boccaccini, Aldo R; Cheeseman, Christopher R

2014-07-01

Mixed color waste glass extracted from municipal solid waste is either not recycled, in which case it is an environmental and financial liability, or it is used in relatively low value applications such as normal weight aggregate. Here, we report on converting it into a novel glass-ceramic lightweight aggregate (LWA), potentially suitable for high added value applications in structural concrete (upcycling). The artificial LWA particles were formed by rapidly sintering (<10 min) waste glass powder with clay mixes using sodium silicate as binder and borate salt as flux. Composition and processing were optimized using response surface methodology (RSM) modeling, and specifically (i) a combined process-mixture dual RSM, and (ii) multiobjective optimization functions. The optimization considered raw materials and energy costs. Mineralogical and physical transformations occur during sintering and a cellular vesicular glass-ceramic composite microstructure is formed, with strong correlations existing between bloating/shrinkage during sintering, density and water adsorption/absorption. The diametrical expansion could be effectively modeled via the RSM and controlled to meet a wide range of specifications; here we optimized for LWA structural concrete. The optimally designed LWA is sintered in comparatively low temperatures (825-835 °C), thus potentially saving costs and lowering emissions; it had exceptionally low water adsorption/absorption (6.1-7.2% w/wd; optimization target: 1.5-7.5% w/wd); while remaining substantially lightweight (density: 1.24-1.28 g.cm(-3); target: 0.9-1.3 g.cm(-3)). This is a considerable advancement for designing effective environmentally friendly lightweight concrete constructions, and boosting resource efficiency of waste glass flows.

Biodegradation of waste lubricants by a newly isolated Ochrobactrum sp. C1.

PubMed

Bhattacharya, Munna; Biswas, Dipa; Sana, Santanu; Datta, Sriparna

2015-10-01

A potential degrader of paraffinic and aromatic hydrocarbons was isolated from oil-contaminated soil from steel plant effluent area in Burnpur, India. The strain was investigated for degradation of waste lubricants (waste engine oil and waste transformer oil) that often contain EPA (Environmental Protection Agency, USA) classified priority pollutants and was identified as Ochrobactrum sp. C1 by 16S rRNA gene sequencing. The strain C1 was found to tolerate unusually high waste lubricant concentration along with emulsification capability of the culture broth, and its degradation efficiency was 48.5 ± 0.5 % for waste engine oil and 30.47 ± 0.25 % for waste transformer oil during 7 days incubation period. In order to get optimal degradation efficiency, a three level Box-Behnken design was employed to optimize the physical parameters namely pH, temperature and waste oil concentration. The results indicate that at temperature 36.4 °C, pH 7.3 and with 4.6 % (v/v) oil concentration, the percentage degradation of waste engine oil will be 57 % within 7 days. At this optimized condition, the experimental values (56.7 ± 0.25 %) are in a good agreement with the predicted values with a calculated R 2 to be 0.998 and significant correlation between biodegradation and emulsification activity (E 24  = 69.42 ± 0.32 %) of the culture broth toward engine oil was found with a correlation coefficient of 0.972. This is the first study showing that an Ochrobactrum sp. strain is capable of degrading waste lubricants, which might contribute to the bioremediation of waste lubricating oil-contaminated soil.

Three-dimensional optimization and sensitivity analysis of dental implant thread parameters using finite element analysis.

PubMed

Geramizadeh, Maryam; Katoozian, Hamidreza; Amid, Reza; Kadkhodazadeh, Mahdi

2018-04-01

This study aimed to optimize the thread depth and pitch of a recently designed dental implant to provide uniform stress distribution by means of a response surface optimization method available in finite element (FE) software. The sensitivity of simulation to different mechanical parameters was also evaluated. A three-dimensional model of a tapered dental implant with micro-threads in the upper area and V-shaped threads in the rest of the body was modeled and analyzed using finite element analysis (FEA). An axial load of 100 N was applied to the top of the implants. The model was optimized for thread depth and pitch to determine the optimal stress distribution. In this analysis, micro-threads had 0.25 to 0.3 mm depth and 0.27 to 0.33 mm pitch, and V-shaped threads had 0.405 to 0.495 mm depth and 0.66 to 0.8 mm pitch. The optimized depth and pitch were 0.307 and 0.286 mm for micro-threads and 0.405 and 0.808 mm for V-shaped threads, respectively. In this design, the most effective parameters on stress distribution were the depth and pitch of the micro-threads based on sensitivity analysis results. Based on the results of this study, the optimal implant design has micro-threads with 0.307 and 0.286 mm depth and pitch, respectively, in the upper area and V-shaped threads with 0.405 and 0.808 mm depth and pitch in the rest of the body. These results indicate that micro-thread parameters have a greater effect on stress and strain values.

A short-term and high-resolution distribution system load forecasting approach using support vector regression with hybrid parameters optimization

DOE PAGES

Jiang, Huaiguang; Zhang, Yingchen; Muljadi, Eduard; ...

2016-01-01

This paper proposes an approach for distribution system load forecasting, which aims to provide highly accurate short-term load forecasting with high resolution utilizing a support vector regression (SVR) based forecaster and a two-step hybrid parameters optimization method. Specifically, because the load profiles in distribution systems contain abrupt deviations, a data normalization is designed as the pretreatment for the collected historical load data. Then an SVR model is trained by the load data to forecast the future load. For better performance of SVR, a two-step hybrid optimization algorithm is proposed to determine the best parameters. In the first step of themore » hybrid optimization algorithm, a designed grid traverse algorithm (GTA) is used to narrow the parameters searching area from a global to local space. In the second step, based on the result of the GTA, particle swarm optimization (PSO) is used to determine the best parameters in the local parameter space. After the best parameters are determined, the SVR model is used to forecast the short-term load deviation in the distribution system. The performance of the proposed approach is compared to some classic methods in later sections of the paper.« less

Kinetic parameter estimation model for anaerobic co-digestion of waste activated sludge and microalgae.

PubMed

Lee, Eunyoung; Cumberbatch, Jewel; Wang, Meng; Zhang, Qiong

2017-03-01

Anaerobic co-digestion has a potential to improve biogas production, but limited kinetic information is available for co-digestion. This study introduced regression-based models to estimate the kinetic parameters for the co-digestion of microalgae and Waste Activated Sludge (WAS). The models were developed using the ratios of co-substrates and the kinetic parameters for the single substrate as indicators. The models were applied to the modified first-order kinetics and Monod model to determine the rate of hydrolysis and methanogenesis for the co-digestion. The results showed that the model using a hyperbola function was better for the estimation of the first-order kinetic coefficients, while the model using inverse tangent function closely estimated the Monod kinetic parameters. The models can be used for estimating kinetic parameters for not only microalgae-WAS co-digestion but also other substrates' co-digestion such as microalgae-swine manure and WAS-aquatic plants. Copyright © 2016 Elsevier Ltd. All rights reserved.

Engineering Parameters in Bioreactor's Design: A Critical Aspect in Tissue Engineering

PubMed Central

Amoabediny, Ghassem; Pouran, Behdad; Tabesh, Hadi; Shokrgozar, Mohammad Ali; Haghighipour, Nooshin; Khatibi, Nahid; Mottaghy, Khosrow; Zandieh-Doulabi, Behrouz

2013-01-01

Bioreactors are important inevitable part of any tissue engineering (TE) strategy as they aid the construction of three-dimensional functional tissues. Since the ultimate aim of a bioreactor is to create a biological product, the engineering parameters, for example, internal and external mass transfer, fluid velocity, shear stress, electrical current distribution, and so forth, are worth to be thoroughly investigated. The effects of such engineering parameters on biological cultures have been addressed in only a few preceding studies. Furthermore, it would be highly inefficient to determine the optimal engineering parameters by trial and error method. A solution is provided by emerging modeling and computational tools and by analyzing oxygen, carbon dioxide, and nutrient and metabolism waste material transports, which can simulate and predict the experimental results. Discovering the optimal engineering parameters is crucial not only to reduce the cost and time of experiments, but also to enhance efficacy and functionality of the tissue construct. This review intends to provide an inclusive package of the engineering parameters together with their calculation procedure in addition to the modeling techniques in TE bioreactors. PMID:24000327

Engineering parameters in bioreactor's design: a critical aspect in tissue engineering.

PubMed

Salehi-Nik, Nasim; Amoabediny, Ghassem; Pouran, Behdad; Tabesh, Hadi; Shokrgozar, Mohammad Ali; Haghighipour, Nooshin; Khatibi, Nahid; Anisi, Fatemeh; Mottaghy, Khosrow; Zandieh-Doulabi, Behrouz

2013-01-01

Bioreactors are important inevitable part of any tissue engineering (TE) strategy as they aid the construction of three-dimensional functional tissues. Since the ultimate aim of a bioreactor is to create a biological product, the engineering parameters, for example, internal and external mass transfer, fluid velocity, shear stress, electrical current distribution, and so forth, are worth to be thoroughly investigated. The effects of such engineering parameters on biological cultures have been addressed in only a few preceding studies. Furthermore, it would be highly inefficient to determine the optimal engineering parameters by trial and error method. A solution is provided by emerging modeling and computational tools and by analyzing oxygen, carbon dioxide, and nutrient and metabolism waste material transports, which can simulate and predict the experimental results. Discovering the optimal engineering parameters is crucial not only to reduce the cost and time of experiments, but also to enhance efficacy and functionality of the tissue construct. This review intends to provide an inclusive package of the engineering parameters together with their calculation procedure in addition to the modeling techniques in TE bioreactors.

[Simulation of vegetation indices optimizing under retrieval of vegetation biochemical parameters based on PROSPECT + SAIL model].

PubMed

Wu, Ling; Liu, Xiang-Nan; Zhou, Bo-Tian; Liu, Chuan-Hao; Li, Lu-Feng

2012-12-01

This study analyzed the sensitivities of three vegetation biochemical parameters [chlorophyll content (Cab), leaf water content (Cw), and leaf area index (LAI)] to the changes of canopy reflectance, with the effects of each parameter on the wavelength regions of canopy reflectance considered, and selected three vegetation indices as the optimization comparison targets of cost function. Then, the Cab, Cw, and LAI were estimated, based on the particle swarm optimization algorithm and PROSPECT + SAIL model. The results showed that retrieval efficiency with vegetation indices as the optimization comparison targets of cost function was better than that with all spectral reflectance. The correlation coefficients (R2) between the measured and estimated values of Cab, Cw, and LAI were 90.8%, 95.7%, and 99.7%, and the root mean square errors of Cab, Cw, and LAI were 4.73 microg x cm(-2), 0.001 g x cm(-2), and 0.08, respectively. It was suggested that to adopt vegetation indices as the optimization comparison targets of cost function could effectively improve the efficiency and precision of the retrieval of biochemical parameters based on PROSPECT + SAIL model.

Biological optimization of simultaneous boost on intra-prostatic lesions (DILs): sensitivity to TCP parameters.

PubMed

Azzeroni, R; Maggio, A; Fiorino, C; Mangili, P; Cozzarini, C; De Cobelli, F; Di Muzio, N G; Calandrino, R

2013-11-01

The aim of this investigation was to explore the potential of biological optimization in the case of simultaneous integrated boost on intra-prostatic dominant lesions (DIL) and evaluating the impact of TCP parameters uncertainty. Different combination of TCP parameters (TD50 and γ50 in the Poisson-like model), were considered for DILs and the prostate outside DILs (CTV) for 7 intermediate/high-risk prostate patients. The aim was to maximize TCP while constraining NTCPs below 5% for all organs at risk. TCP values were highly depending on the parameters used and ranged between 38.4% and 99.9%; the optimized median physical doses were in the range 94-116 Gy and 69-77 Gy for DIL and CTV respectively. TCP values were correlated with the overlap PTV-rectum and the minimum distance between rectum and DIL. In conclusion, biological optimization for selective dose escalation is feasible and suggests prescribed dose around 90-120 Gy to the DILs. The obtained result is critically depending on the assumptions concerning the higher radioresistence in the DILs. In case of very resistant clonogens into the DIL, it may be difficult to maximize TCP to acceptable levels without violating NTCP constraints. Copyright © 2012 Associazione Italiana di Fisica Medica. Published by Elsevier Ltd. All rights reserved.

Optimization of Equation of State and Burn Model Parameters for Explosives

NASA Astrophysics Data System (ADS)

Bergh, Magnus; Wedberg, Rasmus; Lundgren, Jonas

2017-06-01

A reactive burn model implemented in a multi-dimensional hydrocode can be a powerful tool for predicting non-ideal effects as well as initiation phenomena in explosives. Calibration against experiment is, however, critical and non-trivial. Here, a procedure is presented for calibrating the Ignition and Growth Model utilizing hydrocode simulation in conjunction with the optimization program LS-OPT. The model is applied to the explosive PBXN-109. First, a cylinder expansion test is presented together with a new automatic routine for product equation of state calibration. Secondly, rate stick tests and instrumented gap tests are presented. Data from these experiments are used to calibrate burn model parameters. Finally, we discuss the applicability and development of this optimization routine.

[Temporal and spatial heterogeneity analysis of optimal value of sensitive parameters in ecological process model: The BIOME-BGC model as an example.

PubMed

Li, Yi Zhe; Zhang, Ting Long; Liu, Qiu Yu; Li, Ying

2018-01-01

The ecological process models are powerful tools for studying terrestrial ecosystem water and carbon cycle at present. However, there are many parameters for these models, and weather the reasonable values of these parameters were taken, have important impact on the models simulation results. In the past, the sensitivity and the optimization of model parameters were analyzed and discussed in many researches. But the temporal and spatial heterogeneity of the optimal parameters is less concerned. In this paper, the BIOME-BGC model was used as an example. In the evergreen broad-leaved forest, deciduous broad-leaved forest and C3 grassland, the sensitive parameters of the model were selected by constructing the sensitivity judgment index with two experimental sites selected under each vegetation type. The objective function was constructed by using the simulated annealing algorithm combined with the flux data to obtain the monthly optimal values of the sensitive parameters at each site. Then we constructed the temporal heterogeneity judgment index, the spatial heterogeneity judgment index and the temporal and spatial heterogeneity judgment index to quantitatively analyze the temporal and spatial heterogeneity of the optimal values of the model sensitive parameters. The results showed that the sensitivity of BIOME-BGC model parameters was different under different vegetation types, but the selected sensitive parameters were mostly consistent. The optimal values of the sensitive parameters of BIOME-BGC model mostly presented time-space heterogeneity to different degrees which varied with vegetation types. The sensitive parameters related to vegetation physiology and ecology had relatively little temporal and spatial heterogeneity while those related to environment and phenology had generally larger temporal and spatial heterogeneity. In addition, the temporal heterogeneity of the optimal values of the model sensitive parameters showed a significant linear correlation

Parameters optimization of laser brazing in crimping butt using Taguchi and BPNN-GA

NASA Astrophysics Data System (ADS)

Rong, Youmin; Zhang, Zhen; Zhang, Guojun; Yue, Chen; Gu, Yafei; Huang, Yu; Wang, Chunming; Shao, Xinyu

2015-04-01

The laser brazing (LB) is widely used in the automotive industry due to the advantages of high speed, small heat affected zone, high quality of welding seam, and low heat input. Welding parameters play a significant role in determining the bead geometry and hence quality of the weld joint. This paper addresses the optimization of the seam shape in LB process with welding crimping butt of 0.8 mm thickness using back propagation neural network (BPNN) and genetic algorithm (GA). A 3-factor, 5-level welding experiment is conducted by Taguchi L25 orthogonal array through the statistical design method. Then, the input parameters are considered here including welding speed, wire speed rate, and gap with 5 levels. The output results are efficient connection length of left side and right side, top width (WT) and bottom width (WB) of the weld bead. The experiment results are embed into the BPNN network to establish relationship between the input and output variables. The predicted results of the BPNN are fed to GA algorithm that optimizes the process parameters subjected to the objectives. Then, the effects of welding speed (WS), wire feed rate (WF), and gap (GAP) on the sum values of bead geometry is discussed. Eventually, the confirmation experiments are carried out to demonstrate the optimal values were effective and reliable. On the whole, the proposed hybrid method, BPNN-GA, can be used to guide the actual work and improve the efficiency and stability of LB process.

Performance optimization and validation of ADM1 simulations under anaerobic thermophilic conditions.

PubMed

Atallah, Nabil M; El-Fadel, Mutasem; Ghanimeh, Sophia; Saikaly, Pascal; Abou-Najm, Majdi

2014-12-01

In this study, two experimental sets of data each involving two thermophilic anaerobic digesters treating food waste, were simulated using the Anaerobic Digestion Model No. 1 (ADM1). A sensitivity analysis was conducted, using both data sets of one digester, for parameter optimization based on five measured performance indicators: methane generation, pH, acetate, total COD, ammonia, and an equally weighted combination of the five indicators. The simulation results revealed that while optimization with respect to methane alone, a commonly adopted approach, succeeded in simulating methane experimental results, it predicted other intermediary outputs less accurately. On the other hand, the multi-objective optimization has the advantage of providing better results than methane optimization despite not capturing the intermediary output. The results from the parameter optimization were validated upon their independent application on the data sets of the second digester. Copyright © 2014 Elsevier Ltd. All rights reserved.

Effects of operational parameters on dark fermentative hydrogen production from biodegradable complex waste biomass.

PubMed

Ghimire, Anish; Sposito, Fabio; Frunzo, Luigi; Trably, Eric; Escudié, Renaud; Pirozzi, Francesco; Lens, Piet N L; Esposito, Giovanni

2016-04-01

This work aimed to investigate the effect of the initial pH, combination of food to microorganism ratio (F/M) and initial pH, substrate pre-treatment and different inoculum sources on the dark fermentative biohydrogen (H2) yields. Three model complex waste biomasses (food waste, olive mill wastewater (OMWW) and rice straw) were used to assess the effect of the aforementioned parameters. The effect of the initial pH between 4.5 and 7.0 was investigated in batch tests carried out with food waste. The highest H2 yields were shown at initial pH 4.5 (60.6 ± 9.0 mL H2/g VS) and pH 5.0 (50.7 ± 0.8 mL H2/g VS). Furthermore, tests carried out with F/M ratios of 0.5, 1.0 and 1.5 at initial pH 5.0 and 6.5 revealed that a lower F/M ratio (0.5 and 1.0) favored the H2 production at an initial pH 5.0 compared to pH 6.5. Alkaline pre-treatment of raw rice straw using 4% and 8% NaOH at 55°C for 24h, increased the H2 yield by 26 and 57-fold, respectively. In the dark fermentation of OMWW, the H2 yield was doubled when heat-shock pre-treated activated sludge was used as inoculum in comparison to anaerobic sludge. Overall, this study shows that the application of different operating parameters to maximize the H2 yields strongly depends on the biodegradability of the substrate. Copyright © 2016 Elsevier Ltd. All rights reserved.

Optimization of digital breast tomosynthesis (DBT) acquisition parameters for human observers: effect of reconstruction algorithms

NASA Astrophysics Data System (ADS)

Zeng, Rongping; Badano, Aldo; Myers, Kyle J.

2017-04-01

We showed in our earlier work that the choice of reconstruction methods does not affect the optimization of DBT acquisition parameters (angular span and number of views) using simulated breast phantom images in detecting lesions with a channelized Hotelling observer (CHO). In this work we investigate whether the model-observer based conclusion is valid when using humans to interpret images. We used previously generated DBT breast phantom images and recruited human readers to find the optimal geometry settings associated with two reconstruction algorithms, filtered back projection (FBP) and simultaneous algebraic reconstruction technique (SART). The human reader results show that image quality trends as a function of the acquisition parameters are consistent between FBP and SART reconstructions. The consistent trends confirm that the optimization of DBT system geometry is insensitive to the choice of reconstruction algorithm. The results also show that humans perform better in SART reconstructed images than in FBP reconstructed images. In addition, we applied CHOs with three commonly used channel models, Laguerre-Gauss (LG) channels, square (SQR) channels and sparse difference-of-Gaussian (sDOG) channels. We found that LG channels predict human performance trends better than SQR and sDOG channel models for the task of detecting lesions in tomosynthesis backgrounds. Overall, this work confirms that the choice of reconstruction algorithm is not critical for optimizing DBT system acquisition parameters.

Optimization of operating parameters in polysilicon chemical vapor deposition reactor with response surface methodology

NASA Astrophysics Data System (ADS)

An, Li-sha; Liu, Chun-jiao; Liu, Ying-wen

2018-05-01

In the polysilicon chemical vapor deposition reactor, the operating parameters are complex to affect the polysilicon's output. Therefore, it is very important to address the coupling problem of multiple parameters and solve the optimization in a computationally efficient manner. Here, we adopted Response Surface Methodology (RSM) to analyze the complex coupling effects of different operating parameters on silicon deposition rate (R) and further achieve effective optimization of the silicon CVD system. Based on finite numerical experiments, an accurate RSM regression model is obtained and applied to predict the R with different operating parameters, including temperature (T), pressure (P), inlet velocity (V), and inlet mole fraction of H2 (M). The analysis of variance is conducted to describe the rationality of regression model and examine the statistical significance of each factor. Consequently, the optimum combination of operating parameters for the silicon CVD reactor is: T = 1400 K, P = 3.82 atm, V = 3.41 m/s, M = 0.91. The validation tests and optimum solution show that the results are in good agreement with those from CFD model and the deviations of the predicted values are less than 4.19%. This work provides a theoretical guidance to operate the polysilicon CVD process.

Optimization of process parameters in CNC turning of aluminium alloy using hybrid RSM cum TLBO approach

NASA Astrophysics Data System (ADS)

Rudrapati, R.; Sahoo, P.; Bandyopadhyay, A.

2016-09-01

The main aim of the present work is to analyse the significance of turning parameters on surface roughness in computer numerically controlled (CNC) turning operation while machining of aluminium alloy material. Spindle speed, feed rate and depth of cut have been considered as machining parameters. Experimental runs have been conducted as per Box-Behnken design method. After experimentation, surface roughness is measured by using stylus profile meter. Factor effects have been studied through analysis of variance. Mathematical modelling has been done by response surface methodology, to made relationships between the input parameters and output response. Finally, process optimization has been made by teaching learning based optimization (TLBO) algorithm. Predicted turning condition has been validated through confirmatory experiment.

Robust optimization on sustainable biodiesel supply chain produced from waste cooking oil under price uncertainty.

PubMed

Zhang, Yong; Jiang, Yunjian

2017-02-01

Waste cooking oil (WCO)-for-biodiesel conversion is regarded as the "waste-to-wealthy" industry. This paper addresses the design of a WCO-for-biodiesel supply chain at both strategic and tactical levels. The supply chain of this problem is studied, which is based on a typical mode of the waste collection (from restaurants' kitchen) and conversion in the cities. The supply chain comprises three stakeholders: WCO supplier, integrated bio-refinery and demand zone. Three key problems should be addressed for the optimal design of the supply chain: (1) the number, sizes and locations of bio-refinery; (2) the sites and amount of WCO collected; (3) the transportation plans of WCO and biodiesel. A robust mixed integer linear model with muti-objective (economic, environmental and social objectives) is proposed for these problems. Finally, a large-scale practical case study is adopted based on Suzhou, a city in the east of China, to verify the proposed models. Copyright © 2016 Elsevier Ltd. All rights reserved.

Experimental design approach to the process parameter optimization for laser welding of martensitic stainless steels in a constrained overlap configuration

NASA Astrophysics Data System (ADS)

Khan, M. M. A.; Romoli, L.; Fiaschi, M.; Dini, G.; Sarri, F.

2011-02-01

This paper presents an experimental design approach to process parameter optimization for the laser welding of martensitic AISI 416 and AISI 440FSe stainless steels in a constrained overlap configuration in which outer shell was 0.55 mm thick. To determine the optimal laser-welding parameters, a set of mathematical models were developed relating welding parameters to each of the weld characteristics. These were validated both statistically and experimentally. The quality criteria set for the weld to determine optimal parameters were the minimization of weld width and the maximization of weld penetration depth, resistance length and shearing force. Laser power and welding speed in the range 855-930 W and 4.50-4.65 m/min, respectively, with a fiber diameter of 300 μm were identified as the optimal set of process parameters. However, the laser power and welding speed can be reduced to 800-840 W and increased to 4.75-5.37 m/min, respectively, to obtain stronger and better welds.

Analysis and optimization of machining parameters of laser cutting for polypropylene composite

NASA Astrophysics Data System (ADS)

Deepa, A.; Padmanabhan, K.; Kuppan, P.

2017-11-01

Present works explains about machining of self-reinforced Polypropylene composite fabricated using hot compaction method. The objective of the experiment is to find optimum machining parameters for Polypropylene (PP). Laser power and Machining speed were the parameters considered in response to tensile test and Flexure test. Taguchi method is used for experimentation. Grey Relational Analysis (GRA) is used for multiple process parameter optimization. ANOVA (Analysis of Variance) is used to find impact for process parameter. Polypropylene has got the great application in various fields like, it is used in the form of foam in model aircraft and other radio-controlled vehicles, thin sheets (∼2-20μm) used as a dielectric, PP is also used in piping system, it is also been used in hernia and pelvic organ repair or protect new herrnis in the same location.

Improving the surface properties of municipal solid waste-derived pyrolysis biochar by chemical and thermal activation: Optimization of process parameters and environmental application.

PubMed

Genuino, Divine Angela D; de Luna, Mark Daniel G; Capareda, Sergio C

2018-02-01

Biochar produced from the slow pyrolysis of municipal solid waste was activated with KOH and thermal treatments to enhance its surface and adsorptive properties. The effects of KOH concentration, activation temperature and time on the specific surface area (SSA) of the activated biochar were evaluated and optimized using central composite design (CCD) of the response surface methodology (RSM). Results showed that the activation of biochar enhanced its SSA from 402.8 ± 12.5 to 662.4 ± 28.6 m 2  g -1 . The adsorptive capacities of the pristine biochar (PBC) and activated biochar (ABC) were compared using methylene blue (MB) dye as model compound. For MB concentrations up to 25 mg L -1 , more than 99% dye removal was achieved with ABC, while only a maximum of 51% was obtained with PBC. Results of the isotherm study showed that the Langmuir model best described MB adsorption on ABC with adsorption capacity of 37.0-41.2 mg g -1 . Copyright © 2017 Elsevier Ltd. All rights reserved.

Space shuttle propulsion parameter estimation using optimal estimation techniques

NASA Technical Reports Server (NTRS)

1983-01-01

The first twelve system state variables are presented with the necessary mathematical developments for incorporating them into the filter/smoother algorithm. Other state variables, i.e., aerodynamic coefficients can be easily incorporated into the estimation algorithm, representing uncertain parameters, but for initial checkout purposes are treated as known quantities. An approach for incorporating the NASA propulsion predictive model results into the optimal estimation algorithm was identified. This approach utilizes numerical derivatives and nominal predictions within the algorithm with global iterations of the algorithm. The iterative process is terminated when the quality of the estimates provided no longer significantly improves.

Optimal parameters for arterial repair using light-activated surgical adhesives.

PubMed

Soller, Eric C; Hoffman, Grant T; McNally-Heintzelman, Karen M

2003-01-01

The clinical acceptance of laser-tissue repair techniques is dependent on the reproducibility of viable repairs. Reproducibility is dependent on two factors: (i) the choice of materials to be used as the adhesive; and (ii) obtaining temperatures high enough to cause protein denaturation at the vital tissue interface without causing excessive thermal damage to the surrounding tissue. The use of a polymer scaffold as a carrier for the protein solder provides for uniform application of the solder to the tissue, thus allowing for pre-selection of optimal laser parameters. The scaffold also facilitates precise tissue alignment and ease of clinical application. In addition, the scaffold can be doped with various pharmaceuticals such as hemostatic and thrombogenic agents to aid wound healing. An ex vivo study was performed to correlate solder and tissue temperature with the tensile strength of arterial repairs formed using scaffold-enhanced light-activated surgical adhesives. Previous studies by our group using solid protein solder without the scaffold indicate that a solder/tissue, interface temperature of 65 degrees C is optimal. Using this parameter as a benchmark, laser irradiance was varied and temperatures were recorded at the surface and at the tissue interface of scaffold-enhanced protein solder using an infrared temperature monitoring system, designed by the researchers, and a type-K thermocouple, respectively.

Optimization of kinetic parameters for the degradation of plasmid DNA in rat plasma

NASA Astrophysics Data System (ADS)

Chaudhry, Q. A.

2014-12-01

Biotechnology is a rapidly growing area of research work in the field of pharmaceutical sciences. The study of pharmacokinetics of plasmid DNA (pDNA) is an important area of research work. It has been observed that the process of gene delivery faces many troubles on the transport of pDNA towards their target sites. The topoforms of pDNA has been termed as super coiled (S-C), open circular (O-C) and linear (L), the kinetic model of which will be presented in this paper. The kinetic model gives rise to system of ordinary differential equations (ODEs), the exact solution of which has been found. The kinetic parameters, which are responsible for the degradation of super coiled, and the formation of open circular and linear topoforms have a great significance not only in vitro but for modeling of further processes as well, therefore need to be addressed in great detail. For this purpose, global optimization techniques have been adopted, thus finding the optimal results for the said model. The results of the model, while using the optimal parameters, were compared against the measured data, which gives a nice agreement.

Forecasting the Amount of Waste-Sewage Water Discharged into the Yangtze River Basin Based on the Optimal Fractional Order Grey Model

PubMed Central

Li, Shuliang; Meng, Wei; Xie, Yufeng

2017-01-01

With the rapid development of the Yangtze River economic belt, the amount of waste-sewage water discharged into the Yangtze River basin increases sharply year by year, which has impeded the sustainable development of the Yangtze River basin. The water security along the Yangtze River basin is very important for China, It is something about water security of roughly one-third of China’s population and the sustainable development of the 19 provinces, municipalities and autonomous regions among the Yangtze River basin. Therefore, a scientific prediction of the amount of waste-sewage water discharged into Yangtze River basin has a positive significance on sustainable development of industry belt along with Yangtze River basin. This paper builds the fractional DWSGM (1,1) (DWSGM (1,1) model is short for Discharge amount of Waste Sewage Grey Model for one order equation and one variable) model based on the fractional accumulating generation operator and fractional reducing operator, and calculates the optimal order of “r” by using particle swarm optimization (PSO) algorithm for solving the minimum average relative simulation error. Meanwhile, the simulation performance of DWSGM (1,1) model with the optimal fractional order is tested by comparing the simulation results of grey prediction models with different orders. Finally, the optimal fractional order DWSGM (1,1) grey model is applied to predict the amount of waste-sewage water discharged into the Yangtze River basin, and corresponding countermeasures and suggestions are put forward through analyzing and comparing the prediction results. This paper has positive significance on enriching the fractional order modeling method of the grey system. PMID:29295517

Rapid optimization of MRM-MS instrument parameters by subtle alteration of precursor and product m/z targets.

PubMed

Sherwood, Carly A; Eastham, Ashley; Lee, Lik Wee; Risler, Jenni; Mirzaei, Hamid; Falkner, Jayson A; Martin, Daniel B

2009-07-01

Multiple reaction monitoring (MRM) is a highly sensitive method of targeted mass spectrometry (MS) that can be used to selectively detect and quantify peptides based on the screening of specified precursor peptide-to-fragment ion transitions. MRM-MS sensitivity depends critically on the tuning of instrument parameters, such as collision energy and cone voltage, for the generation of maximal product ion signal. Although generalized equations and values exist for such instrument parameters, there is no clear indication that optimal signal can be reliably produced for all types of MRM transitions using such an algorithmic approach. To address this issue, we have devised a workflow functional on both Waters Quattro Premier and ABI 4000 QTRAP triple quadrupole instruments that allows rapid determination of the optimal value of any programmable instrument parameter for each MRM transition. Here, we demonstrate the strategy for the optimizations of collision energy and cone voltage, but the method could be applied to other instrument parameters, such as declustering potential, as well. The workflow makes use of the incremental adjustment of the precursor and product m/z values at the hundredth decimal place to create a series of MRM targets at different collision energies that can be cycled through in rapid succession within a single run, avoiding any run-to-run variability in execution or comparison. Results are easily visualized and quantified using the MRM software package Mr. M to determine the optimal instrument parameters for each transition.

Rapid Optimization of MRM-MS Instrument Parameters by Subtle Alteration of Precursor and Product m/z Targets

PubMed Central

Sherwood, Carly A.; Eastham, Ashley; Lee, Lik Wee; Risler, Jenni; Mirzaei, Hamid; Falkner, Jayson A.; Martin, Daniel B.

2009-01-01

Multiple reaction monitoring (MRM) is a highly sensitive method of targeted mass spectrometry (MS) that can be used to selectively detect and quantify peptides based on the screening of specified precursor peptide-to-fragment ion transitions. MRM-MS sensitivity depends critically on the tuning of instrument parameters, such as collision energy and cone voltage, for the generation of maximal product ion signal. Although generalized equations and values exist for such instrument parameters, there is no clear indication that optimal signal can be reliably produced for all types of MRM transitions using such an algorithmic approach. To address this issue, we have devised a workflow functional on both Waters Quattro Premier and ABI 4000 QTRAP triple quadrupole instruments that allows rapid determination of the optimal value of any programmable instrument parameter for each MRM transition. Here, we demonstrate the strategy for the optimizations of collision energy and cone voltage, but the method could be applied to other instrument parameters, such as declustering potential, as well. The workflow makes use of the incremental adjustment of the precursor and product m/z values at the hundredth decimal place to create a series of MRM targets at different collision energies that can be cycled through in rapid succession within a single run, avoiding any run-to-run variability in execution or comparison. Results are easily visualized and quantified using the MRM software package Mr. M to determine the optimal instrument parameters for each transition. PMID:19405522

Optimizing Methods of Obtaining Stellar Parameters for the H3 Survey

NASA Astrophysics Data System (ADS)

Ivory, KeShawn; Conroy, Charlie; Cargile, Phillip

2018-01-01

The Stellar Halo at High Resolution with Hectochelle Survey (H3) is in the process of observing and collecting stellar parameters for stars in the Milky Way's halo. With a goal of measuring radial velocities for fainter stars, it is crucial that we have optimal methods of obtaining this and other parameters from the data from these stars.The method currently developed is The Payne, named after Cecilia Payne-Gaposchkin, a code that uses neural networks and Markov Chain Monte Carlo methods to utilize both spectra and photometry to obtain values for stellar parameters. This project was to investigate the benefit of fitting both spectra and spectral energy distributions (SED). Mock spectra using the parameters of the Sun were created and noise was inserted at various signal to noise values. The Payne then fit each mock spectrum with and without a mock SED also generated from solar parameters. The result was that at high signal to noise, the spectrum dominated and the effect of fitting the SED was minimal. But at low signal to noise, the addition of the SED greatly decreased the standard deviation of the data and resulted in more accurate values for temperature and metallicity.

Han's model parameters for microalgae grown under intermittent illumination: Determined using particle swarm optimization.

PubMed

Pozzobon, Victor; Perre, Patrick

2018-01-21

This work provides a model and the associated set of parameters allowing for microalgae population growth computation under intermittent lightning. Han's model is coupled with a simple microalgae growth model to yield a relationship between illumination and population growth. The model parameters were obtained by fitting a dataset available in literature using Particle Swarm Optimization method. In their work, authors grew microalgae in excess of nutrients under flashing conditions. Light/dark cycles used for these experimentations are quite close to those found in photobioreactor, i.e. ranging from several seconds to one minute. In this work, in addition to producing the set of parameters, Particle Swarm Optimization robustness was assessed. To do so, two different swarm initialization techniques were used, i.e. uniform and random distribution throughout the search-space. Both yielded the same results. In addition, swarm distribution analysis reveals that the swarm converges to a unique minimum. Thus, the produced set of parameters can be trustfully used to link light intensity to population growth rate. Furthermore, the set is capable to describe photodamages effects on population growth. Hence, accounting for light overexposure effect on algal growth. Copyright © 2017 Elsevier Ltd. All rights reserved.

Optimizing the Determination of Roughness Parameters for Model Urban Canopies

NASA Astrophysics Data System (ADS)

Huq, Pablo; Rahman, Auvi

2018-05-01

We present an objective optimization procedure to determine the roughness parameters for very rough boundary-layer flow over model urban canopies. For neutral stratification the mean velocity profile above a model urban canopy is described by the logarithmic law together with the set of roughness parameters of displacement height d, roughness length z_0 , and friction velocity u_* . Traditionally, values of these roughness parameters are obtained by fitting the logarithmic law through (all) the data points comprising the velocity profile. The new procedure generates unique velocity profiles from subsets or combinations of the data points of the original velocity profile, after which all possible profiles are examined. Each of the generated profiles is fitted to the logarithmic law for a sequence of values of d, with the representative value of d obtained from the minima of the summed least-squares errors for all the generated profiles. The representative values of z_0 and u_* are identified by the peak in the bivariate histogram of z_0 and u_* . The methodology has been verified against laboratory datasets of flow above model urban canopies.

Selection of optimal multispectral imaging system parameters for small joint arthritis detection

NASA Astrophysics Data System (ADS)

Dolenec, Rok; Laistler, Elmar; Stergar, Jost; Milanic, Matija

2018-02-01

Early detection and treatment of arthritis is essential for a successful outcome of the treatment, but it has proven to be very challenging with existing diagnostic methods. Novel methods based on the optical imaging of the affected joints are becoming an attractive alternative. A non-contact multispectral imaging (MSI) system for imaging of small joints of human hands and feet is being developed. In this work, a numerical simulation of the MSI system is presented. The purpose of the simulation is to determine the optimal design parameters. Inflamed and unaffected human joint models were constructed with a realistic geometry and tissue distributions, based on a MRI scan of a human finger with a spatial resolution of 0.2 mm. The light transport simulation is based on a weighted-photon 3D Monte Carlo method utilizing CUDA GPU acceleration. An uniform illumination of the finger within the 400-1100 nm spectral range was simulated and the photons exiting the joint were recorded using different acceptance angles. From the obtained reflectance and transmittance images the spectral and spatial features most indicative of inflammation were identified. Optimal acceptance angle and spectral bands were determined. This study demonstrates that proper selection of MSI system parameters critically affects ability of a MSI system to discriminate the unaffected and inflamed joints. The presented system design optimization approach could be applied to other pathologies.

A Minimum Delta V Orbit Maintenance Strategy for Low-Altitude Missions Using Burn Parameter Optimization

NASA Technical Reports Server (NTRS)

Brown, Aaron J.

2011-01-01

Orbit maintenance is the series of burns performed during a mission to ensure the orbit satisfies mission constraints. Low-altitude missions often require non-trivial orbit maintenance Delta V due to sizable orbital perturbations and minimum altitude thresholds. A strategy is presented for minimizing this Delta V using impulsive burn parameter optimization. An initial estimate for the burn parameters is generated by considering a feasible solution to the orbit maintenance problem. An low-lunar orbit example demonstrates the Delta V savings from the feasible solution to the optimal solution. The strategy s extensibility to more complex missions is discussed, as well as the limitations of its use.

Artificial Intelligence Based Selection of Optimal Cutting Tool and Process Parameters for Effective Turning and Milling Operations

NASA Astrophysics Data System (ADS)

Saranya, Kunaparaju; John Rozario Jegaraj, J.; Ramesh Kumar, Katta; Venkateshwara Rao, Ghanta

2016-06-01

With the increased trend in automation of modern manufacturing industry, the human intervention in routine, repetitive and data specific activities of manufacturing is greatly reduced. In this paper, an attempt has been made to reduce the human intervention in selection of optimal cutting tool and process parameters for metal cutting applications, using Artificial Intelligence techniques. Generally, the selection of appropriate cutting tool and parameters in metal cutting is carried out by experienced technician/cutting tool expert based on his knowledge base or extensive search from huge cutting tool database. The present proposed approach replaces the existing practice of physical search for tools from the databooks/tool catalogues with intelligent knowledge-based selection system. This system employs artificial intelligence based techniques such as artificial neural networks, fuzzy logic and genetic algorithm for decision making and optimization. This intelligence based optimal tool selection strategy is developed using Mathworks Matlab Version 7.11.0 and implemented. The cutting tool database was obtained from the tool catalogues of different tool manufacturers. This paper discusses in detail, the methodology and strategies employed for selection of appropriate cutting tool and optimization of process parameters based on multi-objective optimization criteria considering material removal rate, tool life and tool cost.

Acoustical characterization and parameter optimization of polymeric noise control materials

NASA Astrophysics Data System (ADS)

Homsi, Emile N.

2003-10-01

The sound transmission loss (STL) characteristics of polymer-based materials are considered. Analytical models that predict, characterize and optimize the STL of polymeric materials, with respect to physical parameters that affect performance, are developed for single layer panel configuration and adapted for layered panel construction with homogenous core. An optimum set of material parameters is selected and translated into practical applications for validation. Sound attenuating thermoplastic materials designed to be used as barrier systems in the automotive and consumer industries have certain acoustical characteristics that vary in function of the stiffness and density of the selected material. The validity and applicability of existing theory is explored, and since STL is influenced by factors such as the surface mass density of the panel's material, a method is modified to improve STL performance and optimize load-bearing attributes. An experimentally derived function is applied to the model for better correlation. In-phase and out-of-phase motion of top and bottom layers are considered. It was found that the layered construction of the co-injection type would exhibit fused planes at the interface and move in-phase. The model for the single layer case is adapted to the layered case where it would behave as a single panel. Primary physical parameters that affect STL are identified and manipulated. Theoretical analysis is linked to the resin's matrix attribute. High STL material with representative characteristics is evaluated versus standard resins. It was found that high STL could be achieved by altering materials' matrix and by integrating design solution in the low frequency range. A suggested numerical approach is described for STL evaluation of simple and complex geometries. In practice, validation on actual vehicle systems proved the adequacy of the acoustical characterization process.

Application of Modified Particle Swarm Optimization Method for Parameter Extraction of 2-D TEC Mapping

NASA Astrophysics Data System (ADS)

Toker, C.; Gokdag, Y. E.; Arikan, F.; Arikan, O.

2012-04-01

Ionosphere is a very important part of Space Weather. Modeling and monitoring of ionospheric variability is a major part of satellite communication, navigation and positioning systems. Total Electron Content (TEC), which is defined as the line integral of the electron density along a ray path, is one of the parameters to investigate the ionospheric variability. Dual-frequency GPS receivers, with their world wide availability and efficiency in TEC estimation, have become a major source of global and regional TEC modeling. When Global Ionospheric Maps (GIM) of International GPS Service (IGS) centers (http://iono.jpl.nasa.gov/gim.html) are investigated, it can be observed that regional ionosphere along the midlatitude regions can be modeled as a constant, linear or a quadratic surface. Globally, especially around the magnetic equator, the TEC surfaces resemble twisted and dispersed single centered or double centered Gaussian functions. Particle Swarm Optimization (PSO) proved itself as a fast converging and an effective optimization tool in various diverse fields. Yet, in order to apply this optimization technique into TEC modeling, the method has to be modified for higher efficiency and accuracy in extraction of geophysical parameters such as model parameters of TEC surfaces. In this study, a modified PSO (mPSO) method is applied to regional and global synthetic TEC surfaces. The synthetic surfaces that represent the trend and small scale variability of various ionospheric states are necessary to compare the performance of mPSO over number of iterations, accuracy in parameter estimation and overall surface reconstruction. The Cramer-Rao bounds for each surface type and model are also investigated and performance of mPSO are tested with respect to these bounds. For global models, the sample points that are used in optimization are obtained using IGS receiver network. For regional TEC models, regional networks such as Turkish National Permanent GPS Network (TNPGN

Multiobjective optimization in structural design with uncertain parameters and stochastic processes

NASA Technical Reports Server (NTRS)

Rao, S. S.

1984-01-01

The application of multiobjective optimization techniques to structural design problems involving uncertain parameters and random processes is studied. The design of a cantilever beam with a tip mass subjected to a stochastic base excitation is considered for illustration. Several of the problem parameters are assumed to be random variables and the structural mass, fatigue damage, and negative of natural frequency of vibration are considered for minimization. The solution of this three-criteria design problem is found by using global criterion, utility function, game theory, goal programming, goal attainment, bounded objective function, and lexicographic methods. It is observed that the game theory approach is superior in finding a better optimum solution, assuming the proper balance of the various objective functions. The procedures used in the present investigation are expected to be useful in the design of general dynamic systems involving uncertain parameters, stochastic process, and multiple objectives.

Process Parameter Optimization for Wobbling Laser Spot Welding of Ti6Al4V Alloy

NASA Astrophysics Data System (ADS)

Vakili-Farahani, F.; Lungershausen, J.; Wasmer, K.

Laser beam welding (LBW) coupled with "wobble effect" (fast oscillation of the laser beam) is very promising for high precision micro-joining industry. For this process, similarly to the conventional LBW, the laser welding process parameters play a very significant role in determining the quality of a weld joint. Consequently, four process parameters (laser power, wobble frequency, number of rotations within a single laser pulse and focused position) and 5 responses (penetration, width, heat affected zone (HAZ), area of the fusion zone, area of HAZ and hardness) were investigated for spot welding of Ti6Al4V alloy (grade 5) using a design of experiments (DoE) approach. This paper presents experimental results showing the effects of variating the considered most important process parameters on the spot weld quality of Ti6Al4V alloy. Semi-empirical mathematical models were developed to correlate laser welding parameters to each of the measured weld responses. Adequacies of the models were then examined by various methods such as ANOVA. These models not only allows a better understanding of the wobble laser welding process and predict the process performance but also determines optimal process parameters. Therefore, optimal combination of process parameters was determined considering certain quality criteria set.

Optimization of ecosystem model parameters with different temporal variabilities using tower flux data and an ensemble Kalman filter

NASA Astrophysics Data System (ADS)

He, L.; Chen, J. M.; Liu, J.; Mo, G.; Zhen, T.; Chen, B.; Wang, R.; Arain, M.

2013-12-01

Terrestrial ecosystem models have been widely used to simulate carbon, water and energy fluxes and climate-ecosystem interactions. In these models, some vegetation and soil parameters are determined based on limited studies from literatures without consideration of their seasonal variations. Data assimilation (DA) provides an effective way to optimize these parameters at different time scales . In this study, an ensemble Kalman filter (EnKF) is developed and applied to optimize two key parameters of an ecosystem model, namely the Boreal Ecosystem Productivity Simulator (BEPS): (1) the maximum photosynthetic carboxylation rate (Vcmax) at 25 °C, and (2) the soil water stress factor (fw) for stomatal conductance formulation. These parameters are optimized through assimilating observations of gross primary productivity (GPP) and latent heat (LE) fluxes measured in a 74 year-old pine forest, which is part of the Turkey Point Flux Station's age-sequence sites. Vcmax is related to leaf nitrogen concentration and varies slowly over the season and from year to year. In contrast, fw varies rapidly in response to soil moisture dynamics in the root-zone. Earlier studies suggested that DA of vegetation parameters at daily time steps leads to Vcmax values that are unrealistic. To overcome the problem, we developed a three-step scheme to optimize Vcmax and fw. First, the EnKF is applied daily to obtain precursor estimates of Vcmax and fw. Then Vcmax is optimized at different time scales assuming fw is unchanged from first step. The best temporal period or window size is then determined by analyzing the magnitude of the minimized cost-function, and the coefficient of determination (R2) and Root-mean-square deviation (RMSE) of GPP and LE between simulation and observation. Finally, the daily fw value is optimized for rain free days corresponding to the Vcmax curve from the best window size. The optimized fw is then used to model its relationship with soil moisture. We found that

Optimal design of monitoring networks for multiple groundwater quality parameters using a Kalman filter: application to the Irapuato-Valle aquifer.

PubMed

Júnez-Ferreira, H E; Herrera, G S; González-Hita, L; Cardona, A; Mora-Rodríguez, J

2016-01-01

A new method for the optimal design of groundwater quality monitoring networks is introduced in this paper. Various indicator parameters were considered simultaneously and tested for the Irapuato-Valle aquifer in Mexico. The steps followed in the design were (1) establishment of the monitoring network objectives, (2) definition of a groundwater quality conceptual model for the study area, (3) selection of the parameters to be sampled, and (4) selection of a monitoring network by choosing the well positions that minimize the estimate error variance of the selected indicator parameters. Equal weight for each parameter was given to most of the aquifer positions and a higher weight to priority zones. The objective for the monitoring network in the specific application was to obtain a general reconnaissance of the water quality, including water types, water origin, and first indications of contamination. Water quality indicator parameters were chosen in accordance with this objective, and for the selection of the optimal monitoring sites, it was sought to obtain a low-uncertainty estimate of these parameters for the entire aquifer and with more certainty in priority zones. The optimal monitoring network was selected using a combination of geostatistical methods, a Kalman filter and a heuristic optimization method. Results show that when monitoring the 69 locations with higher priority order (the optimal monitoring network), the joint average standard error in the study area for all the groundwater quality parameters was approximately 90 % of the obtained with the 140 available sampling locations (the set of pilot wells). This demonstrates that an optimal design can help to reduce monitoring costs, by avoiding redundancy in data acquisition.

Enzymatic conversion of waste cooking oils into alternative fuel--biodiesel.

PubMed

Chen, Guanyi; Ying, Ming; Li, Weizhun

2006-01-01

Production of biodiesel from pure oils through chemical conversion may not be applicable to waste oils/fats. Therefore, enzymatic conversion using immobilized lipase based on Rhizopus orzyae is considered in this article. This article studies this technological process, focusing on optimization of several process parameters, including the molar ratio of methanol to waste oils, biocatalyst load, and adding method, reaction temperature, and water content. The results indicate that methanol/oils ratio of 4, immobilized lipase/oils of 30 wt% and 40 degrees C are suitable for waste oils under 1 atm. The irreversible inactivation of the lipase is presumed and a stepwise addition of methanol to reduce inactivation of immobilized lipases is proposed. Under the optimum conditions the yield of methyl esters is around 88-90%.

Optimal Parameter Exploration for Online Change-Point Detection in Activity Monitoring Using Genetic Algorithms

PubMed Central

Khan, Naveed; McClean, Sally; Zhang, Shuai; Nugent, Chris

2016-01-01

In recent years, smart phones with inbuilt sensors have become popular devices to facilitate activity recognition. The sensors capture a large amount of data, containing meaningful events, in a short period of time. The change points in this data are used to specify transitions to distinct events and can be used in various scenarios such as identifying change in a patient’s vital signs in the medical domain or requesting activity labels for generating real-world labeled activity datasets. Our work focuses on change-point detection to identify a transition from one activity to another. Within this paper, we extend our previous work on multivariate exponentially weighted moving average (MEWMA) algorithm by using a genetic algorithm (GA) to identify the optimal set of parameters for online change-point detection. The proposed technique finds the maximum accuracy and F_measure by optimizing the different parameters of the MEWMA, which subsequently identifies the exact location of the change point from an existing activity to a new one. Optimal parameter selection facilitates an algorithm to detect accurate change points and minimize false alarms. Results have been evaluated based on two real datasets of accelerometer data collected from a set of different activities from two users, with a high degree of accuracy from 99.4% to 99.8% and F_measure of up to 66.7%. PMID:27792177

WE-AB-209-09: Optimization of Rotational Arc Station Parameter Optimized Radiation Therapy

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

Dong, P; Xing, L; Ungun, B

Purpose: To develop a fast optimization method for station parameter optimized radiation therapy (SPORT) and show that SPORT is capable of improving VMAT in both plan quality and delivery efficiency. Methods: The angular space from 0° to 360° was divided into 180 station points (SPs). A candidate aperture was assigned to each of the SPs based on the calculation results using a column generation algorithm. The weights of the apertures were then obtained by optimizing the objective function using a state-of-the-art GPU based Proximal Operator Graph Solver (POGS) within seconds. Apertures with zero or low weight were thrown out. Tomore » avoid being trapped in a local minimum, a stochastic gradient descent method was employed which also greatly increased the convergence rate of the objective function. The above procedure repeated until the plan could not be improved any further. A weighting factor associated with the total plan MU also indirectly controlled the complexities of aperture shapes. The number of apertures for VMAT and SPORT was confined to 180. The SPORT allowed the coexistence of multiple apertures in a single SP. The optimization technique was assessed by using three clinical cases (prostate, H&N and brain). Results: Marked dosimetric quality improvement was demonstrated in the SPORT plans for all three studied cases. Prostate case: the volume of the 50% prescription dose was decreased by 22% for the rectum. H&N case: SPORT improved the mean dose for the left and right parotids by 15% each. Brain case: the doses to the eyes, chiasm and inner ears were all improved. SPORT shortened the treatment time by ∼1 min for the prostate case, ∼0.5 min for brain case, and ∼0.2 min for the H&N case. Conclusion: The superior dosimetric quality and delivery efficiency presented here indicates that SPORT is an intriguing alternative treatment modality.« less

Estimating model parameters for an impact-produced shock-wave simulation: Optimal use of partial data with the extended Kalman filter

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

Kao, Jim; Flicker, Dawn; Ide, Kayo

2006-05-20

This paper builds upon our recent data assimilation work with the extended Kalman filter (EKF) method [J. Kao, D. Flicker, R. Henninger, S. Frey, M. Ghil, K. Ide, Data assimilation with an extended Kalman filter for an impact-produced shock-wave study, J. Comp. Phys. 196 (2004) 705-723.]. The purpose is to test the capability of EKF in optimizing a model's physical parameters. The problem is to simulate the evolution of a shock produced through a high-speed flyer plate. In the earlier work, we have showed that the EKF allows one to estimate the evolving state of the shock wave from amore » single pressure measurement, assuming that all model parameters are known. In the present paper, we show that imperfectly known model parameters can also be estimated accordingly, along with the evolving model state, from the same single measurement. The model parameter optimization using the EKF can be achieved through a simple modification of the original EKF formalism by including the model parameters into an augmented state variable vector. While the regular state variables are governed by both deterministic and stochastic forcing mechanisms, the parameters are only subject to the latter. The optimally estimated model parameters are thus obtained through a unified assimilation operation. We show that improving the accuracy of the model parameters also improves the state estimate. The time variation of the optimized model parameters results from blending the data and the corresponding values generated from the model and lies within a small range, of less than 2%, from the parameter values of the original model. The solution computed with the optimized parameters performs considerably better and has a smaller total variance than its counterpart using the original time-constant parameters. These results indicate that the model parameters play a dominant role in the performance of the shock-wave hydrodynamic code at hand.« less

Interval-parameter semi-infinite fuzzy-stochastic mixed-integer programming approach for environmental management under multiple uncertainties.

PubMed

Guo, P; Huang, G H

2010-03-01

In this study, an interval-parameter semi-infinite fuzzy-chance-constrained mixed-integer linear programming (ISIFCIP) approach is developed for supporting long-term planning of waste-management systems under multiple uncertainties in the City of Regina, Canada. The method improves upon the existing interval-parameter semi-infinite programming (ISIP) and fuzzy-chance-constrained programming (FCCP) by incorporating uncertainties expressed as dual uncertainties of functional intervals and multiple uncertainties of distributions with fuzzy-interval admissible probability of violating constraint within a general optimization framework. The binary-variable solutions represent the decisions of waste-management-facility expansion, and the continuous ones are related to decisions of waste-flow allocation. The interval solutions can help decision-makers to obtain multiple decision alternatives, as well as provide bases for further analyses of tradeoffs between waste-management cost and system-failure risk. In the application to the City of Regina, Canada, two scenarios are considered. In Scenario 1, the City's waste-management practices would be based on the existing policy over the next 25 years. The total diversion rate for the residential waste would be approximately 14%. Scenario 2 is associated with a policy for waste minimization and diversion, where 35% diversion of residential waste should be achieved within 15 years, and 50% diversion over 25 years. In this scenario, not only landfill would be expanded, but also CF and MRF would be expanded. Through the scenario analyses, useful decision support for the City's solid-waste managers and decision-makers has been generated. Three special characteristics of the proposed method make it unique compared with other optimization techniques that deal with uncertainties. Firstly, it is useful for tackling multiple uncertainties expressed as intervals, functional intervals, probability distributions, fuzzy sets, and their

Optimizing Photosynthetic and Respiratory Parameters Based on the Seasonal Variation Pattern in Regional Net Ecosystem Productivity Obtained from Atmospheric Inversion

NASA Astrophysics Data System (ADS)

Chen, Z.; Chen, J.; Zheng, X.; Jiang, F.; Zhang, S.; Ju, W.; Yuan, W.; Mo, G.

2014-12-01

In this study, we explore the feasibility of optimizing ecosystem photosynthetic and respiratory parameters from the seasonal variation pattern of the net carbon flux. An optimization scheme is proposed to estimate two key parameters (Vcmax and Q10) by exploiting the seasonal variation in the net ecosystem carbon flux retrieved by an atmospheric inversion system. This scheme is implemented to estimate Vcmax and Q10 of the Boreal Ecosystem Productivity Simulator (BEPS) to improve its NEP simulation in the Boreal North America (BNA) region. Simultaneously, in-situ NEE observations at six eddy covariance sites are used to evaluate the NEE simulations. The results show that the performance of the optimized BEPS is superior to that of the BEPS with the default parameter values. These results have the implication on using atmospheric CO2 data for optimizing ecosystem parameters through atmospheric inversion or data assimilation techniques.

Control Parameters Optimization Based on Co-Simulation of a Mechatronic System for an UA-Based Two-Axis Inertially Stabilized Platform.

PubMed

Zhou, Xiangyang; Zhao, Beilei; Gong, Guohao

2015-08-14

This paper presents a method based on co-simulation of a mechatronic system to optimize the control parameters of a two-axis inertially stabilized platform system (ISP) applied in an unmanned airship (UA), by which high control performance and reliability of the ISP system are achieved. First, a three-dimensional structural model of the ISP is built by using the three-dimensional parametric CAD software SOLIDWORKS(®); then, to analyze the system's kinematic and dynamic characteristics under operating conditions, dynamics modeling is conducted by using the multi-body dynamics software ADAMS™, thus the main dynamic parameters such as displacement, velocity, acceleration and reaction curve are obtained, respectively, through simulation analysis. Then, those dynamic parameters were input into the established MATLAB(®) SIMULINK(®) controller to simulate and test the performance of the control system. By these means, the ISP control parameters are optimized. To verify the methods, experiments were carried out by applying the optimized parameters to the control system of a two-axis ISP. The results show that the co-simulation by using virtual prototyping (VP) is effective to obtain optimized ISP control parameters, eventually leading to high ISP control performance.

Control Parameters Optimization Based on Co-Simulation of a Mechatronic System for an UA-Based Two-Axis Inertially Stabilized Platform

PubMed Central

Zhou, Xiangyang; Zhao, Beilei; Gong, Guohao

2015-01-01

This paper presents a method based on co-simulation of a mechatronic system to optimize the control parameters of a two-axis inertially stabilized platform system (ISP) applied in an unmanned airship (UA), by which high control performance and reliability of the ISP system are achieved. First, a three-dimensional structural model of the ISP is built by using the three-dimensional parametric CAD software SOLIDWORKS®; then, to analyze the system’s kinematic and dynamic characteristics under operating conditions, dynamics modeling is conducted by using the multi-body dynamics software ADAMS™, thus the main dynamic parameters such as displacement, velocity, acceleration and reaction curve are obtained, respectively, through simulation analysis. Then, those dynamic parameters were input into the established MATLAB® SIMULINK® controller to simulate and test the performance of the control system. By these means, the ISP control parameters are optimized. To verify the methods, experiments were carried out by applying the optimized parameters to the control system of a two-axis ISP. The results show that the co-simulation by using virtual prototyping (VP) is effective to obtain optimized ISP control parameters, eventually leading to high ISP control performance. PMID:26287210

Optimization of processing parameters for the preparation of phytosterol microemulsions by the solvent displacement method.

PubMed

Leong, Wai Fun; Che Man, Yaakob B; Lai, Oi Ming; Long, Kamariah; Misran, Misni; Tan, Chin Ping

2009-09-23

The purpose of this study was to optimize the parameters involved in the production of water-soluble phytosterol microemulsions for use in the food industry. In this study, response surface methodology (RSM) was employed to model and optimize four of the processing parameters, namely, the number of cycles of high-pressure homogenization (1-9 cycles), the pressure used for high-pressure homogenization (100-500 bar), the evaporation temperature (30-70 degrees C), and the concentration ratio of microemulsions (1-5). All responses-particle size (PS), polydispersity index (PDI), and percent ethanol residual (%ER)-were well fit by a reduced cubic model obtained by multiple regression after manual elimination. The coefficient of determination (R(2)) and absolute average deviation (AAD) value for PS, PDI, and %ER were 0.9628 and 0.5398%, 0.9953 and 0.7077%, and 0.9989 and 1.0457%, respectively. The optimized processing parameters were 4.88 (approximately 5) homogenization cycles, homogenization pressure of 400 bar, evaporation temperature of 44.5 degrees C, and concentration ratio of microemulsions of 2.34 cycles (approximately 2 cycles) of high-pressure homogenization. The corresponding responses for the optimized preparation condition were a minimal particle size of 328 nm, minimal polydispersity index of 0.159, and <0.1% of ethanol residual. The chi-square test verified the model, whereby the experimental values of PS, PDI, and %ER agreed with the predicted values at a 0.05 level of significance.

An improved chaotic fruit fly optimization based on a mutation strategy for simultaneous feature selection and parameter optimization for SVM and its applications.

PubMed

Ye, Fei; Lou, Xin Yuan; Sun, Lin Fu

2017-01-01

This paper proposes a new support vector machine (SVM) optimization scheme based on an improved chaotic fly optimization algorithm (FOA) with a mutation strategy to simultaneously perform parameter setting turning for the SVM and feature selection. In the improved FOA, the chaotic particle initializes the fruit fly swarm location and replaces the expression of distance for the fruit fly to find the food source. However, the proposed mutation strategy uses two distinct generative mechanisms for new food sources at the osphresis phase, allowing the algorithm procedure to search for the optimal solution in both the whole solution space and within the local solution space containing the fruit fly swarm location. In an evaluation based on a group of ten benchmark problems, the proposed algorithm's performance is compared with that of other well-known algorithms, and the results support the superiority of the proposed algorithm. Moreover, this algorithm is successfully applied in a SVM to perform both parameter setting turning for the SVM and feature selection to solve real-world classification problems. This method is called chaotic fruit fly optimization algorithm (CIFOA)-SVM and has been shown to be a more robust and effective optimization method than other well-known methods, particularly in terms of solving the medical diagnosis problem and the credit card problem.

[Removal of toluene from waste gas by honeycomb adsorption rotor with modified 13X molecular sieves].

PubMed

Wang, Jia-De; Zheng, Liang-Wei; Zhu, Run-Ye; Yu, Yun-Feng

2013-12-01

The removal of toluene from waste gas by Honeycomb Adsorption Rotor with modified 13X molecular sieves was systematically investigated. The effects of the rotor operating parameters and the feed gas parameters on the adsorption efficiency were clarified. The experimental results indicated that the honeycomb adsorption rotor had a good humidity resistance. The removal efficiency of honeycomb adsorption rotor achieved the maximal value with optimal rotor speed and optimal generation air temperature. Moreover, for an appropriate flow rate ratio the removal efficiency and energy consumption should be taken into account. When the recommended operating parameters were regeneration air temperature of 180 degrees C, rotor speed of 2.8-5 r x h(-1), flow rate ratio of 8-12, the removal efficiency kept over 90% for the toluene gas with concentration of 100 mg x m(-3) and inlet velocity of 2 m x s(-1). The research provided design experience and operating parameters for industrial application of honeycomb adsorption rotor. It showed that lower empty bed velocity, faster rotor speed and higher temperature were necessary to purify organic waste gases of higher concentrations.

Modeling and optimization of anaerobic codigestion of potato waste and aquatic weed by response surface methodology and artificial neural network coupled genetic algorithm.

PubMed

Jacob, Samuel; Banerjee, Rintu

2016-08-01

A novel approach to overcome the acidification problem has been attempted in the present study by codigesting industrial potato waste (PW) with Pistia stratiotes (PS, an aquatic weed). The effectiveness of codigestion of the weed and PW was tested in an equal (1:1) proportion by weight with substrate concentration of 5g total solid (TS)/L (2.5gPW+2.5gPS) which resulted in enhancement of methane yield by 76.45% as compared to monodigestion of PW with a positive synergistic effect. Optimization of process parameters was conducted using central composite design (CCD) based response surface methodology (RSM) and artificial neural network (ANN) coupled genetic algorithm (GA) model. Upon comparison of these two optimization techniques, ANN-GA model obtained through feed forward back propagation methodology was found to be efficient and yielded 447.4±21.43LCH4/kgVSfed (0.279gCH4/kgCODvs) which is 6% higher as compared to the CCD-RSM based approach. Copyright © 2016 Elsevier Ltd. All rights reserved.

Development of a turbomachinery design optimization procedure using a multiple-parameter nonlinear perturbation method

NASA Technical Reports Server (NTRS)

Stahara, S. S.

1984-01-01

An investigation was carried out to complete the preliminary development of a combined perturbation/optimization procedure and associated computational code for designing optimized blade-to-blade profiles of turbomachinery blades. The overall purpose of the procedures developed is to provide demonstration of a rapid nonlinear perturbation method for minimizing the computational requirements associated with parametric design studies of turbomachinery flows. The method combines the multiple parameter nonlinear perturbation method, successfully developed in previous phases of this study, with the NASA TSONIC blade-to-blade turbomachinery flow solver, and the COPES-CONMIN optimization procedure into a user's code for designing optimized blade-to-blade surface profiles of turbomachinery blades. Results of several design applications and a documented version of the code together with a user's manual are provided.

Parameter Sweep and Optimization of Loosely Coupled Simulations Using the DAKOTA Toolkit

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

Elwasif, Wael R; Bernholdt, David E; Pannala, Sreekanth

2012-01-01

The increasing availability of large scale computing capabilities has accelerated the development of high-fidelity coupled simulations. Such simulations typically involve the integration of models that implement various aspects of the complex phenomena under investigation. Coupled simulations are playing an integral role in fields such as climate modeling, earth systems modeling, rocket simulations, computational chemistry, fusion research, and many other computational fields. Model coupling provides scientists with systematic ways to virtually explore the physical, mathematical, and computational aspects of the problem. Such exploration is rarely done using a single execution of a simulation, but rather by aggregating the results from manymore » simulation runs that, together, serve to bring to light novel knowledge about the system under investigation. Furthermore, it is often the case (particularly in engineering disciplines) that the study of the underlying system takes the form of an optimization regime, where the control parameter space is explored to optimize an objective functions that captures system realizability, cost, performance, or a combination thereof. Novel and flexible frameworks that facilitate the integration of the disparate models into a holistic simulation are used to perform this research, while making efficient use of the available computational resources. In this paper, we describe the integration of the DAKOTA optimization and parameter sweep toolkit with the Integrated Plasma Simulator (IPS), a component-based framework for loosely coupled simulations. The integration allows DAKOTA to exploit the internal task and resource management of the IPS to dynamically instantiate simulation instances within a single IPS instance, allowing for greater control over the trade-off between efficiency of resource utilization and time to completion. We present a case study showing the use of the combined DAKOTA-IPS system to aid in the design of a

Solid state anaerobic co-digestion of yard waste and food waste for biogas production.

PubMed

Brown, Dan; Li, Yebo

2013-01-01

Food and yard wastes are available year round at low cost and have the potential to complement each other for SS-AD. The goal of this study was to determine optimal feedstock/effluent (F/E) and food waste/yard waste mixing ratios for optimal biogas production. Co-digestion of yard and food waste was carried out at F/E ratios of 1, 2, and 3. For each F/E ratio, food waste percentages of 0%, 10%, and 20%, based on dry volatile solids, were evaluated. Results showed increased methane yields and volumetric productivities as the percentage of food waste was increased to 10% and 20% of the substrate at F/E ratios of 2 and 1, respectively. This study showed that co-digestion of food waste with yard waste at specific ratios can improve digester operating characteristics and end performance metrics over SS-AD of yard waste alone. Copyright © 2012 Elsevier Ltd. All rights reserved.

Statistical optimization of bioprocess parameters for enhanced gallic acid production from coffee pulp tannins by Penicillium verrucosum.

PubMed