Medium term municipal solid waste generation prediction by autoregressive integrated moving average

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

Younes, Mohammad K.; Nopiah, Z. M.; Basri, Noor Ezlin A.

2014-09-12

Generally, solid waste handling and management are performed by municipality or local authority. In most of developing countries, local authorities suffer from serious solid waste management (SWM) problems and insufficient data and strategic planning. Thus it is important to develop robust solid waste generation forecasting model. It helps to proper manage the generated solid waste and to develop future plan based on relatively accurate figures. In Malaysia, solid waste generation rate increases rapidly due to the population growth and new consumption trends that characterize the modern life style. This paper aims to develop monthly solid waste forecasting model using Autoregressivemore » Integrated Moving Average (ARIMA), such model is applicable even though there is lack of data and will help the municipality properly establish the annual service plan. The results show that ARIMA (6,1,0) model predicts monthly municipal solid waste generation with root mean square error equals to 0.0952 and the model forecast residuals are within accepted 95% confident interval.« less

Forecasting municipal solid waste generation using artificial intelligence modelling approaches.

PubMed

Abbasi, Maryam; El Hanandeh, Ali

2016-10-01

Municipal solid waste (MSW) management is a major concern to local governments to protect human health, the environment and to preserve natural resources. The design and operation of an effective MSW management system requires accurate estimation of future waste generation quantities. The main objective of this study was to develop a model for accurate forecasting of MSW generation that helps waste related organizations to better design and operate effective MSW management systems. Four intelligent system algorithms including support vector machine (SVM), adaptive neuro-fuzzy inference system (ANFIS), artificial neural network (ANN) and k-nearest neighbours (kNN) were tested for their ability to predict monthly waste generation in the Logan City Council region in Queensland, Australia. Results showed artificial intelligence models have good prediction performance and could be successfully applied to establish municipal solid waste forecasting models. Using machine learning algorithms can reliably predict monthly MSW generation by training with waste generation time series. In addition, results suggest that ANFIS system produced the most accurate forecasts of the peaks while kNN was successful in predicting the monthly averages of waste quantities. Based on the results, the total annual MSW generated in Logan City will reach 9.4×10(7)kg by 2020 while the peak monthly waste will reach 9.37×10(6)kg. Copyright © 2016 Elsevier Ltd. All rights reserved.

Case study on prediction of remaining methane potential of landfilled municipal solid waste by statistical analysis of waste composition data.

PubMed

Sel, İlker; Çakmakcı, Mehmet; Özkaya, Bestamin; Suphi Altan, H

2016-10-01

Main objective of this study was to develop a statistical model for easier and faster Biochemical Methane Potential (BMP) prediction of landfilled municipal solid waste by analyzing waste composition of excavated samples from 12 sampling points and three waste depths representing different landfilling ages of closed and active sections of a sanitary landfill site located in İstanbul, Turkey. Results of Principal Component Analysis (PCA) were used as a decision support tool to evaluation and describe the waste composition variables. Four principal component were extracted describing 76% of data set variance. The most effective components were determined as PCB, PO, T, D, W, FM, moisture and BMP for the data set. Multiple Linear Regression (MLR) models were built by original compositional data and transformed data to determine differences. It was observed that even residual plots were better for transformed data the R(2) and Adjusted R(2) values were not improved significantly. The best preliminary BMP prediction models consisted of D, W, T and FM waste fractions for both versions of regressions. Adjusted R(2) values of the raw and transformed models were determined as 0.69 and 0.57, respectively. Copyright © 2016 Elsevier Ltd. All rights reserved.

Prediction of the compression ratio for municipal solid waste using decision tree.

PubMed

Heshmati R, Ali Akbar; Mokhtari, Maryam; Shakiba Rad, Saeed

2014-01-01

The compression ratio of municipal solid waste (MSW) is an essential parameter for evaluation of waste settlement and landfill design. However, no appropriate model has been proposed to estimate the waste compression ratio so far. In this study, a decision tree method was utilized to predict the waste compression ratio (C'c). The tree was constructed using Quinlan's M5 algorithm. A reliable database retrieved from the literature was used to develop a practical model that relates C'c to waste composition and properties, including dry density, dry weight water content, and percentage of biodegradable organic waste using the decision tree method. The performance of the developed model was examined in terms of different statistical criteria, including correlation coefficient, root mean squared error, mean absolute error and mean bias error, recommended by researchers. The obtained results demonstrate that the suggested model is able to evaluate the compression ratio of MSW effectively.

Co-digestion of solid waste: Towards a simple model to predict methane production.

PubMed

Kouas, Mokhles; Torrijos, Michel; Schmitz, Sabine; Sousbie, Philippe; Sayadi, Sami; Harmand, Jérôme

2018-04-01

Modeling methane production is a key issue for solid waste co-digestion. Here, the effect of a step-wise increase in the organic loading rate (OLR) on reactor performance was investigated, and four new models were evaluated to predict methane yields using data acquired in batch mode. Four co-digestion experiments of mixtures of 2 solid substrates were conducted in semi-continuous mode. Experimental methane yields were always higher than the BMP values of mixtures calculated from the BMP of each substrate, highlighting the importance of endogenous production (methane produced from auto-degradation of microbial community and generated solids). The experimental methane productions under increasing OLRs corresponded well to the modeled data using the model with constant endogenous production and kinetics identified at 80% from total batch time. This model provides a simple and useful tool for technical design consultancies and plant operators to optimize the co-digestion and the choice of the OLRs. Copyright © 2018 Elsevier Ltd. All rights reserved.

Prediction of chemical speciation in stabilized/solidified wastes using a general chemical equilibrium model. Part 1: Chemical representation of cementitious binders

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

Park, J.Y.; Batchelor, B.

1999-03-01

Chemical equilibrium models are useful to evaluate stabilized/solidified waste. A general equilibrium model, SOLTEQ, a modified version of MINTEQA2 for S/S, was applied to predict the chemical speciations in the stabilized/solidified waste form. A method was developed to prepare SOLTEQ input data that can chemically represent various stabilized/solidified binders. Taylor`s empirical model was used to describe partitioning of alkali ions. As a result, SOLTEQ could represent chemical speciation in pure binder systems such as ordinary Portland cement and ordinary Portland cement + fly ash. Moreover, SOLTEQ could reasonably describe the effects on the chemical speciation due to variations in water-to-cement,more » fly ash contents, and hydration times of various binder systems. However, this application of SOLTEQ was not accurate in predicting concentrations of Ca, Si, and SO{sub 4} ions, due to uncertainties in the CSH solubility model and K{sub sp} values of cement hydrates at high pH values.« less

Coupling model of aerobic waste degradation considering temperature, initial moisture content and air injection volume.

PubMed

Ma, Jun; Liu, Lei; Ge, Sai; Xue, Qiang; Li, Jiangshan; Wan, Yong; Hui, Xinminnan

2018-03-01

A quantitative description of aerobic waste degradation is important in evaluating landfill waste stability and economic management. This research aimed to develop a coupling model to predict the degree of aerobic waste degradation. On the basis of the first-order kinetic equation and the law of conservation of mass, we first developed the coupling model of aerobic waste degradation that considered temperature, initial moisture content and air injection volume to simulate and predict the chemical oxygen demand in the leachate. Three different laboratory experiments on aerobic waste degradation were simulated to test the model applicability. Parameter sensitivity analyses were conducted to evaluate the reliability of parameters. The coupling model can simulate aerobic waste degradation, and the obtained simulation agreed with the corresponding results of the experiment. Comparison of the experiment and simulation demonstrated that the coupling model is a new approach to predict aerobic waste degradation and can be considered as the basis for selecting the economic air injection volume and appropriate management in the future.

A data base approach for prediction of deforestation-induced mass wasting events

NASA Technical Reports Server (NTRS)

Logan, T. L.

1981-01-01

A major topic of concern in timber management is determining the impact of clear-cutting on slope stability. Deforestation treatments on steep mountain slopes have often resulted in a high frequency of major mass wasting events. The Geographic Information System (GIS) is a potentially useful tool for predicting the location of mass wasting sites. With a raster-based GIS, digitally encoded maps of slide hazard parameters can be overlayed and modeled to produce new maps depicting high probability slide areas. The present investigation has the objective to examine the raster-based information system as a tool for predicting the location of the clear-cut mountain slopes which are most likely to experience shallow soil debris avalanches. A literature overview is conducted, taking into account vegetation, roads, precipitation, soil type, slope-angle and aspect, and models predicting mass soil movements. Attention is given to a data base approach and aspects of slide prediction.

The use of artificial neural networks and multiple linear regression to predict rate of medical waste generation

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

Jahandideh, Sepideh; Jahandideh, Samad; Asadabadi, Ebrahim Barzegari

2009-11-15

Prediction of the amount of hospital waste production will be helpful in the storage, transportation and disposal of hospital waste management. Based on this fact, two predictor models including artificial neural networks (ANNs) and multiple linear regression (MLR) were applied to predict the rate of medical waste generation totally and in different types of sharp, infectious and general. In this study, a 5-fold cross-validation procedure on a database containing total of 50 hospitals of Fars province (Iran) were used to verify the performance of the models. Three performance measures including MAR, RMSE and R{sup 2} were used to evaluate performancemore » of models. The MLR as a conventional model obtained poor prediction performance measure values. However, MLR distinguished hospital capacity and bed occupancy as more significant parameters. On the other hand, ANNs as a more powerful model, which has not been introduced in predicting rate of medical waste generation, showed high performance measure values, especially 0.99 value of R{sup 2} confirming the good fit of the data. Such satisfactory results could be attributed to the non-linear nature of ANNs in problem solving which provides the opportunity for relating independent variables to dependent ones non-linearly. In conclusion, the obtained results showed that our ANN-based model approach is very promising and may play a useful role in developing a better cost-effective strategy for waste management in future.« less

Impacts of Process and Prediction Uncertainties on Projected Hanford Waste Glass Amount

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

Gervasio, V.; Kim, D. S.; Vienna, J. D.

Analyses were performed to evaluate the impacts of using the advanced glass models, constraints, and uncertainty descriptions on projected Hanford glass mass. The maximum allowable waste oxide loading (WOL) was estimated for waste compositions while simultaneously satisfying all applicable glass property and composition constraints with sufficient confidence. Different components of prediction and composition/process uncertainties were systematically included in the calculations to evaluate their impacts on glass mass. The analyses estimated the production of 23,360 MT of immobilized high-level waste (IHLW) glass when no uncertainties were taken into account. Accounting for prediction and composition/process uncertainties resulted in 5.01 relative percent increasemore » in estimated glass mass of 24,531 MT. Roughly equal impacts were found for prediction uncertainties (2.58 RPD) and composition/process uncertainties (2.43 RPD). The immobilized low-activity waste (ILAW) mass was predicted to be 282,350 MT without uncertainty and with waste loading “line” rules in place. Accounting for prediction and composition/process uncertainties resulted in only 0.08 relative percent increase in estimated glass mass of 282,562 MT. Without application of line rules the glass mass decreases by 10.6 relative percent (252,490 MT) for the case with no uncertainties. Addition of prediction uncertainties increases glass mass by 1.32 relative percent and the addition of composition/process uncertainties increase glass mass by an additional 7.73 relative percent (9.06 relative percent increase combined). The glass mass estimate without line rules (275,359 MT) was 2.55 relative percent lower than that with the line rules (282,562 MT), after accounting for all applicable uncertainties.« less

Development and validation of a building design waste reduction model.

PubMed

Llatas, C; Osmani, M

2016-10-01

Reduction in construction waste is a pressing need in many countries. The design of building elements is considered a pivotal process to achieve waste reduction at source, which enables an informed prediction of their wastage reduction levels. However the lack of quantitative methods linking design strategies to waste reduction hinders designing out waste practice in building projects. Therefore, this paper addresses this knowledge gap through the design and validation of a Building Design Waste Reduction Strategies (Waste ReSt) model that aims to investigate the relationships between design variables and their impact on onsite waste reduction. The Waste ReSt model was validated in a real-world case study involving 20 residential buildings in Spain. The validation process comprises three stages. Firstly, design waste causes were analyzed. Secondly, design strategies were applied leading to several alternative low waste building elements. Finally, their potential source reduction levels were quantified and discussed within the context of the literature. The Waste ReSt model could serve as an instrumental tool to simulate designing out strategies in building projects. The knowledge provided by the model could help project stakeholders to better understand the correlation between the design process and waste sources and subsequently implement design practices for low-waste buildings. Copyright © 2016 Elsevier Ltd. All rights reserved.

Prediction of Mass Wasting, Erosion, and Sediment Transport With the Distributed Hydrology-Soil-Vegetation Model

NASA Astrophysics Data System (ADS)

Doten, C. O.; Lanini, J. S.; Bowling, L. C.; Lettenmaier, D. P.

2004-12-01

Erosion and sediment transport in a temperate forested watershed are predicted with a new sediment module linked to the Distributed Hydrology-Soil-Vegetation Model (DHSVM). The DHSVM sediment module represents the main sources of sediment generation in forested environments: mass wasting, hillslope erosion and road surface erosion. It produces failures based on a factor-of-safety analysis with the infinite slope model through use of stochastically generated soil and vegetation parameters. Failed material is routed downslope with a rule-based scheme that determines sediment delivery to streams. Sediment from hillslopes and road surfaces is also transported to the channel network. Basin sediment yield is predicted with a simple channel sediment routing scheme. The model was applied to the Rainy Creek catchment, a tributary of the Wenatchee River which drains the east slopes of the Cascade Mountains, and Hard and Ware Creeks on the west slopes of the Cascades. In these initial applications, the model produced plausible sediment yield and ratios of landsliding and surface erosion , when compared to published rates for similar catchments in the Pacific Northwest. We have also used the model to examine the implications of fires and logging road removal on sediment generation in the Rainy Creek catchment. Generally, in absolute value, the predicted changes (increased sediment generation) following fires, which are primarily associated with increased slope failures, are much larger than the modest changes (reductions in sediment yield) associated with road obliteration, although the small sensitivity to forest road obliteration may be due in part to the relatively low road density in the Rainy Creek catchment, and to mechanisms, such as culvert failure, that are not represented in the model.

Verifying the performance of artificial neural network and multiple linear regression in predicting the mean seasonal municipal solid waste generation rate: A case study of Fars province, Iran.

PubMed

Azadi, Sama; Karimi-Jashni, Ayoub

2016-02-01

Predicting the mass of solid waste generation plays an important role in integrated solid waste management plans. In this study, the performance of two predictive models, Artificial Neural Network (ANN) and Multiple Linear Regression (MLR) was verified to predict mean Seasonal Municipal Solid Waste Generation (SMSWG) rate. The accuracy of the proposed models is illustrated through a case study of 20 cities located in Fars Province, Iran. Four performance measures, MAE, MAPE, RMSE and R were used to evaluate the performance of these models. The MLR, as a conventional model, showed poor prediction performance. On the other hand, the results indicated that the ANN model, as a non-linear model, has a higher predictive accuracy when it comes to prediction of the mean SMSWG rate. As a result, in order to develop a more cost-effective strategy for waste management in the future, the ANN model could be used to predict the mean SMSWG rate. Copyright © 2015 Elsevier Ltd. All rights reserved.

Modeling of urban solid waste management system: The case of Dhaka city

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

Sufian, M.A.; Bala, B.K.

2007-07-01

This paper presents a system dynamics computer model to predict solid waste generation, collection capacity and electricity generation from solid waste and to assess the needs for waste management of the urban city of Dhaka, Bangladesh. Simulated results show that solid waste generation, collection capacity and electricity generation potential from solid waste increase with time. Population, uncleared waste, untreated waste, composite index and public concern are projected to increase with time for Dhaka city. Simulated results also show that increasing the budget for collection capacity alone does not improve environmental quality; rather an increased budget is required for both collectionmore » and treatment of solid wastes of Dhaka city. Finally, this model can be used as a computer laboratory for urban solid waste management (USWM) policy analysis.« less

Energy and time modelling of kerbside waste collection: Changes incurred when adding source separated food waste.

PubMed

Edwards, Joel; Othman, Maazuza; Burn, Stewart; Crossin, Enda

2016-10-01

The collection of source separated kerbside municipal FW (SSFW) is being incentivised in Australia, however such a collection is likely to increase the fuel and time a collection truck fleet requires. Therefore, waste managers need to determine whether the incentives outweigh the cost. With literature scarcely describing the magnitude of increase, and local parameters playing a crucial role in accurately modelling kerbside collection; this paper develops a new general mathematical model that predicts the energy and time requirements of a collection regime whilst incorporating the unique variables of different jurisdictions. The model, Municipal solid waste collect (MSW-Collect), is validated and shown to be more accurate at predicting fuel consumption and trucks required than other common collection models. When predicting changes incurred for five different SSFW collection scenarios, results show that SSFW scenarios require an increase in fuel ranging from 1.38% to 57.59%. There is also a need for additional trucks across most SSFW scenarios tested. All SSFW scenarios are ranked and analysed in regards to fuel consumption; sensitivity analysis is conducted to test key assumptions. Crown Copyright © 2016. Published by Elsevier Ltd. All rights reserved.

Analysis on 3RWB model (Reduce, reuse, recycle, and waste bank) in comprehensive waste management toward community-based zero waste

NASA Astrophysics Data System (ADS)

Affandy, Nur Azizah; Isnaini, Enik; Laksono, Arif Budi

2017-06-01

Waste management becomes a serious issue in Indonesia. Significantly, waste production in Lamongan Regency is increasing in linear with the growth of population and current people activities, creating a gap between waste production and waste management. It is a critical problem that should be solved immediately. As a reaction to the issue, the Government of Lamongan Regency has enacted a new policy regarding waste management through a program named Lamongan Green and Clean (LGC). From the collected data, it showed that the "wet waste" or "organic waste" was approximately 63% of total domestic waste. With such condition, it can be predicted that the trashes will decompose quite quickly. From the observation, it was discovered that the generated waste was approximately 0.25 kg/person/day. Meanwhile, the number of population in Tumenggungan Village, Lamongan (data obtained from Monograph in Lamongan district, 2012) was 4651 people. Thus, it can be estimated the total waste in Lamongan was approximately 0.25 kg/person/day x 4651 characters = 930 kg/day. Within 3RWB Model, several stages have to be conducted. In the planning stage, the promotion of self-awareness among the communities in selecting and managing waste due to their interest in a potential benefit, is done. It indicated that community's awareness of waste management waste grew significantly. Meanwhile in socialization stage, each village staff, environmental expert, and policymaker should bear significant role in disseminating the awareness among the people. In the implementation phase, waste management with 3RWB model is promoted by applying it among of the community, starting from selection, waste management, until recycled products sale through the waste bank. In evaluation stage, the village managers, environmental expert, and waste managers are expected to regularly supervise and evaluate the whole activity of the waste management.

Waste tyre pyrolysis: modelling of a moving bed reactor.

PubMed

Aylón, E; Fernández-Colino, A; Murillo, R; Grasa, G; Navarro, M V; García, T; Mastral, A M

2010-12-01

This paper describes the development of a new model for waste tyre pyrolysis in a moving bed reactor. This model comprises three different sub-models: a kinetic sub-model that predicts solid conversion in terms of reaction time and temperature, a heat transfer sub-model that calculates the temperature profile inside the particle and the energy flux from the surroundings to the tyre particles and, finally, a hydrodynamic model that predicts the solid flow pattern inside the reactor. These three sub-models have been integrated in order to develop a comprehensive reactor model. Experimental results were obtained in a continuous moving bed reactor and used to validate model predictions, with good approximation achieved between the experimental and simulated results. In addition, a parametric study of the model was carried out, which showed that tyre particle heating is clearly faster than average particle residence time inside the reactor. Therefore, this fast particle heating together with fast reaction kinetics enables total solid conversion to be achieved in this system in accordance with the predictive model. Copyright © 2010 Elsevier Ltd. All rights reserved.

Predictive Surface Complexation Modeling

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

Sverjensky, Dimitri A.

Surface complexation plays an important role in the equilibria and kinetics of processes controlling the compositions of soilwaters and groundwaters, the fate of contaminants in groundwaters, and the subsurface storage of CO 2 and nuclear waste. Over the last several decades, many dozens of individual experimental studies have addressed aspects of surface complexation that have contributed to an increased understanding of its role in natural systems. However, there has been no previous attempt to develop a model of surface complexation that can be used to link all the experimental studies in order to place them on a predictive basis. Overall,more » my research has successfully integrated the results of the work of many experimentalists published over several decades. For the first time in studies of the geochemistry of the mineral-water interface, a practical predictive capability for modeling has become available. The predictive correlations developed in my research now enable extrapolations of experimental studies to provide estimates of surface chemistry for systems not yet studied experimentally and for natural and anthropogenically perturbed systems.« less

Development of a Thermodynamic Model for the Hanford Tank Waste Operations Simulator - 12193

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

Carter, Robert; Seniow, Kendra

The Hanford Tank Waste Operations Simulator (HTWOS) is the current tool used by the Hanford Tank Operations Contractor for system planning and assessment of different operational strategies. Activities such as waste retrievals in the Hanford tank farms and washing and leaching of waste in the Waste Treatment and Immobilization Plant (WTP) are currently modeled in HTWOS. To predict phase compositions during these activities, HTWOS currently uses simple wash and leach factors that were developed many years ago. To improve these predictions, a rigorous thermodynamic framework has been developed based on the multi-component Pitzer ion interaction model for use with severalmore » important chemical species in Hanford tank waste. These chemical species are those with the greatest impact on high-level waste glass production in the WTP and whose solubility depends on the processing conditions. Starting with Pitzer parameter coefficients and species chemical potential coefficients collated from open literature sources, reconciliation with published experimental data led to a self-consistent set of coefficients known as the HTWOS Pitzer database. Using Gibbs energy minimization with the Pitzer ion interaction equations in Microsoft Excel,1 a number of successful predictions were made for the solubility of simple mixtures of the chosen species. Currently, this thermodynamic framework is being programmed into HTWOS as the mechanism for determining the solid-liquid phase distributions for the chosen species, replacing their simple wash and leach factors. Starting from a variety of open literature sources, a collection of Pitzer parameters and species chemical potentials, as functions of temperature, was tested for consistency and accuracy by comparison with available experimental thermodynamic data (e.g., osmotic coefficients and solubility). Reconciliation of the initial set of parameter coefficients with the experimental data led to the development of the self

Life cycle assessment modelling of waste-to-energy incineration in Spain and Portugal.

PubMed

Margallo, M; Aldaco, R; Irabien, A; Carrillo, V; Fischer, M; Bala, A; Fullana, P

2014-06-01

In recent years, waste management systems have been evaluated using a life cycle assessment (LCA) approach. A main shortcoming of prior studies was the focus on a mixture of waste with different characteristics. The estimation of emissions and consumptions associated with each waste fraction in these studies presented allocation problems. Waste-to-energy (WTE) incineration is a clear example in which municipal solid waste (MSW), comprising many types of materials, is processed to produce several outputs. This paper investigates an approach to better understand incineration processes in Spain and Portugal by applying a multi-input/output allocation model. The application of this model enabled predictions of WTE inputs and outputs, including the consumption of ancillary materials and combustibles, air emissions, solid wastes, and the energy produced during the combustion of each waste fraction. © The Author(s) 2014.

Thermal Predictions of the Cooling of Waste Glass Canisters

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

Donna Post Guillen

2014-11-01

Radioactive liquid waste from five decades of weapons production is slated for vitrification at the Hanford site. The waste will be mixed with glass forming additives and heated to a high temperature, then poured into canisters within a pour cave where the glass will cool and solidify into a stable waste form for disposal. Computer simulations were performed to predict the heat rejected from the canisters and the temperatures within the glass during cooling. Four different waste glass compositions with different thermophysical properties were evaluated. Canister centerline temperatures and the total amount of heat transfer from the canisters to themore » surrounding air are reported.« less

Preliminary ECLSS waste water model

NASA Technical Reports Server (NTRS)

Carter, Donald L.; Holder, Donald W., Jr.; Alexander, Kevin; Shaw, R. G.; Hayase, John K.

1991-01-01

A preliminary waste water model for input to the Space Station Freedom (SSF) Environmental Control and Life Support System (ECLSS) Water Processor (WP) has been generated for design purposes. Data have been compiled from various ECLSS tests and flight sample analyses. A discussion of the characterization of the waste streams comprising the model is presented, along with a discussion of the waste water model and the rationale for the inclusion of contaminants in their respective concentrations. The major objective is to establish a methodology for the development of a waste water model and to present the current state of that model.

Rapid biochemical methane potential prediction of urban organic waste with near-infrared reflectance spectroscopy.

PubMed

Fitamo, T; Triolo, J M; Boldrin, A; Scheutz, C

2017-08-01

The anaerobic digestibility of various biomass feedstocks in biogas plants is determined with biochemical methane potential (BMP) assays. However, experimental BMP analysis is time-consuming, costly and challenging to optimise stock management and feeding to achieve improved biogas production. The aim of the present study is to develop a fast and reliable model based on near-infrared reflectance spectroscopy (NIRS) for the BMP prediction of urban organic waste (UOW). The model comprised 87 UOW samples. Additionally, 88 plant biomass samples were included, to develop a combined model predicting BMP. The coefficient of determination (R 2 ) and root mean square error in prediction (RMSE P ) of the UOW model were 0.88 and 44 mL CH 4 /g VS, while the combined model was 0.89 and 50 mL CH 4 /g VS. Improved model performance was obtained for the two individual models compared to the combined version. The BMP prediction with NIRS was satisfactory and moderately successful. Copyright © 2017 Elsevier Ltd. All rights reserved.

A degradation model for high kitchen waste content municipal solid waste.

PubMed

Chen, Yunmin; Guo, Ruyang; Li, Yu-Chao; Liu, Hailong; Zhan, Tony Liangtong

2016-12-01

Municipal solid waste (MSW) in developing countries has a high content of kitchen waste (KW), and therefore contains large quantities of water and non-hollocellulose degradable organics. The degradation of high KW content MSW cannot be well simulated by the existing degradation models, which are mostly established for low KW content MSW in developed countries. This paper presents a two-stage anaerobic degradation model for high KW content MSW with degradations of hollocellulose, sugars, proteins and lipids considered. The ranges of the proportions of chemical compounds in MSW components are summarized with the recommended values given. Waste components are grouped into rapidly or slowly degradable categories in terms of the degradation rates under optimal water conditions for degradation. In the proposed model, the unionized VFA inhibitions of hydrolysis/acidogenesis and methanogenesis are considered as well as the pH inhibition of methanogenesis. Both modest and serious VFA inhibitions can be modeled by the proposed model. Default values for the parameters in the proposed method can be used for predictions of degradations of both low and high KW content MSW. The proposed model was verified by simulating two laboratory experiments, in which low and high KW content MSW were used, respectively. The simulated results are in good agreement with the measured data of the experiments. The results show that under low VFA concentrations, the pH inhibition of methanogenesis is the main inhibition to be considered, while the inhibitions of both hydrolysis/acidogenesis and methanogenesis caused by unionized VFA are significant under high VFA concentrations. The model is also used to compare the degradation behaviors of low and high KW content MSW under a favorable environmental condition, and it shows that the gas potential of high KW content MSW releases more quickly. Copyright © 2016 Elsevier Ltd. All rights reserved.

Heating value prediction for combustible fraction of municipal solid waste in Semarang using backpropagation neural network

NASA Astrophysics Data System (ADS)

Khuriati, Ainie; Setiabudi, Wahyu; Nur, Muhammad; Istadi, Istadi

2015-12-01

Backpropgation neural network was trained to predict of combustible fraction heating value of MSW from the physical composition. Waste-to-Energy (WtE) is a viable option for municipal solid waste (MSW) management. The influence of the heating value of municipal solid waste (MSW) is very important on the implementation of WtE systems. As MSW is heterogeneous material, direct heating value measurements are often not feasible. In this study an empirical model was developed to describe the heating value of the combustible fraction of municipal solid waste as a function of its physical composition of MSW using backpropagation neural network. Sampling process was carried out at Jatibarang landfill. The weight of each sorting sample taken from each discharged MSW vehicle load is 100 kg. The MSW physical components were grouped into paper wastes, absorbent hygiene product waste, styrofoam waste, HD plastic waste, plastic waste, rubber waste, textile waste, wood waste, yard wastes, kitchen waste, coco waste, and miscellaneous combustible waste. Network was trained by 24 datasets with 1200, 769, and 210 epochs. The results of this analysis showed that the correlation from the physical composition is better than multiple regression method .

Predictive modeling of crystal accumulation in high-level waste glass melters processing radioactive waste

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

Matyáš, Josef; Gervasio, Vivianaluxa; Sannoh, Sulaiman E.

The effectiveness of HLW vitrification is limited by precipitation/accumulation of spinel crystals [(Fe, Ni, Mn, Zn)(Fe, Cr)2O4] in the glass discharge riser of Joule-heated ceramic melters during idling. These crystals do not affect glass durability; however, if accumulated in thick layer, they can clog the melter and prevent discharge of molten glass into canisters. To address this problem, an empirical model was developed that can predict thicknesses of accumulated layers as a function of glass composition. This model predicts well the accumulation of single crystals and/or small-scale agglomerates, but, excessive agglomeration observed in high-Ni-Fe glass resulted in an under-prediction ofmore » accumulated layers, which gradually worsen over time as an increased number of agglomerates formed. Accumulation rate of ~53.8 ± 3.7 µm/h determined for this glass will result in ~26 mm thick layer in 20 days of melter idling.« less

Modeling the combustion behavior of hazardous waste in a rotary kiln incinerator.

PubMed

Yang, Yongxiang; Pijnenborg, Marc J A; Reuter, Markus A; Verwoerd, Joep

2005-01-01

Hazardous wastes have complex physical forms and chemical compositions and are normally incinerated in rotary kilns for safe disposal and energy recovery. In the rotary kiln, the multifeed stream and wide variation of thermal, physical, and chemical properties of the wastes cause the incineration system to be highly heterogeneous, with severe temperature fluctuations and unsteady combustion chemistry. Incomplete combustion is often the consequence, and the process is difficult to control. In this article, modeling of the waste combustion is described by using computational fluid dynamics (CFD). Through CFD simulation, gas flow and mixing, turbulent combustion, and heat transfer inside the incinerator were predicted and visualized. As the first step, the waste in various forms was modeled to a hydrocarbon-based virtual fuel mixture. The combustion of the simplified waste was then simulated with a seven-gas combustion model within a CFD framework. Comparison was made with previous global three-gas combustion model with which no chemical behavior can be derived. The distribution of temperature and chemical species has been investigated. The waste combustion model was validated with temperature measurements. Various operating conditions and the influence on the incineration performance were then simulated. Through this research, a better process understanding and potential optimization of the design were attained.

A review of statistical updating methods for clinical prediction models.

PubMed

Su, Ting-Li; Jaki, Thomas; Hickey, Graeme L; Buchan, Iain; Sperrin, Matthew

2018-01-01

A clinical prediction model is a tool for predicting healthcare outcomes, usually within a specific population and context. A common approach is to develop a new clinical prediction model for each population and context; however, this wastes potentially useful historical information. A better approach is to update or incorporate the existing clinical prediction models already developed for use in similar contexts or populations. In addition, clinical prediction models commonly become miscalibrated over time, and need replacing or updating. In this article, we review a range of approaches for re-using and updating clinical prediction models; these fall in into three main categories: simple coefficient updating, combining multiple previous clinical prediction models in a meta-model and dynamic updating of models. We evaluated the performance (discrimination and calibration) of the different strategies using data on mortality following cardiac surgery in the United Kingdom: We found that no single strategy performed sufficiently well to be used to the exclusion of the others. In conclusion, useful tools exist for updating existing clinical prediction models to a new population or context, and these should be implemented rather than developing a new clinical prediction model from scratch, using a breadth of complementary statistical methods.

Development of numerical model for predicting heat generation and temperatures in MSW landfills.

PubMed

Hanson, James L; Yeşiller, Nazli; Onnen, Michael T; Liu, Wei-Lien; Oettle, Nicolas K; Marinos, Janelle A

2013-10-01

A numerical modeling approach has been developed for predicting temperatures in municipal solid waste landfills. Model formulation and details of boundary conditions are described. Model performance was evaluated using field data from a landfill in Michigan, USA. The numerical approach was based on finite element analysis incorporating transient conductive heat transfer. Heat generation functions representing decomposition of wastes were empirically developed and incorporated to the formulation. Thermal properties of materials were determined using experimental testing, field observations, and data reported in literature. The boundary conditions consisted of seasonal temperature cycles at the ground surface and constant temperatures at the far-field boundary. Heat generation functions were developed sequentially using varying degrees of conceptual complexity in modeling. First a step-function was developed to represent initial (aerobic) and residual (anaerobic) conditions. Second, an exponential growth-decay function was established. Third, the function was scaled for temperature dependency. Finally, an energy-expended function was developed to simulate heat generation with waste age as a function of temperature. Results are presented and compared to field data for the temperature-dependent growth-decay functions. The formulations developed can be used for prediction of temperatures within various components of landfill systems (liner, waste mass, cover, and surrounding subgrade), determination of frost depths, and determination of heat gain due to decomposition of wastes. Copyright © 2013 Elsevier Ltd. All rights reserved.

Thermodynamic model of natural, medieval and nuclear waste glass durability

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

Jantzen, C.M.; Plodinec, M.J.

1983-01-01

A thermodynamic model of glass durability based on hydration of structural units has been applied to natural glass, medieval window glasses, and glasses containing nuclear waste. The relative durability predicted from the calculated thermodynamics correlates directly with the experimentally observed release of structural silicon in the leaching solution in short-term laboratory tests. By choosing natural glasses and ancient glasses whose long-term performance is known, and which bracket the durability of waste glasses, the long-term stability of nuclear waste glasses can be interpolated among these materials. The current Savannah River defense waste glass formulation is as durable as natural basalt frommore » the Hanford Reservation (10/sup 6/ years old). The thermodynamic hydration energy is shown to be related to the bond energetics of the glass. 69 references, 2 figures, 1 table.« less

Waste generated in high-rise buildings construction: a quantification model based on statistical multiple regression.

PubMed

Parisi Kern, Andrea; Ferreira Dias, Michele; Piva Kulakowski, Marlova; Paulo Gomes, Luciana

2015-05-01

Reducing construction waste is becoming a key environmental issue in the construction industry. The quantification of waste generation rates in the construction sector is an invaluable management tool in supporting mitigation actions. However, the quantification of waste can be a difficult process because of the specific characteristics and the wide range of materials used in different construction projects. Large variations are observed in the methods used to predict the amount of waste generated because of the range of variables involved in construction processes and the different contexts in which these methods are employed. This paper proposes a statistical model to determine the amount of waste generated in the construction of high-rise buildings by assessing the influence of design process and production system, often mentioned as the major culprits behind the generation of waste in construction. Multiple regression was used to conduct a case study based on multiple sources of data of eighteen residential buildings. The resulting statistical model produced dependent (i.e. amount of waste generated) and independent variables associated with the design and the production system used. The best regression model obtained from the sample data resulted in an adjusted R(2) value of 0.694, which means that it predicts approximately 69% of the factors involved in the generation of waste in similar constructions. Most independent variables showed a low determination coefficient when assessed in isolation, which emphasizes the importance of assessing their joint influence on the response (dependent) variable. Copyright © 2015 Elsevier Ltd. All rights reserved.

Uranium and strontium fate in waste-weathered sediments: Scaling of molecular processes to predict reactive transport

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

Chorover, Jon; Mueller, Karl; O'Day, Peggy

2016-04-02

transport behaviors that occured in bench-scale studies of waste-sediment interaction with parallel model systems studies of homogeneous nucleation and neo-phase dissolution. Initial plans were to compare results with core sample extractions from the acid uranium waste impacted U-8 and U-12 Cribs at Hanford (see original proposal and letter of collaboration from J. Zachara). However, this part of the project was impossible because funding for core extractions were eliminated from the DoE budget. Three distinct crib waste aqueous simulants (whose composition is based on the most up-to-date information from field site investigations) were reacted with Hanford sediments in batch and column systems. Coupling of contaminant uptake to mineral weathering was monitored using a suite of methods both during waste-sediment interaction, and after, when waste-weathered sediments were subjected to infusion with circumneutral background pore water solutions. Our research was designed to adapt as needed to maintain a strong dialogue between laboratory and modeling investigations so that model development was increasingly constrained by emergent data and understanding. Potential impact of the project to DOE: Better prediction of contaminant uranium transport was achieved by employing multi-faceted lines of inquiry to build a strong bridge between molecular- and field-scale information. By focusing multiple lines and scales of observation on a common experimental design, our collaborative team revealed non-linear and emergent behavior in contaminated weathering systems. A goal of the current project was to expand our modeling capabilities, originally focused on hyperalkaline legacy waste streams, to include acidic weathering reactions that, as described above, were expected to result in profoundly different products. We were able to achieve this goal, and showed that these products nonetheless undergo analogous silicate and non-silicate transformation, ripening and aging processes. Our

Geochemical transformations and modeling of two deep-well injected hazardous wastes

USGS Publications Warehouse

Roy, W.R.; Seyler, B.; Steele, J.D.; Mravik, S.C.; Moore, D.M.; Krapac, I.G.; Peden, J.M.; Griffin, R.A.

1991-01-01

Two liquid hazardous wastes (an alkaline brine-like solution and a dilute acidic waste) were mixed with finely ground rock samples of three injection-related lithologies (sandstone, dolomite, and siltstone) for 155 to 230 days at 325??K-10.8 MPa. The pH and inorganic chemical composition of the alkaline waste were not significantly altered by any of the rock samples after 230 days of mixing. The acidic waste was neutralized as a consequence of carbonate dissolution, ion exchange, or clay-mineral dissolution, and hence was transformed into a nonhazardous waste. Mixing the alkaline waste with the solid phases yielded several reaction products: brucite, Mg(OH)2; calcite, CaCO3; and possibly a type of sodium metasilicate. Clay-like minerals formed in the sandstone, and hydrotalcite, Mg6Al2-CO3(OH)16??4H2O, may have formed in the siltstone at trace levels. Mixing the alkaline waste with a synthetic brine yielded brucite, calcite, and whewellite (CaC2O4??H2O). The thermodynamic model PHRQPITZ predicted that brucite and calcite would precipitate from solution in the dolomite and siltstone mixtures and in the alkaline waste-brine system. The dilute acidic waste did not significantly alter the mineralogical composition of the three rock types after 155 days of contact. The model PHREEQE indicated that the calcite was thermodynamically stable in the dolomite and siltstone mixtures.

Demolition waste generation for development of a regional management chain model.

PubMed

Bernardo, Miguel; Gomes, Marta Castilho; de Brito, Jorge

2016-03-01

Even though construction and demolition waste (CDW) is the bulkiest waste stream, its estimation and composition in specific regions still faces major difficulties. Therefore new methods are required especially when it comes to make predictions limited to small areas, such as counties. This paper proposes one such method, which makes use of data collected from real demolition works and statistical information on the geographical area under study. Based on a correlation analysis between the demolition waste estimates and indicators such as population density, buildings ageing index, buildings density and land occupation type, relationships are established that can be used to determine demolition waste outputs in a given area. The derived models are presented and explained. This methodology is independent from the specific region with which it is exemplified (the Lisbon Metropolitan Area) and can therefore be applied to any region of the world, from the country to the county level. Generation of demolition waste data at the county level is the basis of the design of a systemic model for CDW management in a region. Future developments proposed include a mixed-integer linear programming formulation of such recycling network. Copyright © 2016 Elsevier Ltd. All rights reserved.

A model based on feature objects aided strategy to evaluate the methane generation from food waste by anaerobic digestion.

PubMed

Yu, Meijuan; Zhao, Mingxing; Huang, Zhenxing; Xi, Kezhong; Shi, Wansheng; Ruan, Wenquan

2018-02-01

A model based on feature objects (FOs) aided strategy was used to evaluate the methane generation from food waste by anaerobic digestion. The kinetics of feature objects was tested by the modified Gompertz model and the first-order kinetic model, and the first-order kinetic hydrolysis constants were used to estimate the reaction rate of homemade and actual food waste. The results showed that the methane yields of four feature objects were significantly different. The anaerobic digestion of homemade food waste and actual food waste had various methane yields and kinetic constants due to the different contents of FOs in food waste. Combining the kinetic equations with the multiple linear regression equation could well express the methane yield of food waste, as the R 2 of food waste was more than 0.9. The predictive methane yields of the two actual food waste were 528.22 mL g -1  TS and 545.29 mL g -1  TS with the model, while the experimental values were 527.47 mL g -1  TS and 522.1 mL g -1  TS, respectively. The relative error between the experimental cumulative methane yields and the predicted cumulative methane yields were both less than 5%. Copyright © 2017 Elsevier Ltd. All rights reserved.

Development of a hybrid model to predict construction and demolition waste: China as a case study.

PubMed

Song, Yiliao; Wang, Yong; Liu, Feng; Zhang, Yixin

2017-01-01

Construction and demolition waste (C&DW) is currently a worldwide issue, and the situation is the worst in China due to a rapid increase in the construction industry and the short life span of China's buildings. To create an opportunity out of this problem, comprehensive prevention measures and effective management strategies are urgently needed. One major gap in the literature of waste management is a lack of estimations on future C&DW generation. Therefore, this paper presents a forecasting procedure for C&DW in China that can forecast the quantity of each component in such waste. The proposed approach is based on a GM-SVR model that improves the forecasting effectiveness of the gray model (GM), which is achieved by adjusting the residual series by a support vector regression (SVR) method and a transition matrix that aims to estimate the discharge of each component in the C&DW. Through the proposed method, future C&DW volume are listed and analyzed containing their potential components and distribution in different provinces in China. Besides, model testing process provides mathematical evidence to validate the proposed model is an effective way to give future information of C&DW for policy makers. Copyright © 2016 Elsevier Ltd. All rights reserved.

Use of a Dual-Structure Constitutive Model for Predicting the Long-Term Behavior of an Expansive Clay Buffer in a Nuclear Waste Repository

DOE PAGES

Vilarrasa, Víctor; Rutqvist, Jonny; Blanco Martin, Laura; ...

2015-12-31

Expansive soils are suitable as backfill and buffer materials in engineered barrier systems to isolate heat-generating nuclear waste in deep geological formations. The canisters containing nuclear waste would be placed in tunnels excavated at a depth of several hundred meters. The expansive soil should provide enough swelling capacity to support the tunnel walls, thereby reducing the impact of the excavation-damaged zone on the long-term mechanical and flow-barrier performance. In addition to their swelling capacity, expansive soils are characterized by accumulating irreversible strain on suction cycles and by effects of microstructural swelling on water permeability that for backfill or buffer materialsmore » can significantly delay the time it takes to reach full saturation. In order to simulate these characteristics of expansive soils, a dual-structure constitutive model that includes two porosity levels is necessary. The authors present the formulation of a dual-structure model and describe its implementation into a coupled fluid flow and geomechanical numerical simulator. The authors use the Barcelona Basic Model (BBM), which is an elastoplastic constitutive model for unsaturated soils, to model the macrostructure, and it is assumed that the strains of the microstructure, which are volumetric and elastic, induce plastic strain to the macrostructure. The authors tested and demonstrated the capabilities of the implemented dual-structure model by modeling and reproducing observed behavior in two laboratory tests of expansive clay. As observed in the experiments, the simulations yielded nonreversible strain accumulation with suction cycles and a decreasing swelling capacity with increasing confining stress. Finally, the authors modeled, for the first time using a dual-structure model, the long-term (100,000 years) performance of a generic heat-generating nuclear waste repository with waste emplacement in horizontal tunnels backfilled with expansive clay and

Neural and hybrid modeling: an alternative route to efficiently predict the behavior of biotechnological processes aimed at biofuels obtainment.

PubMed

Curcio, Stefano; Saraceno, Alessandra; Calabrò, Vincenza; Iorio, Gabriele

2014-01-01

The present paper was aimed at showing that advanced modeling techniques, based either on artificial neural networks or on hybrid systems, might efficiently predict the behavior of two biotechnological processes designed for the obtainment of second-generation biofuels from waste biomasses. In particular, the enzymatic transesterification of waste-oil glycerides, the key step for the obtainment of biodiesel, and the anaerobic digestion of agroindustry wastes to produce biogas were modeled. It was proved that the proposed modeling approaches provided very accurate predictions of systems behavior. Both neural network and hybrid modeling definitely represented a valid alternative to traditional theoretical models, especially when comprehensive knowledge of the metabolic pathways, of the true kinetic mechanisms, and of the transport phenomena involved in biotechnological processes was difficult to be achieved.

Dose rate prediction methodology for remote handled transuranic waste workers at the waste isolation pilot plant.

PubMed

Hayes, Robert

2002-10-01

An approach is described for estimating future dose rates to Waste Isolation Pilot Plant workers processing remote handled transuranic waste. The waste streams will come from the entire U.S. Department of Energy complex and can take on virtually any form found from the processing sequences for defense-related production, radiochemistry, activation and related work. For this reason, the average waste matrix from all generator sites is used to estimate the average radiation fields over the facility lifetime. Innovative new techniques were applied to estimate expected radiation fields. Non-linear curve fitting techniques were used to predict exposure rate profiles from cylindrical sources using closed form equations for lines and disks. This information becomes the basis for Safety Analysis Report dose rate estimates and for present and future ALARA design reviews when attempts are made to reduce worker doses.

Neural and Hybrid Modeling: An Alternative Route to Efficiently Predict the Behavior of Biotechnological Processes Aimed at Biofuels Obtainment

PubMed Central

Saraceno, Alessandra; Calabrò, Vincenza; Iorio, Gabriele

2014-01-01

The present paper was aimed at showing that advanced modeling techniques, based either on artificial neural networks or on hybrid systems, might efficiently predict the behavior of two biotechnological processes designed for the obtainment of second-generation biofuels from waste biomasses. In particular, the enzymatic transesterification of waste-oil glycerides, the key step for the obtainment of biodiesel, and the anaerobic digestion of agroindustry wastes to produce biogas were modeled. It was proved that the proposed modeling approaches provided very accurate predictions of systems behavior. Both neural network and hybrid modeling definitely represented a valid alternative to traditional theoretical models, especially when comprehensive knowledge of the metabolic pathways, of the true kinetic mechanisms, and of the transport phenomena involved in biotechnological processes was difficult to be achieved. PMID:24516363

Leaching, geochemical modelling and field verification of a municipal solid waste and a predominantly non-degradable waste landfill.

PubMed

van der Sloot, H A; Kosson, D S; van Zomeren, A

2017-05-01

In spite of the known heterogeneity, wastes destined for landfilling can be characterised for their leaching behaviour by the same protocols as soil, contaminated soil, sediments, sludge, compost, wood, waste and construction products. Characterisation leaching tests used in conjunction with chemical speciation modelling results in much more detailed insights into release controlling processes and factors than single step batch leaching tests like TCLP (USEPA) and EN12457 (EU Landfill Directive). Characterisation testing also can provide the potential for mechanistic impact assessments by making use of a chemical speciation fingerprint (CSF) derived from pH dependence leaching test results. This CSF then forms the basis for subsequent chemical equilibrium and reactive transport modelling to assess environmental impact in a landfill scenario under relevant exposure conditions, including conditions not readily evaluated through direct laboratory testing. This approach has been applied to municipal solid waste (MSW) and predominantly non-degradable waste (PNW) that is representative of a significant part of waste currently being landfilled. This work has shown that a multi-element modelling approach provides a useful description of the release from each of these matrices because relevant release controlling properties and parameters (mineral dissolution/precipitation, sorption on Fe and Al oxides, clay interaction, interaction with dissolved and particulate organic carbon and incorporation in solid solutions) are taken into consideration. Inclusion of dissolved and particulate organic matter in the model is important to properly describe release of the low concentration trace constituents observed in the leachate. The CSF allows the prediction of release under different redox and degradation conditions in the landfill by modifying the redox status and level of dissolved and particulate organic matter in the model runs. The CSF for MSW provides a useful starting point

Multiple system modelling of waste management

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

Eriksson, Ola, E-mail: ola.eriksson@hig.se; Department of Building, Energy and Environmental Engineering, University of Gaevle, SE 801 76 Gaevle; Bisaillon, Mattias, E-mail: mattias.bisaillon@profu.se

2011-12-15

Highlights: > Linking of models will provide a more complete, correct and credible picture of the systems. > The linking procedure is easy to perform and also leads to activation of project partners. > The simulation procedure is a bit more complicated and calls for the ability to run both models. - Abstract: Due to increased environmental awareness, planning and performance of waste management has become more and more complex. Therefore waste management has early been subject to different types of modelling. Another field with long experience of modelling and systems perspective is energy systems. The two modelling traditions havemore » developed side by side, but so far there are very few attempts to combine them. Waste management systems can be linked together with energy systems through incineration plants. The models for waste management can be modelled on a quite detailed level whereas surrounding systems are modelled in a more simplistic way. This is a problem, as previous studies have shown that assumptions on the surrounding system often tend to be important for the conclusions. In this paper it is shown how two models, one for the district heating system (MARTES) and another one for the waste management system (ORWARE), can be linked together. The strengths and weaknesses with model linking are discussed when compared to simplistic assumptions on effects in the energy and waste management systems. It is concluded that the linking of models will provide a more complete, correct and credible picture of the consequences of different simultaneous changes in the systems. The linking procedure is easy to perform and also leads to activation of project partners. However, the simulation procedure is a bit more complicated and calls for the ability to run both models.« less

Model development for household waste prevention behaviour

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

Bortoleto, Ana Paula, E-mail: a.bortoleto@sheffield.ac.uk; Kurisu, Kiyo H.; Hanaki, Keisuke

Highlights: Black-Right-Pointing-Pointer We model waste prevention behaviour using structure equation modelling. Black-Right-Pointing-Pointer We merge attitude-behaviour theories with wider models from environmental psychology. Black-Right-Pointing-Pointer Personal norms and perceived behaviour control are the main behaviour predictors. Black-Right-Pointing-Pointer Environmental concern, moral obligation and inconvenience are the main influence on the behaviour. Black-Right-Pointing-Pointer Waste prevention and recycling are different dimensions of waste management behaviour. - Abstract: Understanding waste prevention behaviour (WPB) could enable local governments and decision makers to design more-effective policies for reducing the amount of waste that is generated. By merging well-known attitude-behaviour theories with elements from wider models from environmental psychology,more » an extensive cognitive framework that provides new and valuable insights is developed for understanding the involvement of individuals in waste prevention. The results confirm the usefulness of the theory of planned behaviour and of Schwartz's altruistic behaviour model as bases for modelling participation in waste prevention. A more elaborate integrated model of prevention was shown to be necessary for the complete analysis of attitudinal aspects associated with waste prevention. A postal survey of 158 respondents provided empirical support for eight of 12 hypotheses. The proposed structural equation indicates that personal norms and perceived behaviour control are the main predictors and that, unlike the case of recycling, subjective norms have a weak influence on WPB. It also suggests that, since social norms have not presented a direct influence, WPB is likely to be influenced by a concern for the environment and the community as well by perceptions of moral obligation and inconvenience. Results also proved that recycling and waste prevention represent different dimensions of

BIOLEACH: Coupled modeling of leachate and biogas production on solid waste landfills

NASA Astrophysics Data System (ADS)

Rodrigo-Clavero, Maria-Elena; Rodrigo-Ilarri, Javier

2015-04-01

One of the most important factors to address when performing the environmental impact assessment of urban solid waste landfills is to evaluate the leachate production. Leachate management (collection and treatment) is also one of the most relevant economical aspects to take into account during the landfill life. Leachate is formed as a solution of biological and chemical components during operational and post-operational phases on urban solid waste landfills as a combination of different processes that involve water gains and looses inside the solid waste mass. Infiltration of external water coming from precipitation is the most important component on this water balance. However, anaerobic waste decomposition and biogas formation processes play also a role on the balance as water-consuming processes. The production of leachate one biogas is therefore a coupled process. Biogas production models usually consider optimal conditions of water content on the solid waste mass. However, real conditions during the operational phase of the landfill may greatly differ from these optimal conditions. In this work, the first results obtained to predict both the leachate and the biogas production as a single coupled phenomenon on real solid waste landfills are shown. The model is applied on a synthetic case considering typical climatological conditions of Mediterranean catchments.

Time-series-based hybrid mathematical modelling method adapted to forecast automotive and medical waste generation: Case study of Lithuania.

PubMed

Karpušenkaitė, Aistė; Ruzgas, Tomas; Denafas, Gintaras

2018-05-01

The aim of the study was to create a hybrid forecasting method that could produce higher accuracy forecasts than previously used 'pure' time series methods. Mentioned methods were already tested with total automotive waste, hazardous automotive waste, and total medical waste generation, but demonstrated at least a 6% error rate in different cases and efforts were made to decrease it even more. Newly developed hybrid models used a random start generation method to incorporate different time-series advantages and it helped to increase the accuracy of forecasts by 3%-4% in hazardous automotive waste and total medical waste generation cases; the new model did not increase the accuracy of total automotive waste generation forecasts. Developed models' abilities to forecast short- and mid-term forecasts were tested using prediction horizon.

Electrochemical Corrosion Studies for Modeling Metallic Waste Form Release Rates

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

Poineau, Frederic; Tamalis, Dimitri

The isotope 99Tc is an important fission product generated from nuclear power production. Because of its long half-life (t 1/2 = 2.13 ∙ 10 5 years) and beta-radiotoxicity (β⁻ = 292 keV), it is a major concern in the long-term management of spent nuclear fuel. In the spent nuclear fuel, Tc is present as an alloy with Mo, Ru, Rh, and Pd called the epsilon-phase, the relative amount of which increases with fuel burn-up. In some separation schemes for spent nuclear fuel, Tc would be separated from the spent fuel and disposed of in a durable waste form. Technetium wastemore » forms under consideration include metallic alloys, oxide ceramics and borosilicate glass. In the development of a metallic waste form, after separation from the spent fuel, Tc would be converted to the metal, incorporated into an alloy and the resulting waste form stored in a repository. Metallic alloys under consideration include Tc–Zr alloys, Tc–stainless steel alloys and Tc–Inconel alloys (Inconel is an alloy of Ni, Cr and iron which is resistant to corrosion). To predict the long-term behavior of the metallic Tc waste form, understanding the corrosion properties of Tc metal and Tc alloys in various chemical environments is needed, but efforts to model the behavior of Tc metallic alloys are limited. One parameter that should also be considered in predicting the long-term behavior of the Tc waste form is the ingrowth of stable Ru that occurs from the radioactive decay of 99Tc ( 99Tc → 99Ru + β⁻). After a geological period of time, significant amounts of Ru will be present in the Tc and may affect its corrosion properties. Studying the effect of Ru on the corrosion behavior of Tc is also of importance. In this context, we studied the electrochemical behavior of Tc metal, Tc-Ni alloys (to model Tc-Inconel alloy) and Tc-Ru alloys in acidic media. The study of Tc-U alloys has also been performed in order to better understand the nature of Tc in metallic spent fuel. Computational

Forecasting municipal solid waste generation using prognostic tools and regression analysis.

PubMed

Ghinea, Cristina; Drăgoi, Elena Niculina; Comăniţă, Elena-Diana; Gavrilescu, Marius; Câmpean, Teofil; Curteanu, Silvia; Gavrilescu, Maria

2016-11-01

For an adequate planning of waste management systems the accurate forecast of waste generation is an essential step, since various factors can affect waste trends. The application of predictive and prognosis models are useful tools, as reliable support for decision making processes. In this paper some indicators such as: number of residents, population age, urban life expectancy, total municipal solid waste were used as input variables in prognostic models in order to predict the amount of solid waste fractions. We applied Waste Prognostic Tool, regression analysis and time series analysis to forecast municipal solid waste generation and composition by considering the Iasi Romania case study. Regression equations were determined for six solid waste fractions (paper, plastic, metal, glass, biodegradable and other waste). Accuracy Measures were calculated and the results showed that S-curve trend model is the most suitable for municipal solid waste (MSW) prediction. Copyright © 2016 Elsevier Ltd. All rights reserved.

A model to minimize joint total costs for industrial waste producers and waste management companies.

PubMed

Tietze-Stöckinger, Ingela; Fichtner, Wolf; Rentz, Otto

2004-12-01

The model LINKopt is a mixed-integer, linear programming model for mid- and long-term planning of waste management options on an inter-company level. There has been a large increase in the transportation of waste material in Germany, which has been attributed to the implementation of the European Directive 75/442/EEC on waste. Similar situations are expected to emerge in other European countries. The model LINKopt has been developed to determine a waste management system with minimal decision-relevant costs considering transportation, handling, storage and treatment of waste materials. The model can serve as a tool to evaluate various waste management strategies and to obtain the optimal combination of investment options. In addition to costs, ecological aspects are considered by determining the total mileage associated with the waste management system. The model has been applied to a German case study evaluating different investment options for a co-operation between Daimler-Chrysler AG at Rastatt, its suppliers, and the waste management company SITA P+R GmbH. The results show that the installation of waste management facilities at the premises of the waste producer would lead to significant reductions in costs and transportation.

Modeling the energy content of combustible ship-scrapping waste at Alang-Sosiya, India, using multiple regression analysis.

PubMed

Reddy, M Srinivasa; Basha, Shaik; Joshi, H V; Sravan Kumar, V G; Jha, B; Ghosh, P K

2005-01-01

Alang-Sosiya is the largest ship-scrapping yard in the world, established in 1982. Every year an average of 171 ships having a mean weight of 2.10 x 10(6)(+/-7.82 x 10(5)) of light dead weight tonnage (LDT) being scrapped. Apart from scrapped metals, this yard generates a massive amount of combustible solid waste in the form of waste wood, plastic, insulation material, paper, glass wool, thermocol pieces (polyurethane foam material), sponge, oiled rope, cotton waste, rubber, etc. In this study multiple regression analysis was used to develop predictive models for energy content of combustible ship-scrapping solid wastes. The scope of work comprised qualitative and quantitative estimation of solid waste samples and performing a sequential selection procedure for isolating variables. Three regression models were developed to correlate the energy content (net calorific values (LHV)) with variables derived from material composition, proximate and ultimate analyses. The performance of these models for this particular waste complies well with the equations developed by other researchers (Dulong, Steuer, Scheurer-Kestner and Bento's) for estimating energy content of municipal solid waste.

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.

Modeling Organic Contaminant Desorption from Municipal Solid Waste Components

NASA Astrophysics Data System (ADS)

Knappe, D. R.; Wu, B.; Barlaz, M. A.

2002-12-01

Approximately 25% of the sites on the National Priority List (NPL) of Superfund are municipal landfills that accepted hazardous waste. Unlined landfills typically result in groundwater contamination, and priority pollutants such as alkylbenzenes are often present. To select cost-effective risk management alternatives, better information on factors controlling the fate of hydrophobic organic contaminants (HOCs) in landfills is required. The objectives of this study were (1) to investigate the effects of HOC aging time, anaerobic sorbent decomposition, and leachate composition on HOC desorption rates, and (2) to simulate HOC desorption rates from polymers and biopolymer composites with suitable diffusion models. Experiments were conducted with individual components of municipal solid waste (MSW) including polyvinyl chloride (PVC), high-density polyethylene (HDPE), newsprint, office paper, and model food and yard waste (rabbit food). Each of the biopolymer composites (office paper, newsprint, rabbit food) was tested in both fresh and anaerobically decomposed form. To determine the effects of aging on alkylbenzene desorption rates, batch desorption tests were performed after sorbents were exposed to toluene for 30 and 250 days in flame-sealed ampules. Desorption tests showed that alkylbenzene desorption rates varied greatly among MSW components (PVC slowest, fresh rabbit food and newsprint fastest). Furthermore, desorption rates decreased as aging time increased. A single-parameter polymer diffusion model successfully described PVC and HDPE desorption data, but it failed to simulate desorption rate data for biopolymer composites. For biopolymer composites, a three-parameter biphasic polymer diffusion model was employed, which successfully simulated both the initial rapid and the subsequent slow desorption of toluene. Toluene desorption rates from MSW mixtures were predicted for typical MSW compositions in the years 1960 and 1997. For the older MSW mixture, which had a

Limitations to the use of two-dimensional thermal modeling of a nuclear waste repository

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

Davis, B.W.

1979-01-04

Thermal modeling of a nuclear waste repository is basic to most waste management predictive models. It is important that the modeling techniques accurately determine the time-dependent temperature distribution of the waste emplacement media. Recent modeling studies show that the time-dependent temperature distribution can be accurately modeled in the far-field using a 2-dimensional (2-D) planar numerical model; however, the near-field cannot be modeled accurately enough by either 2-D axisymmetric or 2-D planar numerical models for repositories in salt. The accuracy limits of 2-D modeling were defined by comparing results from 3-dimensional (3-D) TRUMP modeling with results from both 2-D axisymmetric andmore » 2-D planar. Both TRUMP and ADINAT were employed as modeling tools. Two-dimensional results from the finite element code, ADINAT were compared with 2-D results from the finite difference code, TRUMP; they showed almost perfect correspondence in the far-field. This result adds substantially to confidence in future use of ADINAT and its companion stress code ADINA for thermal stress analysis. ADINAT was found to be somewhat sensitive to time step and mesh aspect ratio. 13 figures, 4 tables.« less

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

Conceptual models governing leaching behavior and their long-term predictive capability

USGS Publications Warehouse

Claassen, Hans C.

1981-01-01

Six models that may be used to describe the interaction of radioactive waste solids with aqueous solutions are as follows:Simple linear mass transfer;Simple parabolic mass transfer;Parabolic mass transfer with the formation of a diffusion-limiting surface layer at an arbitrary time;Initial parabolic mass transfer followed by linear mass transfer at an arbitrary time;Parabolic (or linear) mass transfer and concomitant surface sorption; andParabolic (or linear) mass transfer and concomitant chemical precipitation.Some of these models lead to either illogical or unrealistic predictions when published data are extrapolated to long times. These predictions result because most data result from short-term experimentation. Probably for longer times, processes will occur that have not been observed in the shorter experiments. This hypothesis has been verified by mass-transfer data from laboratory experiments using natural volcanic glass to predict the composition of groundwater. That such rate-limiting mechanisms do occur is reassuring, although now it is not possible to deduce a single mass-transfer limiting mechanism that could control the solution concentration of all components of all waste forms being investigated. Probably the most reasonable mechanisms are surface sorption and chemical precipitation of the species of interest. Another is limiting of mass transfer by chemical precipitation on the waste form surface of a substance not containing the species of interest, that is, presence of a diffusion-limiting layer. The presence of sorption and chemical precipitation as factors limiting mass transfer has been verified in natural groundwater systems, whereas the diffusion-limiting mechanism has not been verified yet.

Progress toward bridging from atomistic to continuum modeling to predict nuclear waste glass dissolution.

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

Zapol, Peter; Bourg, Ian; Criscenti, Louise Jacqueline

2011-10-01

This report summarizes research performed for the Nuclear Energy Advanced Modeling and Simulation (NEAMS) Subcontinuum and Upscaling Task. The work conducted focused on developing a roadmap to include molecular scale, mechanistic information in continuum-scale models of nuclear waste glass dissolution. This information is derived from molecular-scale modeling efforts that are validated through comparison with experimental data. In addition to developing a master plan to incorporate a subcontinuum mechanistic understanding of glass dissolution into continuum models, methods were developed to generate constitutive dissolution rate expressions from quantum calculations, force field models were selected to generate multicomponent glass structures and gel layers,more » classical molecular modeling was used to study diffusion through nanopores analogous to those in the interfacial gel layer, and a micro-continuum model (K{mu}C) was developed to study coupled diffusion and reaction at the glass-gel-solution interface.« less

Simulating settlement during waste placement at a landfill with waste lifts placed under frozen conditions.

PubMed

Van Geel, Paul J; Murray, Kathleen E

2015-12-01

Twelve instrument bundles were placed within two waste profiles as waste was placed in an operating landfill in Ste. Sophie, Quebec, Canada. The settlement data were simulated using a three-component model to account for primary or instantaneous compression, secondary compression or mechanical creep and biodegradation induced settlement. The regressed model parameters from the first waste layer were able to predict the settlement of the remaining four waste layers with good agreement. The model parameters were compared to values published in the literature. A MSW landfill scenario referenced in the literature was used to illustrate how the parameter values from the different studies predicted settlement. The parameters determined in this study and other studies with total waste heights between 15 and 60 m provided similar estimates of total settlement in the long term while the settlement rates and relative magnitudes of the three components varied. The parameters determined based on studies with total waste heights less than 15m resulted in larger secondary compression indices and lower biodegradation induced settlements. When these were applied to a MSW landfill scenario with a total waste height of 30 m, the settlement was overestimated and provided unrealistic values. This study concludes that more field studies are needed to measure waste settlement during the filling stage of landfill operations and more field data are needed to assess different settlement models and their respective parameters. Copyright © 2015 Elsevier Ltd. All rights reserved.

Modelling the sulfate capacity of simulated radioactive waste borosilicate glasses

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

Bingham, P. A.; Vaishnav, S.; Forder, S. D.

2017-02-01

The capacity of simulated high-level radioactive waste borosilicate glasses to incorporate sulfate has been studied as a function of glass composition. Combined Raman, 57Fe Mössbauer and literature evidence supports the attribution of coordination numbers and oxidation states of constituent cations for the purposes of modelling, and results confirm the validity of correlating sulfate incorporation in multicomponent borosilicate radioactive waste glasses with different models. A strong compositional dependency is observed and this can be described by an inverse linear relationship between incorporated sulfate (mol% SO 4 2-) and total cation field strength index of the glass, Σ(z/a 2), with a highmore » goodness-of-fit (R 2 ≈ 0.950). Similar relationships are also obtained if theoretical optical basicity, Λ th (R 2 ≈ 0.930) or non-bridging oxygen per tetrahedron ratio, NBO/T (R 2 ≈ 0.919), are used. Results support the application of these models, and in particular Σ(z/a 2), as predictive tools to aid the development of new glass compositions with enhanced sulfate capacities.« less

Model development for household waste prevention behaviour.

PubMed

Bortoleto, Ana Paula; Kurisu, Kiyo H; Hanaki, Keisuke

2012-12-01

Understanding waste prevention behaviour (WPB) could enable local governments and decision makers to design more-effective policies for reducing the amount of waste that is generated. By merging well-known attitude-behaviour theories with elements from wider models from environmental psychology, an extensive cognitive framework that provides new and valuable insights is developed for understanding the involvement of individuals in waste prevention. The results confirm the usefulness of the theory of planned behaviour and of Schwartz's altruistic behaviour model as bases for modelling participation in waste prevention. A more elaborate integrated model of prevention was shown to be necessary for the complete analysis of attitudinal aspects associated with waste prevention. A postal survey of 158 respondents provided empirical support for eight of 12 hypotheses. The proposed structural equation indicates that personal norms and perceived behaviour control are the main predictors and that, unlike the case of recycling, subjective norms have a weak influence on WPB. It also suggests that, since social norms have not presented a direct influence, WPB is likely to be influenced by a concern for the environment and the community as well by perceptions of moral obligation and inconvenience. Results also proved that recycling and waste prevention represent different dimensions of waste management behaviour requiring particular approaches to increase individuals' engagement in future policies. Copyright © 2012 Elsevier Ltd. All rights reserved.

A composite numerical model for assessing subsurface transport of oily wastes and chemical constituents

NASA Astrophysics Data System (ADS)

Panday, S.; Wu, Y. S.; Huyakorn, P. S.; Wade, S. C.; Saleem, Z. A.

1997-02-01

Subsurface fate and transport models are utilized to predict concentrations of chemicals leaching from wastes into downgradient receptor wells. The contaminant concentrations in groundwater provide a measure of the risk to human health and the environment. The level of potential risk is currently used by the U.S. Environmental Protection Agency to determine whether management of the wastes should conform to hazardous waste management standards. It is important that the transport and fate of contaminants is simulated realistically. Most models in common use are inappropriate for simulating the migration of wastes containing significant fractions of nonaqueous-phase liquids (NAPLs). The migration of NAPL and its dissolved constituents may not be reliably predicted using conventional aqueous-phase transport simulations. To overcome this deficiency, an efficient and robust regulatory assessment model incorporating multiphase flow and transport in the unsaturated and saturated zones of the subsurface environment has been developed. The proposed composite model takes into account all of the major transport processes including infiltration and ambient flow of NAPL, entrapment of residual NAPL, adsorption, volatilization, degradation, dissolution of chemical constituents, and transport by advection and hydrodynamic dispersion. Conceptually, the subsurface is treated as a composite unsaturated zone-saturated zone system. The composite simulator consists of three major interconnected computational modules representing the following components of the migration pathway: (1) vertical multiphase flow and transport in the unsaturated zone; (2) areal movement of the free-product lens in the saturated zone with vertical equilibrium; and (3) three-dimensional aqueous-phase transport of dissolved chemicals in ambient groundwater. Such a composite model configuration promotes computational efficiency and robustness (desirable for regulatory assessment applications). Two examples are

Accelerated Weathering of Waste Glass at 90°C with the Pressurized Unsaturated Flow (PUF) Apparatus: Implications for Predicting Glass Corrosion with a Reactive Transport Model

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

Pierce, Eric M.; Bacon, Diana H.

2009-09-21

The interest in the long-term durability of waste glass stems from the need to predict radionuclide release rates from the corroding glass over geologic time-scales. Several long-term test methods have been developed to accelerate the glass-water reaction [drip test, vapor hydration test, product consistency test-B, and pressurized unsaturated flow (PUF)]. Currently, the PUF test is the only method that can mimic the unsaturated hydraulic properties expected in a subsurface disposal facility and simultaneously monitor the glass-water reaction. PUF tests are being conducted to accelerate the weathering of glass and validate the model parameters being used to predict long-term glass behavior.more » One dimensional reactive chemical transport simulations of glass dissolution and secondary phase formation during a 1.5-year long PUF experiment was conducted with the subsurface transport over reactive multi-phases (STORM) code. Results show that parameterization of the computer model by combining direct laboratory measurements and thermodynamic data provides an integrated approach to predicting glass behavior over geologic-time scales.« less

A model for quantifying construction waste in projects according to the European waste list.

PubMed

Llatas, C

2011-06-01

The new EU challenge is to recover 70% by weight of C&D waste in 2020. Literature reveals that one major barrier is the lack of data. Therefore, this paper presents a model which allows technicians to estimate C&D waste during the design stage in order to promote prevention and recovery. The types and quantities of CW are estimated and managed according to EU guidelines, by building elements and specifically for each project. The model would allow detection of the source of the waste and to adopt other alternative procedures which delete hazardous waste and reduce CW. Likewise, it develops a systematic structure of the construction process, a waste classification system and some analytical expressions which are based on factors. These factors depend on technology and represent a standard on site. It would allow to develop a database of waste anywhere. A Spanish case study is covered. Factors were obtained by studying over 20 dwellings. The source and types of packaging waste, remains, soil and hazardous waste were estimated in detail and were compared with other studies. Results reveal that the model can be implemented in projects and the chances of reducing and recovery C&D waste could be increased, well above the EU challenge. Copyright © 2011 Elsevier Ltd. All rights reserved.

Mathematical modeling of olive mill waste composting process.

PubMed

Vasiliadou, Ioanna A; Muktadirul Bari Chowdhury, Abu Khayer Md; Akratos, Christos S; Tekerlekopoulou, Athanasia G; Pavlou, Stavros; Vayenas, Dimitrios V

2015-09-01

The present study aimed at developing an integrated mathematical model for the composting process of olive mill waste. The multi-component model was developed to simulate the composting of three-phase olive mill solid waste with olive leaves and different materials as bulking agents. The modeling system included heat transfer, organic substrate degradation, oxygen consumption, carbon dioxide production, water content change, and biological processes. First-order kinetics were used to describe the hydrolysis of insoluble organic matter, followed by formation of biomass. Microbial biomass growth was modeled with a double-substrate limitation by hydrolyzed available organic substrate and oxygen using Monod kinetics. The inhibitory factors of temperature and moisture content were included in the system. The production and consumption of nitrogen and phosphorous were also included in the model. In order to evaluate the kinetic parameters, and to validate the model, six pilot-scale composting experiments in controlled laboratory conditions were used. Low values of hydrolysis rates were observed (0.002841/d) coinciding with the high cellulose and lignin content of the composting materials used. Model simulations were in good agreement with the experimental results. Sensitivity analysis was performed and the modeling efficiency was determined to further evaluate the model predictions. Results revealed that oxygen simulations were more sensitive on the input parameters of the model compared to those of water, temperature and insoluble organic matter. Finally, the Nash and Sutcliff index (E), showed that the experimental data of insoluble organic matter (E>0.909) and temperature (E>0.678) were better simulated than those of water. Copyright © 2015 Elsevier Ltd. All rights reserved.

Nonlinear Autoregressive Exogenous modeling of a large anaerobic digester producing biogas from cattle waste.

PubMed

Dhussa, Anil K; Sambi, Surinder S; Kumar, Shashi; Kumar, Sandeep; Kumar, Surendra

2014-10-01

In waste-to-energy plants, there is every likelihood of variations in the quantity and characteristics of the feed. Although intermediate storage tanks are used, but many times these are of inadequate capacity to dampen the variations. In such situations an anaerobic digester treating waste slurry operates under dynamic conditions. In this work a special type of dynamic Artificial Neural Network model, called Nonlinear Autoregressive Exogenous model, is used to model the dynamics of anaerobic digesters by using about one year data collected on the operating digesters. The developed model consists of two hidden layers each having 10 neurons, and uses 18days delay. There are five neurons in input layer and one neuron in output layer for a day. Model predictions of biogas production rate are close to plant performance within ±8% deviation. Copyright © 2014 Elsevier Ltd. All rights reserved.

Forecasting of municipal solid waste quantity in a developing country using multivariate grey models

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

Intharathirat, Rotchana, E-mail: rotchana.in@gmail.com; Abdul Salam, P., E-mail: salam@ait.ac.th; Kumar, S., E-mail: kumar@ait.ac.th

Highlights: • Grey model can be used to forecast MSW quantity accurately with the limited data. • Prediction interval overcomes the uncertainty of MSW forecast effectively. • A multivariate model gives accuracy associated with factors affecting MSW quantity. • Population, urbanization, employment and household size play role for MSW quantity. - Abstract: In order to plan, manage and use municipal solid waste (MSW) in a sustainable way, accurate forecasting of MSW generation and composition plays a key role. It is difficult to carry out the reliable estimates using the existing models due to the limited data available in the developingmore » countries. This study aims to forecast MSW collected in Thailand with prediction interval in long term period by using the optimized multivariate grey model which is the mathematical approach. For multivariate models, the representative factors of residential and commercial sectors affecting waste collected are identified, classified and quantified based on statistics and mathematics of grey system theory. Results show that GMC (1, 5), the grey model with convolution integral, is the most accurate with the least error of 1.16% MAPE. MSW collected would increase 1.40% per year from 43,435–44,994 tonnes per day in 2013 to 55,177–56,735 tonnes per day in 2030. This model also illustrates that population density is the most important factor affecting MSW collected, followed by urbanization, proportion employment and household size, respectively. These mean that the representative factors of commercial sector may affect more MSW collected than that of residential sector. Results can help decision makers to develop the measures and policies of waste management in long term period.« less

Modelling municipal solid waste generation: a review.

PubMed

Beigl, Peter; Lebersorger, Sandra; Salhofer, Stefan

2008-01-01

The objective of this paper is to review previously published models of municipal solid waste generation and to propose an implementation guideline which will provide a compromise between information gain and cost-efficient model development. The 45 modelling approaches identified in a systematic literature review aim at explaining or estimating the present or future waste generation using economic, socio-demographic or management-orientated data. A classification was developed in order to categorise these highly heterogeneous models according to the following criteria--the regional scale, the modelled waste streams, the hypothesised independent variables and the modelling method. A procedural practice guideline was derived from a discussion of the underlying models in order to propose beneficial design options concerning regional sampling (i.e., number and size of observed areas), waste stream definition and investigation, selection of independent variables and model validation procedures. The practical application of the findings was demonstrated with two case studies performed on different regional scales, i.e., on a household and on a city level. The findings of this review are finally summarised in the form of a relevance tree for methodology selection.

Modeling Nitrogen Decrease in Water Lettuce Ponds from Waste Stabilization Ponds

NASA Astrophysics Data System (ADS)

Putri, Gitta Agnes; Sunarsih

2018-02-01

This paper presents about the dynamic modeling of the Water Lettuce ponds as a form of improvement from the Water Hyacinth ponds. The purpose of this paper is to predict nitrogen decrease and nitrogen transformation in Water Lettuce ponds integrated with Waste Stabilization Ponds. The model consists of 4 mass balances, namely Dissolved Organic Nitrogen (DON), Particulate Organic Nitrogen (PON), ammonium (NH4+), Nitrate and Nitrite (NOx). The process of nitrogen transformation which considered in a Water Lettuce ponds, namely hydrolysis, mineralization, nitrification, denitrification, plant and bacterial uptake processes. Numerical simulations are performed by giving the values of parameters and the initial values of nitrogen compounds based on a review of previous studies. Numerical results show that the rate of change in the concentration of nitrogen compounds in the integration ponds of waste stabilization and water lettuce decreases and reaches stable at different times.

Optimization of waste combinations during in-vessel composting of agricultural waste.

PubMed

Varma, V Sudharsan; Kalamdhad, Ajay S; Kumar, Bimlesh

2017-01-01

In-vessel composting of agricultural waste is a well-described approach for stabilization of compost within a short time period. Although composting studies have shown the different combinations of waste materials for producing good quality compost, studies of the particular ratio of the waste materials in the mix are still limited. In the present study, composting was conducted with a combination of vegetable waste, cow dung, sawdust and dry leaves using a 550 L rotary drum composter. Application of a radial basis functional neural network was used to simulate the composting process. The model utilizes physico-chemical parameters with different waste materials as input variables and three output variables: volatile solids, soluble biochemical oxygen demand and carbon dioxide evolution. For the selected model, the coefficient of determination reached the high value of 0.997. The complicated interaction of agricultural waste components during composting makes it a nonlinear problem so it is difficult to find the optimal waste combinations for producing quality compost. Optimization of a trained radial basis functional model has yielded the optimal proportion as 62 kg, 17 kg and 9 kg for vegetable waste, cow dung and sawdust, respectively. The results showed that the predictive radial basis functional model described for drum composting of agricultural waste was well suited for organic matter degradation and can be successfully applied.

Dynamic metabolic modeling of heterotrophic and mixotrophic microalgal growth on fermentative wastes

PubMed Central

Baroukh, Caroline; Turon, Violette; Bernard, Olivier

2017-01-01

Microalgae are promising microorganisms for the production of numerous molecules of interest, such as pigments, proteins or triglycerides that can be turned into biofuels. Heterotrophic or mixotrophic growth on fermentative wastes represents an interesting approach to achieving higher biomass concentrations, while reducing cost and improving the environmental footprint. Fermentative wastes generally consist of a blend of diverse molecules and it is thus crucial to understand microalgal metabolism in such conditions, where switching between substrates might occur. Metabolic modeling has proven to be an efficient tool for understanding metabolism and guiding the optimization of biomass or target molecule production. Here, we focused on the metabolism of Chlorella sorokiniana growing heterotrophically and mixotrophically on acetate and butyrate. The metabolism was represented by 172 metabolic reactions. The DRUM modeling framework with a mildly relaxed quasi-steady-state assumption was used to account for the switching between substrates and the presence of light. Nine experiments were used to calibrate the model and nine experiments for the validation. The model efficiently predicted the experimental data, including the transient behavior during heterotrophic, autotrophic, mixotrophic and diauxic growth. It shows that an accurate model of metabolism can now be constructed, even in dynamic conditions, with the presence of several carbon substrates. It also opens new perspectives for the heterotrophic and mixotrophic use of microalgae, especially for biofuel production from wastes. PMID:28582469

CHEMICAL ANALYSIS OF SIMULATED HIGH LEVEL WASTE GLASSES TO SUPPORT SULFATE SOLUBILITY MODELING

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

Fox, K.; Marra, J.

2014-08-14

The U.S. Department of Energy (DOE), Office of Environmental Management (EM) is sponsoring an international, collaborative project to develop a fundamental model for sulfate solubility in nuclear waste glass. The solubility of sulfate has a significant impact on the achievable waste loading for nuclear waste forms both within the DOE complex and to some extent at U.K. sites. The development of enhanced borosilicate glass compositions with improved sulfate solubility will allow for higher waste loadings and accelerated cleanup missions. Much of the previous work on improving sulfate retention in waste glasses has been done on an empirical basis, making itmore » difficult to apply the findings to future waste compositions despite the large number of glass systems studied. A more fundamental, rather than empirical, model of sulfate solubility in glass, under development at Sheffield Hallam University (SHU), could provide a solution to the issues of sulfate solubility. The model uses the normalized cation field strength index as a function of glass composition to predict sulfate capacity, and has shown early success for some glass systems. The objective of the current scope is to mature the sulfate solubility model to the point where it can be used to guide glass composition development for DOE waste vitrification efforts, allowing for enhanced waste loadings and waste throughput. A series of targeted glass compositions was selected to resolve data gaps in the current model. SHU fabricated these glasses and sent samples to the Savannah River National Laboratory (SRNL) for chemical composition analysis. SHU will use the resulting data to enhance the sulfate solubility model and resolve any deficiencies. In this report, SRNL provides chemical analyses for simulated waste glasses fabricated SHU in support of sulfate solubility model development. A review of the measured compositions revealed that there are issues with the B{sub 2}O{sub 3} and Fe{sub 2}O{sub 3

Optimal waste-to-energy strategy assisted by GIS For sustainable solid waste management

NASA Astrophysics Data System (ADS)

Tan, S. T.; Hashim, H.

2014-02-01

Municipal solid waste (MSW) management has become more complex and costly with the rapid socio-economic development and increased volume of waste. Planning a sustainable regional waste management strategy is a critical step for the decision maker. There is a great potential for MSW to be used for the generation of renewable energy through waste incineration or landfilling with gas capture system. However, due to high processing cost and cost of resource transportation and distribution throughout the waste collection station and power plant, MSW is mostly disposed in the landfill. This paper presents an optimization model incorporated with GIS data inputs for MSW management. The model can design the multi-period waste-to-energy (WTE) strategy to illustrate the economic potential and tradeoffs for MSW management under different scenarios. The model is capable of predicting the optimal generation, capacity, type of WTE conversion technology and location for the operation and construction of new WTE power plants to satisfy the increased energy demand by 2025 in the most profitable way. Iskandar Malaysia region was chosen as the model city for this study.

Lean waste classification model to support the sustainable operational practice

NASA Astrophysics Data System (ADS)

Sutrisno, A.; Vanany, I.; Gunawan, I.; Asjad, M.

2018-04-01

Driven by growing pressure for a more sustainable operational practice, improvement on the classification of non-value added (waste) is one of the prerequisites to realize sustainability of a firm. While the use of the 7 (seven) types of the Ohno model now becoming a versatile tool to reveal the lean waste occurrence. In many recent investigations, the use of the Seven Waste model of Ohno is insufficient to cope with the types of waste occurred in industrial practices at various application levels. Intended to a narrowing down this limitation, this paper presented an improved waste classification model based on survey to recent studies discussing on waste at various operational stages. Implications on the waste classification model to the body of knowledge and industrial practices are provided.

Modelling the sulfate capacity of simulated radioactive waste borosilicate glasses

DOE PAGES

Bingham, Paul A.; Vaishnav, Shuchi; Forder, Sue D.; ...

2016-11-10

In this paper, the capacity of simulated high-level radioactive waste borosilicate glasses to incorporate sulfate has been studied as a function of glass composition. Combined Raman, 57Fe Mössbauer and literature evidence supports the attribution of coordination numbers and oxidation states of constituent cations for the purposes of modelling, and results confirm the validity of correlating sulfate incorporation in multicomponent borosilicate radioactive waste glasses with different models. A strong compositional dependency is observed and this can be described by an inverse linear relationship between incorporated sulfate (mol% SO 4 2-) and total cation field strength index of the glass, Σ(z/a 2),more » with a high goodness-of-fit (R 2 ≈ 0.950). Similar relationships are also obtained if theoretical optical basicity, Λ th (R 2 ≈ 0.930) or non-bridging oxygen per tetrahedron ratio, NBO/T (R 2 ≈ 0.919), are used. Finally, results support the application of these models, and in particular Σ(z/a 2), as predictive tools to aid the development of new glass compositions with enhanced sulfate capacities.« less

Novel use of geochemical models in evaluating treatment trains for aqueous radioactive waste streams

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

Abitz, R.J.

1996-12-31

Thermodynamic geochemical models have been applied to assess the relative effectiveness of a variety of reagents added to aqueous waste streams for the removal of radioactive elements. Two aqueous waste streams were examined: effluent derived from the processing of uranium ore and irradiated uranium fuel rods. Simulations of the treatment train were performed to estimate the mass of reagents needed per kilogram of solution, identify pH regions corresponding to solubility minimums, and predict the identity and quantity of precipitated solids. Results generated by the simulations include figures that chart the chemical evolution of the waste stream as reagents are addedmore » and summary tables that list mass balances for all reagents and radioactive elements of concern. Model results were used to set initial reagent levels for the treatment trains, minimizing the number of bench-scale tests required to bring the treatment train up to full-scale operation. Additionally, presentation of modeling results at public meetings helps to establish good faith between the federal government, industry, concerned citizens, and media groups. 18 refs., 3 figs., 1 tab.« less

A novel methodology to estimate the evolution of construction waste in construction sites.

PubMed

Katz, Amnon; Baum, Hadassa

2011-02-01

This paper focuses on the accumulation of construction waste generated throughout the erection of new residential buildings. A special methodology was developed in order to provide a model that will predict the flow of construction waste. The amount of waste and its constituents, produced on 10 relatively large construction sites (7000-32,000 m(2) of built area) was monitored periodically for a limited time. A model that predicts the accumulation of construction waste was developed based on these field observations. According to the model, waste accumulates in an exponential manner, i.e. smaller amounts are generated during the early stages of construction and increasing amounts are generated towards the end of the project. The total amount of waste from these sites was estimated at 0.2m(3) per 1m(2) floor area. A good correlation was found between the model predictions and actual data from the field survey. Copyright © 2010 Elsevier Ltd. All rights reserved.

Environmental modelling of use of treated organic waste on agricultural land: a comparison of existing models for life cycle assessment of waste systems.

PubMed

Hansen, Trine Lund; Christensen, Thomas Højlund; Schmidt, Sonia

2006-04-01

Modelling of environmental impacts from the application of treated organic municipal solid waste (MSW) in agriculture differs widely between different models for environmental assessment of waste systems. In this comparative study five models were examined concerning quantification and impact assessment of environmental effects from land application of treated organic MSW: DST (Decision Support Tool, USA), IWM (Integrated Waste Management, U.K.), THE IFEU PROJECT (Germany), ORWARE (ORganic WAste REsearch, Sweden) and EASEWASTE (Environmental Assessment of Solid Waste Systems and Technologies, Denmark). DST and IWM are life cycle inventory (LCI) models, thus not performing actual impact assessment. The DST model includes only one water emission (biological oxygen demand) from compost leaching in the results and IWM considers only air emissions from avoided production of commercial fertilizers. THE IFEU PROJECT, ORWARE and EASEWASTE are life cycle assessment (LCA) models containing more detailed land application modules. A case study estimating the environmental impacts from land application of 1 ton of composted source sorted organic household waste was performed to compare the results from the different models and investigate the origin of any difference in type or magnitude of the results. The contributions from the LCI models were limited and did not depend on waste composition or local agricultural conditions. The three LCA models use the same overall approach for quantifying the impacts of the system. However, due to slightly different assumptions, quantification methods and environmental impact assessment, the obtained results varied clearly between the models. Furthermore, local conditions (e.g. soil type, farm type, climate and legal regulation) and waste composition strongly influenced the results of the environmental assessment.

Household Food Insecurity May Predict Underweightand Wasting among Children Aged 24-59 Months.

PubMed

Abdurahman, Ahmed A; Mirzaei, Khadijeh; Dorosty, Ahmed Reza; Rahimiforoushani, A; Kedir, Haji

2016-01-01

The aim of this study was to examine the association between household food insecurity and nutritional status among children aged 24-59 months in Haromaya District. Children (N = 453) aged 24-59 months were recruited in a community-based cross-sectional survey with a representative sample of households selected by a multistage sampling procedure in Haromaya District. Household Food Insecurity Access Scale and anthropometry were administered. Multinomial logistic regression models were applied to select variables that are candidate for multivariable model. The prevalences of stunting, underweight, and wasting among children aged 24-59 months were 61.1%, 28.1%, and 11.8%, respectively. The mean household food insecurity access scale score was 3.34, and 39.7% of households experienced some degree of food insecurity. By logistic regression analysis and after adjusting for the confounding factors, household food insecurity was significantly predictive of underweight (AOR = 2.48, CI = 1.17-5.24, p = .05) and chronic energy deficiency (AOR = 0.47, CI = 0.23-0.97, p = .04) and marginally significant for wasting (AOR = 0.53, CI = 0.27-1.03, p = .06). It is concluded that household food security improves child growth and nutritional status.

Evaluation of a predictive ground-water solute-transport model at the Idaho National Engineering Laboratory, Idaho

USGS Publications Warehouse

Lewis, Barney D.; Goldstein, Flora J.

1982-01-01

Aqueous chemical and radioactive wastes discharged to shallow ponds and to shallow or deep wells on the Idaho National Engineering Laboratory (INEL) since 1952 have affected the quality of the ground water in the underlying Snake River Plain aquifer. The aqueous wastes have created large and laterally dispersed concentration plumes within the aquifer. The waste plumes with the largest areal distribution are those of chloride , tritium, and with high specific conductance values. The data from eight wells drilled near the southern INEL boundary during the summer of 1980 were used to evaluate the accuracy of a predictive modeling study completed in 1973, and to simulate 1980 positions of chloride and tritium plumes. Data interpretation from the drilling program indicates that the hydrogeologic characteristics of the subsurface rocks have marked effects on the regional ground-water flow regimen and, therefore, the movement of aqueous wastes. As expected, the waste plumes projected by the computer model for 1980, extended somewhat further downgradient than indicated by well data due to conservative worst-case assumptions in the model input and inacurate approximations of subsequent waste discharge and aquifer recharge conditions. (USGS)

Nuclear Energy Advanced Modeling and Simulation Waste Integrated Performance and Safety Codes (NEAMS Waste IPSC).

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

Schultz, Peter Andrew

The objective of the U.S. Department of Energy Office of Nuclear Energy Advanced Modeling and Simulation Waste Integrated Performance and Safety Codes (NEAMS Waste IPSC) is to provide an integrated suite of computational modeling and simulation (M&S) capabilities to quantitatively assess the long-term performance of waste forms in the engineered and geologic environments of a radioactive-waste storage facility or disposal repository. Achieving the objective of modeling the performance of a disposal scenario requires describing processes involved in waste form degradation and radionuclide release at the subcontinuum scale, beginning with mechanistic descriptions of chemical reactions and chemical kinetics at the atomicmore » scale, and upscaling into effective, validated constitutive models for input to high-fidelity continuum scale codes for coupled multiphysics simulations of release and transport. Verification and validation (V&V) is required throughout the system to establish evidence-based metrics for the level of confidence in M&S codes and capabilities, including at the subcontiunuum scale and the constitutive models they inform or generate. This Report outlines the nature of the V&V challenge at the subcontinuum scale, an approach to incorporate V&V concepts into subcontinuum scale modeling and simulation (M&S), and a plan to incrementally incorporate effective V&V into subcontinuum scale M&S destined for use in the NEAMS Waste IPSC work flow to meet requirements of quantitative confidence in the constitutive models informed by subcontinuum scale phenomena.« less

Geographic patterns of cigarette butt waste in the urban environment.

PubMed

Marah, Maacah; Novotny, Thomas E

2011-05-01

This reports the initial phase of a study to quantify the spatial pattern of cigarette butt waste in an urban environment. Geographic Information Systems (GIS) was used to create a weighted overlay analysis model which was then applied to the locations of businesses where cigarettes are sold or are likely to be consumed and venues where higher concentrations of butts may be deposited. The model's utility was tested using a small-scale litter audit in three zip codes of San Diego, California. We found that cigarette butt waste is highly concentrated around businesses where cigarettes are sold or consumed. The mean number of butts for predicted high waste sites was 38.1 (SD 18.87), for predicted low waste sites mean 4.8 (SD 5.9), p<0.001. Cigarette butt waste is not uniformly distributed in the urban environment, its distribution is linked to locations and patterns of sales and consumption. A GIS and weighted overlay model may be a useful tool in predicting urban locations of greater and lesser amounts of cigarette butt waste. These data can in turn be used to develop economic cost studies and plan mitigation strategies in urban communities.

A Spanish model for quantification and management of construction waste.

PubMed

Solís-Guzmán, Jaime; Marrero, Madelyn; Montes-Delgado, Maria Victoria; Ramírez-de-Arellano, Antonio

2009-09-01

Currently, construction and demolition waste (C&D waste) is a worldwide issue that concerns not only governments but also the building actors involved in construction activity. In Spain, a new national decree has been regulating the production and management of C&D waste since February 2008. The present work describes the waste management model that has inspired this decree: the Alcores model implemented with good results in Los Alcores Community (Seville, Spain). A detailed model is also provided to estimate the volume of waste that is expected to be generated on the building site. The quantification of C&D waste volume, from the project stage, is essential for the building actors to properly plan and control its disposal. This quantification model has been developed by studying 100 dwelling projects, especially their bill of quantities, and defining three coefficients to estimate the demolished volume (CT), the wreckage volume (CR) and the packaging volume (CE). Finally, two case studies are included to illustrate the usefulness of the model to estimate C&D waste volume in both new construction and demolition projects.

An analysis of household waste management policy using system dynamics modelling.

PubMed

Inghels, Dirk; Dullaert, Wout

2011-04-01

This paper analyses the Flemish household waste management policy. Based on historical data from the period 1991-2006, literature reviews and interviews, both mathematical and descriptive relationships are derived that describe Flemish waste collection, reuse, recycling and disposal behaviour. This provides insights into how gross domestic product (GDP), population and selective collection behaviour have influenced household waste production and collection over time. These relationships are used to model the dynamic relationships underlying household waste management in Flanders by using a system dynamics (SD) modelling approach. Where most SD models in literature are conceptual and descriptive, in the present study a real-life case with both correlational and descriptive relationships was modelled for Flanders, a European region with an outstanding waste management track record. This model was used to evaluate the current Flemish household waste management policy based on the principles of the waste hierarchy, also referred as the Lansink ranking. The results show that Flemish household waste targets up to 2015 can be achieved by the current waste policy measures. It also shows the sensitivity of some key policy parameters such as prevention and reuse. Given the general nature of the model and its limited data requirements, the authors believe that the approach implemented in this model can also assist waste policy makers in other regions or countries to meet their policy targets by simulating the effect of their current and potential household waste policy measures.

Plasma Processing of Model Residential Solid Waste

NASA Astrophysics Data System (ADS)

Messerle, V. E.; Mossé, A. L.; Nikonchuk, A. N.; Ustimenko, A. B.; Baimuldin, R. V.

2017-09-01

The authors have tested the technology of processing of model residential solid waste. They have developed and created a pilot plasma unit based on a plasma chamber incinerator. The waste processing technology has been tested and prepared for commercialization.

Equilibrium and kinetic modelling of Cd(II) biosorption by algae Gelidium and agar extraction algal waste.

PubMed

Vilar, Vítor J P; Botelho, Cidália M S; Boaventura, Rui A R

2006-01-01

In this study an industrial algal waste from agar extraction has been used as an inexpensive and effective biosorbent for cadmium (II) removal from aqueous solutions. This biosorbent was compared with the algae Gelidium itself, which is the raw material for agar extraction. Equilibrium data follow both Langmuir and Redlich-Peterson models. The parameters of Langmuir equilibrium model are q(max)=18.0 mgg(-1), b=0.19 mgl(-1) and q(max)=9.7 mgg(-1), b=0.16 mgl(-1), respectively for Gelidium and the algal waste. Kinetic experiments were conducted at initial Cd(II) concentrations in the range 6-91 mgl(-1). Data were fitted to pseudo-first- and second-order Lagergren models. For an initial Cd(II) concentration of 91 mgl(-1) the parameters of the pseudo-first-order Lagergren model are k(1,ads)=0.17 and 0.87 min(-1); q(eq)=16.3 and 8.7 mgg(-1), respectively, for Gelidium and algal waste. Kinetic constants vary with the initial metal concentration. The adsorptive behaviour of biosorbent particles was modelled using a batch reactor mass transfer kinetic model. The model successfully predicts Cd(II) concentration profiles and provides significant insights on the biosorbents performance. The homogeneous diffusivity, D(h), is in the range 0.5-2.2 x10(-8) and 2.1-10.4 x10(-8)cm(2)s(-1), respectively, for Gelidium and algal waste.

Waste Reduction Model (WARM) Resources for State and Local Government/Solid Waste Planners

EPA Pesticide Factsheets

This page provides a brief overview of how EPA’s Waste Reduction Model (WARM) can be used by state and local government/solid waste planners. The page includes a brief summary of uses of WARM for the audience and links to other resources.

Geographic patterns of cigarette butt waste in the urban environment

PubMed Central

Novotny, Thomas E

2011-01-01

Background This reports the initial phase of a study to quantify the spatial pattern of cigarette butt waste in an urban environment. Methods Geographic Information Systems (GIS) was used to create a weighted overlay analysis model which was then applied to the locations of businesses where cigarettes are sold or are likely to be consumed and venues where higher concentrations of butts may be deposited. The model's utility was tested using a small-scale litter audit in three zip codes of San Diego, California. Results We found that cigarette butt waste is highly concentrated around businesses where cigarettes are sold or consumed. The mean number of butts for predicted high waste sites was 38.1 (SD 18.87), for predicted low waste sites mean 4.8 (SD 5.9), p<0.001. Conclusions Cigarette butt waste is not uniformly distributed in the urban environment, its distribution is linked to locations and patterns of sales and consumption. A GIS and weighted overlay model may be a useful tool in predicting urban locations of greater and lesser amounts of cigarette butt waste. These data can in turn be used to develop economic cost studies and plan mitigation strategies in urban communities. PMID:21504924

Modeling transient heat transfer in nuclear waste repositories.

PubMed

Yang, Shaw-Yang; Yeh, Hund-Der

2009-09-30

The heat of high-level nuclear waste may be generated and released from a canister at final disposal sites. The waste heat may affect the engineering properties of waste canisters, buffers, and backfill material in the emplacement tunnel and the host rock. This study addresses the problem of the heat generated from the waste canister and analyzes the heat distribution between the buffer and the host rock, which is considered as a radial two-layer heat flux problem. A conceptual model is first constructed for the heat conduction in a nuclear waste repository and then mathematical equations are formulated for modeling heat flow distribution at repository sites. The Laplace transforms are employed to develop a solution for the temperature distributions in the buffer and the host rock in the Laplace domain, which is numerically inverted to the time-domain solution using the modified Crump method. The transient temperature distributions for both the single- and multi-borehole cases are simulated in the hypothetical geological repositories of nuclear waste. The results show that the temperature distributions in the thermal field are significantly affected by the decay heat of the waste canister, the thermal properties of the buffer and the host rock, the disposal spacing, and the thickness of the host rock at a nuclear waste repository.

Forecasting of municipal solid waste quantity in a developing country using multivariate grey models.

PubMed

Intharathirat, Rotchana; Abdul Salam, P; Kumar, S; Untong, Akarapong

2015-05-01

In order to plan, manage and use municipal solid waste (MSW) in a sustainable way, accurate forecasting of MSW generation and composition plays a key role. It is difficult to carry out the reliable estimates using the existing models due to the limited data available in the developing countries. This study aims to forecast MSW collected in Thailand with prediction interval in long term period by using the optimized multivariate grey model which is the mathematical approach. For multivariate models, the representative factors of residential and commercial sectors affecting waste collected are identified, classified and quantified based on statistics and mathematics of grey system theory. Results show that GMC (1, 5), the grey model with convolution integral, is the most accurate with the least error of 1.16% MAPE. MSW collected would increase 1.40% per year from 43,435-44,994 tonnes per day in 2013 to 55,177-56,735 tonnes per day in 2030. This model also illustrates that population density is the most important factor affecting MSW collected, followed by urbanization, proportion employment and household size, respectively. These mean that the representative factors of commercial sector may affect more MSW collected than that of residential sector. Results can help decision makers to develop the measures and policies of waste management in long term period. Copyright © 2015 Elsevier Ltd. All rights reserved.

Impacts of Process and Prediction Uncertainties on Projected Hanford Waste Glass Amount

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

Gervasio, Vivianaluxa; Vienna, John D.; Kim, Dong-Sang

Analyses were performed to evaluate the impacts of using the advanced glass models, constraints (Vienna et al. 2016), and uncertainty descriptions on projected Hanford glass mass. The maximum allowable WOL was estimated for waste compositions while simultaneously satisfying all applicable glass property and composition constraints with sufficient confidence. Different components of prediction and composition/process uncertainties were systematically included in the calculations to evaluate their impacts on glass mass. The analyses estimated the production of 23,360 MT of IHLW glass when no uncertainties were taken into accound. Accounting for prediction and composition/process uncertainties resulted in 5.01 relative percent increase in estimatedmore » glass mass 24,531 MT. Roughly equal impacts were found for prediction uncertainties (2.58 RPD) and composition/process uncertainties (2.43 RPD). ILAW mass was predicted to be 282,350 MT without uncertainty and with weaste loading “line” rules in place. Accounting for prediction and composition/process uncertainties resulted in only 0.08 relative percent increase in estimated glass mass of 282,562 MTG. Without application of line rules the glass mass decreases by 10.6 relative percent (252,490 MT) for the case with no uncertainties. Addition of prediction uncertainties increases glass mass by 1.32 relative percent and the addition of composition/process uncertainties increase glass mass by an additional 7.73 relative percent (9.06 relative percent increase combined). The glass mass estimate without line rules (275,359 MT) was 2.55 relative percent lower than that with the line rules (282,562 MT), after accounting for all applicable uncertainties.« less

WastePlan model implementation for New York State. Final report

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

Visalli, J.R.; Blackman, D.A.

1995-07-01

WastePlan is a computer software tool that models solid waste quantities, costs, and other parameters on a regional basis. The software was developed by the Tellus Institute, a nonprofit research and consulting firm. The project`s objective was to provide local solid waste management planners in New York State responsible to develop and implement comprehensive solid waste management plans authorized by the Solid Waste Management Act of 1988, with a WastePlan model specifically tailored to fit the demographic and other characteristics of New York State and to provide training and technical support to the users. Two-day workshops were held in 1992more » to introduce planners to the existing versions; subsequently, extensive changes were made to the model and a second set of two-day workshops were held in 1993 to introduce planners to the enhanced version of WastePlan. Following user evaluations, WastePlan was further modified to allow users to model systems using a simplified version, and to incorporate report forms required by New York State. A post-project survey of trainees revealed limited regular use of software. Possible reasons include lack of synchronicity with NYSDEC planning process; lack of computer literacy and aptitude among trainees; hardware limitations; software user-friendliness; and the work environment of the trainees. A number of recommendations are made to encourage use of WastePlan by local solid waste management planners.« less

Modelling of cementitious backfill interactions with vitrified intermediate-level waste

NASA Astrophysics Data System (ADS)

Baston, Graham; Heath, Timothy; Hunter, Fiona; Swanton, Stephen

2017-06-01

New types of wasteform are being considered for the geological disposal of radioactive intermediate-level waste (ILW) in the UK. These include vitrified ILW products arising from the application of thermal treatment processes. For disposal of such wasteforms in a geological disposal facility, a range of concepts are under consideration, including those with a high-pH cementitious backfill (the NRVB, Nirex Reference Vault Backfill). Alternatively, a cement-based material that buffers to a less alkaline pH could be used (an LPB, Low-pH Backfill). To assess the compatibility of these potential new wasteforms with cement-based disposal concepts, it is necessary to understand their impacts on the long-term evolution of the backfill. A scoping thermodynamic modelling study was undertaken to help understand the possible effects of these wasteforms on the performance of the backfill. The model primarily considers the interactions occurring between the vitirified waste, the porewater and the backfill, within a static and (in most cases) totally closed system. The approach was simplified by assuming equilibrium between the backfill and the corroded glass available at selected times, rather than involving detailed, reactive transport modelling. The aim was to provide an understanding of whether the impacts of the vitrified wastes on backfill performance are sufficient to compromise disposal in such environments. The calculations indicated that for NRVB, the overall alkaline buffering capacity of the backfill is not expected to be impaired by interactions with vitrified waste; rather the buffering will be to less alkaline pH values (above pH 9) but for a longer period. For the LPB, slightly lower pH values were predicted in some cases. The sorption capacities of the backfills are unlikely to be impaired by interactions with vitrified ILW. Indeed they may be increased, due to the additional C-S-H phase formation. The results of this study suggest that disposal of vitrified ILW

Geochemical modeling of leaching of Ca, Mg, Al, and Pb from cementitious waste forms

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

Martens, E., E-mail: evelien.martens@csiro.a; Jacques, D.; Van Gerven, T.

2010-08-15

Results from extraction tests on cement-waste samples were simulated with a thermodynamic equilibrium model using a consistent database, to which lead data were added. Subsequent diffusion tests were modeled by means of a 3D diffusive transport model combined with the geochemical model derived from the extraction tests. Modeling results of the leached major element concentrations for both uncarbonated and (partially) carbonated samples agreed well with the extraction test using the set of pure minerals and solid solutions present in the database. The observed decrease in Ca leaching with increasing carbonation level was qualitatively predicted. Simulations also revealed that Pb leachingmore » is not controlled by dissolution/precipitation only. The addition of the calcite-cerrusite solid solution and adsorption reactions on amorphous Fe- and Al-oxides improved the predictions and are considered to control the Pb leaching during the extractions tests. The dynamic diffusive leaching tests were appropriately modeled for Na, K, Ca and Pb.« less

Chaotic time series prediction for prenatal exposure to polychlorinated biphenyls in umbilical cord blood using the least squares SEATR model

NASA Astrophysics Data System (ADS)

Xu, Xijin; Tang, Qian; Xia, Haiyue; Zhang, Yuling; Li, Weiqiu; Huo, Xia

2016-04-01

Chaotic time series prediction based on nonlinear systems showed a superior performance in prediction field. We studied prenatal exposure to polychlorinated biphenyls (PCBs) by chaotic time series prediction using the least squares self-exciting threshold autoregressive (SEATR) model in umbilical cord blood in an electronic waste (e-waste) contaminated area. The specific prediction steps basing on the proposal methods for prenatal PCB exposure were put forward, and the proposed scheme’s validity was further verified by numerical simulation experiments. Experiment results show: 1) seven kinds of PCB congeners negatively correlate with five different indices for birth status: newborn weight, height, gestational age, Apgar score and anogenital distance; 2) prenatal PCB exposed group at greater risks compared to the reference group; 3) PCBs increasingly accumulated with time in newborns; and 4) the possibility of newborns suffering from related diseases in the future was greater. The desirable numerical simulation experiments results demonstrated the feasibility of applying mathematical model in the environmental toxicology field.

Chaotic time series prediction for prenatal exposure to polychlorinated biphenyls in umbilical cord blood using the least squares SEATR model

PubMed Central

Xu, Xijin; Tang, Qian; Xia, Haiyue; Zhang, Yuling; Li, Weiqiu; Huo, Xia

2016-01-01

Chaotic time series prediction based on nonlinear systems showed a superior performance in prediction field. We studied prenatal exposure to polychlorinated biphenyls (PCBs) by chaotic time series prediction using the least squares self-exciting threshold autoregressive (SEATR) model in umbilical cord blood in an electronic waste (e-waste) contaminated area. The specific prediction steps basing on the proposal methods for prenatal PCB exposure were put forward, and the proposed scheme’s validity was further verified by numerical simulation experiments. Experiment results show: 1) seven kinds of PCB congeners negatively correlate with five different indices for birth status: newborn weight, height, gestational age, Apgar score and anogenital distance; 2) prenatal PCB exposed group at greater risks compared to the reference group; 3) PCBs increasingly accumulated with time in newborns; and 4) the possibility of newborns suffering from related diseases in the future was greater. The desirable numerical simulation experiments results demonstrated the feasibility of applying mathematical model in the environmental toxicology field. PMID:27118260

Estimating methane emissions from landfills based on rainfall, ambient temperature, and waste composition: The CLEEN model.

PubMed

Karanjekar, Richa V; Bhatt, Arpita; Altouqui, Said; Jangikhatoonabad, Neda; Durai, Vennila; Sattler, Melanie L; Hossain, M D Sahadat; Chen, Victoria

2015-12-01

Accurately estimating landfill methane emissions is important for quantifying a landfill's greenhouse gas emissions and power generation potential. Current models, including LandGEM and IPCC, often greatly simplify treatment of factors like rainfall and ambient temperature, which can substantially impact gas production. The newly developed Capturing Landfill Emissions for Energy Needs (CLEEN) model aims to improve landfill methane generation estimates, but still require inputs that are fairly easy to obtain: waste composition, annual rainfall, and ambient temperature. To develop the model, methane generation was measured from 27 laboratory scale landfill reactors, with varying waste compositions (ranging from 0% to 100%); average rainfall rates of 2, 6, and 12 mm/day; and temperatures of 20, 30, and 37°C, according to a statistical experimental design. Refuse components considered were the major biodegradable wastes, food, paper, yard/wood, and textile, as well as inert inorganic waste. Based on the data collected, a multiple linear regression equation (R(2)=0.75) was developed to predict first-order methane generation rate constant values k as functions of waste composition, annual rainfall, and temperature. Because, laboratory methane generation rates exceed field rates, a second scale-up regression equation for k was developed using actual gas-recovery data from 11 landfills in high-income countries with conventional operation. The Capturing Landfill Emissions for Energy Needs (CLEEN) model was developed by incorporating both regression equations into the first-order decay based model for estimating methane generation rates from landfills. CLEEN model values were compared to actual field data from 6 US landfills, and to estimates from LandGEM and IPCC. For 4 of the 6 cases, CLEEN model estimates were the closest to actual. Copyright © 2015 Elsevier Ltd. All rights reserved.

Using STELLA System Dynamic Model to Analyze Greenhouse Gases' Emission From Solid Waste Management in Taiwan

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

Horng, Jao-Jia; Lee, R.F.; Liao, K.Y.

2004-03-31

Using a system dynamic model (SDM), such as STELLA, to analyze the waste management policy is a new trial for Taiwan's research communities. We have developed an easy and relatively accurate model for analyzing the greenhouse gases emission for the wastes from animal farming and municipalities. With the local research data of the past decade, we extract the most prominent factors and assemble the SDM. The results and scenarios were compared with the national inventory. By comparing to the past data, we found these models reasonably represent the situation in Taiwan. However, SDM can program many scenarios and produce amore » lot of prediction data. With the development of many program control tools on STELLA, we believe the models could be further used by researchers or policy-makers to find the needed research topics, to set the future scenarios and to determine the management tools.« less

Future missions studies: Combining Schatten's solar activity prediction model with a chaotic prediction model

NASA Technical Reports Server (NTRS)

Ashrafi, S.

1991-01-01

K. Schatten (1991) recently developed a method for combining his prediction model with our chaotic model. The philosophy behind this combined model and his method of combination is explained. Because the Schatten solar prediction model (KS) uses a dynamo to mimic solar dynamics, accurate prediction is limited to long-term solar behavior (10 to 20 years). The Chaotic prediction model (SA) uses the recently developed techniques of nonlinear dynamics to predict solar activity. It can be used to predict activity only up to the horizon. In theory, the chaotic prediction should be several orders of magnitude better than statistical predictions up to that horizon; beyond the horizon, chaotic predictions would theoretically be just as good as statistical predictions. Therefore, chaos theory puts a fundamental limit on predictability.

Energy recovery from solid waste. [production engineering model

NASA Technical Reports Server (NTRS)

Dalton, C.; Huang, C. J.

1974-01-01

A recent group study on the problem of solid waste disposal provided a decision making model for a community to use in determining the future for its solid waste. The model is a combination of the following factors: technology, legal, social, political, economic and environmental. An assessment of local or community needs determines what form of energy recovery is desirable. A market for low pressure steam or hot water would direct a community to recover energy from solid waste by incineration to generate steam. A fuel gas could be produced by a process known as pyrolysis if there is a local market for a low heating value gaseous fuel. Solid waste can also be used directly as a fuel supplemental to coal in a steam generator. An evaluation of these various processes is made.

Analysis of solid waste from ships and modeling of its generation on the river Danube in Serbia.

PubMed

Ulniković, Vladanka Presburger; Vukić, Marija; Milutinović-Nikolić, Aleksandra

2013-06-01

This study focuses on the issues related to the waste management in river ports in general and, particularly, in ports on the river Danube's flow through Serbia. The ports of Apatin, Bezdan, Backa Palanka, Novi Sad, Belgrade, Smederevo, Veliko Gradiste, Prahovo and Kladovo were analyzed. The input data (number of watercrafts, passengers and crew members) were obtained from harbor authorities for the period 2005-2009. The quantities of solid waste generated on both cruise and cargo ships are considered in this article. As there is no strategy for waste treatment in the ports in Serbia, these data are extremely valuable for further design of equipment for waste treatment and collection. Trends in data were analyzed and regression models were used to predict the waste quantities in each port in next 3 years. The obtained trends could be utilized as the basis for the calculation of the equipment capacities for waste selection, collection, storage and treatment. The results presented in this study establish the need for an organized management system for this type of waste, as well as suggest where the terminals for collection, storage and treatment of solid waste from ships should be located.

Waste Reduction Model

EPA Pesticide Factsheets

To help solid waste planners and organizations track/report GHG emissions reductions from various waste management practices. To assist in calculating GHG emissions of baseline and alternative waste management practices and provide the history of WARM.

Upgrade to Ion Exchange Modeling for Removal of Technetium from Hanford Waste Using SuperLig® 639 Resin

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

Hamm, L.; Smith, F.; Aleman, S.

2013-05-16

This report documents the development and application of computer models to describe the sorption of pertechnetate [TcO₄⁻], and its surrogate perrhenate [ReO₄⁻], on SuperLig® 639 resin. Two models have been developed: 1) A thermodynamic isotherm model, based on experimental data, that predicts [TcO₄⁻] and [ReO₄⁻] sorption as a function of solution composition and temperature and 2) A column model that uses the isotherm calculated by the first model to simulate the performance of a full-scale sorption process. The isotherm model provides a synthesis of experimental data collected from many different sources to give a best estimate prediction of the behaviormore » of the pertechnetate-SuperLig® 639 system and an estimate of the uncertainty in this prediction. The column model provides a prediction of the expected performance of the plant process by determining the volume of waste solution that can be processed based on process design parameters such as column size, flow rate and resin physical properties.« less

Modeling of Solid Waste Processing Options in BIO-Plex

NASA Technical Reports Server (NTRS)

Rodriguez, Luis F.; Finn, Cory; Kang, Sukwon; Hogan, John; Luna, Bernadette (Technical Monitor)

2000-01-01

BIO-Plex is a ground-based test bed currently under development by NASA for testing technologies and practices that may be utilized in future long-term life support missions. All aspects of such an Advanced Life Support (ALS) System must be considered to confidently construct a reliable system, which will not only allow the crew to survive in harsh environments, but allow the crew time to perform meaningful research. Effective handling of solid wastes is a critical aspect of the system, especially when recovery of resources contained in the waste is required. This is particularly important for ALS Systems configurations that include a Biomass Production Chamber. In these cases, significant amounts of inedible biomass waste may be produced, which can ultimately serve as a repository of necessary resources for sustaining life, notably carbon, water, and plant nutrients. Numerous biological and physicochemical solid waste processing options have been considered. Biological options include composting, aerobic digestion, and anaerobic digestion. Physicochemical options include pyrolysis, SCWO (supercritical water oxidation), various incineration configurations, microwave incineration, magnetically assisted gasification, and low temperature plasma reaction. Modeling of these options is a necessary step to assist in the design process. A previously developed top-level model of BIO-Plex implemented in MATLAB Simulink (r) for the use of systems analysis and design has been adopted for this analysis. Presently, this model only considered incineration for solid waste processing. Present work, reported here, includes the expansion of this model to include a wider array of solid waste processing options selected from the above options, bearing in mind potential, near term solid waste treatment systems. Furthermore, a trade study has also been performed among these solid waste processing technologies in an effort to determine the ideal technology for long-term life support

Predictive modeling of hazardous waste landfill total above-ground biomass using passive optical and LIDAR remotely sensed data

NASA Astrophysics Data System (ADS)

Hadley, Brian Christopher

This dissertation assessed remotely sensed data and geospatial modeling technique(s) to map the spatial distribution of total above-ground biomass present on the surface of the Savannah River National Laboratory's (SRNL) Mixed Waste Management Facility (MWMF) hazardous waste landfill. Ordinary least squares (OLS) regression, regression kriging, and tree-structured regression were employed to model the empirical relationship between in-situ measured Bahia (Paspalum notatum Flugge) and Centipede [Eremochloa ophiuroides (Munro) Hack.] grass biomass against an assortment of explanatory variables extracted from fine spatial resolution passive optical and LIDAR remotely sensed data. Explanatory variables included: (1) discrete channels of visible, near-infrared (NIR), and short-wave infrared (SWIR) reflectance, (2) spectral vegetation indices (SVI), (3) spectral mixture analysis (SMA) modeled fractions, (4) narrow-band derivative-based vegetation indices, and (5) LIDAR derived topographic variables (i.e. elevation, slope, and aspect). Results showed that a linear combination of the first- (1DZ_DGVI), second- (2DZ_DGVI), and third-derivative of green vegetation indices (3DZ_DGVI) calculated from hyperspectral data recorded over the 400--960 nm wavelengths of the electromagnetic spectrum explained the largest percentage of statistical variation (R2 = 0.5184) in the total above-ground biomass measurements. In general, the topographic variables did not correlate well with the MWMF biomass data, accounting for less than five percent of the statistical variation. It was concluded that tree-structured regression represented the optimum geospatial modeling technique due to a combination of model performance and efficiency/flexibility factors.

Understanding leachate flow in municipal solid waste landfills by combining time-lapse ERT and subsurface flow modelling - Part II: Constraint methodology of hydrodynamic models.

PubMed

Audebert, M; Oxarango, L; Duquennoi, C; Touze-Foltz, N; Forquet, N; Clément, R

2016-09-01

Leachate recirculation is a key process in the operation of municipal solid waste landfills as bioreactors. To ensure optimal water content distribution, bioreactor operators need tools to design leachate injection systems. Prediction of leachate flow by subsurface flow modelling could provide useful information for the design of such systems. However, hydrodynamic models require additional data to constrain them and to assess hydrodynamic parameters. Electrical resistivity tomography (ERT) is a suitable method to study leachate infiltration at the landfill scale. It can provide spatially distributed information which is useful for constraining hydrodynamic models. However, this geophysical method does not allow ERT users to directly measure water content in waste. The MICS (multiple inversions and clustering strategy) methodology was proposed to delineate the infiltration area precisely during time-lapse ERT survey in order to avoid the use of empirical petrophysical relationships, which are not adapted to a heterogeneous medium such as waste. The infiltration shapes and hydrodynamic information extracted with MICS were used to constrain hydrodynamic models in assessing parameters. The constraint methodology developed in this paper was tested on two hydrodynamic models: an equilibrium model where, flow within the waste medium is estimated using a single continuum approach and a non-equilibrium model where flow is estimated using a dual continuum approach. The latter represents leachate flows into fractures. Finally, this methodology provides insight to identify the advantages and limitations of hydrodynamic models. Furthermore, we suggest an explanation for the large volume detected by MICS when a small volume of leachate is injected. Copyright © 2016 Elsevier Ltd. All rights reserved.

Neural network models for biological waste-gas treatment systems.

PubMed

Rene, Eldon R; Estefanía López, M; Veiga, María C; Kennes, Christian

2011-12-15

This paper outlines the procedure for developing artificial neural network (ANN) based models for three bioreactor configurations used for waste-gas treatment. The three bioreactor configurations chosen for this modelling work were: biofilter (BF), continuous stirred tank bioreactor (CSTB) and monolith bioreactor (MB). Using styrene as the model pollutant, this paper also serves as a general database of information pertaining to the bioreactor operation and important factors affecting gas-phase styrene removal in these biological systems. Biological waste-gas treatment systems are considered to be both advantageous and economically effective in treating a stream of polluted air containing low to moderate concentrations of the target contaminant, over a rather wide range of gas-flow rates. The bioreactors were inoculated with the fungus Sporothrix variecibatus, and their performances were evaluated at different empty bed residence times (EBRT), and at different inlet styrene concentrations (C(i)). The experimental data from these bioreactors were modelled to predict the bioreactors performance in terms of their removal efficiency (RE, %), by adequate training and testing of a three-layered back propagation neural network (input layer-hidden layer-output layer). Two models (BIOF1 and BIOF2) were developed for the BF with different combinations of easily measurable BF parameters as the inputs, that is concentration (gm(-3)), unit flow (h(-1)) and pressure drop (cm of H(2)O). The model developed for the CSTB used two inputs (concentration and unit flow), while the model for the MB had three inputs (concentration, G/L (gas/liquid) ratio, and pressure drop). Sensitivity analysis in the form of absolute average sensitivity (AAS) was performed for all the developed ANN models to ascertain the importance of the different input parameters, and to assess their direct effect on the bioreactors performance. The performance of the models was estimated by the regression

Mixing-controlled uncertainty in long-term predictions of acid rock drainage from heterogeneous waste-rock piles

NASA Astrophysics Data System (ADS)

Pedretti, D.; Beckie, R. D.; Mayer, K. U.

2015-12-01

The chemistry of drainage from waste-rock piles at mine sites is difficult to predict because of a number of uncertainties including heterogeneous reactive mineral content, distribution of minerals, weathering rates and physical flow properties. In this presentation, we examine the effects of mixing on drainage chemistry over timescales of 100s of years. We use a 1-D streamtube conceptualization of flow in waste rocks and multicomponent reactive transport modeling. We simplify the reactive system to consist of acid-producing sulfide minerals and acid-neutralizing carbonate minerals and secondary sulfate and iron oxide minerals. We create multiple realizations of waste-rock piles with distinct distributions of reactive minerals along each flow path and examine the uncertainty of drainage geochemistry through time. The limited mixing of streamtubes that is characteristic of the vertical unsaturated flow in many waste-rock piles, allows individual flowpaths to sustain acid or neutral conditions to the base of the pile, where the streamtubes mix. Consequently, mixing and the acidity/alkalinity balance of the streamtube waters, and not the overall acid- and base-producing mineral contents, control the instantaneous discharge chemistry. Our results show that the limited mixing implied by preferential flow and the heterogeneous distribution of mineral contents lead to large uncertainty in drainage chemistry over short and medium time scales. However, over longer timescales when one of either the acid-producing or neutralizing primary phases is depleted, the drainage chemistry becomes less controlled by mixing and in turn less uncertain. A correct understanding of the temporal variability of uncertainty is key to make informed long-term decisions in mining settings regarding the management of waste material.

Correlation models for waste tank sludges and slurries

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

Mahoney, L.A.; Trent, D.S.

This report presents the results of work conducted to support the TEMPEST computer modeling under the Flammable Gas Program (FGP) and to further the comprehension of the physical processes occurring in the Hanford waste tanks. The end products of this task are correlation models (sets of algorithms) that can be added to the TEMPEST computer code to improve the reliability of its simulation of the physical processes that occur in Hanford tanks. The correlation models can be used to augment, not only the TEMPEST code, but other computer codes that can simulate sludge motion and flammable gas retention. This reportmore » presents the correlation models, also termed submodels, that have been developed to date. The submodel-development process is an ongoing effort designed to increase our understanding of sludge behavior and improve our ability to realistically simulate the sludge fluid characteristics that have an impact on safety analysis. The effort has employed both literature searches and data correlation to provide an encyclopedia of tank waste properties in forms that are relatively easy to use in modeling waste behavior. These properties submodels will be used in other tasks to simulate waste behavior in the tanks. Density, viscosity, yield strength, surface tension, heat capacity, thermal conductivity, salt solubility, and ammonia and water vapor pressures were compiled for solutions and suspensions of sodium nitrate and other salts (where data were available), and the data were correlated by linear regression. In addition, data for simulated Hanford waste tank supernatant were correlated to provide density, solubility, surface tension, and vapor pressure submodels for multi-component solutions containing sodium hydroxide, sodium nitrate, sodium nitrite, and sodium aluminate.« less

Thermal Model Predictions of Advanced Stirling Radioisotope Generator Performance

NASA Technical Reports Server (NTRS)

Wang, Xiao-Yen J.; Fabanich, William Anthony; Schmitz, Paul C.

2014-01-01

This paper presents recent thermal model results of the Advanced Stirling Radioisotope Generator (ASRG). The three-dimensional (3D) ASRG thermal power model was built using the Thermal Desktop(trademark) thermal analyzer. The model was correlated with ASRG engineering unit test data and ASRG flight unit predictions from Lockheed Martin's (LM's) I-deas(trademark) TMG thermal model. The auxiliary cooling system (ACS) of the ASRG is also included in the ASRG thermal model. The ACS is designed to remove waste heat from the ASRG so that it can be used to heat spacecraft components. The performance of the ACS is reported under nominal conditions and during a Venus flyby scenario. The results for the nominal case are validated with data from Lockheed Martin. Transient thermal analysis results of ASRG for a Venus flyby with a representative trajectory are also presented. In addition, model results of an ASRG mounted on a Cassini-like spacecraft with a sunshade are presented to show a way to mitigate the high temperatures of a Venus flyby. It was predicted that the sunshade can lower the temperature of the ASRG alternator by 20 C for the representative Venus flyby trajectory. The 3D model also was modified to predict generator performance after a single Advanced Stirling Convertor failure. The geometry of the Microtherm HT insulation block on the outboard side was modified to match deformation and shrinkage observed during testing of a prototypic ASRG test fixture by LM. Test conditions and test data were used to correlate the model by adjusting the thermal conductivity of the deformed insulation to match the post-heat-dump steady state temperatures. Results for these conditions showed that the performance of the still-functioning inboard ACS was unaffected.

Crime prediction modeling

NASA Technical Reports Server (NTRS)

1971-01-01

A study of techniques for the prediction of crime in the City of Los Angeles was conducted. Alternative approaches to crime prediction (causal, quasicausal, associative, extrapolative, and pattern-recognition models) are discussed, as is the environment within which predictions were desired for the immediate application. The decision was made to use time series (extrapolative) models to produce the desired predictions. The characteristics of the data and the procedure used to choose equations for the extrapolations are discussed. The usefulness of different functional forms (constant, quadratic, and exponential forms) and of different parameter estimation techniques (multiple regression and multiple exponential smoothing) are compared, and the quality of the resultant predictions is assessed.

DECHLORINATION-CONTROLLED POLYCHLORINATED DIBENZOFURAN FROM MUNICIPAL WASTE INCINERATORS

EPA Science Inventory

The ability to predict polychlorinated dibenzofuran (PCDF) isomer patterns from municipal waste incinerators (MWIs) enables an understanding of PCDF formation that may provide preventive measures. This work develops a model for the pattern prediction, assuming that the peak rati...

Modelling of composting process of different organic waste at pilot scale: Biodegradability and odor emissions.

PubMed

Gutiérrez, M C; Siles, J A; Diz, J; Chica, A F; Martín, M A

2017-01-01

The composting process of six different compostable substrates and one of these with the addition of bacterial inoculums carried out in a dynamic respirometer was evaluated. Despite the heterogeneity of the compostable substrates, cumulative oxygen demand (OD, mgO 2 kgVS) was fitted adequately to an exponential regression growing until reaching a maximum in all cases. According to the kinetic constant of the reaction (K) values obtained, the wastes that degraded more slowly were those containing lignocellulosic material (green wastes) or less biodegradable wastes (sewage sludge). The odor emissions generated during the composting processes were also fitted in all cases to a Gaussian regression with R 2 values within the range 0.8-0.9. The model was validated representing real odor concentration near the maximum value against predicted odor concentration of each substrate, (R 2 =0.9314; 95% prediction interval). The variables of maximum odor concentration (ou E /m 3 ) and the time (h) at which the maximum was reached were also evaluated statistically using ANOVA and a post-hoc Tukey test taking the substrate as a factor, which allowed homogeneous groups to be obtained according to one or both of these variables. The maximum oxygen consumption rate or organic matter degradation during composting was directly related to the maximum odor emission generation rate (R 2 =0.9024, 95% confidence interval) when only the organic wastes with a low content in lignocellulosic materials and no inoculated waste (HRIO) were considered. Finally, the composting of OFMSW would produce a higher odor impact than the other substrates if this process was carried out without odor control or open systems. Copyright © 2016 Elsevier Ltd. All rights reserved.

Modified version of ADM1 model for agro-waste application.

PubMed

Galí, A; Benabdallah, T; Astals, S; Mata-Alvarez, J

2009-06-01

Agro-residues account for a large proportion of the wastes generated around the world. There is thus a need for a model to simulate the anaerobic digestion processes used in their treatment. We have developed model based on ADM1, to be applied to agro-wastes. We examined and tested the biodegradability of apple, pear, orange, rape, sunflower, pig manure and glycerol wastes to be used as the basis for feeding the model. Moreover, the fractions of particulate COD (X(c)) were calculated, and the disintegration constant was obtained from biodegradability profiles, considering disintegration to be the limiting process. The other kinetic and stoichiometric parameters were taken from the ADM1 model. The model operating under mono-substrate and co-substrate conditions was then validated with batch tests. At the same time the model was validated on a continuous anaerobic reactor operating with pig manure at lab scale. In both cases the correlation between the model and the experimental results was satisfactory. We conclude that the anaerobic digestion model is a reliable tool for the design and operation of plants in which agro-wastes are treated.

Model for the separate collection of packaging waste in Portuguese low-performing recycling regions.

PubMed

Oliveira, V; Sousa, V; Vaz, J M; Dias-Ferreira, C

2018-06-15

Separate collection of packaging waste (glass; plastic/metals; paper/cardboard), is currently a widespread practice throughout Europe. It enables the recovery of good quality recyclable materials. However, separate collection performance are quite heterogeneous, with some countries reaching higher levels than others. In the present work, separate collection of packaging waste has been evaluated in a low-performance recycling region in Portugal in order to investigate which factors are most affecting the performance in bring-bank collection system. The variability of separate collection yields (kg per inhabitant per year) among 42 municipalities was scrutinized for the year 2015 against possible explanatory factors. A total of 14 possible explanatory factors were analysed, falling into two groups: socio-economic/demographic and waste collection service related. Regression models were built in an attempt to evaluate the individual effect of each factor on separate collection yields and predict changes on the collection yields by acting on those factors. The best model obtained is capable to explain 73% of the variation found in the separate collection yields. The model includes the following statistically significant indicators affecting the success of separate collection yields: i) inhabitants per bring-bank; ii) relative accessibility to bring-banks; iii) degree of urbanization; iv) number of school years attended; and v) area. The model presented in this work was developed specifically for the bring-bank system, has an explanatory power and quantifies the impact of each factor on separate collection yields. It can therefore be used as a support tool by local and regional waste management authorities in the definition of future strategies to increase collection of recyclables of good quality and to achieve national and regional targets. Copyright © 2017 Elsevier Ltd. All rights reserved.

Multi-objective reverse logistics model for integrated computer waste management.

PubMed

Ahluwalia, Poonam Khanijo; Nema, Arvind K

2006-12-01

This study aimed to address the issues involved in the planning and design of a computer waste management system in an integrated manner. A decision-support tool is presented for selecting an optimum configuration of computer waste management facilities (segregation, storage, treatment/processing, reuse/recycle and disposal) and allocation of waste to these facilities. The model is based on an integer linear programming method with the objectives of minimizing environmental risk as well as cost. The issue of uncertainty in the estimated waste quantities from multiple sources is addressed using the Monte Carlo simulation technique. An illustrated example of computer waste management in Delhi, India is presented to demonstrate the usefulness of the proposed model and to study tradeoffs between cost and risk. The results of the example problem show that it is possible to reduce the environmental risk significantly by a marginal increase in the available cost. The proposed model can serve as a powerful tool to address the environmental problems associated with exponentially growing quantities of computer waste which are presently being managed using rudimentary methods of reuse, recovery and disposal by various small-scale vendors.

Glass Property Data and Models for Estimating High-Level Waste Glass Volume

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

Vienna, John D.; Fluegel, Alexander; Kim, Dong-Sang

2009-10-05

This report describes recent efforts to develop glass property models that can be used to help estimate the volume of high-level waste (HLW) glass that will result from vitrification of Hanford tank waste. The compositions of acceptable and processable HLW glasses need to be optimized to minimize the waste-form volume and, hence, to save cost. A database of properties and associated compositions for simulated waste glasses was collected for developing property-composition models. This database, although not comprehensive, represents a large fraction of data on waste-glass compositions and properties that were available at the time of this report. Glass property-composition modelsmore » were fit to subsets of the database for several key glass properties. These models apply to a significantly broader composition space than those previously publised. These models should be considered for interim use in calculating properties of Hanford waste glasses.« less

Waste IPSC : Thermal-Hydrologic-Chemical-Mechanical (THCM) modeling and simulation.

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

Freeze, Geoffrey A.; Wang, Yifeng; Arguello, Jose Guadalupe, Jr.

2010-10-01

Waste IPSC Objective is to develop an integrated suite of high performance computing capabilities to simulate radionuclide movement through the engineered components and geosphere of a radioactive waste storage or disposal system: (1) with robust thermal-hydrologic-chemical-mechanical (THCM) coupling; (2) for a range of disposal system alternatives (concepts, waste form types, engineered designs, geologic settings); (3) for long time scales and associated large uncertainties; (4) at multiple model fidelities (sub-continuum, high-fidelity continuum, PA); and (5) in accordance with V&V and software quality requirements. THCM Modeling collaborates with: (1) Other Waste IPSC activities: Sub-Continuum Processes (and FMM), Frameworks and Infrastructure (and VU,more » ECT, and CT); (2) Waste Form Campaign; (3) Used Fuel Disposition (UFD) Campaign; and (4) ASCEM.« less

Ion Exchange Modeling of Crystalline Silicotitanate (IONSIV(R) IE-911) Column for Cesium Removal from Argentine Waste

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

Hang, T.

2003-07-16

The U.S. Department of Energy (DOE) and the Nuclear Energy Commission of Argentina (CNEA) have a collaborative project to separate cesium/strontium from waste resulting from the production of Mo-99. The Pacific Northwest National Laboratory (PNNL) is assisting DOE on this joint project by providing technical guidance to CNEA scientists. As part of the collaboration, PNNL staff works with staff at the Savannah River Technology Center (SRTC) to run the VERSE-LC model for removal of cesium from the Mo-99 waste using the crystalline silicotitanate (CST) material (IONSIV(R) IE-911, UOP LLC, DesPlaines, IL) based on technical data provided by CNEA. This reportmore » discusses the VERSE-LC ion-exchange-column model and the predicted results of CNEA test cases.« less

A Nuclear Waste Management Cost Model for Policy Analysis

NASA Astrophysics Data System (ADS)

Barron, R. W.; Hill, M. C.

2017-12-01

Although integrated assessments of climate change policy have frequently identified nuclear energy as a promising alternative to fossil fuels, these studies have often treated nuclear waste disposal very simply. Simple assumptions about nuclear waste are problematic because they may not be adequate to capture relevant costs and uncertainties, which could result in suboptimal policy choices. Modeling nuclear waste management costs is a cross-disciplinary, multi-scale problem that involves economic, geologic and environmental processes that operate at vastly different temporal scales. Similarly, the climate-related costs and benefits of nuclear energy are dependent on environmental sensitivity to CO2 emissions and radiation, nuclear energy's ability to offset carbon emissions, and the risk of nuclear accidents, factors which are all deeply uncertain. Alternative value systems further complicate the problem by suggesting different approaches to valuing intergenerational impacts. Effective policy assessment of nuclear energy requires an integrated approach to modeling nuclear waste management that (1) bridges disciplinary and temporal gaps, (2) supports an iterative, adaptive process that responds to evolving understandings of uncertainties, and (3) supports a broad range of value systems. This work develops the Nuclear Waste Management Cost Model (NWMCM). NWMCM provides a flexible framework for evaluating the cost of nuclear waste management across a range of technology pathways and value systems. We illustrate how NWMCM can support policy analysis by estimating how different nuclear waste disposal scenarios developed using the NWMCM framework affect the results of a recent integrated assessment study of alternative energy futures and their effects on the cost of achieving carbon abatement targets. Results suggest that the optimism reflected in previous works is fragile: Plausible nuclear waste management costs and discount rates appropriate for intergenerational cost

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.

Cultural Resource Predictive Modeling

DTIC Science & Technology

2017-10-01

property to manage ? a. Yes 2) Do you use CRPM (Cultural Resource Predictive Modeling) No, but I use predictive modelling informally . For example...resource program and provide support to the test ranges for their missions. This document will provide information such as lessons learned, points...of contact, and resources to the range cultural resource managers . Objective/Scope: Identify existing cultural resource predictive models and

Kinetic modelling of anaerobic hydrolysis of solid wastes, including disintegration processes

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

García-Gen, Santiago; Sousbie, Philippe; Rangaraj, Ganesh

2015-01-15

Highlights: • Fractionation of solid wastes into readily and slowly biodegradable fractions. • Kinetic coefficients estimation from mono-digestion batch assays. • Validation of kinetic coefficients with a co-digestion continuous experiment. • Simulation of batch and continuous experiments with an ADM1-based model. - Abstract: A methodology to estimate disintegration and hydrolysis kinetic parameters of solid wastes and validate an ADM1-based anaerobic co-digestion model is presented. Kinetic parameters of the model were calibrated from batch reactor experiments treating individually fruit and vegetable wastes (among other residues) following a new protocol for batch tests. In addition, decoupled disintegration kinetics for readily and slowlymore » biodegradable fractions of solid wastes was considered. Calibrated parameters from batch assays of individual substrates were used to validate the model for a semi-continuous co-digestion operation treating simultaneously 5 fruit and vegetable wastes. The semi-continuous experiment was carried out in a lab-scale CSTR reactor for 15 weeks at organic loading rate ranging between 2.0 and 4.7 g VS/L d. The model (built in Matlab/Simulink) fit to a large extent the experimental results in both batch and semi-continuous mode and served as a powerful tool to simulate the digestion or co-digestion of solid wastes.« less

Developing a model for moisture in saltcake waste tanks: Progress report

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

Simmons, C.S.; Aimo, N.; Fayer, M.J.

1997-07-01

This report describes a modeling effort to provide a computer simulation capability for estimating the distribution and movement of moisture in the saltcake-type waste contained in Hanford`s single-shell radioactive waste storage tanks. This moisture model goes beyond an earlier version because it describes water vapor movement as well as the interstitial liquid held in a saltcake waste. The work was performed by Pacific Northwest National Laboratory to assist Duke Engineering and Services Hanford with the Organic Tank Safety Program. The Organic Tank Safety Program is concerned whether saltcake waste, when stabilized by jet pumping, will retain sufficient moisture near themore » surface to preclude any possibility of an accidental ignition and propagation of burning. The nitrate/nitrite saltcake, which might also potentially include combustible organic chemicals might not always retain enough moisture near the surface to preclude any such accident. Draining liquid from a tank by pumping, coupled with moisture evaporating into a tank`s head space, may cause a dry waste surface that is not inherently safe. The moisture model was devised to help examine this safety question. The model accounts for water being continually cycled by evaporation into the head space and returned to the waste by condensation or partly lost through venting to the external atmosphere. Water evaporation occurs even in a closed tank, because it is driven by the transfer to the outside of the heat load generated by radioactivity within the waste. How dry a waste may become over time depends on the particular hydraulic properties of a saltcake, and the model uses those properties to describe the capillary flow of interstitial liquid as well as the water vapor flow caused by thermal differences within the porous waste.« less

Thermal/structural modeling of a large scale in situ overtest experiment for defense high level waste at the Waste Isolation Pilot Plant Facility

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

Morgan, H.S.; Stone, C.M.; Krieg, R.D.

Several large scale in situ experiments in bedded salt formations are currently underway at the Waste Isolation Pilot Plant (WIPP) near Carlsbad, New Mexico, USA. In these experiments, the thermal and creep responses of salt around several different underground room configurations are being measured. Data from the tests are to be compared to thermal and structural responses predicted in pretest reference calculations. The purpose of these comparisons is to evaluate computational models developed from laboratory data prior to fielding of the in situ experiments. In this paper, the computational models used in the pretest reference calculation for one of themore » large scale tests, The Overtest for Defense High Level Waste, are described; and the pretest computed thermal and structural responses are compared to early data from the experiment. The comparisons indicate that computed and measured temperatures for the test agree to within ten percent but that measured deformation rates are between two and three times greater than corresponsing computed rates. 10 figs., 3 tabs.« less

Numerical weather prediction model tuning via ensemble prediction system

NASA Astrophysics Data System (ADS)

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

2011-12-01

This paper discusses a novel approach to tune predictive skill of numerical weather prediction (NWP) models. NWP models contain tunable parameters which appear in parameterizations schemes of sub-grid scale physical processes. Currently, numerical values of these parameters are specified manually. In a recent dual manuscript (QJRMS, revised) we developed a new concept and method for on-line estimation of the NWP model parameters. The EPPES ("Ensemble prediction and parameter estimation system") method requires only minimal changes to the existing operational ensemble prediction infra-structure and it seems very cost-effective because 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 each member of the ensemble of predictions using 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 the 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 atmospheric general circulation model based ensemble prediction system show that the NWP model tuning capacity of EPPES scales up to realistic models and ensemble prediction systems. Finally, a global top-end NWP model tuning exercise with preliminary results is published.

Natural geochemical analogues of the near field of high-level nuclear waste repositories

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

Apps, J.A.

1995-09-01

United States practice has been to design high-level nuclear waste (HLW) geological repositories with waste densities sufficiently high that repository temperatures surrounding the waste will exceed 100{degrees}C and could reach 250{degrees}C. Basalt and devitrified vitroclastic tuff are among the host rocks considered for waste emplacement. Near-field repository thermal behavior and chemical alteration in such rocks is expected to be similar to that observed in many geothermal systems. Therefore, the predictive modeling required for performance assessment studies of the near field could be validated and calibrated using geothermal systems as natural analogues. Examples are given which demonstrate the need for refinementmore » of the thermodynamic databases used in geochemical modeling of near-field natural analogues and the extent to which present models can predict conditions in geothermal fields.« less

Translating landfill methane generation parameters among first-order decay models.

PubMed

Krause, Max J; Chickering, Giles W; Townsend, Timothy G

2016-11-01

Landfill gas (LFG) generation is predicted by a first-order decay (FOD) equation that incorporates two parameters: a methane generation potential (L 0 ) and a methane generation rate (k). Because non-hazardous waste landfills may accept many types of waste streams, multiphase models have been developed in an attempt to more accurately predict methane generation from heterogeneous waste streams. The ability of a single-phase FOD model to predict methane generation using weighted-average methane generation parameters and tonnages translated from multiphase models was assessed in two exercises. In the first exercise, waste composition from four Danish landfills represented by low-biodegradable waste streams was modeled in the Afvalzorg Multiphase Model and methane generation was compared to the single-phase Intergovernmental Panel on Climate Change (IPCC) Waste Model and LandGEM. In the second exercise, waste composition represented by IPCC waste components was modeled in the multiphase IPCC and compared to single-phase LandGEM and Australia's Solid Waste Calculator (SWC). In both cases, weight-averaging of methane generation parameters from waste composition data in single-phase models was effective in predicting cumulative methane generation from -7% to +6% of the multiphase models. The results underscore the understanding that multiphase models will not necessarily improve LFG generation prediction because the uncertainty of the method rests largely within the input parameters. A unique method of calculating the methane generation rate constant by mass of anaerobically degradable carbon was presented (k c ) and compared to existing methods, providing a better fit in 3 of 8 scenarios. Generally, single phase models with weighted-average inputs can accurately predict methane generation from multiple waste streams with varied characteristics; weighted averages should therefore be used instead of regional default values when comparing models. Translating multiphase

A conflict model for the international hazardous waste disposal dispute.

PubMed

Hu, Kaixian; Hipel, Keith W; Fang, Liping

2009-12-15

A multi-stage conflict model is developed to analyze international hazardous waste disposal disputes. More specifically, the ongoing toxic waste conflicts are divided into two stages consisting of the dumping prevention and dispute resolution stages. The modeling and analyses, based on the methodology of graph model for conflict resolution (GMCR), are used in both stages in order to grasp the structure and implications of a given conflict from a strategic viewpoint. Furthermore, a specific case study is investigated for the Ivory Coast hazardous waste conflict. In addition to the stability analysis, sensitivity and attitude analyses are conducted to capture various strategic features of this type of complicated dispute.

A multi-objective model for sustainable recycling of municipal solid waste.

PubMed

Mirdar Harijani, Ali; Mansour, Saeed; Karimi, Behrooz

2017-04-01

The efficient management of municipal solid waste is a major problem for large and populated cities. In many countries, the majority of municipal solid waste is landfilled or dumped owing to an inefficient waste management system. Therefore, an optimal and sustainable waste management strategy is needed. This study introduces a recycling and disposal network for sustainable utilisation of municipal solid waste. In order to optimise the network, we develop a multi-objective mixed integer linear programming model in which the economic, environmental and social dimensions of sustainability are concurrently balanced. The model is able to: select the best combination of waste treatment facilities; specify the type, location and capacity of waste treatment facilities; determine the allocation of waste to facilities; consider the transportation of waste and distribution of processed products; maximise the profit of the system; minimise the environmental footprint; maximise the social impacts of the system; and eventually generate an optimal and sustainable configuration for municipal solid waste management. The proposed methodology could be applied to any region around the world. Here, the city of Tehran, Iran, is presented as a real case study to show the applicability of the methodology.

Decision support models for solid waste management: Review and game-theoretic approaches

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

Karmperis, Athanasios C., E-mail: athkarmp@mail.ntua.gr; Army Corps of Engineers, Hellenic Army General Staff, Ministry of Defence; Aravossis, Konstantinos

Highlights: ► The mainly used decision support frameworks for solid waste management are reviewed. ► The LCA, CBA and MCDM models are presented and their strengths, weaknesses, similarities and possible combinations are analyzed. ► The game-theoretic approach in a solid waste management context is presented. ► The waste management bargaining game is introduced as a specific decision support framework. ► Cooperative and non-cooperative game-theoretic approaches to decision support for solid waste management are discussed. - Abstract: This paper surveys decision support models that are commonly used in the solid waste management area. Most models are mainly developed within three decisionmore » support frameworks, which are the life-cycle assessment, the cost–benefit analysis and the multi-criteria decision-making. These frameworks are reviewed and their strengths and weaknesses as well as their critical issues are analyzed, while their possible combinations and extensions are also discussed. Furthermore, the paper presents how cooperative and non-cooperative game-theoretic approaches can be used for the purpose of modeling and analyzing decision-making in situations with multiple stakeholders. Specifically, since a waste management model is sustainable when considering not only environmental and economic but also social aspects, the waste management bargaining game is introduced as a specific decision support framework in which future models can be developed.« less

Predictive modeling of complications.

PubMed

Osorio, Joseph A; Scheer, Justin K; Ames, Christopher P

2016-09-01

Predictive analytic algorithms are designed to identify patterns in the data that allow for accurate predictions without the need for a hypothesis. Therefore, predictive modeling can provide detailed and patient-specific information that can be readily applied when discussing the risks of surgery with a patient. There are few studies using predictive modeling techniques in the adult spine surgery literature. These types of studies represent the beginning of the use of predictive analytics in spine surgery outcomes. We will discuss the advancements in the field of spine surgery with respect to predictive analytics, the controversies surrounding the technique, and the future directions.

Trickling filter for urea and bio-waste processing - dynamic modelling of nitrogen cycle

NASA Astrophysics Data System (ADS)

Zhukov, Anton; Hauslage, Jens; Tertilt, Gerin; Bornemann, Gerhild

Mankind’s exploration of the solar system requires reliable Life Support Systems (LSS) enabling long duration manned space missions. In the absence of frequent resupply missions, closure of the LSS will play a very important role and its maximisation will to a large extent drive the selection of appropriate LSS architectures. One of the significant issues on the way to full closure is to effectively utilise biological wastes such as urine, inedible biomass etc. A very promising concept of biological waste reprocessing is the use of trickling filters which are currently being developed and investigated by DLR, Cologne, Germany. The concept is called Combined Regenerative Organic-Food Production (C.R.O.P.) and is based on the microbiological treatment of biological wastes and reprocessing them into aqueous fertilizer which can directly be used in a greenhouse for food production. Numerous experiments have been and are being conducted by DLR in order to fully understand and characterize the process. The human space exploration group of the Technical University of Munich (TUM) in cooperation with DLR has started to establish a dynamic model of the trickling filter system to be able to assess its performance on the LSS level. In the first development stage the model covers the nitrogen cycle enabling to simulate urine processing. This paper describes briefly the C.R.O.P. concept and the status of the trickling filter model development. The model is based on enzyme-catalyzed reaction kinetics for the fundamental microbiological reaction chain and is created in MATLAB. Verification and correlation of the developed model with experiment results has been performed. Several predictive studies for batch sequencing behavior have been performed, demonstrating a good capability of C.R.O.P. concept to be used in closed LSS. Achieved results are critically discussed and way forward is presented.

Healthcare waste management: an interpretive structural modeling approach.

PubMed

Thakur, Vikas; Anbanandam, Ramesh

2016-06-13

Purpose - The World Health Organization identified infectious healthcare waste as a threat to the environment and human health. India's current medical waste management system has limitations, which lead to ineffective and inefficient waste handling practices. Hence, the purpose of this paper is to: first, identify the important barriers that hinder India's healthcare waste management (HCWM) systems; second, classify operational, tactical and strategical issues to discuss the managerial implications at different management levels; and third, define all barriers into four quadrants depending upon their driving and dependence power. Design/methodology/approach - India's HCWM system barriers were identified through the literature, field surveys and brainstorming sessions. Interrelationships among all the barriers were analyzed using interpretive structural modeling (ISM). Fuzzy-Matrice d'Impacts Croisés Multiplication Appliquée á un Classement (MICMAC) analysis was used to classify HCWM barriers into four groups. Findings - In total, 25 HCWM system barriers were identified and placed in 12 different ISM model hierarchy levels. Fuzzy-MICMAC analysis placed eight barriers in the second quadrant, five in third and 12 in fourth quadrant to define their relative ISM model importance. Research limitations/implications - The study's main limitation is that all the barriers were identified through a field survey and barnstorming sessions conducted only in Uttarakhand, Northern State, India. The problems in implementing HCWM practices may differ with the region, hence, the current study needs to be replicated in different Indian states to define the waste disposal strategies for hospitals. Practical implications - The model will help hospital managers and Pollution Control Boards, to plan their resources accordingly and make policies, targeting key performance areas. Originality/value - The study is the first attempt to identify India's HCWM system barriers and prioritize

Multiphase, multicomponent flow and transport models for Nuclear Test-Ban Treaty monitoring and nuclear waste disposal applications

NASA Astrophysics Data System (ADS)

Jordan, Amy

Open challenges remain in using numerical models of subsurface flow and transport systems to make useful predictions related to nuclear waste storage and nonproliferation. The work presented here addresses the sensitivity of model results to unknown parameters, states, and processes, particularly uncertainties related to incorporating previously unrepresented processes (e.g., explosion-induced fracturing, hydrous mineral dehydration) into a subsurface flow and transport numerical simulator. The Finite Element Heat and Mass (FEHM) transfer code is used for all numerical models in this research. An experimental campaign intended to validate the predictive capability of numerical models that include the strongly coupled thermal, hydrological, and chemical processes in bedded salt is also presented. Underground nuclear explosions (UNEs) produce radionuclide gases that may seep to the surface over weeks to months. The estimated timing of gas arrival at the surface may be used to deploy personnel and equipment to the site of a suspected UNE, if allowed under the terms of the Comprehensive Nuclear Test-Ban Treaty. A model was developed using FEHM that considers barometrically pumped gas transport through a simplified fractured medium and was used to quantify the impact of uncertainties in hydrologic parameters (fracture aperture, matrix permeability, porosity, and saturation) and season of detonation on the timing of gas breakthrough. Numerical sensitivity analyses were performed for the case of a 1 kt UNE at a 400 m burial depth. Gas arrival time was found to be most affected by matrix permeability and fracture aperture. Gases having higher diffusivity were more sensitive to uncertainty in the rock properties. The effect of seasonality in the barometric pressure forcing was found to be important, with detonations in March the least likely to be detectable based on barometric data for Rainier Mesa, Nevada. Monte Carlo modeling was also used to predict the window of

Solid waste projection model: Model version 1. 0 technical reference manual

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

Wilkins, M.L.; Crow, V.L.; Buska, D.E.

1990-11-01

The Solid Waste Projection Model (SWPM) system is an analytical tool developed by Pacific Northwest Laboratory (PNL) for Westinghouse Hanford Company (WHC). The SWPM system provides a modeling and analysis environment that supports decisions in the process of evaluating various solid waste management alternatives. This document, one of a series describing the SWPM system, contains detailed information regarding the software utilized in developing Version 1.0 of the modeling unit of SWPM. This document is intended for use by experienced software engineers and supports programming, code maintenance, and model enhancement. Those interested in using SWPM should refer to the SWPM Modelmore » User's Guide. This document is available from either the PNL project manager (D. L. Stiles, 509-376-4154) or the WHC program monitor (B. C. Anderson, 509-373-2796). 8 figs.« less

Kinetic modelling of anaerobic hydrolysis of solid wastes, including disintegration processes.

PubMed

García-Gen, Santiago; Sousbie, Philippe; Rangaraj, Ganesh; Lema, Juan M; Rodríguez, Jorge; Steyer, Jean-Philippe; Torrijos, Michel

2015-01-01

A methodology to estimate disintegration and hydrolysis kinetic parameters of solid wastes and validate an ADM1-based anaerobic co-digestion model is presented. Kinetic parameters of the model were calibrated from batch reactor experiments treating individually fruit and vegetable wastes (among other residues) following a new protocol for batch tests. In addition, decoupled disintegration kinetics for readily and slowly biodegradable fractions of solid wastes was considered. Calibrated parameters from batch assays of individual substrates were used to validate the model for a semi-continuous co-digestion operation treating simultaneously 5 fruit and vegetable wastes. The semi-continuous experiment was carried out in a lab-scale CSTR reactor for 15 weeks at organic loading rate ranging between 2.0 and 4.7 gVS/Ld. The model (built in Matlab/Simulink) fit to a large extent the experimental results in both batch and semi-continuous mode and served as a powerful tool to simulate the digestion or co-digestion of solid wastes. Copyright © 2014 Elsevier Ltd. All rights reserved.

Thermodynamic model for uranium release from hanford site tank residual waste.

PubMed

Cantrell, Kirk J; Deutsch, William J; Lindberg, Mike J

2011-02-15

A thermodynamic model of U solid-phase solubility and paragenesis was developed for Hanford Site tank residual waste that will remain in place after tank closure. The model was developed using a combination of waste composition data, waste leach test data, and thermodynamic modeling of the leach test data. The testing and analyses were conducted using actual Hanford Site tank residual waste. Positive identification of U phases by X-ray diffraction was generally not possible either because solids in the waste were amorphous or their concentrations were not detectable by XRD for both as-received and leached residual waste. Three leachant solutions were used in the studies: deionized water, CaCO3 saturated solution, and Ca(OH)2 saturated solution. Analysis of calculated saturation indices indicate that NaUO2PO4·xH2O and Na2U2O7(am) are present in the residual wastes initially. Leaching of the residual wastes with deionized water or CaCO3 saturated solution results in preferential dissolution Na2U2O7(am) and formation of schoepite. Leaching of the residual wastes with Ca(OH)2 saturated solution appears to result in transformation of both NaUO2PO4·xH2O and Na2U2O7(am) to CaUO4. Upon the basis of these results, the paragenetic sequence of secondary phases expected to occur as leaching of residual waste progresses for two tank closure scenarios was identified.

A study of waste liquid crystal display generation in mainland China.

PubMed

Liu, Zhifeng; Xu, Zeying; Huang, Haihong; Li, Bingbing

2016-01-01

The generation of liquid crystal display waste is becoming a serious social problem. Predicting liquid crystal display waste status is the foundation for establishing a recycling network; however, the difficulty in predicting liquid crystal display waste quantity lies in data mining. In order to determine the quantity and the distribution of liquid crystal display waste in China, the four top-selling liquid crystal display products (liquid crystal display TVs, desktop PCs, notebook PCs, and mobile phones) were selected as study objects. Then, the extended logistic model and market supply A method was used to predict the quantity of liquid crystal display waste products. Moreover, the distribution of liquid crystal display waste products in different regions was evaluated by examining the consumption levels of household equipment. The results revealed that the quantity of waste liquid crystal displays would increase rapidly in the next decade. In particular, the predicted quantity of waste liquid crystal displays would rise to approximately 4.262 × 10(9) pieces in 2020, and the total display area (i.e. the surface area of liquid crystal display panels) of waste liquid crystal displays would reach 5.539 × 10(7) m(2). The prediction on the display area of waste liquid crystal display TVs showed that it would account for 71.5% of the total display area by 2020. Meanwhile, the quantity of waste mobile phones would significantly grow, increasing 5.8 times from 2012 to 2020. In terms of distribution, Guangdong is the top waste liquid crystal display-generating province in China, followed by Jiangsu, Shandong, Henan, Zhejiang, and Sichuan. Considering its regional characteristics, Guangdong has been proposed to be the most important location of the recycling network. © The Author(s) 2015.

Coupling scales for modelling heavy metal vaporization from municipal solid waste incineration in a fluid bed by CFD

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

Soria, José, E-mail: jose.soria@probien.gob.ar; Gauthier, Daniel; Flamant, Gilles

2015-09-15

Highlights: • A CFD two-scale model is formulated to simulate heavy metal vaporization from waste incineration in fluidized beds. • MSW particle is modelled with the macroscopic particle model. • Influence of bed dynamics on HM vaporization is included. • CFD predicted results agree well with experimental data reported in literature. • This approach may be helpful for fluidized bed reactor modelling purposes. - Abstract: Municipal Solid Waste Incineration (MSWI) in fluidized bed is a very interesting technology mainly due to high combustion efficiency, great flexibility for treating several types of waste fuels and reduction in pollutants emitted with themore » flue gas. However, there is a great concern with respect to the fate of heavy metals (HM) contained in MSW and their environmental impact. In this study, a coupled two-scale CFD model was developed for MSWI in a bubbling fluidized bed. It presents an original scheme that combines a single particle model and a global fluidized bed model in order to represent the HM vaporization during MSW combustion. Two of the most representative HM (Cd and Pb) with bed temperatures ranging between 923 and 1073 K have been considered. This new approach uses ANSYS FLUENT 14.0 as the modelling platform for the simulations along with a complete set of self-developed user-defined functions (UDFs). The simulation results are compared to the experimental data obtained previously by the research group in a lab-scale fluid bed incinerator. The comparison indicates that the proposed CFD model predicts well the evolution of the HM release for the bed temperatures analyzed. It shows that both bed temperature and bed dynamics have influence on the HM vaporization rate. It can be concluded that CFD is a rigorous tool that provides valuable information about HM vaporization and that the original two-scale simulation scheme adopted allows to better represent the actual particle behavior in a fluid bed incinerator.« less

Atmospheric Dispersion Modeling of the February 2014 Waste Isolation Pilot Plant Release

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

Nasstrom, John; Piggott, Tom; Simpson, Matthew

2015-07-22

This report presents the results of a simulation of the atmospheric dispersion and deposition of radioactivity released from the Waste Isolation Pilot Plant (WIPP) site in New Mexico in February 2014. These simulations were made by the National Atmospheric Release Advisory Center (NARAC) at Lawrence Livermore National Laboratory (LLNL), and supersede NARAC simulation results published in a previous WIPP report (WIPP, 2014). The results presented in this report use additional, more detailed data from WIPP on the specific radionuclides released, radioactivity release amounts and release times. Compared to the previous NARAC simulations, the new simulation results in this report aremore » based on more detailed modeling of the winds, turbulence, and particle dry deposition. In addition, the initial plume rise from the exhaust vent was considered in the new simulations, but not in the previous NARAC simulations. The new model results show some small differences compared to previous results, but do not change the conclusions in the WIPP (2014) report. Presented are the data and assumptions used in these model simulations, as well as the model-predicted dose and deposition on and near the WIPP site. A comparison of predicted and measured radionuclide-specific air concentrations is also presented.« less

Emissions model of waste treatment operations at the Idaho Chemical Processing Plant

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

Schindler, R.E.

1995-03-01

An integrated model of the waste treatment systems at the Idaho Chemical Processing Plant (ICPP) was developed using a commercially-available process simulation software (ASPEN Plus) to calculate atmospheric emissions of hazardous chemicals for use in an application for an environmental permit to operate (PTO). The processes covered by the model are the Process Equipment Waste evaporator, High Level Liquid Waste evaporator, New Waste Calcining Facility and Liquid Effluent Treatment and Disposal facility. The processes are described along with the model and its assumptions. The model calculates emissions of NO{sub x}, CO, volatile acids, hazardous metals, and organic chemicals. Some calculatedmore » relative emissions are summarized and insights on building simulations are discussed.« less

Comparing urban solid waste recycling from the viewpoint of urban metabolism based on physical input-output model: A case of Suzhou in China.

PubMed

Liang, Sai; Zhang, Tianzhu

2012-01-01

Investigating impacts of urban solid waste recycling on urban metabolism contributes to sustainable urban solid waste management and urban sustainability. Using a physical input-output model and scenario analysis, urban metabolism of Suzhou in 2015 is predicted and impacts of four categories of solid waste recycling on urban metabolism are illustrated: scrap tire recycling, food waste recycling, fly ash recycling and sludge recycling. Sludge recycling has positive effects on reducing all material flows. Thus, sludge recycling for biogas is regarded as an accepted method. Moreover, technical levels of scrap tire recycling and food waste recycling should be improved to produce positive effects on reducing more material flows. Fly ash recycling for cement production has negative effects on reducing all material flows except solid wastes. Thus, other fly ash utilization methods should be exploited. In addition, the utilization and treatment of secondary wastes from food waste recycling and sludge recycling should be concerned. Copyright © 2011 Elsevier Ltd. All rights reserved.

Predictive models in urology.

PubMed

Cestari, Andrea

2013-01-01

Predictive modeling is emerging as an important knowledge-based technology in healthcare. The interest in the use of predictive modeling reflects advances on different fronts such as the availability of health information from increasingly complex databases and electronic health records, a better understanding of causal or statistical predictors of health, disease processes and multifactorial models of ill-health and developments in nonlinear computer models using artificial intelligence or neural networks. These new computer-based forms of modeling are increasingly able to establish technical credibility in clinical contexts. The current state of knowledge is still quite young in understanding the likely future direction of how this so-called 'machine intelligence' will evolve and therefore how current relatively sophisticated predictive models will evolve in response to improvements in technology, which is advancing along a wide front. Predictive models in urology are gaining progressive popularity not only for academic and scientific purposes but also into the clinical practice with the introduction of several nomograms dealing with the main fields of onco-urology.

A nanomaterial release model for waste shredding using a Bayesian belief network

NASA Astrophysics Data System (ADS)

Shandilya, Neeraj; Ligthart, Tom; van Voorde, Imelda; Stahlmecke, Burkhard; Clavaguera, Simon; Philippot, Cecile; Ding, Yaobo; Goede, Henk

2018-02-01

The shredding of waste of electrical and electronic equipment (WEEE) and other products, incorporated with nanomaterials, can lead to a substantial release of nanomaterials. Considering the uncertainty, complexity, and scarcity of experimental data on release, we present the development of a Bayesian belief network (BBN) model. This baseline model aims to give a first prediction of the release of nanomaterials (excluding nanofibers) during their mechanical shredding. With a focus on the description of the model development methodology, we characterize nanomaterial release in terms of number, size, mass, and composition of released particles. Through a sensitivity analysis of the model, we find the material-specific parameters like affinity of nanomaterials to the matrix of the composite and their state of dispersion inside the matrix to reduce the nanomaterial release up to 50%. The shredder-specific parameters like number of shafts in a shredder and input and output size of the material for shredding could minimize it up to 98%. The comparison with two experimental test cases shows promising outcome on the prediction capacity of the model. As additional experimental data on nanomaterial release becomes available, the model is able to further adapt and update risk forecasts. When adapting the model with additional expert beliefs, experts should be selected using criteria, e.g., substantial contribution to nanomaterial and/or particulate matter release-related scientific literature, the capacity and willingness to contribute to further development of the BBN model, and openness to accepting deviating opinions. [Figure not available: see fulltext.

Verification of a model for predicting the effect of inconstant temperature on embryonic development of lake whitefish (Coregonus clupeaformis)

USGS Publications Warehouse

Berlin, William H.; Brooke, L.T.; Stone, Linda J.

1977-01-01

The model was used to predict the effects of small temperature increases (caused by a hypothetical waste-heat discharge) on the rate of development and time of hatching of lake whitefish eggs. According to this simulation, continuous addition of waste heat sufficient to raise the temperature 1, 2, or 3 C above ambient on the spawning grounds during December-April would advance the time of hatching 8, 16, or 21 days, respectively. Possible effects of this advancement on the reproductive success of whitefish are discussed.

Evaluation of the predictive capability of coupled thermo-hydro-mechanical models for a heated bentonite/clay system (HE-E) in the Mont Terri Rock Laboratory

DOE PAGES

Garitte, B.; Shao, H.; Wang, X. R.; ...

2017-01-09

Process understanding and parameter identification using numerical methods based on experimental findings are a key aspect of the international cooperative project DECOVALEX. Comparing the predictions from numerical models against experimental results increases confidence in the site selection and site evaluation process for a radioactive waste repository in deep geological formations. In the present phase of the project, DECOVALEX-2015, eight research teams have developed and applied models for simulating an in-situ heater experiment HE-E in the Opalinus Clay in the Mont Terri Rock Laboratory in Switzerland. The modelling task was divided into two study stages, related to prediction and interpretation ofmore » the experiment. A blind prediction of the HE-E experiment was performed based on calibrated parameter values for both the Opalinus Clay, that were based on the modelling of another in-situ experiment (HE-D), and modelling of laboratory column experiments on MX80 granular bentonite and a sand/bentonite mixture .. After publication of the experimental data, additional coupling functions were analysed and considered in the different models. Moreover, parameter values were varied to interpret the measured temperature, relative humidity and pore pressure evolution. The analysis of the predictive and interpretative results reveals the current state of understanding and predictability of coupled THM behaviours associated with geologic nuclear waste disposal in clay formations.« less

Evaluation of the predictive capability of coupled thermo-hydro-mechanical models for a heated bentonite/clay system (HE-E) in the Mont Terri Rock Laboratory

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

Garitte, B.; Shao, H.; Wang, X. R.

Process understanding and parameter identification using numerical methods based on experimental findings are a key aspect of the international cooperative project DECOVALEX. Comparing the predictions from numerical models against experimental results increases confidence in the site selection and site evaluation process for a radioactive waste repository in deep geological formations. In the present phase of the project, DECOVALEX-2015, eight research teams have developed and applied models for simulating an in-situ heater experiment HE-E in the Opalinus Clay in the Mont Terri Rock Laboratory in Switzerland. The modelling task was divided into two study stages, related to prediction and interpretation ofmore » the experiment. A blind prediction of the HE-E experiment was performed based on calibrated parameter values for both the Opalinus Clay, that were based on the modelling of another in-situ experiment (HE-D), and modelling of laboratory column experiments on MX80 granular bentonite and a sand/bentonite mixture .. After publication of the experimental data, additional coupling functions were analysed and considered in the different models. Moreover, parameter values were varied to interpret the measured temperature, relative humidity and pore pressure evolution. The analysis of the predictive and interpretative results reveals the current state of understanding and predictability of coupled THM behaviours associated with geologic nuclear waste disposal in clay formations.« less

Review of the Scientific Understanding of Radioactive Waste at the U.S. DOE Hanford Site

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

Peterson, Reid A.; Buck, Edgar C.; Chun, Jaehun

This paper reviews the origin and chemical and rheological complexity of radioactive waste at the U.S. Department of Energy’s Hanford Site. The waste, stored in underground tanks, was generated via three distinct processes over decades of plutonium extraction operations. Although close records were kept of original waste disposition, tank-to-tank transfers and conditions that impede equilibrium complicate our understanding of the chemistry, phase composition, and rheology of the waste. Tank waste slurries comprise particles and aggregates from nano to micron scales, with varying densities, morphologies, heterogeneous compositions, and complicated responses to flow regimes and process conditions. Further, remnant or changing radiationmore » fields may affect the stability and rheology of the waste. These conditions pose challenges for transport through conduits or pipes to treatment plants for vitrification. Additionally, recalcitrant boehmite degrades glass quality and must be reduced prior to vitrification, but dissolves much more slowly than predicted given surface normalized rates. Existing empirical models based on ex situ experiments and observations lack true predictive capabilities. Recent advances in in situ microscopy, aberration corrected TEM, theoretical modeling across scales, and experimental methods for probing the physics and chemistry at mineral-fluid and mineral-mineral interfaces are being implemented to build robustly predictive physics-based models.« less

Evaluating the ability of artificial neural network and PCA-M5P models in predicting leachate COD load in landfills.

PubMed

Azadi, Sama; Amiri, Hamid; Rakhshandehroo, G Reza

2016-09-01

Waste burial in uncontrolled landfills can cause serious environmental damages and unpleasant consequences. Leachates produced in landfills have the potential to contaminate soil and groundwater resources. Leachate management is one of the major issues with respect to landfills environmental impacts. Improper design of landfills can lead to leachate spread in the environment, and hence, engineered landfills are required to have leachate monitoring programs. The high cost of such programs may be greatly reduced and cost efficiency of the program may be optimized if one can predict leachate contamination level and foresee management and treatment strategies. The aim of this study is to develop two expert systems consisting of Artificial Neural Network (ANN) and Principal Component Analysis-M5P (PCA-M5P) models to predict Chemical Oxygen Demand (COD) load in leachates produced in lab-scale landfills. Measured data from three landfill lysimeters, including rainfall depth, number of days after waste deposition, thickness of top and bottom Compacted Clay Liners (CCLs), and thickness of top cover over the lysimeter, were utilized to develop, train, validate, and test the expert systems and predict the leachate COD load. Statistical analysis of the prediction results showed that both models possess good prediction ability with a slight superiority for ANN over PCA-M5P. Based on test datasets, the mean absolute percentage error for ANN and PCA-M5P models were 4% and 12%, respectively, and the correlation coefficient for both models was greater than 0.98. Developed models may be used as a rough estimate for leachate COD load prediction in primary landfill designs, where the effect of a top and/or bottom liner is disputed. Copyright © 2016 Elsevier Ltd. All rights reserved.

New mechanistically based model for predicting reduction of biosolids waste by ozonation of return activated sludge.

PubMed

Isazadeh, Siavash; Feng, Min; Urbina Rivas, Luis Enrique; Frigon, Dominic

2014-04-15

Two pilot-scale activated sludge reactors were operated for 98 days to provide the necessary data to develop and validate a new mathematical model predicting the reduction of biosolids production by ozonation of the return activated sludge (RAS). Three ozone doses were tested during the study. In addition to the pilot-scale study, laboratory-scale experiments were conducted with mixed liquor suspended solids and with pure cultures to parameterize the biomass inactivation process during exposure to ozone. The experiments revealed that biomass inactivation occurred even at the lowest doses, but that it was not associated with extensive COD solubilization. For validation, the model was used to simulate the temporal dynamics of the pilot-scale operational data. Increasing the description accuracy of the inactivation process improved the precision of the model in predicting the operational data. Copyright © 2014 Elsevier B.V. All rights reserved.

Radioactive waste disposal in the marine environment

NASA Astrophysics Data System (ADS)

Anderson, D. R.

In order to find the optimal solution to waste disposal problems, it is necessary to make comparisons between disposal media. It has become obvious to many within the scientific community that the single medium approach leads to over protection of one medium at the expense of the others. Cross media comparisons are being conducted in the Department of Energy ocean disposal programs for several radioactive wastes. Investigations in three areas address model development, comparisons of laboratory tests with field results and predictions, and research needs in marine disposal of radioactive waste. Tabulated data are included on composition of liquid high level waste and concentration of some natural radionuclides in the sea.

System dynamic modeling on construction waste management in Shenzhen, China.

PubMed

Tam, Vivian W Y; Li, Jingru; Cai, Hong

2014-05-01

This article examines the complexity of construction waste management in Shenzhen, Mainland China. In-depth analysis of waste generation, transportation, recycling, landfill and illegal dumping of various inherent management phases is explored. A system dynamics modeling using Stella model is developed. Effects of landfill charges and also penalties from illegal dumping are also simulated. The results show that the implementation of comprehensive policy on both landfill charges and illegal dumping can effectively control the illegal dumping behavior, and achieve comprehensive construction waste minimization. This article provides important recommendations for effective policy implementation and explores new perspectives for Shenzhen policy makers.

Experimental and modeling approaches for food waste composting: a review.

PubMed

Li, Zhentong; Lu, Hongwei; Ren, Lixia; He, Li

2013-10-01

Composting has been used as a method to dispose food waste (FW) and recycle organic matter to improve soil structure and fertility. Considering the significance of composting in FW treatment, many researchers have paid their attention on how to improve FW composting efficiency, reduce operating cost, and mitigate the associated environmental damage. This review focuses on the overall studies of FW composting, not only various parameters significantly affecting the processes and final results, but also a number of simulation approaches that are greatly instrumental in well understanding the process mechanism and/or results prediction. Implications of many key ingredients on FW composting performance are also discussed. Perspects of effective laboratory experiments and computer-based simulation are finally investigated, demonstrating many demanding areas for enhanced research efforts, which include the screening of multi-functional additives, volatile organiccompound emission control, necessity of modeling and post-modeling analysis, and usefulness of developing more conjunctive AI-based process control techniques. Copyright © 2013 Elsevier Ltd. All rights reserved.

Long-term settlement prediction at open dumping area using Hossein and Gabr method for new development

NASA Astrophysics Data System (ADS)

Pauzi, Nur Irfah Mohd; Shariffuddin, Ahmad Sulaimi; Omar, Husaini; Misran, Halina

2017-07-01

In Malaysia, the most common method of disposal is landfill/open dumping. The soil at the dumping area are mixed with waste and soil. Thus, it was called as waste soil. Due to its heterogeneity properties, waste soil has a different settlement rate because different types of waste tends to settle differently. The Hussein and Gabr model which used empirical model was proposed to compute the long-term settlement. This Hussein and Gabr model is one of the soil settlement model that can be used to predict the long-term settlement at the dumping area. The model relates between the compression index and the time factor. The time factor are t1, t2, t3 and t4. The compression index is Cα1=compression index and Cβ is biodegradation index. The duration for initial compression, the compression, the biological compression and time creep are included in the model. The sample of waste soil is taken from closed dumping area in Lukut, Negeri Sembilan with the height of waste approximately 1 to 3 meters. The sample is tested using consolidation test for determining the geotechnical parameters and compressibility index. Based on the Hossein and Gabr model, the predicted long-term settlement for 20 years (ΔH) for the waste height of 1 to 3 meters are 0.21m, 0.42m and 0.63m respectively and are below the percentages of proposed maximum settlement for waste soil which is acceptable for new development to takes place.. The types of deep or shallow foundation are proposed based on the predicted settlement. The abandoned open dumping area can now be reused for the new development after the long-term settlement are predicted and some of the precaution measures has been taken as a safety measures.

Inverse and Predictive Modeling

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

Syracuse, Ellen Marie

The LANL Seismo-Acoustic team has a strong capability in developing data-driven models that accurately predict a variety of observations. These models range from the simple – one-dimensional models that are constrained by a single dataset and can be used for quick and efficient predictions – to the complex – multidimensional models that are constrained by several types of data and result in more accurate predictions. Team members typically build models of geophysical characteristics of Earth and source distributions at scales of 1 to 1000s of km, the techniques used are applicable for other types of physical characteristics at an evenmore » greater range of scales. The following cases provide a snapshot of some of the modeling work done by the Seismo- Acoustic team at LANL.« less

Archaeological predictive model set.

DOT National Transportation Integrated Search

2015-03-01

This report is the documentation for Task 7 of the Statewide Archaeological Predictive Model Set. The goal of this project is to : develop a set of statewide predictive models to assist the planning of transportation projects. PennDOT is developing t...

Modelling the anaerobic digestion of solid organic waste - Substrate characterisation method for ADM1 using a combined biochemical and kinetic parameter estimation approach.

PubMed

Poggio, D; Walker, M; Nimmo, W; Ma, L; Pourkashanian, M

2016-07-01

This work proposes a novel and rigorous substrate characterisation methodology to be used with ADM1 to simulate the anaerobic digestion of solid organic waste. The proposed method uses data from both direct substrate analysis and the methane production from laboratory scale anaerobic digestion experiments and involves assessment of four substrate fractionation models. The models partition the organic matter into a mixture of particulate and soluble fractions with the decision on the most suitable model being made on quality of fit between experimental and simulated data and the uncertainty of the calibrated parameters. The method was tested using samples of domestic green and food waste and using experimental data from both short batch tests and longer semi-continuous trials. The results showed that in general an increased fractionation model complexity led to better fit but with increased uncertainty. When using batch test data the most suitable model for green waste included one particulate and one soluble fraction, whereas for food waste two particulate fractions were needed. With richer semi-continuous datasets, the parameter estimation resulted in less uncertainty therefore allowing the description of the substrate with a more complex model. The resulting substrate characterisations and fractionation models obtained from batch test data, for both waste samples, were used to validate the method using semi-continuous experimental data and showed good prediction of methane production, biogas composition, total and volatile solids, ammonia and alkalinity. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

A BIM-based system for demolition and renovation waste estimation and planning.

PubMed

Cheng, Jack C P; Ma, Lauren Y H

2013-06-01

Due to the rising worldwide awareness of green environment, both government and contractors have to consider effective construction and demolition (C&D) waste management practices. The last two decades have witnessed the growing importance of demolition and renovation (D&R) works and the growing amount of D&R waste disposed to landfills every day, especially in developed cities like Hong Kong. Quantitative waste prediction is crucial for waste management. It can enable contractors to pinpoint critical waste generation processes and to plan waste control strategies. In addition, waste estimation could also facilitate some government waste management policies, such as the waste disposal charging scheme in Hong Kong. Currently, tools that can accurately and conveniently estimate the amount of waste from construction, renovation, and demolition projects are lacking. In the light of this research gap, this paper presents a building information modeling (BIM) based system that we have developed for estimation and planning of D&R waste. BIM allows multi-disciplinary information to be superimposed within one digital building model. Our system can extract material and volume information through the BIM model and integrate the information for detailed waste estimation and planning. Waste recycling and reuse are also considered in our system. Extracted material information can be provided to recyclers before demolition or renovation to make recycling stage more cooperative and more efficient. Pick-up truck requirements and waste disposal charging fee for different waste facilities will also be predicted through our system. The results could provide alerts to contractors ahead of time at project planning stage. This paper also presents an example scenario with a 47-floor residential building in Hong Kong to demonstrate our D&R waste estimation and planning system. As the BIM technology has been increasingly adopted in the architectural, engineering and construction industry

Numerical simulation of organic waste aerobic biodegradation: a new way to correlate respiration kinetics and organic matter fractionation.

PubMed

Denes, Jeremy; Tremier, Anne; Menasseri-Aubry, Safya; Walter, Christian; Gratteau, Laurette; Barrington, Suzelle

2015-02-01

Composting wastes permits the reuse of organic matter (OM) as agricultural amendments. The fate of OM during composting and the subsequent degradation of composts in soils largely depend on waste OM quality. The proposed study aimed at developing a model to predict the evolution in organic matter quality during the aerobic degradation of organic waste, based on the quantification of the various OM fractions contained in the wastes. The model was calibrated from data gathered during the monitoring of four organic wastes (two non-treated wastes and their digestates) exposed to respirometric tests. The model was successfully fitted for all four wastes and permitted to predict respiration kinetics, expressed as CO2 production rates, and the evolution of OM fractions. The calibrated model demonstrated that hydrolysis rates of OM fractions were similar for all four wastes whereas the parameters related to microbial activity (eg. growth and death rates) were specific to each substrate. These later parameters have been estimated by calibration on respirometric data, thus demonstrating that coupling analyses of OM fractions in initial wastes and respirometric tests permit the simulation of the biodegradation of various type of waste. The biodegradation model presented in this paper could thereafter be integrated in a composting model by implementing mass and heat balance equations. Copyright © 2014 Elsevier Ltd. All rights reserved.

A novel multiple batch extraction test to assess contaminant mobilization from porous waste materials

NASA Astrophysics Data System (ADS)

Iden, S. C.; Durner, W.; Delay, M.; Frimmel, F. H.

2009-04-01

Contaminated porous materials, like soils, dredged sediments or waste materials must be tested before they can be used as filling materials in order to minimize the risk of groundwater pollution. We applied a multiple batch extraction test at varying liquid-to-solid (L/S) ratios to a demolition waste material and a municipal waste incineration product and investigated the release of chloride, sulphate, sodium, copper, chromium and dissolved organic carbon from both waste materials. The liquid phase test concentrations were used to estimate parameters of a relatively simple mass balance model accounting for equilibrium partitioning. The model parameters were estimated within a Bayesian framework by applying an efficient MCMC sampler and the uncertainties of the model parameters and model predictions were quantified. We tested isotherms of the linear, Freundlich and Langmuir type and selected the optimal isotherm model by use of the Deviance Information Criterion (DIC). Both the excellent fit to the experimental data and a comparison between the model-predicted and independently measured concentrations at the L/S ratios of 0.25 and 0.5 L/kg demonstrate the applicability of the model for almost all studied substances and both waste materials. We conclude that batch extraction tests at varying L/S ratios provide, at moderate experimental cost, a powerful complement to established test designs like column leaching or single batch extraction tests. The method constitutes an important tool in risk assessments, because concentrations at soil water contents representative for the field situation can be predicted from easier-to-obtain test concentrations at larger L/S ratios. This helps to circumvent the experimental difficulties of the soil saturation extract and eliminates the need to apply statistical approaches to predict such representative concentrations which have been shown to suffer dramatically from poor correlations.

Preliminary study on enhancing waste management best practice model in Malaysia construction industry

NASA Astrophysics Data System (ADS)

Jamaludin, Amril Hadri; Karim, Nurulzatushima Abdul; Noor, Raja Nor Husna Raja Mohd; Othman, Nurulhidayah; Malik, Sulaiman Abdul

2017-08-01

Construction waste management (CWM) is the practice of minimizing and diverting construction waste, demolition debris, and land-clearing debris from disposal and redirecting recyclable resources back into the construction process. Best practice model means best choice from the collection of other practices that was built for purpose of construction waste management. The practice model can help the contractors in minimizing waste before the construction activities will be started. The importance of minimizing wastage will have direct impact on time, cost and quality of a construction project. This paper is focusing on the preliminary study to determine the factors of waste generation in the construction sites and identify the effectiveness of existing construction waste management practice conducted in Malaysia. The paper will also include the preliminary works of planned research location, data collection method, and analysis to be done by using the Analytical Hierarchy Process (AHP) to help in developing suitable waste management best practice model that can be used in the country.

Versions of the Waste Reduction Model (WARM)

EPA Pesticide Factsheets

This page provides a brief chronology of changes made to EPA’s Waste Reduction Model (WARM), organized by WARM version number. The page includes brief summaries of changes and updates since the previous version.

Versions of the Waste Reduction Model (WARM)

EPA Pesticide Factsheets

2017-02-14

This page provides a brief chronology of changes made to EPA’s Waste Reduction Model (WARM), organized by WARM version number. The page includes brief summaries of changes and updates since the previous version.

Development of a methodology for electronic waste estimation: A material flow analysis-based SYE-Waste Model.

PubMed

Yedla, Sudhakar

2016-01-01

Improved living standards and the share of services sector to the economy in Asia, and the use of electronic equipment is on the rise and results in increased electronic waste generation. A peculiarity of electronic waste is that it has a 'significant' value even after its life time, and to add complication, even after its extended life in its 'dump' stage. Thus, in Indian situations, after its life time is over, the e-material changes hands more than once and finally ends up either in the hands of informal recyclers or in the store rooms of urban dwellings. This character makes it extremely difficult to estimate electronic waste generation. The present study attempts to develop a functional model based on a material flow analysis approach by considering all possible end uses of the material, its transformed goods finally arriving at disposal. It considers various degrees of uses derived of the e-goods regarding their primary use (life time), secondary use (first degree extension of life), third-hand use (second degree extension of life), donation, retention at the respective places (without discarding), fraction shifted to scrap vendor, and the components reaching the final dump site from various end points of use. This 'generic functional model' named SYE-Waste Model, developed based on a material flow analysis approach, can be used to derive 'obsolescence factors' for various degrees of usage of e-goods and also to make a comprehensive estimation of electronic waste in any city/country. © The Author(s) 2015.

Predicting readmission risk with institution-specific prediction models.

PubMed

Yu, Shipeng; Farooq, Faisal; van Esbroeck, Alexander; Fung, Glenn; Anand, Vikram; Krishnapuram, Balaji

2015-10-01

The ability to predict patient readmission risk is extremely valuable for hospitals, especially under the Hospital Readmission Reduction Program of the Center for Medicare and Medicaid Services which went into effect starting October 1, 2012. There is a plethora of work in the literature that deals with developing readmission risk prediction models, but most of them do not have sufficient prediction accuracy to be deployed in a clinical setting, partly because different hospitals may have different characteristics in their patient populations. We propose a generic framework for institution-specific readmission risk prediction, which takes patient data from a single institution and produces a statistical risk prediction model optimized for that particular institution and, optionally, for a specific condition. This provides great flexibility in model building, and is also able to provide institution-specific insights in its readmitted patient population. We have experimented with classification methods such as support vector machines, and prognosis methods such as the Cox regression. We compared our methods with industry-standard methods such as the LACE model, and showed the proposed framework is not only more flexible but also more effective. We applied our framework to patient data from three hospitals, and obtained some initial results for heart failure (HF), acute myocardial infarction (AMI), pneumonia (PN) patients as well as patients with all conditions. On Hospital 2, the LACE model yielded AUC 0.57, 0.56, 0.53 and 0.55 for AMI, HF, PN and All Cause readmission prediction, respectively, while the proposed model yielded 0.66, 0.65, 0.63, 0.74 for the corresponding conditions, all significantly better than the LACE counterpart. The proposed models that leverage all features at discharge time is more accurate than the models that only leverage features at admission time (0.66 vs. 0.61 for AMI, 0.65 vs. 0.61 for HF, 0.63 vs. 0.56 for PN, 0.74 vs. 0.60 for All

Ensuring Longevity: Ancient Glasses Help Predict Durability of Vitrified Nuclear Waste

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

Weaver, Jamie L.; McCloy, John S.; Ryan, Joseph V.

How does glass alter with time? For the last hundred years this has been an important question to the fields of object conservation and archeology to ensure the preservation of glass artifacts. This same question is part of the development and assessment of durable glass waste forms for the immobilization of nuclear wastes. Researchers have developed experiments ranging from simple to highly sophisticated to answer this question, and, as a result, have gained significant insight into the mechanisms that drive glass alteration. However, the gathered data have been predominately applicable to only short-term alteration times, i.e. over the course ofmore » decades. What has remained elusive is the long-term mechanisms of glass alteration[1]. These mechanisms are of particular interest to the international nuclear waste glass community as they strive to ensure that vitrified products will be durable for thousands to tens of thousands of years. For the last thirty years this community has been working to fill this research gap by partnering with archeologists, museum curators, and geologists to identify hundred to million-year old glass analogues that have altered in environments representative of those expected at potential nuclear waste disposal sites. The process of identifying a waste glass relevant analogue is challenging as it requires scientists to relate data collected from short-term laboratory experiments to observations made from long-term analogues and extensive geochemical modeling.« less

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.

Review of the Scientific Understanding of Radioactive Waste at the U.S. DOE Hanford Site.

PubMed

Peterson, Reid A; Buck, Edgar C; Chun, Jaehun; Daniel, Richard C; Herting, Daniel L; Ilton, Eugene S; Lumetta, Gregg J; Clark, Sue B

2018-01-16

This Critical Review reviews the origin and chemical and rheological complexity of radioactive waste at the U.S. Department of Energy Hanford Site. The waste, stored in underground tanks, was generated via three distinct processes over decades of plutonium extraction operations. Although close records were kept of original waste disposition, tank-to-tank transfers and conditions that impede equilibrium complicate our understanding of the chemistry, phase composition, and rheology of the waste. Tank waste slurries comprise particles and aggregates from nano to micro scales, with varying densities, morphologies, heterogeneous compositions, and complicated responses to flow regimes and process conditions. Further, remnant or changing radiation fields may affect the stability and rheology of the waste. These conditions pose challenges for transport through conduits or pipes to treatment plants for vitrification. Additionally, recalcitrant boehmite degrades glass quality and the high aluminum content must be reduced prior to vitrification for the manufacture of waste glass of acceptable durability. However, caustic leaching indicates that boehmite dissolves much more slowly than predicted given surface normalized rates. Existing empirical models based on ex situ experiments and observations generally only describe material balances and have not effectively predicted process performance. Recent advances in the areas of in situ microscopy, aberration-corrected transmission electron microscopy, theoretical modeling across scales, and experimental methods for probing the physics and chemistry at mineral-fluid and mineral-mineral interfaces are being implemented to build robustly predictive physics-based models.

A mathematical model for municipal solid waste management - A case study in Hong Kong.

PubMed

Lee, C K M; Yeung, C L; Xiong, Z R; Chung, S H

2016-12-01

With the booming economy and increasing population, the accumulation of waste has become an increasingly arduous issue and has aroused the attention from all sectors of society. Hong Kong which has a relative high daily per capita domestic waste generation rate in Asia has not yet established a comprehensive waste management system. This paper conducts a review of waste management approaches and models. Researchers highlight that mathematical models provide useful information for decision-makers to select appropriate choices and save cost. It is suggested to consider municipal solid waste management in a holistic view and improve the utilization of waste management infrastructures. A mathematical model which adopts integer linear programming and mixed integer programming has been developed for Hong Kong municipal solid waste management. A sensitivity analysis was carried out to simulate different scenarios which provide decision-makers important information for establishing Hong Kong waste management system. Copyright © 2016 Elsevier Ltd. All rights reserved.

IONSIV(R) IE-911 Performance in Savannah River Site Radioactive Waste

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

Walker, D.D.

2001-06-04

This report describes cesium sorption from high-level radioactive waste solutions onto IONSIV(R) IE-911 at ambient temperature. Researchers characterized six radioactive waste samples from five high-level waste tanks in the Savannah River Site tank farm, diluted the wastes to 5.6 M Na+, and made equilibrium and kinetic measurements of cesium sorption. The equilibrium measurements were compared to ZAM (Zheng, Anthony, and Martin) model predictions. The kinetic measurements were compared to simulant solutions whose column performance has been measured.

Waste Reduction Model (WARM) Resources for Students

EPA Pesticide Factsheets

This page provides a brief overview of how EPA’s Waste Reduction Model (WARM) can be used by students. The page includes a brief summary of uses of WARM for the audience and links to other resources.

Use of EDTA in modified kinetic testing for contaminated drainage prediction from waste rocks: case of the Lac Tio mine.

PubMed

Plante, Benoît; Benzaazoua, Mostafa; Bussière, Bruno; Kandji, El-Hadji-Babacar; Chopard, Aurélie; Bouzahzah, Hassan

2015-05-01

The tools developed for acid mine drainage (AMD) prediction were proven unsuccessful to predict the geochemical behavior of mine waste rocks having a significant chemical sorption capacity, which delays the onset of contaminated neutral drainage (CND). The present work was performed in order to test a new approach of water quality prediction, by using a chelating agent solution (0.03 M EDTA, or ethylenediaminetetraacetic acid) in kinetic testing used for the prediction of the geochemical behavior of geologic material. The hypothesis underlying the proposed approach is that the EDTA solution should chelate the metals as soon as they are released by sulfide oxidation, inhibiting their sorption or secondary precipitation, and therefore reproduce a worst-case scenario where very low metal attenuation mechanisms are present in the drainage waters. Fresh and weathered waste rocks from the Lac Tio mine (Rio tinto, Iron and Titanium), which are known to generate Ni-CND at the field scale, were submitted to small-scale humidity cells in control tests (using deionized water) and using an EDTA solution. Results show that EDTA effectively prevents the metals to be sorbed or to precipitate as secondary minerals, therefore enabling to bypass the delay associated with metal sorption in the prediction of water quality from these materials. This work shows that the use of a chelating agent solution is a promising novel approach of water quality prediction and provides general guidelines to be used in further studies, which will help both practitioners and regulators to plan more efficient management and disposal strategies of mine wastes.

Seal Formation Mechanism Beneath Animal Waste Holding Ponds

NASA Astrophysics Data System (ADS)

Cihan, A.; Tyner, J. S.; Wright, W. C.

2005-12-01

Infiltration of animal waste from holding ponds can cause contamination of groundwater. Typically, the initial flux from a pond decreases rapidly as a seal of animal waste particulates is deposited at the base of the pond. The purpose of this study was to investigate the mechanism of the seal formation. Twenty-four soil columns (10-cm diameter by 43-cm long) were hand-packed with sand, silty loam or clay soils. A 2.3 m column of dairy or swine waste was applied to the top of the each column. The leakage rate from each column was measured with respect to time to analyze the effect of seal formation on different soil textures and animal waste types. We tested our hypothesis that seal growth and the subsequent decrease of leachate production adheres to a filter cake growth model. Said model predicts that the cumulative leakage rate is proportional to the square root of time and to the square root of the height of the waste.

Accurate and dynamic predictive model for better prediction in medicine and healthcare.

PubMed

Alanazi, H O; Abdullah, A H; Qureshi, K N; Ismail, A S

2018-05-01

Information and communication technologies (ICTs) have changed the trend into new integrated operations and methods in all fields of life. The health sector has also adopted new technologies to improve the systems and provide better services to customers. Predictive models in health care are also influenced from new technologies to predict the different disease outcomes. However, still, existing predictive models have suffered from some limitations in terms of predictive outcomes performance. In order to improve predictive model performance, this paper proposed a predictive model by classifying the disease predictions into different categories. To achieve this model performance, this paper uses traumatic brain injury (TBI) datasets. TBI is one of the serious diseases worldwide and needs more attention due to its seriousness and serious impacts on human life. The proposed predictive model improves the predictive performance of TBI. The TBI data set is developed and approved by neurologists to set its features. The experiment results show that the proposed model has achieved significant results including accuracy, sensitivity, and specificity.

Local CFD kinetic model of cadmium vaporization during fluid bed incineration of municipal solid waste.

PubMed

Soria, J; Gauthier, D; Falcoz, Q; Flamant, G; Mazza, G

2013-03-15

The emissions of heavy metals during incineration of Municipal Solid Waste (MSW) are a major issue to health and the environment. It is then necessary to well quantify these emissions in order to accomplish an adequate control and prevent the heavy metals from leaving the stacks. In this study the kinetic behavior of Cadmium during Fluidized Bed Incineration (FBI) of artificial MSW pellets, for bed temperatures ranging from 923 to 1073 K, was modeled. FLUENT 12.1.4 was used as the modeling framework for the simulations and implemented together with a complete set of user-defined functions (UDFs). The CFD model combines the combustion of a single solid waste particle with heavy metal (HM) vaporization from the burning particle, and it takes also into account both pyrolysis and volatiles' combustion. A kinetic rate law for the Cd release, derived from the CFD thermal analysis of the combusting particle, is proposed. The simulation results are compared with experimental data obtained in a lab-scale fluidized bed incinerator reported in literature, and with the predicted values from a particulate non-isothermal model, formerly developed by the authors. The comparison shows that the proposed CFD model represents very well the evolution of the HM release for the considered range of bed temperature. Copyright © 2013 Elsevier B.V. All rights reserved.

Waste production and regional growth of marine activities an econometric model.

PubMed

Bramati, Maria Caterina

2016-11-15

Coastal regions are characterized by intense human activity and climatic pressures, often intensified by competing interests in the use of marine waters. To assess the effect of public spending on the regional economy, an econometric model is here proposed. Not only are the regional investment and the climatic risks included in the model, but also variables related to the anthropogenic pressure, such as population, economic activities and waste production. Feedback effects of economic and demographic expansion on the pollution of coastal areas are also considered. It is found that dangerous waste increases with growing shipping and transportation activities and with growing population density in non-touristic coastal areas. On the other hand, the amount of non-dangerous wastes increases with marine mining, defense and offshore energy production activities. However, lower waste production occurs in areas where aquaculture and touristic industry are more exploited, and accompanied by increasing regional investment in waste disposal. Copyright © 2016 Elsevier Ltd. All rights reserved.

Documentation for the Waste Reduction Model (WARM)

EPA Pesticide Factsheets

This page describes the WARM documentation files and provides links to all documentation files associated with EPA’s Waste Reduction Model (WARM). The page includes a brief summary of the chapters documenting the greenhouse gas emission and energy factors.

A steady state model of agricultural waste pyrolysis: A mini review.

PubMed

Trninić, M; Jovović, A; Stojiljković, D

2016-09-01

Agricultural waste is one of the main renewable energy resources available, especially in an agricultural country such as Serbia. Pyrolysis has already been considered as an attractive alternative for disposal of agricultural waste, since the technique can convert this special biomass resource into granular charcoal, non-condensable gases and pyrolysis oils, which could furnish profitable energy and chemical products owing to their high calorific value. In this regard, the development of thermochemical processes requires a good understanding of pyrolysis mechanisms. Experimental and some literature data on the pyrolysis characteristics of corn cob and several other agricultural residues under inert atmosphere were structured and analysed in order to obtain conversion behaviour patterns of agricultural residues during pyrolysis within the temperature range from 300 °C to 1000 °C. Based on experimental and literature data analysis, empirical relationships were derived, including relations between the temperature of the process and yields of charcoal, tar and gas (CO2, CO, H2 and CH4). An analytical semi-empirical model was then used as a tool to analyse the general trends of biomass pyrolysis. Although this semi-empirical model needs further refinement before application to all types of biomass, its prediction capability was in good agreement with results obtained by the literature review. The compact representation could be used in other applications, to conveniently extrapolate and interpolate these results to other temperatures and biomass types. © The Author(s) 2016.

Posterior Predictive Bayesian Phylogenetic Model Selection

PubMed Central

Lewis, Paul O.; Xie, Wangang; Chen, Ming-Hui; Fan, Yu; Kuo, Lynn

2014-01-01

We present two distinctly different posterior predictive approaches to Bayesian phylogenetic model selection and illustrate these methods using examples from green algal protein-coding cpDNA sequences and flowering plant rDNA sequences. The Gelfand–Ghosh (GG) approach allows dissection of an overall measure of model fit into components due to posterior predictive variance (GGp) and goodness-of-fit (GGg), which distinguishes this method from the posterior predictive P-value approach. The conditional predictive ordinate (CPO) method provides a site-specific measure of model fit useful for exploratory analyses and can be combined over sites yielding the log pseudomarginal likelihood (LPML) which is useful as an overall measure of model fit. CPO provides a useful cross-validation approach that is computationally efficient, requiring only a sample from the posterior distribution (no additional simulation is required). Both GG and CPO add new perspectives to Bayesian phylogenetic model selection based on the predictive abilities of models and complement the perspective provided by the marginal likelihood (including Bayes Factor comparisons) based solely on the fit of competing models to observed data. [Bayesian; conditional predictive ordinate; CPO; L-measure; LPML; model selection; phylogenetics; posterior predictive.] PMID:24193892

Waste Reduction Model (WARM) Material Descriptions and ...

EPA Pesticide Factsheets

2017-02-14

This page provides a summary of the materials included in EPA’s Waste Reduction Model (WARM). The page includes a list of materials, a description of the material as defined in the primary data source, and citations for primary data sources.

Multi-model ensemble hydrologic prediction using Bayesian model averaging

NASA Astrophysics Data System (ADS)

Duan, Qingyun; Ajami, Newsha K.; Gao, Xiaogang; Sorooshian, Soroosh

2007-05-01

Multi-model ensemble strategy is a means to exploit the diversity of skillful predictions from different models. This paper studies the use of Bayesian model averaging (BMA) scheme to develop more skillful and reliable probabilistic hydrologic predictions from multiple competing predictions made by several hydrologic models. BMA is a statistical procedure that infers consensus predictions by weighing individual predictions based on their probabilistic likelihood measures, with the better performing predictions receiving higher weights than the worse performing ones. Furthermore, BMA provides a more reliable description of the total predictive uncertainty than the original ensemble, leading to a sharper and better calibrated probability density function (PDF) for the probabilistic predictions. In this study, a nine-member ensemble of hydrologic predictions was used to test and evaluate the BMA scheme. This ensemble was generated by calibrating three different hydrologic models using three distinct objective functions. These objective functions were chosen in a way that forces the models to capture certain aspects of the hydrograph well (e.g., peaks, mid-flows and low flows). Two sets of numerical experiments were carried out on three test basins in the US to explore the best way of using the BMA scheme. In the first set, a single set of BMA weights was computed to obtain BMA predictions, while the second set employed multiple sets of weights, with distinct sets corresponding to different flow intervals. In both sets, the streamflow values were transformed using Box-Cox transformation to ensure that the probability distribution of the prediction errors is approximately Gaussian. A split sample approach was used to obtain and validate the BMA predictions. The test results showed that BMA scheme has the advantage of generating more skillful and equally reliable probabilistic predictions than original ensemble. The performance of the expected BMA predictions in terms of

A Novel Triggerless Approach for Modeling Mass Wasting Susceptibility

NASA Astrophysics Data System (ADS)

Aly, M. H.; Rowden, K. W.

2017-12-01

Common approaches for modeling mass wasting susceptibility rely on using triggers, which are catalysts for failure, as critical inputs. Frequently used triggers include removal of the toe of a slope or vegetation and time correlated events such as seismicity or heavy precipitation. When temporal data are unavailable, correlating triggers with a particular mass wasting event (MWE) is futile. Meanwhile, geologic structures directly influence slope stability and are typically avoided in alternative modeling approaches. Depending on strata's dip direction, underlying geology can make a slope either stronger or weaker. To heuristically understand susceptibility and reliably infer risk, without being constrained by the previously mentioned limitations, a novel triggerless approach is conceived in this study. Core requisites include a digital elevation model and digitized geologic maps containing geologic formations delineated as polygons encompassing adequate distribution of structural attitudes. Tolerably simple geology composed of gently deformed, relatively flat-lying Carboniferous strata with minimal faulting or monoclines, ideal for applying this new triggerless approach, is found in the Boston Mountains, NW Arkansas, where 47 MWEs are documented. Two models are then created; one model has integrated Empirical Bayesian Kriging (EBK) and fuzzy logic, while the second model has employed a standard implementation of a weighted overlay. Statistical comparisons show that the first model has identified 83%, compared to only 28% for the latter model, of the failure events in categories ranging from moderate to very high susceptibility. These results demonstrate that the introduced triggerless approach is efficiently capable of modeling mass wasting susceptibility, by incorporating EBK and fuzzy logic, in areas lacking temporal datasets.

Multi-model analysis in hydrological prediction

NASA Astrophysics Data System (ADS)

Lanthier, M.; Arsenault, R.; Brissette, F.

2017-12-01

Hydrologic modelling, by nature, is a simplification of the real-world hydrologic system. Therefore ensemble hydrological predictions thus obtained do not present the full range of possible streamflow outcomes, thereby producing ensembles which demonstrate errors in variance such as under-dispersion. Past studies show that lumped models used in prediction mode can return satisfactory results, especially when there is not enough information available on the watershed to run a distributed model. But all lumped models greatly simplify the complex processes of the hydrologic cycle. To generate more spread in the hydrologic ensemble predictions, multi-model ensembles have been considered. In this study, the aim is to propose and analyse a method that gives an ensemble streamflow prediction that properly represents the forecast probabilities and reduced ensemble bias. To achieve this, three simple lumped models are used to generate an ensemble. These will also be combined using multi-model averaging techniques, which generally generate a more accurate hydrogram than the best of the individual models in simulation mode. This new predictive combined hydrogram is added to the ensemble, thus creating a large ensemble which may improve the variability while also improving the ensemble mean bias. The quality of the predictions is then assessed on different periods: 2 weeks, 1 month, 3 months and 6 months using a PIT Histogram of the percentiles of the real observation volumes with respect to the volumes of the ensemble members. Initially, the models were run using historical weather data to generate synthetic flows. This worked for individual models, but not for the multi-model and for the large ensemble. Consequently, by performing data assimilation at each prediction period and thus adjusting the initial states of the models, the PIT Histogram could be constructed using the observed flows while allowing the use of the multi-model predictions. The under-dispersion has been

Description of waste pretreatment and interfacing systems dynamic simulation model

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

Garbrick, D.J.; Zimmerman, B.D.

1995-05-01

The Waste Pretreatment and Interfacing Systems Dynamic Simulation Model was created to investigate the required pretreatment facility processing rates for both high level and low level waste so that the vitrification of tank waste can be completed according to the milestones defined in the Tri-Party Agreement (TPA). In order to achieve this objective, the processes upstream and downstream of the pretreatment facilities must also be included. The simulation model starts with retrieval of tank waste and ends with vitrification for both low level and high level wastes. This report describes the results of three simulation cases: one based on suggestedmore » average facility processing rates, one with facility rates determined so that approximately 6 new DSTs are required, and one with facility rates determined so that approximately no new DSTs are required. It appears, based on the simulation results, that reasonable facility processing rates can be selected so that no new DSTs are required by the TWRS program. However, this conclusion must be viewed with respect to the modeling assumptions, described in detail in the report. Also included in the report, in an appendix, are results of two sensitivity cases: one with glass plant water recycle steams recycled versus not recycled, and one employing the TPA SST retrieval schedule versus a more uniform SST retrieval schedule. Both recycling and retrieval schedule appear to have a significant impact on overall tank usage.« less

Learning Instance-Specific Predictive Models

PubMed Central

Visweswaran, Shyam; Cooper, Gregory F.

2013-01-01

This paper introduces a Bayesian algorithm for constructing predictive models from data that are optimized to predict a target variable well for a particular instance. This algorithm learns Markov blanket models, carries out Bayesian model averaging over a set of models to predict a target variable of the instance at hand, and employs an instance-specific heuristic to locate a set of suitable models to average over. We call this method the instance-specific Markov blanket (ISMB) algorithm. The ISMB algorithm was evaluated on 21 UCI data sets using five different performance measures and its performance was compared to that of several commonly used predictive algorithms, including nave Bayes, C4.5 decision tree, logistic regression, neural networks, k-Nearest Neighbor, Lazy Bayesian Rules, and AdaBoost. Over all the data sets, the ISMB algorithm performed better on average on all performance measures against all the comparison algorithms. PMID:25045325

Waste management activities and carbon emissions in Africa.

PubMed

Couth, R; Trois, C

2011-01-01

This paper summarizes research into waste management activities and carbon emissions from territories in sub-Saharan Africa with the main objective of quantifying emission reductions (ERs) that can be gained through viable improvements to waste management in Africa. It demonstrates that data on waste and carbon emissions is poor and generally inadequate for prediction models. The paper shows that the amount of waste produced and its composition are linked to national Gross Domestic Product (GDP). Waste production per person is around half that in developed countries with a mean around 230 kg/hd/yr. Sub-Saharan territories produce waste with a biogenic carbon content of around 56% (+/-25%), which is approximately 40% greater than developed countries. This waste is disposed in uncontrolled dumps that produce large amounts of methane gas. Greenhouse gas (GHG) emissions from waste will rise with increasing urbanization and can only be controlled through funding mechanisms from developed countries. Copyright © 2010 Elsevier Ltd. All rights reserved.

Analysis and modeling of metals release from MBT wastes through batch and up-flow column tests.

PubMed

Pantini, Sara; Verginelli, Iason; Lombardi, Francesco

2015-04-01

The leaching behavior of wastes coming out from Mechanical Biological Treatment (MBT) plants is still poorly investigated in literature. This work presents an attempt to provide a deeper insight about the contaminants release from this type of waste. To this end, results of several batch and up-flow percolation tests, carried out on different biologically treated waste samples collected from an Italian MBT plant, are reported. The obtained results showed that, despite MBT wastes are characterized by relatively high heavy metals content, only a limited amount was actually soluble and thus bioavailable. Namely, the release percentage was generally lower than 5% of the total content with the only exception of dissolved organic carbon (DOC), Zn, Ni and Co with release percentages up to 20%. The information provided by the different tests also allowed to highlight some key factors governing the kinetics release of DOC and metals from this type of material. In particular, results of up-flow column percolation tests showed that metals such as Cr, Mg, Ni and Zn followed essentially the leaching trend of DOC suggesting that these elements were mainly released as organo-compounds. Actually, a strong linear correlation (R(2) > 0.8) between DOC and metals concentration in eluates was observed, especially for Cr, Ni and Zn (R(2)>0.94). Thus, combining the results of batch and up-flow column percolation tests, partition coefficients between DOC and metals concentration were derived. These data, coupled with a simplified screening model for DOC release, allowed to get a very good prediction of metal release during the different column tests. Finally, combining the experimental data with a simplified model provided some useful indications for the evaluation of long-term emissions from this type of waste in landfill disposal scenarios. Copyright © 2014 Elsevier Ltd. All rights reserved.

Model-based predictions for dopamine.

PubMed

Langdon, Angela J; Sharpe, Melissa J; Schoenbaum, Geoffrey; Niv, Yael

2018-04-01

Phasic dopamine responses are thought to encode a prediction-error signal consistent with model-free reinforcement learning theories. However, a number of recent findings highlight the influence of model-based computations on dopamine responses, and suggest that dopamine prediction errors reflect more dimensions of an expected outcome than scalar reward value. Here, we review a selection of these recent results and discuss the implications and complications of model-based predictions for computational theories of dopamine and learning. Copyright © 2017. Published by Elsevier Ltd.

The Component Slope Linear Model for Calculating Intensive Partial Molar Properties: Application to Waste Glasses

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

Reynolds, Jacob G.

2013-01-11

Partial molar properties are the changes occurring when the fraction of one component is varied while the fractions of all other component mole fractions change proportionally. They have many practical and theoretical applications in chemical thermodynamics. Partial molar properties of chemical mixtures are difficult to measure because the component mole fractions must sum to one, so a change in fraction of one component must be offset with a change in one or more other components. Given that more than one component fraction is changing at a time, it is difficult to assign a change in measured response to a changemore » in a single component. In this study, the Component Slope Linear Model (CSLM), a model previously published in the statistics literature, is shown to have coefficients that correspond to the intensive partial molar properties. If a measured property is plotted against the mole fraction of a component while keeping the proportions of all other components constant, the slope at any given point on a graph of this curve is the partial molar property for that constituent. Actually plotting this graph has been used to determine partial molar properties for many years. The CSLM directly includes this slope in a model that predicts properties as a function of the component mole fractions. This model is demonstrated by applying it to the constant pressure heat capacity data from the NaOH-NaAl(OH{sub 4}H{sub 2}O system, a system that simplifies Hanford nuclear waste. The partial molar properties of H{sub 2}O, NaOH, and NaAl(OH){sub 4} are determined. The equivalence of the CSLM and the graphical method is verified by comparing results detennined by the two methods. The CSLM model has been previously used to predict the liquidus temperature of spinel crystals precipitated from Hanford waste glass. Those model coefficients are re-interpreted here as the partial molar spinel liquidus temperature of the glass components.« less

Managing Dog Waste: Campaign Insights from the Health Belief Model

ERIC Educational Resources Information Center

Typhina, Eli; Yan, Changmin

2014-01-01

Aiming to help municipalities develop effective education and outreach campaigns to reduce stormwater pollutants, such as pet waste, this study applied the Health Belief Model (HBM) to identify perceptions of dog waste and corresponding collection behaviors from dog owners living in a small U.S. city. Results of 455 online survey responses…

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.

Greenhouse gases emissions from waste management practices using Life Cycle Inventory model.

PubMed

Chen, Tsao-Chou; Lin, Cheng-Fang

2008-06-30

When exploring the correlation between municipal solid waste management and green house gas emission, the volume and physical composition of the waste matter must be taken into account. Due to differences in local environments and lifestyles the quantity and composition of waste often vary. This leads to differences in waste treatment methods and causes different volumes of greenhouse gases (GHGs), highlighting the need for local research. In this study the Life Cycle Inventory method was used with global warming indicator GHGs as the variables. By quantifying the data and adopting a region-based approach, this created a model of household MSWM in Taipei City, a metropolitan region in Taiwan. To allow analysis and comparison a compensatory system was then added to expand the system boundary. The results of the analysis indicated that out of all the solid waste management sub-models for a function unit, recycling was the most effective method for reducing GHG emissions while using kitchen food waste as swine feeding resulted in the most GHG emissions. As for the impact of waste collection vehicles on emissions, if the efficiency of transportation could be improved and energy consumption reduced, this will help solid waste management to achieve its goal of reducing GHG emissions.

Improved cyberinfrastructure for integrated hydrometeorological predictions within the fully-coupled WRF-Hydro modeling system

NASA Astrophysics Data System (ADS)

gochis, David; hooper, Rick; parodi, Antonio; Jha, Shantenu; Yu, Wei; Zaslavsky, Ilya; Ganapati, Dinesh

2014-05-01

The community WRF-Hydro system is currently being used in a variety of flood prediction and regional hydroclimate impacts assessment applications around the world. Despite its increasingly wide use certain cyberinfrastructure bottlenecks exist in the setup, execution and post-processing of WRF-Hydro model runs. These bottlenecks result in wasted time, labor, data transfer bandwidth and computational resource use. Appropriate development and use of cyberinfrastructure to setup and manage WRF-Hydro modeling applications will streamline the entire workflow of hydrologic model predictions. This talk will present recent advances in the development and use of new open-source cyberinfrastructure tools for the WRF-Hydro architecture. These tools include new web-accessible pre-processing applications, supercomputer job management applications and automated verification and visualization applications. The tools will be described successively and then demonstrated in a set of flash flood use cases for recent destructive flood events in the U.S. and in Europe. Throughout, an emphasis on the implementation and use of community data standards for data exchange is made.

Kinetic study of solid waste pyrolysis using distributed activation energy model.

PubMed

Bhavanam, Anjireddy; Sastry, R C

2015-02-01

The pyrolysis characteristics of municipal solid waste, agricultural residues such as ground nut shell, cotton husk and their blends are investigated using non-isothermal thermogravimetric analysis (TGA) with in a temperature range of 30-900 °C at different heating rates of 10 °C, 30 °C and 50 °C/min in inert atmosphere. From the thermograms obtained from TGA, it is observed that the maximum rate of degradation occurred in the second stage of the pyrolysis process for all the solid wastes. The distributed activation energy model (DAEM) is used to study the pyrolysis kinetics of the solid wastes. The kinetic parameters E (activation energy), k0 (frequency factor) are calculated from this model. It is found that the range of activation energies for agricultural residues are lower than the municipal solid waste. The activation energies for the municipal solid waste pyrolysis process drastically decreased with addition of agricultural residues. The proposed DAEM is successfully validated with TGA experimental data. Copyright © 2014 Elsevier Ltd. All rights reserved.

Kinetics and equilibrium modelling of lead uptake by algae Gelidium and algal waste from agar extraction industry.

PubMed

Vilar, Vítor J P; Botelho, Cidália M S; Boaventura, Rui A R

2007-05-08

Pb(II) biosorption onto algae Gelidium, algal waste from agar extraction industry and a composite material was studied. Discrete and continuous site distribution models were used to describe the biosorption equilibrium at different pH (5.3, 4 and 3), considering competition among Pb(II) ions and protons. The affinity distribution function of Pb(II) on the active sites was calculated by the Sips distribution. The Langmuir equilibrium constant was compared with the apparent affinity calculated by the discrete model, showing higher affinity for lead ions at higher pH values. Kinetic experiments were conducted at initial Pb(II) concentrations of 29-104 mgl(-1) and data fitted to pseudo-first Lagergren and second-order models. The adsorptive behaviour of biosorbent particles was modelled using a batch mass transfer kinetic model, which successfully predicts Pb(II) concentration profiles at different initial lead concentration and pH, and provides significant insights on the biosorbents performance. Average values of homogeneous diffusivity, D(h), are 3.6 x 10(-8); 6.1 x 10(-8) and 2.4 x 10(-8)cm(2)s(-1), respectively, for Gelidium, algal waste and composite material. The concentration of lead inside biosorbent particles follows a parabolic profile that becomes linear near equilibrium.

An incentive-based source separation model for sustainable municipal solid waste management in China.

PubMed

Xu, Wanying; Zhou, Chuanbin; Lan, Yajun; Jin, Jiasheng; Cao, Aixin

2015-05-01

Municipal solid waste (MSW) management (MSWM) is most important and challenging in large urban communities. Sound community-based waste management systems normally include waste reduction and material recycling elements, often entailing the separation of recyclable materials by the residents. To increase the efficiency of source separation and recycling, an incentive-based source separation model was designed and this model was tested in 76 households in Guiyang, a city of almost three million people in southwest China. This model embraced the concepts of rewarding households for sorting organic waste, government funds for waste reduction, and introducing small recycling enterprises for promoting source separation. Results show that after one year of operation, the waste reduction rate was 87.3%, and the comprehensive net benefit under the incentive-based source separation model increased by 18.3 CNY tonne(-1) (2.4 Euros tonne(-1)), compared to that under the normal model. The stakeholder analysis (SA) shows that the centralized MSW disposal enterprises had minimum interest and may oppose the start-up of a new recycling system, while small recycling enterprises had a primary interest in promoting the incentive-based source separation model, but they had the least ability to make any change to the current recycling system. The strategies for promoting this incentive-based source separation model are also discussed in this study. © The Author(s) 2015.

Fixed recurrence and slip models better predict earthquake behavior than the time- and slip-predictable models: 2. Laboratory earthquakes

NASA Astrophysics Data System (ADS)

Rubinstein, Justin L.; Ellsworth, William L.; Beeler, Nicholas M.; Kilgore, Brian D.; Lockner, David A.; Savage, Heather M.

2012-02-01

The behavior of individual stick-slip events observed in three different laboratory experimental configurations is better explained by a "memoryless" earthquake model with fixed inter-event time or fixed slip than it is by the time- and slip-predictable models for earthquake occurrence. We make similar findings in the companion manuscript for the behavior of natural repeating earthquakes. Taken together, these results allow us to conclude that the predictions of a characteristic earthquake model that assumes either fixed slip or fixed recurrence interval should be preferred to the predictions of the time- and slip-predictable models for all earthquakes. Given that the fixed slip and recurrence models are the preferred models for all of the experiments we examine, we infer that in an event-to-event sense the elastic rebound model underlying the time- and slip-predictable models does not explain earthquake behavior. This does not indicate that the elastic rebound model should be rejected in a long-term-sense, but it should be rejected for short-term predictions. The time- and slip-predictable models likely offer worse predictions of earthquake behavior because they rely on assumptions that are too simple to explain the behavior of earthquakes. Specifically, the time-predictable model assumes a constant failure threshold and the slip-predictable model assumes that there is a constant minimum stress. There is experimental and field evidence that these assumptions are not valid for all earthquakes.

Model-free and model-based reward prediction errors in EEG.

PubMed

Sambrook, Thomas D; Hardwick, Ben; Wills, Andy J; Goslin, Jeremy

2018-05-24

Learning theorists posit two reinforcement learning systems: model-free and model-based. Model-based learning incorporates knowledge about structure and contingencies in the world to assign candidate actions with an expected value. Model-free learning is ignorant of the world's structure; instead, actions hold a value based on prior reinforcement, with this value updated by expectancy violation in the form of a reward prediction error. Because they use such different learning mechanisms, it has been previously assumed that model-based and model-free learning are computationally dissociated in the brain. However, recent fMRI evidence suggests that the brain may compute reward prediction errors to both model-free and model-based estimates of value, signalling the possibility that these systems interact. Because of its poor temporal resolution, fMRI risks confounding reward prediction errors with other feedback-related neural activity. In the present study, EEG was used to show the presence of both model-based and model-free reward prediction errors and their place in a temporal sequence of events including state prediction errors and action value updates. This demonstration of model-based prediction errors questions a long-held assumption that model-free and model-based learning are dissociated in the brain. Copyright © 2018 Elsevier Inc. All rights reserved.

Biogas production from Pongamia biomass wastes and a model to estimate biodegradability from their composition.

PubMed

Gunaseelan, Victor Nallathambi

2014-02-01

In this study, I investigated the chemical characteristics, biochemical methane potential, conversion kinetics and biodegradability of untreated and NaOH-treated Pongamia plant parts, and pod husk and press cake from the biodiesel industry to evaluate their suitability as an alternative feedstock for biogas production. The untreated Pongamia seeds exhibited the maximum CH4 yield of 473 ml g (-1) volatile solid (VS) added. Yellow, withered leaves gave a yield as low as 122 ml CH4 g (-1) VS added. There were significant variations in the CH4 production rate constants, which ranged from 0.02 to 0.15 d (-1), and biodegradability, which ranged from 0.25 to 0.98. NaOH treatment of leaf and pod husk, which were highly rich in fibers, increased the yields by 15-22% and CH4 production rate constants by 20-75%. Utilization of Pongamia wastes in biogas digesters not only influences the economics of biodiesel production but also yields CH4 fuel and protects the environment. The experimental data from this study were used to develop a multiple regression model, which could estimate biodegradability based on biochemical characteristics. The model predicted the biodegradability of previously published biomass wastes (r(2) = 0.88) from their biochemical composition. The theoretical CH4 yields estimated as 350 ml g(-1) chemical oxygen demand destroyed are much higher than the experimental yields as 100% biodegradability is assumed for each substrate. Upon correcting the theoretical CH4 yields with biodegradability data obtained from chemical analyses of substrates, their ultimate CH4 yields could be predicted rapidly.

Personalized Modeling for Prediction with Decision-Path Models

PubMed Central

Visweswaran, Shyam; Ferreira, Antonio; Ribeiro, Guilherme A.; Oliveira, Alexandre C.; Cooper, Gregory F.

2015-01-01

Deriving predictive models in medicine typically relies on a population approach where a single model is developed from a dataset of individuals. In this paper we describe and evaluate a personalized approach in which we construct a new type of decision tree model called decision-path model that takes advantage of the particular features of a given person of interest. We introduce three personalized methods that derive personalized decision-path models. We compared the performance of these methods to that of Classification And Regression Tree (CART) that is a population decision tree to predict seven different outcomes in five medical datasets. Two of the three personalized methods performed statistically significantly better on area under the ROC curve (AUC) and Brier skill score compared to CART. The personalized approach of learning decision path models is a new approach for predictive modeling that can perform better than a population approach. PMID:26098570

Growth and food consumption by tiger muskellunge: Effects of temperature and ration level on bioenergetic model predictions

USGS Publications Warehouse

Chipps, S.R.; Einfalt, L.M.; Wahl, David H.

2000-01-01

We measured growth of age-0 tiger muskellunge as a function of ration size (25, 50, 75, and 100% C(max))and water temperature (7.5-25??C) and compared experimental results with those predicted from a bioenergetic model. Discrepancies between actual and predicted values varied appreciably with water temperature and growth rate. On average, model output overestimated winter consumption rates at 10 and 7.5??C by 113 to 328%, respectively, whereas model predictions in summer and autumn (20-25??C) were in better agreement with actual values (4 to 58%). We postulate that variation in model performance was related to seasonal changes in esocid metabolic rate, which were not accounted for in the bioenergetic model. Moreover, accuracy of model output varied with feeding and growth rate of tiger muskellunge. The model performed poorly for fish fed low rations compared with estimates based on fish fed ad libitum rations and was attributed, in part, to the influence of growth rate on the accuracy of bioenergetic predictions. Based on modeling simulations, we found that errors associated with bioenergetic parameters had more influence on model output when growth rate was low, which is consistent with our observations. In addition, reduced conversion efficiency at high ration levels may contribute to variable model performance, thereby implying that waste losses should be modeled as a function of ration size for esocids. Our findings support earlier field tests of the esocid bioenergetic model and indicate that food consumption is generally overestimated by the model, particularly in winter months and for fish exhibiting low feeding and growth rates.

Extracting falsifiable predictions from sloppy models.

PubMed

Gutenkunst, Ryan N; Casey, Fergal P; Waterfall, Joshua J; Myers, Christopher R; Sethna, James P

2007-12-01

Successful predictions are among the most compelling validations of any model. Extracting falsifiable predictions from nonlinear multiparameter models is complicated by the fact that such models are commonly sloppy, possessing sensitivities to different parameter combinations that range over many decades. Here we discuss how sloppiness affects the sorts of data that best constrain model predictions, makes linear uncertainty approximations dangerous, and introduces computational difficulties in Monte-Carlo uncertainty analysis. We also present a useful test problem and suggest refinements to the standards by which models are communicated.

SIMSWASTE-AD - A modelling framework for the environmental assessment of agricultural waste management strategies: Anaerobic digestion.

PubMed

Pardo, Guillermo; Moral, Raúl; Del Prado, Agustín

2017-01-01

On-farm anaerobic digestion (AD) has been promoted due to its improved environmental performance, which is based on a number of life cycle assessments (LCA). However, the influence of site-specific conditions and practices on AD performance is rarely captured in LCA studies and the effects on C and N cycles are often overlooked. In this paper, a new model for AD (SIMS WASTE-AD ) is described in full and tested against a selection of available measured data. Good agreement between modelled and measured values was obtained, reflecting the model capability to predict biogas production (r 2 =0.84) and N mineralization (r 2 =0.85) under a range of substrate mixtures and operational conditions. SIMS WASTE-AD was also used to simulate C and N flows and GHG emissions for a set of scenarios exploring different AD technology levels, feedstock mixtures and climate conditions. The importance of post-digestion emissions and its relationship with the AD performance have been stressed as crucial factors to reduce the net GHG emissions (-75%) but also to enhance digestate fertilizer potential (15%). Gas tight digestate storage with residual biogas collection is highly recommended (especially in temperate to warm climates), as well as those operational conditions that can improve the process efficiency on degrading VS (e.g. thermophilic range, longer hydraulic retention time). Beyond the effects on the manure management stage, SIMS WASTE-AD also aims to help account for potential effects of AD on other stages by providing the C and nutrient flows. While primarily designed to be applied within the SIMS DAIRY modelling framework, it can also interact with other models implemented in integrated approaches. Such system scope assessments are essential for stakeholders and policy makers in order to develop effective strategies for reducing GHG emissions and environmental issues in the agriculture sector. Copyright © 2016 Elsevier B.V. All rights reserved.

Creating Economic Incentives for Waste Disposal in Developing Countries Using the MixAlco Process.

PubMed

Lonkar, Sagar; Fu, Zhihong; Wales, Melinda; Holtzapple, Mark

2017-01-01

In rapidly growing developing countries, waste disposal is a major challenge. Current waste disposal methods (e.g., landfills and sewage treatment) incur costs and often are not employed; thus, wastes accumulate in the environment. To address this challenge, it is advantageous to create economic incentives to collect and process wastes. One approach is the MixAlco process, which uses methane-inhibited anaerobic fermentation to convert waste biomass into carboxylate salts, which are chemically converted to industrial chemicals and fuels. In this paper, humanure (raw human feces and urine) is explored as a possible nutrient source for fermentation. This work focuses on fermenting municipal solid waste (energy source) and humanure (nutrient source) in batch fermentations. Using the Continuum Particle Distribution Model (CPDM), the performance of continuous countercurrent fermentation was predicted at different volatile solid loading rates (VSLR) and liquid residence times (LRT). For a four-stage countercurrent fermentation system at VSLR = 4 g/(L∙day), LRT = 30 days, and solids concentration = 100 g/L liquid, the model predicts carboxylic acid concentration of 68 g/L and conversion of 78.5 %.

Prevalence of nutritional wasting in populations: building explanatory models using secondary data.

PubMed Central

Fernandez, Isabel D.; Himes, John H.; de Onis, Mercedes

2002-01-01

OBJECTIVE: To understand how social context affects the nutritional status of populations, as reflected by the prevalence of wasting in children under 5 years of age from Africa, Latin America, and Asia; to present a systematic way of building models for wasting prevalence, using a conceptual framework for the determinants of malnutrition; and to examine the feasibility of using readily available data collected over time to build models of wasting prevalence in populations. METHODS: Associations between prevalence of wasting and environmental variables were examined in the three regions. General linear mixed models were fitted using anthropometric survey data for countries within each region. FINDINGS: Low birth weight (LBW), measles incidence, and access to a safe water supply explained 64% of wasting variability in Asia. In Latin America, LBW and survey year explained 38%; in Africa, LBW, survey year, and adult literacy explained 7%. CONCLUSION: LBW emerged as a predictor of wasting prevalence in all three regions. Actions regarding women's rights may have an effect on the nutritional status of children since LBW seems to reflect several aspects of the conditions of women in society. Databases have to be made compatible with each other to facilitate integrated analysis for nutritional research and policy decision-making. In addition, the validity of the variables representing the conceptual framework should be improved. PMID:12075364

Review of Concrete Biodeterioration in Relation to Buried Nuclear Waste

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

Turick, C; Berry, C.

Long-term storage of low level radioactive material in below ground concrete disposal units (DUs) (Saltstone Disposal Facility) is a means of depositing wastes generated from nuclear operations of the U.S. Department of Energy. Based on the currently modeled degradation mechanisms, possible microbial induced effects on the structural integrity of buried low level wastes must be addressed. Previous international efforts related to microbial impacts on concrete structures that house low level radioactive waste showed that microbial activity can play a significant role in the process of concrete degradation and ultimately structural deterioration. This literature review examines the recent research in thismore » field and is focused on specific parameters that are applicable to modeling and prediction of the fate of concrete vaults housing stored wastes and the wastes themselves. Rates of concrete biodegradation vary with the environmental conditions, illustrating a need to understand the bioavailability of key compounds involved in microbial activity. Specific parameters require pH and osmotic pressure to be within a certain range to allow for microbial growth as well as the availability and abundance of energy sources like components involved in sulfur, iron and nitrogen oxidation. Carbon flow and availability are also factors to consider in predicting concrete biodegradation. The results of this review suggest that microbial activity in Saltstone, (grouted low level radioactive waste) is unlikely due to very high pH and osmotic pressure. Biodegradation of the concrete vaults housing the radioactive waste however, is a possibility. The rate and degree of concrete biodegradation is dependent on numerous physical, chemical and biological parameters. Results from this review point to parameters to focus on for modeling activities and also, possible options for mitigation that would minimize concrete biodegradation. In addition, key chemical components that drive

Fixed recurrence and slip models better predict earthquake behavior than the time- and slip-predictable models 1: repeating earthquakes

USGS Publications Warehouse

Rubinstein, Justin L.; Ellsworth, William L.; Chen, Kate Huihsuan; Uchida, Naoki

2012-01-01

The behavior of individual events in repeating earthquake sequences in California, Taiwan and Japan is better predicted by a model with fixed inter-event time or fixed slip than it is by the time- and slip-predictable models for earthquake occurrence. Given that repeating earthquakes are highly regular in both inter-event time and seismic moment, the time- and slip-predictable models seem ideally suited to explain their behavior. Taken together with evidence from the companion manuscript that shows similar results for laboratory experiments we conclude that the short-term predictions of the time- and slip-predictable models should be rejected in favor of earthquake models that assume either fixed slip or fixed recurrence interval. This implies that the elastic rebound model underlying the time- and slip-predictable models offers no additional value in describing earthquake behavior in an event-to-event sense, but its value in a long-term sense cannot be determined. These models likely fail because they rely on assumptions that oversimplify the earthquake cycle. We note that the time and slip of these events is predicted quite well by fixed slip and fixed recurrence models, so in some sense they are time- and slip-predictable. While fixed recurrence and slip models better predict repeating earthquake behavior than the time- and slip-predictable models, we observe a correlation between slip and the preceding recurrence time for many repeating earthquake sequences in Parkfield, California. This correlation is not found in other regions, and the sequences with the correlative slip-predictable behavior are not distinguishable from nearby earthquake sequences that do not exhibit this behavior.

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.

Integrated models for solid waste management in tourism regions: Langkawi Island, Malaysia.

PubMed

Shamshiry, Elmira; Nadi, Behzad; Mokhtar, Mazlin Bin; Komoo, Ibrahim; Hashim, Halimaton Saadiah; Yahaya, Nadzri

2011-01-01

The population growth, changing consumption patterns, and rapid urbanization contribute significantly to the growing volumes of solid waste that are generated in urban settings. As the rate of urbanization increases, demand on the services of solid waste management increases. The rapid urban growth in Langkawi Island, Malaysia, combined with the increasing rates of solid waste production has provided evidence that the traditional solid waste management practices, particularly the methods of waste collection and disposal, are inefficient and quite nonsustainable. Accordingly, municipal managers and planners in Langkawi need to look for and adopt a model for solid waste management that emphasizes an efficient and sustainable management of solid wastes in Langkawi Island. This study presents the current practices of solid waste management in Langkawi Island, describes the composition of the solid waste generated in that area, and presents views of local residents and tourist on issues related to solid waste management like the aesthetic value of the island environment. The most important issue of this paper is that it is the first time that integrated solid waste management is investigated in the Langkawi Island.

Source term evaluation model for high-level radioactive waste repository with decay chain build-up.

PubMed

Chopra, Manish; Sunny, Faby; Oza, R B

2016-09-18

A source term model based on two-component leach flux concept is developed for a high-level radioactive waste repository. The long-lived radionuclides associated with high-level waste may give rise to the build-up of activity because of radioactive decay chains. The ingrowths of progeny are incorporated in the model using Bateman decay chain build-up equations. The model is applied to different radionuclides present in the high-level radioactive waste, which form a part of decay chains (4n to 4n + 3 series), and the activity of the parent and daughter radionuclides leaching out of the waste matrix is estimated. Two cases are considered: one when only parent is present initially in the waste and another where daughters are also initially present in the waste matrix. The incorporation of in situ production of daughter radionuclides in the source is important to carry out realistic estimates. It is shown that the inclusion of decay chain build-up is essential to avoid underestimation of the radiological impact assessment of the repository. The model can be a useful tool for evaluating the source term of the radionuclide transport models used for the radiological impact assessment of high-level radioactive waste repositories.

Waste collection multi objective model with real time traceability data.

PubMed

Faccio, Maurizio; Persona, Alessandro; Zanin, Giorgia

2011-12-01

Waste collection is a highly visible municipal service that involves large expenditures and difficult operational problems, plus it is expensive to operate in terms of investment costs (i.e. vehicles fleet), operational costs (i.e. fuel, maintenances) and environmental costs (i.e. emissions, noise and traffic congestions). Modern traceability devices, like volumetric sensors, identification RFID (Radio Frequency Identification) systems, GPRS (General Packet Radio Service) and GPS (Global Positioning System) technology, permit to obtain data in real time, which is fundamental to implement an efficient and innovative waste collection routing model. The basic idea is that knowing the real time data of each vehicle and the real time replenishment level at each bin makes it possible to decide, in function of the waste generation pattern, what bin should be emptied and what should not, optimizing different aspects like the total covered distance, the necessary number of vehicles and the environmental impact. This paper describes a framework about the traceability technology available in the optimization of solid waste collection, and introduces an innovative vehicle routing model integrated with the real time traceability data, starting the application in an Italian city of about 100,000 inhabitants. The model is tested and validated using simulation and an economical feasibility study is reported at the end of the paper. Copyright © 2011 Elsevier Ltd. All rights reserved.

Aspen Plus® and economic modeling of equine waste utilization for localized hot water heating via fast pyrolysis.

PubMed

Hammer, Nicole L; Boateng, Akwasi A; Mullen, Charles A; Wheeler, M Clayton

2013-10-15

Aspen Plus(®) based simulation models have been developed to design a pyrolysis process for on-site production and utilization of pyrolysis oil from equine waste at the Equine Rehabilitation Center at Morrisville State College (MSC). The results indicate that utilization of all the available waste from the site's 41 horses requires a 6 oven dry metric ton per day (ODMTPD) pyrolysis system but it will require a 15 ODMTPD system for waste generated by an additional 150 horses at the expanded area including the College and its vicinity. For this a dual fluidized bed combustion reduction integrated pyrolysis system (CRIPS) developed at USDA's Agricultural Research Service (ARS) was identified as the technology of choice for pyrolysis oil production. The Aspen Plus(®) model was further used to consider the combustion of the produced pyrolysis oil (bio-oil) in the existing boilers that generate hot water for space heating at the Equine Center. The model results show the potential for both the equine facility and the College to displace diesel fuel (fossil) with renewable pyrolysis oil and alleviate a costly waste disposal problem. We predict that all the heat required to operate the pyrolyzer could be supplied by non-condensable gas and about 40% of the biochar co-produced with bio-oil. Techno-economic Analysis shows neither design is economical at current market conditions; however the 15 ODMTPD CRIPS design would break even when diesel prices reach $11.40/gal. This can be further improved to $7.50/gal if the design capacity is maintained at 6 ODMTPD but operated at 4950 h per annum. Published by Elsevier Ltd.

Overview of waste stabilization with cement.

PubMed

Batchelor, B

2006-01-01

Cement can treat a variety of wastes by improving physical characteristics (solidification) and reducing the toxicity and mobility of contaminants (stabilization). Potentially adverse waste-binder interactions are an important consideration because they can limit solidification. Stabilization occurs when a contaminant is converted from the dissolved (mobile) phase to a solid (immobile) phase by reactions, such as precipitation, sorption, or substitution. These reactions are often strongly affected by pH, so the presence of components of the waste that control pH are critical to stabilization reactions. Evaluating environmental impacts can be accomplished in a tiered strategy in which simplest approach would be to measure the maximum amount of contaminant that could be released. Alternatively, the sequence of release can be determined, either by microcosm tests that attempt to simulate conditions in the disposal zone or by mechanistic models that attempt to predict behavior using fundamental characteristics of the treated waste.

Modelling of Two-Stage Methane Digestion With Pretreatment of Biomass

NASA Astrophysics Data System (ADS)

Dychko, A.; Remez, N.; Opolinskyi, I.; Kraychuk, S.; Ostapchuk, N.; Yevtieieva, L.

2018-04-01

Systems of anaerobic digestion should be used for processing of organic waste. Managing the process of anaerobic recycling of organic waste requires reliable predicting of biogas production. Development of mathematical model of process of organic waste digestion allows determining the rate of biogas output at the two-stage process of anaerobic digestion considering the first stage. Verification of Konto's model, based on the studied anaerobic processing of organic waste, is implemented. The dependencies of biogas output and its rate from time are set and may be used to predict the process of anaerobic processing of organic waste.

Impact of microbial activity on the radioactive waste disposal: long term prediction of biocorrosion processes.

PubMed

Libert, Marie; Schütz, Marta Kerber; Esnault, Loïc; Féron, Damien; Bildstein, Olivier

2014-06-01

This study emphasizes different experimental approaches and provides perspectives to apprehend biocorrosion phenomena in the specific disposal environment by investigating microbial activity with regard to the modification of corrosion rate, which in turn can have an impact on the safety of radioactive waste geological disposal. It is found that iron-reducing bacteria are able to use corrosion products such as iron oxides and "dihydrogen" as new energy sources, especially in the disposal environment which contains low amounts of organic matter. Moreover, in the case of sulphate-reducing bacteria, the results show that mixed aerobic and anaerobic conditions are the most hazardous for stainless steel materials, a situation which is likely to occur in the early stage of a geological disposal. Finally, an integrated methodological approach is applied to validate the understanding of the complex processes and to design experiments aiming at the acquisition of kinetic data used in long term predictive modelling of biocorrosion processes. © 2013.

Finite element analysis of ion transport in solid state nuclear waste form materials

NASA Astrophysics Data System (ADS)

Rabbi, F.; Brinkman, K.; Amoroso, J.; Reifsnider, K.

2017-09-01

Release of nuclear species from spent fuel ceramic waste form storage depends on the individual constituent properties as well as their internal morphology, heterogeneity and boundary conditions. Predicting the release rate is essential for designing a ceramic waste form, which is capable of effectively storing the spent fuel without contaminating the surrounding environment for a longer period of time. To predict the release rate, in the present work a conformal finite element model is developed based on the Nernst Planck Equation. The equation describes charged species transport through different media by convection, diffusion, or migration. And the transport can be driven by chemical/electrical potentials or velocity fields. The model calculates species flux in the waste form with different diffusion coefficient for each species in each constituent phase. In the work reported, a 2D approach is taken to investigate the contributions of different basic parameters in a waste form design, i.e., volume fraction, phase dispersion, phase surface area variation, phase diffusion co-efficient, boundary concentration etc. The analytical approach with preliminary results is discussed. The method is postulated to be a foundation for conformal analysis based design of heterogeneous waste form materials.

Review of concrete biodeterioration in relation to nuclear waste.

PubMed

Turick, Charles E; Berry, Christopher J

2016-01-01

Storage of radioactive waste in concrete structures is a means of containing wastes and related radionuclides generated from nuclear operations in many countries. Previous efforts related to microbial impacts on concrete structures that are used to contain radioactive waste showed that microbial activity can play a significant role in the process of concrete degradation and ultimately structural deterioration. This literature review examines the research in this field and is focused on specific parameters that are applicable to modeling and prediction of the fate of concrete structures used to store or dispose of radioactive waste. Rates of concrete biodegradation vary with the environmental conditions, illustrating a need to understand the bioavailability of key compounds involved in microbial activity. Specific parameters require pH and osmotic pressure to be within a certain range to allow for microbial growth as well as the availability and abundance of energy sources such as components involved in sulfur, iron and nitrogen oxidation. Carbon flow and availability are also factors to consider in predicting concrete biodegradation. The microbial contribution to degradation of the concrete structures containing radioactive waste is a constant possibility. The rate and degree of concrete biodegradation is dependent on numerous physical, chemical and biological parameters. Parameters to focus on for modeling activities and possible options for mitigation that would minimize concrete biodegradation are discussed and include key conditions that drive microbial activity on concrete surfaces. Copyright © 2015. Published by Elsevier Ltd.

Analytical method of waste allocation in waste management systems: Concept, method and case study

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

Bergeron, Francis C., E-mail: francis.b.c@videotron.ca

Waste is not a rejected item to dispose anymore but increasingly a secondary resource to exploit, influencing waste allocation among treatment operations in a waste management (WM) system. The aim of this methodological paper is to present a new method for the assessment of the WM system, the “analytical method of the waste allocation process” (AMWAP), based on the concept of the “waste allocation process” defined as the aggregation of all processes of apportioning waste among alternative waste treatment operations inside or outside the spatial borders of a WM system. AMWAP contains a conceptual framework and an analytical approach. Themore » conceptual framework includes, firstly, a descriptive model that focuses on the description and classification of the WM system. It includes, secondly, an explanatory model that serves to explain and to predict the operation of the WM system. The analytical approach consists of a step-by-step analysis for the empirical implementation of the conceptual framework. With its multiple purposes, AMWAP provides an innovative and objective modular method to analyse a WM system which may be integrated in the framework of impact assessment methods and environmental systems analysis tools. Its originality comes from the interdisciplinary analysis of the WAP and to develop the conceptual framework. AMWAP is applied in the framework of an illustrative case study on the household WM system of Geneva (Switzerland). It demonstrates that this method provides an in-depth and contextual knowledge of WM. - Highlights: • The study presents a new analytical method based on the waste allocation process. • The method provides an in-depth and contextual knowledge of the waste management system. • The paper provides a reproducible procedure for professionals, experts and academics. • It may be integrated into impact assessment or environmental system analysis tools. • An illustrative case study is provided based on household waste

Incorporating uncertainty in predictive species distribution modelling.

PubMed

Beale, Colin M; Lennon, Jack J

2012-01-19

Motivated by the need to solve ecological problems (climate change, habitat fragmentation and biological invasions), there has been increasing interest in species distribution models (SDMs). Predictions from these models inform conservation policy, invasive species management and disease-control measures. However, predictions are subject to uncertainty, the degree and source of which is often unrecognized. Here, we review the SDM literature in the context of uncertainty, focusing on three main classes of SDM: niche-based models, demographic models and process-based models. We identify sources of uncertainty for each class and discuss how uncertainty can be minimized or included in the modelling process to give realistic measures of confidence around predictions. Because this has typically not been performed, we conclude that uncertainty in SDMs has often been underestimated and a false precision assigned to predictions of geographical distribution. We identify areas where development of new statistical tools will improve predictions from distribution models, notably the development of hierarchical models that link different types of distribution model and their attendant uncertainties across spatial scales. Finally, we discuss the need to develop more defensible methods for assessing predictive performance, quantifying model goodness-of-fit and for assessing the significance of model covariates.

Comparing urban solid waste recycling from the viewpoint of urban metabolism based on physical input-output model: A case of Suzhou in China

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

Liang Sai, E-mail: liangsai09@gmail.com; Zhang Tianzhu, E-mail: zhangtz@mail.tsinghua.edu.cn

Highlights: Black-Right-Pointing-Pointer Impacts of solid waste recycling on Suzhou's urban metabolism in 2015 are analyzed. Black-Right-Pointing-Pointer Sludge recycling for biogas is regarded as an accepted method. Black-Right-Pointing-Pointer Technical levels of reusing scrap tires and food wastes should be improved. Black-Right-Pointing-Pointer Other fly ash utilization methods should be exploited. Black-Right-Pointing-Pointer Secondary wastes from reusing food wastes and sludge should be concerned. - Abstract: Investigating impacts of urban solid waste recycling on urban metabolism contributes to sustainable urban solid waste management and urban sustainability. Using a physical input-output model and scenario analysis, urban metabolism of Suzhou in 2015 is predicted and impactsmore » of four categories of solid waste recycling on urban metabolism are illustrated: scrap tire recycling, food waste recycling, fly ash recycling and sludge recycling. Sludge recycling has positive effects on reducing all material flows. Thus, sludge recycling for biogas is regarded as an accepted method. Moreover, technical levels of scrap tire recycling and food waste recycling should be improved to produce positive effects on reducing more material flows. Fly ash recycling for cement production has negative effects on reducing all material flows except solid wastes. Thus, other fly ash utilization methods should be exploited. In addition, the utilization and treatment of secondary wastes from food waste recycling and sludge recycling should be concerned.« less

Modeling the economics of landfilling organic processing waste streams

NASA Astrophysics Data System (ADS)

Rosentrater, Kurt A.

2005-11-01

As manufacturing industries become more cognizant of the ecological effects that their firms have on the surrounding environment, their waste streams are increasingly becoming viewed not only as materials in need of disposal, but also as resources that can be reused, recycled, or reprocessed into valuable products. Within the food processing sector are many examples of various liquid, sludge, and solid biological and organic waste streams that require remediation. Alternative disposal methods for food and other bio-organic manufacturing waste streams are increasingly being investigated. Direct shipping, blending, extrusion, pelleting, and drying are commonly used to produce finished human food, animal feed, industrial products, and components ready for further manufacture. Landfilling, the traditional approach to waste remediation, however, should not be dismissed entirely. It does provide a baseline to which all other recycling and reprocessing options should be compared. This paper discusses the implementation of a computer model designed to examine the economics of landfilling bio-organic processing waste streams. Not only are these results applicable to food processing operations, but any industrial or manufacturing firm would benefit from examining the trends discussed here.

Host culling as an adaptive management tool for chronic wasting disease in white-tailed deer: a modelling study.

PubMed

Wasserberg, Gideon; Osnas, Erik E; Rolley, Robert E; Samuel, Michael D

2009-04-01

Emerging wildlife diseases pose a significant threat to natural and human systems. Because of real or perceived risks of delayed actions, disease management strategies such as culling are often implemented before thorough scientific knowledge of disease dynamics is available. Adaptive management is a valuable approach in addressing the uncertainty and complexity associated with wildlife disease problems and can be facilitated by using a formal model.We developed a multi-state computer simulation model using age, sex, infection-stage, and seasonality as a tool for scientific learning and managing chronic wasting disease (CWD) in white-tailed deer Odocoileus virginianus. Our matrix model used disease transmission parameters based on data collected through disease management activities. We used this model to evaluate management issues on density- (DD) and frequency-dependent (FD) transmission, time since disease introduction, and deer culling on the demographics, epizootiology, and management of CWD.Both DD and FD models fit the Wisconsin data for a harvested white-tailed deer population, but FD was slightly better. Time since disease introduction was estimated as 36 (95% CI, 24-50) and 188 (41->200) years for DD and FD transmission, respectively. Deer harvest using intermediate to high non-selective rates can be used to reduce uncertainty between DD and FD transmission and improve our prediction of long-term epidemic patterns and host population impacts. A higher harvest rate allows earlier detection of these differences, but substantially reduces deer abundance.Results showed that CWD has spread slowly within Wisconsin deer populations, and therefore, epidemics and disease management are expected to last for decades. Non-hunted deer populations can develop and sustain a high level of infection, generating a substantial risk of disease spread. In contrast, CWD prevalence remains lower in hunted deer populations, but at a higher prevalence the disease competes with

Prediction Models for Dynamic Demand Response

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

Aman, Saima; Frincu, Marc; Chelmis, Charalampos

2015-11-02

As Smart Grids move closer to dynamic curtailment programs, Demand Response (DR) events will become necessary not only on fixed time intervals and weekdays predetermined by static policies, but also during changing decision periods and weekends to react to real-time demand signals. Unique challenges arise in this context vis-a-vis demand prediction and curtailment estimation and the transformation of such tasks into an automated, efficient dynamic demand response (D 2R) process. While existing work has concentrated on increasing the accuracy of prediction models for DR, there is a lack of studies for prediction models for D 2R, which we address inmore » this paper. Our first contribution is the formal definition of D 2R, and the description of its challenges and requirements. Our second contribution is a feasibility analysis of very-short-term prediction of electricity consumption for D 2R over a diverse, large-scale dataset that includes both small residential customers and large buildings. Our third, and major contribution is a set of insights into the predictability of electricity consumption in the context of D 2R. Specifically, we focus on prediction models that can operate at a very small data granularity (here 15-min intervals), for both weekdays and weekends - all conditions that characterize scenarios for D 2R. We find that short-term time series and simple averaging models used by Independent Service Operators and utilities achieve superior prediction accuracy. We also observe that workdays are more predictable than weekends and holiday. Also, smaller customers have large variation in consumption and are less predictable than larger buildings. Key implications of our findings are that better models are required for small customers and for non-workdays, both of which are critical for D 2R. Also, prediction models require just few days’ worth of data indicating that small amounts of historical training data can be used to make reliable predictions

Integrated Models for Solid Waste Management in Tourism Regions: Langkawi Island, Malaysia

PubMed Central

Shamshiry, Elmira; Nadi, Behzad; Bin Mokhtar, Mazlin; Komoo, Ibrahim; Saadiah Hashim, Halimaton; Yahaya, Nadzri

2011-01-01

The population growth, changing consumption patterns, and rapid urbanization contribute significantly to the growing volumes of solid waste that are generated in urban settings. As the rate of urbanization increases, demand on the services of solid waste management increases. The rapid urban growth in Langkawi Island, Malaysia, combined with the increasing rates of solid waste production has provided evidence that the traditional solid waste management practices, particularly the methods of waste collection and disposal, are inefficient and quite nonsustainable. Accordingly, municipal managers and planners in Langkawi need to look for and adopt a model for solid waste management that emphasizes an efficient and sustainable management of solid wastes in Langkawi Island. This study presents the current practices of solid waste management in Langkawi Island, describes the composition of the solid waste generated in that area, and presents views of local residents and tourist on issues related to solid waste management like the aesthetic value of the island environment. The most important issue of this paper is that it is the first time that integrated solid waste management is investigated in the Langkawi Island. PMID:21904559

Natural Analogues - One Way to Help Build Public Confidence in the Predicted Performance of a Mined Geologic Repository for Nuclear Waste

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

Stuckless, J. S.

2002-02-26

The general public needs to have a way to judge the predicted long-term performance of the potential high-level nuclear waste repository at Yucca Mountain. The applicability and reliability of mathematical models used to make this prediction are neither easily understood nor accepted by the public. Natural analogues can provide the average person with a tool to assess the predicted performance and other scientific conclusions. For example, hydrologists with the Yucca Mountain Project have predicted that most of the water moving through the unsaturated zone at Yucca Mountain, Nevada will move through the host rock and around tunnels. Thus, seepage intomore » tunnels is predicted to be a small percentage of available infiltration. This hypothesis can be tested experimentally and with some quantitative analogues. It can also be tested qualitatively using a variety of analogues such as (1) well-preserved Paleolithic to Neolithic paintings in caves and rock shelters, (2) biological remains preserved in caves and rock shelters, and (3) artifacts and paintings preserved in man-made underground openings. These examples can be found in materials that are generally available to the non-scientific public and can demonstrate the surprising degree of preservation of fragile and easily destroyed materials for very long periods of time within the unsaturated zone.« less

MJO prediction skill of the subseasonal-to-seasonal (S2S) prediction models

NASA Astrophysics Data System (ADS)

Son, S. W.; Lim, Y.; Kim, D.

2017-12-01

The Madden-Julian Oscillation (MJO), the dominant mode of tropical intraseasonal variability, provides the primary source of tropical and extratropical predictability on subseasonal to seasonal timescales. To better understand its predictability, this study conducts quantitative evaluation of MJO prediction skill in the state-of-the-art operational models participating in the subseasonal-to-seasonal (S2S) prediction project. Based on bivariate correlation coefficient of 0.5, the S2S models exhibit MJO prediction skill ranging from 12 to 36 days. These prediction skills are affected by both the MJO amplitude and phase errors, the latter becoming more important with forecast lead times. Consistent with previous studies, the MJO events with stronger initial amplitude are typically better predicted. However, essentially no sensitivity to the initial MJO phase is observed. Overall MJO prediction skill and its inter-model spread are further related with the model mean biases in moisture fields and longwave cloud-radiation feedbacks. In most models, a dry bias quickly builds up in the deep tropics, especially across the Maritime Continent, weakening horizontal moisture gradient. This likely dampens the organization and propagation of MJO. Most S2S models also underestimate the longwave cloud-radiation feedbacks in the tropics, which may affect the maintenance of the MJO convective envelop. In general, the models with a smaller bias in horizontal moisture gradient and longwave cloud-radiation feedbacks show a higher MJO prediction skill, suggesting that improving those processes would enhance MJO prediction skill.

Combining an experimental study and ANFIS modeling to predict landfill leachate transport in underlying soil-a case study in north of Iran.

PubMed

Yousefi Kebria, D; Ghavami, M; Javadi, S; Goharimanesh, M

2017-12-16

In the contemporary world, urbanization and progressive industrial activities increase the rate of waste material generated in many developed countries. Municipal solid waste landfills (MSWs) are designed to dispose the waste from urban areas. However, discharged landfill leachate, the soluble water mixture that filters through solid waste landfills, can potentially migrate into the soil and affect living organisms by making harmful biological changes in the ecosystem. Due to well-documented landfill problems involving contamination, it is necessary to investigate the long-term influence of discharged leachate on the consistency of the soil beds beneath MSW landfills. To do so, the current study collected vertical deep core samples from different locations in the same unlined landfill. The impacts of effluent leachate on physical and chemical properties of the soil and its propagation depth were studied, and the leachate-transport pattern between successive boreholes was predicted by a developed mathematical model using an adaptive neuro-fuzzy inference system (ANFIS). The decomposition of organic leachate admixtures in the landfill yield is to produce organic acids as well as carbon dioxide, which diminishes the pH level of the landfill soil. The chemical analysis of discharged leachate in the soil samples showed that the concentrations of heavy metals are much lower than those of chloride, COD, BOD 5 , and bicarbonate. Using linear regression and mean square errors between the measured and predicted data, the accuracy of the proposed ANFIS model has been validated. Results show a high correlation between observed and predicated data.

Evaluating Predictive Uncertainty of Hyporheic Exchange Modelling

NASA Astrophysics Data System (ADS)

Chow, R.; Bennett, J.; Dugge, J.; Wöhling, T.; Nowak, W.

2017-12-01

Hyporheic exchange is the interaction of water between rivers and groundwater, and is difficult to predict. One of the largest contributions to predictive uncertainty for hyporheic fluxes have been attributed to the representation of heterogeneous subsurface properties. This research aims to evaluate which aspect of the subsurface representation - the spatial distribution of hydrofacies or the model for local-scale (within-facies) heterogeneity - most influences the predictive uncertainty. Also, we seek to identify data types that help reduce this uncertainty best. For this investigation, we conduct a modelling study of the Steinlach River meander, in Southwest Germany. The Steinlach River meander is an experimental site established in 2010 to monitor hyporheic exchange at the meander scale. We use HydroGeoSphere, a fully integrated surface water-groundwater model, to model hyporheic exchange and to assess the predictive uncertainty of hyporheic exchange transit times (HETT). A highly parameterized complex model is built and treated as `virtual reality', which is in turn modelled with simpler subsurface parameterization schemes (Figure). Then, we conduct Monte-Carlo simulations with these models to estimate the predictive uncertainty. Results indicate that: Uncertainty in HETT is relatively small for early times and increases with transit times. Uncertainty from local-scale heterogeneity is negligible compared to uncertainty in the hydrofacies distribution. Introducing more data to a poor model structure may reduce predictive variance, but does not reduce predictive bias. Hydraulic head observations alone cannot constrain the uncertainty of HETT, however an estimate of hyporheic exchange flux proves to be more effective at reducing this uncertainty. Figure: Approach for evaluating predictive model uncertainty. A conceptual model is first developed from the field investigations. A complex model (`virtual reality') is then developed based on that conceptual model

Actinide Sorption in a Brine/Dolomite Rock System: Evaluating the Degree of Conservatism in Kd Ranges used in Performance Assessment Modeling for the WIPP Nuclear Waste Repository

NASA Astrophysics Data System (ADS)

Dittrich, T. M.; Reed, D. T.

2015-12-01

The Waste Isolation Pilot Plant (WIPP) near Carlsbad, NM is the only operating nuclear waste repository in the US and has been accepting transuranic (TRU) waste since 1999. The WIPP is located in a salt deposit approximately 650 m below the surface and performance assessment (PA) modeling for a 10,000 year period is required to recertify the operating license with the US EPA every five years. The main pathway of concern for environmental release of radioactivity is a human intrusion caused by drilling into a pressurized brine reservoir below the repository. This could result in the flooding of the repository and subsequent transport in the high transmissivity layer (dolomite-rich Culebra formation) above the waste disposal rooms. We evaluate the degree of conservatism in the estimated sorption partition coefficients (Kds) ranges used in the PA based on an approach developed with granite rock and actinides (Dittrich and Reimus, 2015; Dittrich et al., 2015). Sorption onto the waste storage material (Fe drums) may also play a role in mobile actinide concentrations. We will present (1) a conceptual overview of how Kds are used in the PA model, (2) technical background of the evolution of the ranges and (3) results from batch and column experiments and model predictions for Kds with WIPP dolomite and clays, brine with various actinides, and ligands (e.g., acetate, citrate, EDTA) that could promote transport. The current Kd ranges used in performance models are based on oxidation state and are 5-400, 0.5-10,000, 0.03-200, and 0.03-20 mL g-1 for elements with oxidation states of III, IV, V, and VI, respectively. Based on redox conditions predicted in the brines, possible actinide species include Pu(III), Pu(IV), U(IV), U(VI), Np(IV), Np(V), Am(III), and Th(IV). We will also discuss the challenges of upscaling from lab experiments to field scale predictions, the role of colloids, and the effect of engineered barrier materials (e.g., MgO) on transport conditions. Dittrich

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.

Analysis of a novel class of predictive microbial growth models and application to coculture growth.

PubMed

Poschet, F; Vereecken, K M; Geeraerd, A H; Nicolaï, B M; Van Impe, J F

2005-04-15

In this paper, a novel class of microbial growth models is analysed. In contrast with the currently used logistic type models (e.g., the model of Baranyi and Roberts [Baranyi, J., Roberts, T.A., 1994. A dynamic approach to predicting bacterial growth in food. International Journal of Food Microbiology 23, 277-294]), the novel model class, presented in Van Impe et al. (Van Impe, J.F., Poschet, F., Geeraerd, A.H., Vereecken, K.M., 2004. Towards a novel class of predictive microbial growth models. International Journal of Food Microbiology, this issue), explicitly incorporates nutrient exhaustion and/or metabolic waste product effects inducing stationary phase behaviour. As such, these novel model types can be extended in a natural way towards microbial interactions in cocultures and microbial growth in structured foods. Two illustrative case studies of the novel model types are thoroughly analysed and compared to the widely used model of Baranyi and Roberts. In a first case study, the stationary phase is assumed to be solely resulting from toxic product inhibition and is described as a function of the pH-evolution. In the second case study, substrate exhaustion is the sole cause of the stationary phase. Finally, a more complex case study of a so-called P-model is presented, dealing with a coculture inhibition of Listeria innocua mediated by lactic acid production of Lactococcus lactis.

Reverse logistics network for municipal solid waste management: The inclusion of waste pickers as a Brazilian legal requirement

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

Ferri, Giovane Lopes, E-mail: giovane.ferri@aluno.ufes.br; Diniz Chaves, Gisele de Lorena, E-mail: gisele.chaves@ufes.br; Ribeiro, Glaydston Mattos, E-mail: glaydston@pet.coppe.ufrj.br

Highlights: • We propose a reverse logistics network for MSW involving waste pickers. • A generic facility location mathematical model was validated in a Brazilian city. • The results enable to predict the capacity for screening and storage centres (SSC). • We minimise the costs for transporting MSW with screening and storage centres. • The use of SSC can be a potential source of revenue and a better use of MSW. - Abstract: This study proposes a reverse logistics network involved in the management of municipal solid waste (MSW) to solve the challenge of economically managing these wastes considering themore » recent legal requirements of the Brazilian Waste Management Policy. The feasibility of the allocation of MSW material recovery facilities (MRF) as intermediate points between the generators of these wastes and the options for reuse and disposal was evaluated, as well as the participation of associations and cooperatives of waste pickers. This network was mathematically modelled and validated through a scenario analysis of the municipality of São Mateus, which makes the location model more complete and applicable in practice. The mathematical model allows the determination of the number of facilities required for the reverse logistics network, their location, capacities, and product flows between these facilities. The fixed costs of installation and operation of the proposed MRF were balanced with the reduction of transport costs, allowing the inclusion of waste pickers to the reverse logistics network. The main contribution of this study lies in the proposition of a reverse logistics network for MSW simultaneously involving legal, environmental, economic and social criteria, which is a very complex goal. This study can guide practices in other countries that have realities similar to those in Brazil of accelerated urbanisation without adequate planning for solid waste management, added to the strong presence of waste pickers that, through the

Hydrometeorological model for streamflow prediction

USGS Publications Warehouse

Tangborn, Wendell V.

1979-01-01

The hydrometeorological model described in this manual was developed to predict seasonal streamflow from water in storage in a basin using streamflow and precipitation data. The model, as described, applies specifically to the Skokomish, Nisqually, and Cowlitz Rivers, in Washington State, and more generally to streams in other regions that derive seasonal runoff from melting snow. Thus the techniques demonstrated for these three drainage basins can be used as a guide for applying this method to other streams. Input to the computer program consists of daily averages of gaged runoff of these streams, and daily values of precipitation collected at Longmire, Kid Valley, and Cushman Dam. Predictions are based on estimates of the absolute storage of water, predominately as snow: storage is approximately equal to basin precipitation less observed runoff. A pre-forecast test season is used to revise the storage estimate and improve the prediction accuracy. To obtain maximum prediction accuracy for operational applications with this model , a systematic evaluation of several hydrologic and meteorologic variables is first necessary. Six input options to the computer program that control prediction accuracy are developed and demonstrated. Predictions of streamflow can be made at any time and for any length of season, although accuracy is usually poor for early-season predictions (before December 1) or for short seasons (less than 15 days). The coefficient of prediction (CP), the chief measure of accuracy used in this manual, approaches zero during the late autumn and early winter seasons and reaches a maximum of about 0.85 during the spring snowmelt season. (Kosco-USGS)

Survival Regression Modeling Strategies in CVD Prediction.

PubMed

Barkhordari, Mahnaz; Padyab, Mojgan; Sardarinia, Mahsa; Hadaegh, Farzad; Azizi, Fereidoun; Bozorgmanesh, Mohammadreza

2016-04-01

A fundamental part of prevention is prediction. Potential predictors are the sine qua non of prediction models. However, whether incorporating novel predictors to prediction models could be directly translated to added predictive value remains an area of dispute. The difference between the predictive power of a predictive model with (enhanced model) and without (baseline model) a certain predictor is generally regarded as an indicator of the predictive value added by that predictor. Indices such as discrimination and calibration have long been used in this regard. Recently, the use of added predictive value has been suggested while comparing the predictive performances of the predictive models with and without novel biomarkers. User-friendly statistical software capable of implementing novel statistical procedures is conspicuously lacking. This shortcoming has restricted implementation of such novel model assessment methods. We aimed to construct Stata commands to help researchers obtain the aforementioned statistical indices. We have written Stata commands that are intended to help researchers obtain the following. 1, Nam-D'Agostino X 2 goodness of fit test; 2, Cut point-free and cut point-based net reclassification improvement index (NRI), relative absolute integrated discriminatory improvement index (IDI), and survival-based regression analyses. We applied the commands to real data on women participating in the Tehran lipid and glucose study (TLGS) to examine if information relating to a family history of premature cardiovascular disease (CVD), waist circumference, and fasting plasma glucose can improve predictive performance of Framingham's general CVD risk algorithm. The command is adpredsurv for survival models. Herein we have described the Stata package "adpredsurv" for calculation of the Nam-D'Agostino X 2 goodness of fit test as well as cut point-free and cut point-based NRI, relative and absolute IDI, and survival-based regression analyses. We hope this

A one-dimensional, steady-state, dissolved-oxygen model and waste-load assimilation study for Sand Creek, Decatur County, Indiana

USGS Publications Warehouse

Wilber, William G.; Crawford, Charles G.; Peters, James G.

1979-01-01

A digital model calibrated to conditions in Sand Creek near Greensburg, Ind., was used to develop alternatives for future waste loadings that would be compatible with Indiana stream water-quality standards defined for two critical hydrologic conditions, summer and winter low flows. The only point-source waste load affecting Sand Creek in the vicinity of Greensburg is the Greensburg wastewater-treatment facility. Non-point, unrecorded waste loads seemed to be significant during three water-quality surveys done by the Indiana State Board of Health. Natural streamflow in Sand Creek during the summer and annual 7-day, 10-year low flow is zero so no benefit from dilution is provided. Effluent ammonia-nitrogen concentrations from the Greensburg wastewater-treatment facility will not meet Indiana water-quality standards during summer and winter low flows. To meet the water-quality standard the wastewater-effluent would be limited to a maximum total ammonia-nitrogen concentration of 2.5 mg/l for summer months (June through August) and 4.0 mg/l for winter months (November through March). Model simulations indicate that benthic-oxygen demand, nitrification, and the dissolved-oxygen concentration of the wastewater effluent are the most significant factors affecting the in-stream dissolved-oxygen concentration during summer low flows. The model predicts that with a benthic-oxygen demand of 1.5 grams per square meter per day at 20C the stream has no additional waste-load assimilative capacity. Present carbonaceous biochemical-oxygen demand loads from the Greensburg wastewater-treatment facility will not result in violations of the in-stream dissolved-oxygen standard (5 mg/l) during winter low flows. (Kosco-USGS)

A Bayesian Network Model for Assessing Estrogen Fate and Transport in a Swine Waste Lagoon

PubMed Central

Lee, Boknam; Kullman, Seth W.; Yost, Erin; Meyer, Michael T.; Worley-Davis, Lynn; Reckhow, Kenneth H.

2017-01-01

Commercial swine waste lagoons are regarded as a major reservoir of natural estrogens, which have the potential to produce adverse physiological effects on exposed aquatic organisms and wildlife. However, there remains limited understanding of the complex mechanisms of physical, chemical, and biological processes that govern the fate and transport of natural estrogens within an anaerobic swine lagoon. To improve lagoon management and ultimately help control the offsite transport of these compounds from swine operations, a Bayesian network model was developed to predict estrogen fate and budget and compared against data collected from a commercial swine field site. In general, the model was able to predict the estrogen fate and budget in both the slurry and sludge stores within the swine lagoon. Sensitivity analysis within the model, demonstrated that the estrogen input loading from the associated barn facility was the most important factor in controlling estrogen concentrations within the lagoon slurry storage, while the settling rate was the most significant factor in the lagoon sludge storage. The degradation reactions were shown to be minor in both stores based on prediction of average total estrogen concentrations. Management scenario evaluations showed that the best possible management options to reduce estrogen levels in the lagoon are either to adjust the estrogen input loading from swine barn facilities or to effectively enhancing estrogen bonding with suspended solids through the use of organic polymers or inorganic coagulants. PMID:24798317

A spatially distributed model for the dynamic prediction of sediment erosion and transport in mountainous forested watersheds

NASA Astrophysics Data System (ADS)

Doten, Colleen O.; Bowling, Laura C.; Lanini, Jordan S.; Maurer, Edwin P.; Lettenmaier, Dennis P.

2006-04-01

Erosion and sediment transport in a temperate forested watershed are predicted with a new sediment model that represents the main sources of sediment generation in forested environments (mass wasting, hillslope erosion, and road surface erosion) within the distributed hydrology-soil-vegetation model (DHSVM) environment. The model produces slope failures on the basis of a factor-of-safety analysis with the infinite slope model through use of stochastically generated soil and vegetation parameters. Failed material is routed downslope with a rule-based scheme that determines sediment delivery to streams. Sediment from hillslopes and road surfaces is also transported to the channel network. A simple channel routing scheme is implemented to predict basin sediment yield. We demonstrate through an initial application of this model to the Rainy Creek catchment, a tributary of the Wenatchee River, which drains the east slopes of the Cascade Mountains, that the model produces plausible sediment yield and ratios of landsliding and surface erosion when compared to published rates for similar catchments in the Pacific Northwest. A road removal scenario and a basin-wide fire scenario are both evaluated with the model.

Large-scale structure prediction by improved contact predictions and model quality assessment.

PubMed

Michel, Mirco; Menéndez Hurtado, David; Uziela, Karolis; Elofsson, Arne

2017-07-15

Accurate contact predictions can be used for predicting the structure of proteins. Until recently these methods were limited to very big protein families, decreasing their utility. However, recent progress by combining direct coupling analysis with machine learning methods has made it possible to predict accurate contact maps for smaller families. To what extent these predictions can be used to produce accurate models of the families is not known. We present the PconsFold2 pipeline that uses contact predictions from PconsC3, the CONFOLD folding algorithm and model quality estimations to predict the structure of a protein. We show that the model quality estimation significantly increases the number of models that reliably can be identified. Finally, we apply PconsFold2 to 6379 Pfam families of unknown structure and find that PconsFold2 can, with an estimated 90% specificity, predict the structure of up to 558 Pfam families of unknown structure. Out of these, 415 have not been reported before. Datasets as well as models of all the 558 Pfam families are available at http://c3.pcons.net/ . All programs used here are freely available. arne@bioinfo.se. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

Understanding leachate flow in municipal solid waste landfills by combining time-lapse ERT and subsurface flow modelling - Part I: Analysis of infiltration shape on two different waste deposit cells.

PubMed

Audebert, M; Clément, R; Moreau, S; Duquennoi, C; Loisel, S; Touze-Foltz, N

2016-09-01

Landfill bioreactors are based on an acceleration of in-situ waste biodegradation by performing leachate recirculation. To quantify the water content and to evaluate the leachate injection system, in-situ methods are required to obtain spatially distributed information, usually electrical resistivity tomography (ERT). In a previous study, the MICS (multiple inversions and clustering strategy) methodology was proposed to improve the hydrodynamic interpretation of ERT results by a precise delimitation of the infiltration area. In this study, MICS was applied on two ERT time-lapse data sets recorded on different waste deposit cells in order to compare the hydrodynamic behaviour of leachate flow between the two cells. This comparison is based on an analysis of: (i) the volume of wetted waste assessed by MICS and the wetting rate, (ii) the infiltration shapes and (iii) the pore volume used by the leachate flow. This paper shows that leachate hydrodynamic behaviour is comparable from one waste deposit cell to another with: (i) a high leachate infiltration speed at the beginning of the infiltration, which decreases with time, (ii) a horizontal anisotropy of the leachate infiltration shape and (iii) a very small fraction of the pore volume used by the leachate flow. This hydrodynamic information derived from MICS results can be useful for subsurface flow modelling used to predict leachate flow at the landfill scale. Copyright © 2016 Elsevier Ltd. All rights reserved.

Mental models accurately predict emotion transitions.

PubMed

Thornton, Mark A; Tamir, Diana I

2017-06-06

Successful social interactions depend on people's ability to predict others' future actions and emotions. People possess many mechanisms for perceiving others' current emotional states, but how might they use this information to predict others' future states? We hypothesized that people might capitalize on an overlooked aspect of affective experience: current emotions predict future emotions. By attending to regularities in emotion transitions, perceivers might develop accurate mental models of others' emotional dynamics. People could then use these mental models of emotion transitions to predict others' future emotions from currently observable emotions. To test this hypothesis, studies 1-3 used data from three extant experience-sampling datasets to establish the actual rates of emotional transitions. We then collected three parallel datasets in which participants rated the transition likelihoods between the same set of emotions. Participants' ratings of emotion transitions predicted others' experienced transitional likelihoods with high accuracy. Study 4 demonstrated that four conceptual dimensions of mental state representation-valence, social impact, rationality, and human mind-inform participants' mental models. Study 5 used 2 million emotion reports on the Experience Project to replicate both of these findings: again people reported accurate models of emotion transitions, and these models were informed by the same four conceptual dimensions. Importantly, neither these conceptual dimensions nor holistic similarity could fully explain participants' accuracy, suggesting that their mental models contain accurate information about emotion dynamics above and beyond what might be predicted by static emotion knowledge alone.

Mental models accurately predict emotion transitions

PubMed Central

Thornton, Mark A.; Tamir, Diana I.

2017-01-01

Successful social interactions depend on people’s ability to predict others’ future actions and emotions. People possess many mechanisms for perceiving others’ current emotional states, but how might they use this information to predict others’ future states? We hypothesized that people might capitalize on an overlooked aspect of affective experience: current emotions predict future emotions. By attending to regularities in emotion transitions, perceivers might develop accurate mental models of others’ emotional dynamics. People could then use these mental models of emotion transitions to predict others’ future emotions from currently observable emotions. To test this hypothesis, studies 1–3 used data from three extant experience-sampling datasets to establish the actual rates of emotional transitions. We then collected three parallel datasets in which participants rated the transition likelihoods between the same set of emotions. Participants’ ratings of emotion transitions predicted others’ experienced transitional likelihoods with high accuracy. Study 4 demonstrated that four conceptual dimensions of mental state representation—valence, social impact, rationality, and human mind—inform participants’ mental models. Study 5 used 2 million emotion reports on the Experience Project to replicate both of these findings: again people reported accurate models of emotion transitions, and these models were informed by the same four conceptual dimensions. Importantly, neither these conceptual dimensions nor holistic similarity could fully explain participants’ accuracy, suggesting that their mental models contain accurate information about emotion dynamics above and beyond what might be predicted by static emotion knowledge alone. PMID:28533373

Poisson Mixture Regression Models for Heart Disease Prediction.

PubMed

Mufudza, Chipo; Erol, Hamza

2016-01-01

Early heart disease control can be achieved by high disease prediction and diagnosis efficiency. This paper focuses on the use of model based clustering techniques to predict and diagnose heart disease via Poisson mixture regression models. Analysis and application of Poisson mixture regression models is here addressed under two different classes: standard and concomitant variable mixture regression models. Results show that a two-component concomitant variable Poisson mixture regression model predicts heart disease better than both the standard Poisson mixture regression model and the ordinary general linear Poisson regression model due to its low Bayesian Information Criteria value. Furthermore, a Zero Inflated Poisson Mixture Regression model turned out to be the best model for heart prediction over all models as it both clusters individuals into high or low risk category and predicts rate to heart disease componentwise given clusters available. It is deduced that heart disease prediction can be effectively done by identifying the major risks componentwise using Poisson mixture regression model.

Poisson Mixture Regression Models for Heart Disease Prediction

PubMed Central

Erol, Hamza

2016-01-01

Early heart disease control can be achieved by high disease prediction and diagnosis efficiency. This paper focuses on the use of model based clustering techniques to predict and diagnose heart disease via Poisson mixture regression models. Analysis and application of Poisson mixture regression models is here addressed under two different classes: standard and concomitant variable mixture regression models. Results show that a two-component concomitant variable Poisson mixture regression model predicts heart disease better than both the standard Poisson mixture regression model and the ordinary general linear Poisson regression model due to its low Bayesian Information Criteria value. Furthermore, a Zero Inflated Poisson Mixture Regression model turned out to be the best model for heart prediction over all models as it both clusters individuals into high or low risk category and predicts rate to heart disease componentwise given clusters available. It is deduced that heart disease prediction can be effectively done by identifying the major risks componentwise using Poisson mixture regression model. PMID:27999611

A Global Model for Bankruptcy Prediction

PubMed Central

Alaminos, David; del Castillo, Agustín; Fernández, Manuel Ángel

2016-01-01

The recent world financial crisis has increased the number of bankruptcies in numerous countries and has resulted in a new area of research which responds to the need to predict this phenomenon, not only at the level of individual countries, but also at a global level, offering explanations of the common characteristics shared by the affected companies. Nevertheless, few studies focus on the prediction of bankruptcies globally. In order to compensate for this lack of empirical literature, this study has used a methodological framework of logistic regression to construct predictive bankruptcy models for Asia, Europe and America, and other global models for the whole world. The objective is to construct a global model with a high capacity for predicting bankruptcy in any region of the world. The results obtained have allowed us to confirm the superiority of the global model in comparison to regional models over periods of up to three years prior to bankruptcy. PMID:27880810

FTIR-PAS: a powerful tool for characterising the chemical composition and predicting the labile C fraction of various organic waste products.

PubMed

Bekiaris, Georgios; Bruun, Sander; Peltre, Clément; Houot, Sabine; Jensen, Lars S

2015-05-01

Fourier transform infrared (FT-IR) spectroscopy has been used for several years as a fast, low-cost, reliable technique for characterising a large variety of materials. However, the strong influence of sample particle size and the inability to measure the absorption of very dark and opaque samples have made FTIR unsuitable for many waste materials. FTIR-photoacoustic spectroscopy (FTIR-PAS) can eliminate some of the shortcomings of traditional FTIR caused by scattering effects and reflection issues, and recent advances in PAS technology have made commercial instruments available. In this study, FTIR-PAS was used to characterise a wide range of organic waste products and predict their labile carbon fraction, which is normally determined from time-consuming assays. FTIR-PAS was found to be capable of predicting the labile fraction of carbon as efficiently as near infrared spectroscopy (NIR) and furthermore of identifying the compounds that are correlated with the predicted parameter, thus facilitating a more mechanistic interpretation. Copyright © 2015 Elsevier Ltd. All rights reserved.

Mathematical model of organic substrate degradation in solid waste windrow composting.

PubMed

Seng, Bunrith; Kristanti, Risky Ayu; Hadibarata, Tony; Hirayama, Kimiaki; Katayama-Hirayama, Keiko; Kaneko, Hidehiro

2016-01-01

Organic solid waste composting is a complex process that involves many coupled physical, chemical and biological mechanisms. To understand this complexity and to ease in planning, design and management of the composting plant, mathematical model for simulation is usually applied. The aim of this paper is to develop a mathematical model of organic substrate degradation and its performance evaluation in solid waste windrow composting system. The present model is a biomass-dependent model, considering biological growth processes under the limitation of moisture, oxygen and substrate contents, and temperature. The main output of this model is substrate content which was divided into two categories: slowly and rapidly degradable substrates. To validate the model, it was applied to a laboratory scale windrow composting of a mixture of wood chips and dog food. The wastes were filled into a cylindrical reactor of 6 cm diameter and 1 m height. The simulation program was run for 3 weeks with 1 s stepwise. The simulated results were in reasonably good agreement with the experimental results. The MC and temperature of model simulation were found to be matched with those of experiment, but limited for rapidly degradable substrates. Under anaerobic zone, the degradation of rapidly degradable substrate needs to be incorporated into the model to achieve full simulation of a long period static pile composting. This model is a useful tool to estimate the changes of substrate content during composting period, and acts as a basic model for further development of a sophisticated model.

Experimental Determination and Thermodynamic Modeling of Electrical Conductivity of SRS Waste Tank Supernate

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

Pike, J.; Reboul, S.

2015-06-01

SRS High Level Waste Tank Farm personnel rely on conductivity probes for detection of incipient overflow conditions in waste tanks. Minimal information is available concerning the sensitivity that must be achieved such that that liquid detection is assured. Overly sensitive electronics results in numerous nuisance alarms for these safety-related instruments. In order to determine the minimum sensitivity required of the probe, Tank Farm Engineering personnel need adequate conductivity data to improve the existing designs. Little or no measurements of liquid waste conductivity exist; however, the liquid phase of the waste consists of inorganic electrolytes for which the conductivity may bemore » calculated. Savannah River Remediation (SRR) Tank Farm Facility Engineering requested SRNL to determine the conductivity of the supernate resident in SRS waste Tank 40 experimentally as well as computationally. In addition, SRNL was requested to develop a correlation, if possible, that would be generally applicable to liquid waste resident in SRS waste tanks. A waste sample from Tank 40 was analyzed for composition and electrical conductivity as shown in Table 4-6, Table 4-7, and Table 4-9. The conductivity for undiluted Tank 40 sample was 0.087 S/cm. The accuracy of OLI Analyzer™ was determined using available literature data. Overall, 95% of computed estimates of electrical conductivity are within ±15% of literature values for component concentrations from 0 to 15 M and temperatures from 0 to 125 °C. Though the computational results are generally in good agreement with the measured data, a small portion of literature data deviates as much as ±76%. A simplified model was created that can be used readily to estimate electrical conductivity of waste solution in computer spreadsheets. The variability of this simplified approach deviates up to 140% from measured values. Generally, this model can be applied to estimate the conductivity within a factor of two. The

Waste Form and Indrift Colloids-Associated Radionuclide Concentrations: Abstraction and Summary

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

R. Aguilar

This Model Report describes the analysis and abstractions of the colloids process model for the waste form and engineered barrier system components of the total system performance assessment calculations to be performed with the Total System Performance Assessment-License Application model. Included in this report is a description of (1) the types and concentrations of colloids that could be generated in the waste package from degradation of waste forms and the corrosion of the waste package materials, (2) types and concentrations of colloids produced from the steel components of the repository and their potential role in radionuclide transport, and (3) typesmore » and concentrations of colloids present in natural waters in the vicinity of Yucca Mountain. Additionally, attachment/detachment characteristics and mechanisms of colloids anticipated in the repository are addressed and discussed. The abstraction of the process model is intended to capture the most important characteristics of radionuclide-colloid behavior for use in predicting the potential impact of colloid-facilitated radionuclide transport on repository performance.« less

Using connectome-based predictive modeling to predict individual behavior from brain connectivity

PubMed Central

Shen, Xilin; Finn, Emily S.; Scheinost, Dustin; Rosenberg, Monica D.; Chun, Marvin M.; Papademetris, Xenophon; Constable, R Todd

2017-01-01

Neuroimaging is a fast developing research area where anatomical and functional images of human brains are collected using techniques such as functional magnetic resonance imaging (fMRI), diffusion tensor imaging (DTI), and electroencephalography (EEG). Technical advances and large-scale datasets have allowed for the development of models capable of predicting individual differences in traits and behavior using brain connectivity measures derived from neuroimaging data. Here, we present connectome-based predictive modeling (CPM), a data-driven protocol for developing predictive models of brain-behavior relationships from connectivity data using cross-validation. This protocol includes the following steps: 1) feature selection, 2) feature summarization, 3) model building, and 4) assessment of prediction significance. We also include suggestions for visualizing the most predictive features (i.e., brain connections). The final result should be a generalizable model that takes brain connectivity data as input and generates predictions of behavioral measures in novel subjects, accounting for a significant amount of the variance in these measures. It has been demonstrated that the CPM protocol performs equivalently or better than most of the existing approaches in brain-behavior prediction. However, because CPM focuses on linear modeling and a purely data-driven driven approach, neuroscientists with limited or no experience in machine learning or optimization would find it easy to implement the protocols. Depending on the volume of data to be processed, the protocol can take 10–100 minutes for model building, 1–48 hours for permutation testing, and 10–20 minutes for visualization of results. PMID:28182017

A one-dimensional, steady-state, dissolved-oxygen model and waste-load assimilation study for Wabash River, Huntington County, Indiana

USGS Publications Warehouse

Crawford, Charles G.; Wilber, William G.; Peters, James G.

1980-01-01

A digital model calibrated to conditions in the Wabash River in Huntington County, Ind., was used to predict alternatives for future waste loadings that would be compatible with Indiana stream water-quality standards defined for two critical hydrologic conditons, summer and winter low flows. The major point-source waste load affecting the Wabash River in Huntington County is the Huntington wastewater-treatment facility. The most significnt factor potentially affecting the dissolved-oxygen concentration during summer low flows is nitrification. However, nitrification should not be a limiting factor on the allowable nitrogenous and carbonaceous waste loads for the Huntington wastewater-treatment facility during summer low flows if the ammonia-nitrogen toxicity standard for Indiana streams is met. The disolved-oxygen standard for Indiana stream, an average of 5.0 milligrams per liter, should be met during summer and winter low flows if the National Pollution Discharge Elimination System 's 5-day, carbonaceous biochemical-oxygen demands of a monthly average concentration of 30 milligrams per liter and a maximum weekly average of 45 milligrams per liter are not exceeded. 

Strategy Plan A Methodology to Predict the Uniformity of Double-Shell Tank Waste Slurries Based on Mixing Pump Operation

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

J.A. Bamberger; L.M. Liljegren; P.S. Lowery

This document presents an analysis of the mechanisms influencing mixing within double-shell slurry tanks. A research program to characterize mixing of slurries within tanks has been proposed. The research program presents a combined experimental and computational approach to produce correlations describing the tank slurry concentration profile (and therefore uniformity) as a function of mixer pump operating conditions. The TEMPEST computer code was used to simulate both a full-scale (prototype) and scaled (model) double-shell waste tank to predict flow patterns resulting from a stationary jet centered in the tank. The simulation results were used to evaluate flow patterns in the tankmore » and to determine whether flow patterns are similar between the full-scale prototype and an existing 1/12-scale model tank. The flow patterns were sufficiently similar to recommend conducting scoping experiments at 1/12-scale. Also, TEMPEST modeled velocity profiles of the near-floor jet were compared to experimental measurements of the near-floor jet with good agreement. Reported values of physical properties of double-shell tank slurries were analyzed to evaluate the range of properties appropriate for conducting scaled experiments. One-twelfth scale scoping experiments are recommended to confirm the prioritization of the dimensionless groups (gravitational settling, Froude, and Reynolds numbers) that affect slurry suspension in the tank. Two of the proposed 1/12-scale test conditions were modeled using the TEMPEST computer code to observe the anticipated flow fields. This information will be used to guide selection of sampling probe locations. Additional computer modeling is being conducted to model a particulate laden, rotating jet centered in the tank. The results of this modeling effort will be compared to the scaled experimental data to quantify the agreement between the code and the 1/12-scale experiment. The scoping experiment results will guide selection of parameters

Thermal barrier coating life prediction model

NASA Technical Reports Server (NTRS)

Pilsner, B. H.; Hillery, R. V.; Mcknight, R. L.; Cook, T. S.; Kim, K. S.; Duderstadt, E. C.

1986-01-01

The objectives of this program are to determine the predominant modes of degradation of a plasma sprayed thermal barrier coating system, and then to develop and verify life prediction models accounting for these degradation modes. The program is divided into two phases, each consisting of several tasks. The work in Phase 1 is aimed at identifying the relative importance of the various failure modes, and developing and verifying life prediction model(s) for the predominant model for a thermal barrier coating system. Two possible predominant failure mechanisms being evaluated are bond coat oxidation and bond coat creep. The work in Phase 2 will develop design-capable, causal, life prediction models for thermomechanical and thermochemical failure modes, and for the exceptional conditions of foreign object damage and erosion.

Source term model evaluations for the low-level waste facility performance assessment

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

Yim, M.S.; Su, S.I.

1995-12-31

The estimation of release of radionuclides from various waste forms to the bottom boundary of the waste disposal facility (source term) is one of the most important aspects of LLW facility performance assessment. In this work, several currently used source term models are comparatively evaluated for the release of carbon-14 based on a test case problem. The models compared include PRESTO-EPA-CPG, IMPACTS, DUST and NEFTRAN-II. Major differences in assumptions and approaches between the models are described and key parameters are identified through sensitivity analysis. The source term results from different models are compared and other concerns or suggestions are discussed.

Predicting the degradability of waste activated sludge.

PubMed

Jones, Richard; Parker, Wayne; Zhu, Henry; Houweling, Dwight; Murthy, Sudhir

2009-08-01

The objective of this study was to identify methods for estimating anaerobic digestibility of waste activated sludge (WAS). The WAS streams were generated in three sequencing batch reactors (SBRs) treating municipal wastewater. The wastewater and WAS properties were initially determined through simulation of SBR operation with BioWin (EnviroSim Associates Ltd., Flamborough, Ontario, Canada). Samples of WAS from the SBRs were subsequently characterized through respirometry and batch anaerobic digestion. Respirometry was an effective tool for characterizing the active fraction of WAS and could be a suitable technique for determining sludge composition for input to anaerobic models. Anaerobic digestion of the WAS revealed decreasing methane production and lower chemical oxygen demand removals as the SRT of the sludge increased. BioWin was capable of accurately describing the digestion of the WAS samples for typical digester SRTs. For extended digestion times (i.e., greater than 30 days), some degradation of the endogenous decay products was assumed to achieve accurate simulations for all sludge SRTs.

Estimating national landfill methane emissions: an application of the 2006 Intergovernmental Panel on Climate Change Waste Model in Panama.

PubMed

Weitz, Melissa; Coburn, Jeffrey B; Salinas, Edgar

2008-05-01

This paper estimates national methane emissions from solid waste disposal sites in Panama over the time period 1990-2020 using both the 2006 Intergovernmental Panel on Climate Change (IPCC) Waste Model spreadsheet and the default emissions estimate approach presented in the 1996 IPCC Good Practice Guidelines. The IPCC Waste Model has the ability to calculate emissions from a variety of solid waste disposal site types, taking into account country- or region-specific waste composition and climate information, and can be used with a limited amount of data. Countries with detailed data can also run the model with country-specific values. The paper discusses methane emissions from solid waste disposal; explains the differences between the two methodologies in terms of data needs, assumptions, and results; describes solid waste disposal circumstances in Panama; and presents the results of this analysis. It also demonstrates the Waste Model's ability to incorporate landfill gas recovery data and to make projections. The former default method methane emissions estimates are 25 Gg in 1994, and range from 23.1 Gg in 1990 to a projected 37.5 Gg in 2020. The Waste Model estimates are 26.7 Gg in 1994, ranging from 24.6 Gg in 1990 to 41.6 Gg in 2020. Emissions estimates for Panama produced by the new model were, on average, 8% higher than estimates produced by the former default methodology. The increased estimate can be attributed to the inclusion of all solid waste disposal in Panama (as opposed to only disposal in managed landfills), but the increase was offset somewhat by the different default factors and regional waste values between the 1996 and 2006 IPCC guidelines, and the use of the first-order decay model with a time delay for waste degradation in the IPCC Waste Model.

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.

Delirium prediction in the intensive care unit: comparison of two delirium prediction models.

PubMed

Wassenaar, Annelies; Schoonhoven, Lisette; Devlin, John W; van Haren, Frank M P; Slooter, Arjen J C; Jorens, Philippe G; van der Jagt, Mathieu; Simons, Koen S; Egerod, Ingrid; Burry, Lisa D; Beishuizen, Albertus; Matos, Joaquim; Donders, A Rogier T; Pickkers, Peter; van den Boogaard, Mark

2018-05-05

Accurate prediction of delirium in the intensive care unit (ICU) may facilitate efficient use of early preventive strategies and stratification of ICU patients by delirium risk in clinical research, but the optimal delirium prediction model to use is unclear. We compared the predictive performance and user convenience of the prediction  model for delirium (PRE-DELIRIC) and early prediction model for delirium (E-PRE-DELIRIC) in ICU patients and determined the value of a two-stage calculation. This 7-country, 11-hospital, prospective cohort study evaluated consecutive adults admitted to the ICU who could be reliably assessed for delirium using the Confusion Assessment Method-ICU or the Intensive Care Delirium Screening Checklist. The predictive performance of the models was measured using the area under the receiver operating characteristic curve. Calibration was assessed graphically. A physician questionnaire evaluated user convenience. For the two-stage calculation we used E-PRE-DELIRIC immediately after ICU admission and updated the prediction using PRE-DELIRIC after 24 h. In total 2178 patients were included. The area under the receiver operating characteristic curve was significantly greater for PRE-DELIRIC (0.74 (95% confidence interval 0.71-0.76)) compared to E-PRE-DELIRIC (0.68 (95% confidence interval 0.66-0.71)) (z score of - 2.73 (p < 0.01)). Both models were well-calibrated. The sensitivity improved when using the two-stage calculation in low-risk patients. Compared to PRE-DELIRIC, ICU physicians (n = 68) rated the E-PRE-DELIRIC model more feasible. While both ICU delirium prediction models have moderate-to-good performance, the PRE-DELIRIC model predicts delirium better. However, ICU physicians rated the user convenience of E-PRE-DELIRIC superior to PRE-DELIRIC. In low-risk patients the delirium prediction further improves after an update with the PRE-DELIRIC model after 24 h. ClinicalTrials.gov, NCT02518646 . Registered on 21 July 2015.

Estimating Model Prediction Error: Should You Treat Predictions as Fixed or Random?

NASA Technical Reports Server (NTRS)

Wallach, Daniel; Thorburn, Peter; Asseng, Senthold; Challinor, Andrew J.; Ewert, Frank; Jones, James W.; Rotter, Reimund; Ruane, Alexander

2016-01-01

Crop models are important tools for impact assessment of climate change, as well as for exploring management options under current climate. It is essential to evaluate the uncertainty associated with predictions of these models. We compare two criteria of prediction error; MSEP fixed, which evaluates mean squared error of prediction for a model with fixed structure, parameters and inputs, and MSEP uncertain( X), which evaluates mean squared error averaged over the distributions of model structure, inputs and parameters. Comparison of model outputs with data can be used to estimate the former. The latter has a squared bias term, which can be estimated using hindcasts, and a model variance term, which can be estimated from a simulation experiment. The separate contributions to MSEP uncertain (X) can be estimated using a random effects ANOVA. It is argued that MSEP uncertain (X) is the more informative uncertainty criterion, because it is specific to each prediction situation.

A facility location model for municipal solid waste management system under uncertain environment.

PubMed

Yadav, Vinay; Bhurjee, A K; Karmakar, Subhankar; Dikshit, A K

2017-12-15

In municipal solid waste management system, decision makers have to develop an insight into the processes namely, waste generation, collection, transportation, processing, and disposal methods. Many parameters (e.g., waste generation rate, functioning costs of facilities, transportation cost, and revenues) in this system are associated with uncertainties. Often, these uncertainties of parameters need to be modeled under a situation of data scarcity for generating probability distribution function or membership function for stochastic mathematical programming or fuzzy mathematical programming respectively, with only information of extreme variations. Moreover, if uncertainties are ignored, then the problems like insufficient capacities of waste management facilities or improper utilization of available funds may be raised. To tackle uncertainties of these parameters in a more efficient manner an algorithm, based on interval analysis, has been developed. This algorithm is applied to find optimal solutions for a facility location model, which is formulated to select economically best locations of transfer stations in a hypothetical urban center. Transfer stations are an integral part of contemporary municipal solid waste management systems, and economic siting of transfer stations ensures financial sustainability of this system. The model is written in a mathematical programming language AMPL with KNITRO as a solver. The developed model selects five economically best locations out of ten potential locations with an optimum overall cost of [394,836, 757,440] Rs. 1 /day ([5906, 11,331] USD/day) approximately. Further, the requirement of uncertainty modeling is explained based on the results of sensitivity analysis. Copyright © 2017 Elsevier B.V. All rights reserved.

Risk terrain modeling predicts child maltreatment.

PubMed

Daley, Dyann; Bachmann, Michael; Bachmann, Brittany A; Pedigo, Christian; Bui, Minh-Thuy; Coffman, Jamye

2016-12-01

As indicated by research on the long-term effects of adverse childhood experiences (ACEs), maltreatment has far-reaching consequences for affected children. Effective prevention measures have been elusive, partly due to difficulty in identifying vulnerable children before they are harmed. This study employs Risk Terrain Modeling (RTM), an analysis of the cumulative effect of environmental factors thought to be conducive for child maltreatment, to create a highly accurate prediction model for future substantiated child maltreatment cases in the City of Fort Worth, Texas. The model is superior to commonly used hotspot predictions and more beneficial in aiding prevention efforts in a number of ways: 1) it identifies the highest risk areas for future instances of child maltreatment with improved precision and accuracy; 2) it aids the prioritization of risk-mitigating efforts by informing about the relative importance of the most significant contributing risk factors; 3) since predictions are modeled as a function of easily obtainable data, practitioners do not have to undergo the difficult process of obtaining official child maltreatment data to apply it; 4) the inclusion of a multitude of environmental risk factors creates a more robust model with higher predictive validity; and, 5) the model does not rely on a retrospective examination of past instances of child maltreatment, but adapts predictions to changing environmental conditions. The present study introduces and examines the predictive power of this new tool to aid prevention efforts seeking to improve the safety, health, and wellbeing of vulnerable children. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

Predicting characteristics of rainfall driven estrogen runoff and transport from swine AFO spray fields.

PubMed

Lee, Boknam; Kullman, Seth W; Yost, Erin E; Meyer, Michael T; Worley-Davis, Lynn; Williams, C Michael; Reckhow, Kenneth H

2015-11-01

Animal feeding operations (AFOs) have been implicated as potentially major sources of estrogenic contaminants into the aquatic environment due to the relatively minimal treatment of waste and potential mobilization and transport of waste components from spray fields. In this study a Bayesian network (BN) model was developed to inform management decisions and better predict the transport and fate of natural steroidal estrogens from these sites. The developed BN model integrates processes of surface runoff and sediment loss with the modified universal soil loss equation (MUSLE) and the soil conservation service curve number (SCS-CN) runoff model. What-if scenario simulations of lagoon slurry wastes to the spray fields were conducted for the most abundant natural estrogen estrone (E1) observed in the system. It was found that E1 attenuated significantly after 2 months following waste slurry application in both spring and summer seasons, with the overall attenuation rate predicted to be higher in the summer compared to the spring. Using simulations of rainfall events in conjunction with waste slurry application rates, it was predicted that the magnitude of E1 runoff loss is significantly higher in the spring as compared to the summer months, primarily due to spray field crop management plans. Our what-if scenario analyses suggest that planting Bermuda grass in the spray fields is likely to reduce runoff losses of natural estrogens near the water bodies and ecosystems, as compared to planting of soybeans. Copyright © 2015 Elsevier B.V. All rights reserved.

Predicting Characteristics of Rainfall Driven Estrogen Runoff and Transport from Swine AFO Spray Fields

PubMed Central

Lee, Boknam; Kullman, Seth W.; Yost, Erin E.; Meyer, Michael T.; Worley-Davis, Lynn; Williams, C. Michael; Reckhow, Kenneth H.

2017-01-01

Animal feeding operations (AFOs) have been implicated as potentially major sources of estrogenic contaminants into the aquatic environment due to the relatively minimal treatment of waste and potential mobilization and transport of waste components from spray fields. In this study a Bayesian network (BN) model was developed to inform management decisions and better predict the transport and fate of natural steroidal estrogens from these sites. The developed BN model integrates processes of surface runoff and sediment loss with the modified universal soil loss equation (MUSLE) and the soil conservation service curve number (SCS-CN) runoff model. What-if scenario simulations of lagoon slurry wastes to the spray fields were conducted for the most abundant natural estrogen estrone (E1) observed in the system. It was found that E1 attenuated significantly after 2 months following waste slurry application in both spring and summer seasons, with the overall attenuation rate predicted to be higher in the summer compared to the spring. Using simulations of rainfall events in conjunction with waste slurry application rates, it was predicted that the magnitude of E1 runoff loss is significantly higher in the spring as compared to the summer months, primarily due to spray field crop management plans. Our what-if scenario analyses suggest that planting Bermuda grass in the spray fields is likely to reduce runoff losses of natural estrogens near the water bodies and ecosystems, as compared to planting of soybeans. PMID:26102057

Artificial neural network based modelling approach for municipal solid waste gasification in a fluidized bed reactor.

PubMed

Pandey, Daya Shankar; Das, Saptarshi; Pan, Indranil; Leahy, James J; Kwapinski, Witold

2016-12-01

In this paper, multi-layer feed forward neural networks are used to predict the lower heating value of gas (LHV), lower heating value of gasification products including tars and entrained char (LHV p ) and syngas yield during gasification of municipal solid waste (MSW) during gasification in a fluidized bed reactor. These artificial neural networks (ANNs) with different architectures are trained using the Levenberg-Marquardt (LM) back-propagation algorithm and a cross validation is also performed to ensure that the results generalise to other unseen datasets. A rigorous study is carried out on optimally choosing the number of hidden layers, number of neurons in the hidden layer and activation function in a network using multiple Monte Carlo runs. Nine input and three output parameters are used to train and test various neural network architectures in both multiple output and single output prediction paradigms using the available experimental datasets. The model selection procedure is carried out to ascertain the best network architecture in terms of predictive accuracy. The simulation results show that the ANN based methodology is a viable alternative which can be used to predict the performance of a fluidized bed gasifier. Copyright © 2016 Elsevier Ltd. All rights reserved.

Mathematical Modeling to Reduce Waste of Compounded Sterile Products in Hospital Pharmacies

PubMed Central

Dobson, Gregory; Haas, Curtis E.; Tilson, David

2014-01-01

Abstract In recent years, many US hospitals embarked on “lean” projects to reduce waste. One advantage of the lean operational improvement methodology is that it relies on process observation by those engaged in the work and requires relatively little data. However, the thoughtful analysis of the data captured by operational systems allows the modeling of many potential process options. Such models permit the evaluation of likely waste reductions and financial savings before actual process changes are made. Thus the most promising options can be identified prospectively, change efforts targeted accordingly, and realistic targets set. This article provides one example of such a datadriven process redesign project focusing on waste reduction in an in-hospital pharmacy. A mathematical model of the medication prepared and delivered by the pharmacy is used to estimate the savings from several potential redesign options (rescheduling the start of production, scheduling multiple batches, or reordering production within a batch) as well as the impact of information system enhancements. The key finding is that mathematical modeling can indeed be a useful tool. In one hospital setting, it estimated that waste could be realistically reduced by around 50% by using several process changes and that the greatest benefit would be gained by rescheduling the start of production (for a single batch) away from the period when most order cancellations are made. PMID:25477580

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.

Impact of iron redox chemistry on nuclear waste disposal

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

Pearce, Carolyn I.; Rosso, Kevin M.; Pattrick, Richard

For the safe disposal of nuclear waste, the ability to predict the changes in oxidation states of redox active actinide elements and fission products, such as U, Pu, Tc and Np is a key factor in determining their long term mobility. Both in the Geological Disposal Facility (GDF) near-field and in the far-field subsurface environment, the oxidation states of radionuclides are closely tied to changes in the redox condition of other elements in the subsurface such as iron. Iron pervades all aspects of the waste package environment, from the steel in the waste containers, through corrosion products, to the ironmore » minerals present in the host rock. Over the long period required for nuclear waste disposal, the chemical conditions of the subsurface waste package will vary along the entire continuum from oxidizing to reducing conditions. This variability leads to the expectation that redox-active components such as Fe oxides can undergo phase transformations or dissolution; to understand and quantify such a system with respect to potential impacts on waste package integrity and radionuclide fate is clearly a serious challenge. Traditional GDF performance assessment models currently rely upon surface adsorption or single phase solubility experiments and do not deal with the incorporation of radionuclides into specific crystallographic sites within the evolving Fe phases. In this chapter, we focus on the iron-bearing phases that are likely to be present in both the near and far-field of a GDF, examining their potential for redox activity and interaction with radionuclides. To support this, thermodynamic and molecular modelling is particularly important in predicting radionuclide behaviour in the presence of Fe-phases. Examination of radionuclide contamination of the natural environment provides further evidence of the importance of Fe-phases in far-field processes; these can be augmented by experimental and analogue studies.« less

An equivalent-time-lines model for municipal solid waste based on its compression characteristics.

PubMed

Gao, Wu; Bian, Xuecheng; Xu, Wenjie; Chen, Yunmin

2017-10-01

Municipal solid waste (MSW) demonstrates a noticeable time-dependent stress-strain behavior, which contributes greatly to the settlement of landfills and therefore influences both the storage capacity of landfills and the integrity of internal structures. The long-term compression tests for MSW under different biodegradation conditions were analyzed. It showed that the primary compression can affect the secondary compression due to the biodegradation and mechanical creep. Based on the time-lines model for clays and the compression characteristics of MSW, relationships between MSW's viscous strain rate and equivalent time were established, and then the viscous strain functions of MSW under different biodegradation conditions were deduced, and an equivalent-time-lines model for MSW settlement for two biodegradation conditions was developed, including the Type I model for the enhanced biodegradation condition and the Type II model for the normal biodegradation condition. The simulated compression results of laboratory and field compression tests under different biodegradation conditions were consistent with the measured data, which showed the reliability of both types of the equivalent-time-lines model for MSW. In addition, investigations of the long-term settlement of landfills from the literature indicated that the Type I model is suitable for predicting settlement in MSW landfills with a distinct biodegradation progress of MSW, a high content of organics in MSW, a short fill age or under an enhanced biodegradation environment; while the Type II model is good at predicting settlement in MSW landfills with a distinct progress of mechanical creep compression, a low content of organics in MSW, a long fill age or under a normal biodegradation condition. Furthermore, relationships between model parameters and the fill age of landfills were summarized. Finally, the similarities and differences between the equivalent-time-lines model for MSW and the stress

A Model of Solid Waste Management Based Multilateral Co-Operation in Semi-Urban Community

ERIC Educational Resources Information Center

Kanchanabhandhu, Chanchai; Woraphong, Seree

2016-01-01

The purpose of this research was to construct a model of solid waste management based on multilateral cooperation in semi-urban community. Its specific objectives were to 1) study the solid waste situation and involvement of community in the solid waste management in Wangtaku Sub-district, Muang District, Nakhon Pathom Province; 2) construct a…

Quantification of landfill methane using modified Intergovernmental Panel on Climate Change's waste model and error function analysis.

PubMed

Govindan, Siva Shangari; Agamuthu, P

2014-10-01

Waste management can be regarded as a cross-cutting environmental 'mega-issue'. Sound waste management practices support the provision of basic needs for general health, such as clean air, clean water and safe supply of food. In addition, climate change mitigation efforts can be achieved through reduction of greenhouse gas emissions from waste management operations, such as landfills. Landfills generate landfill gas, especially methane, as a result of anaerobic degradation of the degradable components of municipal solid waste. Evaluating the mode of generation and collection of landfill gas has posted a challenge over time. Scientifically, landfill gas generation rates are presently estimated using numerical models. In this study the Intergovernmental Panel on Climate Change's Waste Model is used to estimate the methane generated from a Malaysian sanitary landfill. Key parameters of the model, which are the decay rate and degradable organic carbon, are analysed in two different approaches; the bulk waste approach and waste composition approach. The model is later validated using error function analysis and optimum decay rate, and degradable organic carbon for both approaches were also obtained. The best fitting values for the bulk waste approach are a decay rate of 0.08 y(-1) and degradable organic carbon value of 0.12; and for the waste composition approach the decay rate was found to be 0.09 y(-1) and degradable organic carbon value of 0.08. From this validation exercise, the estimated error was reduced by 81% and 69% for the bulk waste and waste composition approach, respectively. In conclusion, this type of modelling could constitute a sensible starting point for landfills to introduce careful planning for efficient gas recovery in individual landfills. © The Author(s) 2014.

Prediction using patient comparison vs. modeling: a case study for mortality prediction.

PubMed

Hoogendoorn, Mark; El Hassouni, Ali; Mok, Kwongyen; Ghassemi, Marzyeh; Szolovits, Peter

2016-08-01

Information in Electronic Medical Records (EMRs) can be used to generate accurate predictions for the occurrence of a variety of health states, which can contribute to more pro-active interventions. The very nature of EMRs does make the application of off-the-shelf machine learning techniques difficult. In this paper, we study two approaches to making predictions that have hardly been compared in the past: (1) extracting high-level (temporal) features from EMRs and building a predictive model, and (2) defining a patient similarity metric and predicting based on the outcome observed for similar patients. We analyze and compare both approaches on the MIMIC-II ICU dataset to predict patient mortality and find that the patient similarity approach does not scale well and results in a less accurate model (AUC of 0.68) compared to the modeling approach (0.84). We also show that mortality can be predicted within a median of 72 hours.

Rainfall-runoff model for prediction of waterborne viral contamination in a small river catchment

NASA Astrophysics Data System (ADS)

Gelati, E.; Dommar, C.; Lowe, R.; Polcher, J.; Rodó, X.

2013-12-01

We present a lumped rainfall-runoff model aimed at providing useful information for the prediction of waterborne viral contamination in small rivers. Viral contamination of water bodies may occur because of the discharge of sewage effluents and of surface runoff over areas affected by animal waste loads. Surface runoff is caused by precipitation that cannot infiltrate due to its intensity and to antecedent soil water content. It may transport animal feces to adjacent water bodies and cause viral contamination. We model streamflow by separating it into two components: subsurface flow, which is produced by infiltrated precipitation; and surface runoff. The model estimates infiltrated and non-infiltrated precipitation and uses impulse-response functions to compute the corresponding fractions of streamflow. The developed methodologies are applied to the Glafkos river, whose catchment extends for 102 km2 and includes the city of Patra. Streamflow and precipitation observations are available at a daily time resolution. Waterborne virus concentration measurements were performed approximately every second week from the beginning of 2011 to mid 2012. Samples were taken at several locations: in river water upstream of Patras and in the urban area; in sea water at the river outlet and approximately 2 km south-west of Patras; in sewage effluents before and after treatment. The rainfall-runoff model was calibrated and validated using observed streamflow and precipitation data. The model contribution to waterborne viral contamination prediction was benchmarked by analyzing the virus concentration measurements together with the estimated surface runoff values. The presented methodology may be a first step towards the development of waterborne viral contamination alert systems. Predicting viral contamination of water bodies would benefit sectors such as water supply and tourism.

Waste Reduction Model (WARM) Material Descriptions and Data Sources

EPA Pesticide Factsheets

This page provides a summary of the materials included in EPA’s Waste Reduction Model (WARM). The page includes a list of materials, a description of the material as defined in the primary data source, and citations for primary data sources.

Modeling of 3d Space-time Surface of Potential Fields and Hydrogeologic Modeling of Nuclear Waste Disposal Sites

NASA Astrophysics Data System (ADS)

Shestopalov, V.; Bondarenko, Y.; Zayonts, I.; Rudenko, Y.

extracted from the total vertical and hori- zontal gradient respectively, both shaded from the 5 northeast to 355 northwest. The dip of multi-layer surfaces indicates the down -"gradient" direction in the fields. The methodology of 3D STSI is based on the analysis of vertical and horizontal anisotropy of gravity and magnetic fields, as well as of multi-layer 3D space-time surface model (3D STSM) of the stress fields. The 3D STSM is multi-layer topology structure of 1 lineaments or gradients (edges) and surfaces calculated by uniform matrices of the geophysical fields. One of the information components of the stress fields character- istics is the aspects and slopes for compressive and tensile stresses. Overlaying of the 3D STSI and lineaments with maps of multi-layer gradients enables to create highly reliable 3D Space-Time Kinematic Model "3D STKM". The analysis of 3D STKM in- cluded: - the space-time reconstruct of forces direction and strain distribution scheme during formation of geological structures and structural paragenesis (lineaments) of potential fields; - predict the real location of expected tectonic dislocations, zones of rock fracturing and disintegration, and mass-stable blocks. Based on these data, the 3D STSM are drawn which reflect the geodynamics of territory development on the ground of paleotectonic reconstruction of successive activity stages having formed the present-day lithosphere. Thus three-dimensional STSM allows to construct an un- mixing geodynamic processes in any interval of fixed space-time in coordinates x, y, t(z). The integrated of the 3D STSM and 3D seismic models enables also to create structural-kinematic and geodynamic maps of the Earth's crust at different depth. As a result, the classification of CNPP areas is performed into zones of compressive and tensile stresses characterized by enhanced permeability of rocks, and zones of consoli- dation with minimal rocks permeability. In addition, the vertically alternating zones of

The component slope linear model for calculating intensive partial molar properties /application to waste glasses and aluminate solutions

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

Reynolds, Jacob G.

2013-01-11

Partial molar properties are the changes occurring when the fraction of one component is varied while the fractions of all other component mole fractions change proportionally. They have many practical and theoretical applications in chemical thermodynamics. Partial molar properties of chemical mixtures are difficult to measure because the component mole fractions must sum to one, so a change in fraction of one component must be offset with a change in one or more other components. Given that more than one component fraction is changing at a time, it is difficult to assign a change in measured response to a changemore » in a single component. In this study, the Component Slope Linear Model (CSLM), a model previously published in the statistics literature, is shown to have coefficients that correspond to the intensive partial molar properties. If a measured property is plotted against the mole fraction of a component while keeping the proportions of all other components constant, the slope at any given point on a graph of this curve is the partial molar property for that constituent. Actually plotting this graph has been used to determine partial molar properties for many years. The CSLM directly includes this slope in a model that predicts properties as a function of the component mole fractions. This model is demonstrated by applying it to the constant pressure heat capacity data from the NaOH-NaAl(OH){sub 4}-H{sub 2}O system, a system that simplifies Hanford nuclear waste. The partial molar properties of H{sub 2}O, NaOH, and NaAl(OH){sub 4} are determined. The equivalence of the CSLM and the graphical method is verified by comparing results determined by the two methods. The CSLM model has been previously used to predict the liquidus temperature of spinel crystals precipitated from Hanford waste glass. Those model coefficients are re-interpreted here as the partial molar spinel liquidus temperature of the glass components.« less

Comparative evaluation of anaerobic digestion for sewage sludge and various organic wastes with simple modeling.

PubMed

Hidaka, Taira; Wang, Feng; Tsumori, Jun

2015-09-01

Anaerobic co-digestion of sewage sludge and other organic wastes, such as kitchen garbage, food waste, and agricultural waste, at a wastewater treatment plant (WWTP) is a promising method for both energy and material recovery. Substrate characteristics and the anaerobic digestion performance of sewage sludge and various organic wastes were compared using experiments and modeling. Co-digestion improved the value of digested sewage sludge as a fertilizer. The relationship between total and soluble elemental concentrations was correlated with the periodic table: most Na and K (alkali metals) were soluble, and around 20-40% of Mg and around 10-20% of Ca (alkaline earth metals) were soluble. The ratio of biodegradable chemical oxygen demand of organic wastes was 65-90%. The methane conversion ratio and methane production rate under mesophilic conditions were evaluated using a simplified mathematical model. There was reasonably close agreement between the model simulations and the experimental results in terms of methane production and nitrogen concentration. These results provide valuable information and indicate that the model can be used as a pre-evaluation tool to facilitate the introduction of co-digestion at WWTPs. Copyright © 2015 Elsevier Ltd. All rights reserved.

Prediction skill of rainstorm events over India in the TIGGE weather prediction models

NASA Astrophysics Data System (ADS)

Karuna Sagar, S.; Rajeevan, M.; Vijaya Bhaskara Rao, S.; Mitra, A. K.

2017-12-01

Extreme rainfall events pose a serious threat of leading to severe floods in many countries worldwide. Therefore, advance prediction of its occurrence and spatial distribution is very essential. In this paper, an analysis has been made to assess the skill of numerical weather prediction models in predicting rainstorms over India. Using gridded daily rainfall data set and objective criteria, 15 rainstorms were identified during the monsoon season (June to September). The analysis was made using three TIGGE (THe Observing System Research and Predictability Experiment (THORPEX) Interactive Grand Global Ensemble) models. The models considered are the European Centre for Medium-Range Weather Forecasts (ECMWF), National Centre for Environmental Prediction (NCEP) and the UK Met Office (UKMO). Verification of the TIGGE models for 43 observed rainstorm days from 15 rainstorm events has been made for the period 2007-2015. The comparison reveals that rainstorm events are predictable up to 5 days in advance, however with a bias in spatial distribution and intensity. The statistical parameters like mean error (ME) or Bias, root mean square error (RMSE) and correlation coefficient (CC) have been computed over the rainstorm region using the multi-model ensemble (MME) mean. The study reveals that the spread is large in ECMWF and UKMO followed by the NCEP model. Though the ensemble spread is quite small in NCEP, the ensemble member averages are not well predicted. The rank histograms suggest that the forecasts are under prediction. The modified Contiguous Rain Area (CRA) technique was used to verify the spatial as well as the quantitative skill of the TIGGE models. Overall, the contribution from the displacement and pattern errors to the total RMSE is found to be more in magnitude. The volume error increases from 24 hr forecast to 48 hr forecast in all the three models.

Municipal household solid waste fee based on an increasing block pricing model in Beijing, China.

PubMed

Chu, Zhujie; Wu, Yunga; Zhuang, Jun

2017-03-01

This article aims to design an increasing block pricing model to estimate the waste fee with the consideration of the goals and principles of municipal household solid waste pricing. The increasing block pricing model is based on the main consideration of the per capita disposable income of urban residents, household consumption expenditure, production rate of waste disposal industry, and inflation rate. The empirical analysis is based on survey data of 5000 households in Beijing, China. The results indicate that the current uniform price of waste disposal is set too high for low-income people, and waste fees to the household disposable income or total household spending ratio are too low for the medium- and high-income families. An increasing block pricing model can prevent this kind of situation, and not only solve the problem of lack of funds, but also enhance the residents' awareness of environmental protection. A comparative study based on the grey system model is made by having a preliminary forecast for the waste emissions reduction effect of the pay-as-you-throw programme in the next 5 years of Beijing, China. The results show that the effect of the pay-as-you-throw programme is not only to promote the energy conservation and emissions reduction, but also giving a further improvement of the environmental quality.

Pyrolysis kinetics behavior of solid tire wastes available in Bangladesh.

PubMed

Islam, M Rofiqul; Haniu, H; Fardoushi, J

2009-02-01

Pyrolysis kinetics of available bicycle/rickshaw, motorcycle and truck tire wastes in Bangladesh have been investigated thermogravimetrically in a nitrogen atmosphere at heating rates of 10 and 60 degrees C/min over a temperature range of 30-800 degrees C. The three tire wastes exhibited similar behaviors in that, when heating rate was increased, the initial reaction temperature decreased but the reaction range and reaction rate increased. The percentage of total weight loss was higher for truck tire waste and lower for bicycle/rickshaw tire waste. The pyrolysis of truck tire waste was found to be easier than that of bicycle/rickshaw and motorcycle tire wastes while it was comparatively more difficult for motorcycle tire waste. The overall rate equation for the three tire wastes has been modeled satisfactorily by one simplified equation from which the kinetic parameters of unreacted materials based on the Arrhenius form can be determined. The predicted rate equation compares fairly well with the measured TG and DTG data. DTA curves for all of the samples show that the degradation reactions are three main exotherms and one endotherm.

Predicting climate-induced range shifts: model differences and model reliability.

Treesearch

Joshua J. Lawler; Denis White; Ronald P. Neilson; Andrew R. Blaustein

2006-01-01

Predicted changes in the global climate are likely to cause large shifts in the geographic ranges of many plant and animal species. To date, predictions of future range shifts have relied on a variety of modeling approaches with different levels of model accuracy. Using a common data set, we investigated the potential implications of alternative modeling approaches for...

Comparing field investigations with laboratory models to predict landfill leachate emissions

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

Fellner, Johann; Doeberl, Gernot; Allgaier, Gerhard

2009-06-15

Investigations into laboratory reactors and landfills are used for simulating and predicting emissions from municipal solid waste landfills. We examined water flow and solute transport through the same waste body for different volumetric scales (laboratory experiment: 0.08 m{sup 3}, landfill: 80,000 m{sup 3}), and assessed the differences in water flow and leachate emissions of chloride, total organic carbon and Kjeldahl nitrogen. The results indicate that, due to preferential pathways, the flow of water in field-scale landfills is less uniform than in laboratory reactors. Based on tracer experiments, it can be discerned that in laboratory-scale experiments around 40% of pore watermore » participates in advective solute transport, whereas this fraction amounts to less than 0.2% in the investigated full-scale landfill. Consequences of the difference in water flow and moisture distribution are: (1) leachate emissions from full-scale landfills decrease faster than predicted by laboratory experiments, and (2) the stock of materials remaining in the landfill body, and thus the long-term emission potential, is likely to be underestimated by laboratory landfill simulations.« less

Atmospheric prediction model survey

NASA Technical Reports Server (NTRS)

Wellck, R. E.

1976-01-01

As part of the SEASAT Satellite program of NASA, a survey of representative primitive equation atmospheric prediction models that exist in the world today was written for the Jet Propulsion Laboratory. Seventeen models developed by eleven different operational and research centers throughout the world are included in the survey. The surveys are tutorial in nature describing the features of the various models in a systematic manner.

An Interoceptive Predictive Coding Model of Conscious Presence

PubMed Central

Seth, Anil K.; Suzuki, Keisuke; Critchley, Hugo D.

2011-01-01

We describe a theoretical model of the neurocognitive mechanisms underlying conscious presence and its disturbances. The model is based on interoceptive prediction error and is informed by predictive models of agency, general models of hierarchical predictive coding and dopaminergic signaling in cortex, the role of the anterior insular cortex (AIC) in interoception and emotion, and cognitive neuroscience evidence from studies of virtual reality and of psychiatric disorders of presence, specifically depersonalization/derealization disorder. The model associates presence with successful suppression by top-down predictions of informative interoceptive signals evoked by autonomic control signals and, indirectly, by visceral responses to afferent sensory signals. The model connects presence to agency by allowing that predicted interoceptive signals will depend on whether afferent sensory signals are determined, by a parallel predictive-coding mechanism, to be self-generated or externally caused. Anatomically, we identify the AIC as the likely locus of key neural comparator mechanisms. Our model integrates a broad range of previously disparate evidence, makes predictions for conjoint manipulations of agency and presence, offers a new view of emotion as interoceptive inference, and represents a step toward a mechanistic account of a fundamental phenomenological property of consciousness. PMID:22291673

Simulated Annealing Based Hybrid Forecast for Improving Daily Municipal Solid Waste Generation Prediction

PubMed Central

Song, Jingwei; He, Jiaying; Zhu, Menghua; Tan, Debao; Zhang, Yu; Ye, Song; Shen, Dingtao; Zou, Pengfei

2014-01-01

A simulated annealing (SA) based variable weighted forecast model is proposed to combine and weigh local chaotic model, artificial neural network (ANN), and partial least square support vector machine (PLS-SVM) to build a more accurate forecast model. The hybrid model was built and multistep ahead prediction ability was tested based on daily MSW generation data from Seattle, Washington, the United States. The hybrid forecast model was proved to produce more accurate and reliable results and to degrade less in longer predictions than three individual models. The average one-week step ahead prediction has been raised from 11.21% (chaotic model), 12.93% (ANN), and 12.94% (PLS-SVM) to 9.38%. Five-week average has been raised from 13.02% (chaotic model), 15.69% (ANN), and 15.92% (PLS-SVM) to 11.27%. PMID:25301508

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

Assessment and modeling of E-waste generation based on growth rate from different telecom companies in the State of Kuwait.

PubMed

Al-Anzi, Bader S; Al-Burait, Abdul Aziz; Thomas, Ashly; Ong, Chi Siang

2017-12-01

The present work assesses the production rate of cell phone e-waste in Kuwait by comparing the number of clients in three telecommunication service providers like Zain, Ooredoo, and Viva in the state of Kuwait over a period of 7 years from 2008 to 2015. An online survey was conducted to evaluate the growth in the number of clients in three cell phone companies, and the data analysis was carried out using statistical package for the social sciences (SPSS) software. The prediction of the growth percentage of the number of clients in each telecommunication company was analyzed using analysis of variance (ANOVA) test and followed by the regression model. The study shows that there is an increase in the number of clients in all three companies (Zain, Ooredoo, and Viva) between year 2008 and 2015, and it was estimated that approximately 7.9 million cell phone users would be achieved in the first quarter of 2015. Based on this predicted number of cell phone users, the production of e-waste would be 3 kt per year with an average growth of 12.7%.

PredictABEL: an R package for the assessment of risk prediction models.

PubMed

Kundu, Suman; Aulchenko, Yurii S; van Duijn, Cornelia M; Janssens, A Cecile J W

2011-04-01

The rapid identification of genetic markers for multifactorial diseases from genome-wide association studies is fuelling interest in investigating the predictive ability and health care utility of genetic risk models. Various measures are available for the assessment of risk prediction models, each addressing a different aspect of performance and utility. We developed PredictABEL, a package in R that covers descriptive tables, measures and figures that are used in the analysis of risk prediction studies such as measures of model fit, predictive ability and clinical utility, and risk distributions, calibration plot and the receiver operating characteristic plot. Tables and figures are saved as separate files in a user-specified format, which include publication-quality EPS and TIFF formats. All figures are available in a ready-made layout, but they can be customized to the preferences of the user. The package has been developed for the analysis of genetic risk prediction studies, but can also be used for studies that only include non-genetic risk factors. PredictABEL is freely available at the websites of GenABEL ( http://www.genabel.org ) and CRAN ( http://cran.r-project.org/).

Risk prediction model: Statistical and artificial neural network approach

NASA Astrophysics Data System (ADS)

Paiman, Nuur Azreen; Hariri, Azian; Masood, Ibrahim

2017-04-01

Prediction models are increasingly gaining popularity and had been used in numerous areas of studies to complement and fulfilled clinical reasoning and decision making nowadays. The adoption of such models assist physician's decision making, individual's behavior, and consequently improve individual outcomes and the cost-effectiveness of care. The objective of this paper is to reviewed articles related to risk prediction model in order to understand the suitable approach, development and the validation process of risk prediction model. A qualitative review of the aims, methods and significant main outcomes of the nineteen published articles that developed risk prediction models from numerous fields were done. This paper also reviewed on how researchers develop and validate the risk prediction models based on statistical and artificial neural network approach. From the review done, some methodological recommendation in developing and validating the prediction model were highlighted. According to studies that had been done, artificial neural network approached in developing the prediction model were more accurate compared to statistical approach. However currently, only limited published literature discussed on which approach is more accurate for risk prediction model development.

The potential role of aerobic biological waste treatment in regenerative life support systems

NASA Technical Reports Server (NTRS)

Shuler, M. L.; Nafis, D.; Sze, E.

1981-01-01

The purpose of the paper is to make a preliminary assessment of the feasibility of using aerobic biological waste treatment in closed systems. Issues that are addressed in this paper are: (1) how high a degree of material balance is possible, (2) how much might such a system weigh, and (3) how would system closure and weight be affected if animals were included in the system. A computer model has been developed to calculate for different scenarios the compositions and amounts of the streams entering or leaving the waste treatment system and to estimate the launch weight of such a system. A bench scale apparatus has been built to mimic the proposed waste treatment system; the experiments are used to verify model predictions and to improve model parameter estimations.

Predictive models of moth development

USDA-ARS?s Scientific Manuscript database

Degree-day models link ambient temperature to insect life-stages, making such models valuable tools in integrated pest management. These models increase management efficacy by predicting pest phenology. In Wisconsin, the top insect pest of cranberry production is the cranberry fruitworm, Acrobasis v...

Isothermal approach to predict the removal efficiency of β-carotene adsorption from CPO using activated carbon produced from tea waste

NASA Astrophysics Data System (ADS)

Harahap, S. A. A.; Nazar, A.; Yunita, M.; Pasaribu, RA; Panjaitan, F.; Yanuar, F.; Misran, E.

2018-02-01

Adsorption of β-carotene in crude palm oil (CPO) was studied using activated carbon produced from tea waste (ACTW) an adsorbent. Isothermal studies were carried out at 60 °C with the ratio of activated carbon to CPO were 1:3, 1:4, 1:5, and 1:6, respectively. The ACTW showed excellent performance as the percentage of adsorption of β-carotene from CPO was > 99%. The best percentage removal (R) was achieved at ACTW to CPO ratio equal to 1:3, which was 99.61%. The appropriate isotherm model for this study was Freundlich isotherm model. The combination of Freundlich isotherm equation and mass balance equation showed a good agreement when validated to the experimental data. The equation subsequently executed to predict the removal efficiency under given sets of operating conditions. At a targetted R, CPO volume can be estimated for a certain initial concentration β-carotene in CPO C0 and mass of ACTW adsorbent M used.

A dynamic model for assessing the effects of management strategies on the reduction of construction and demolition waste.

PubMed

Yuan, Hongping; Chini, Abdol R; Lu, Yujie; Shen, Liyin

2012-03-01

During the past few decades, construction and demolition (C&D) waste has received increasing attention from construction practitioners and researchers worldwide. A plethora of research regarding C&D waste management has been published in various academic journals. However, it has been determined that existing studies with respect to C&D waste reduction are mainly carried out from a static perspective, without considering the dynamic and interdependent nature of the whole waste reduction system. This might lead to misunderstanding about the actual effect of implementing any waste reduction strategies. Therefore, this research proposes a model that can serve as a decision support tool for projecting C&D waste reduction in line with the waste management situation of a given construction project, and more importantly, as a platform for simulating effects of various management strategies on C&D waste reduction. The research is conducted using system dynamics methodology, which is a systematic approach that deals with the complexity - interrelationships and dynamics - of any social, economic and managerial system. The dynamic model integrates major variables that affect C&D waste reduction. In this paper, seven causal loop diagrams that can deepen understanding about the feedback relationships underlying C&D waste reduction system are firstly presented. Then a stock-flow diagram is formulated by using software for system dynamics modeling. Finally, a case study is used to illustrate the validation and application of the proposed model. Results of the case study not only built confidence in the model so that it can be used for quantitative analysis, but also assessed and compared the effect of three designed policy scenarios on C&D waste reduction. One major contribution of this study is the development of a dynamic model for evaluating C&D waste reduction strategies under various scenarios, so that best management strategies could be identified before being implemented

Three multimedia models used at hazardous and radioactive waste sites

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

Moskowitz, P.D.; Pardi, R.; Fthenakis, V.M.

1996-02-01

Multimedia models are used commonly in the initial phases of the remediation process where technical interest is focused on determining the relative importance of various exposure pathways. This report provides an approach for evaluating and critically reviewing the capabilities of multimedia models. This study focused on three specific models MEPAS Version 3.0, MMSOILS Version 2.2, and PRESTO-EPA-CPG Version 2.0. These models evaluate the transport and fate of contaminants from source to receptor through more than a single pathway. The presence of radioactive and mixed wastes at a site poses special problems. Hence, in this report, restrictions associated with the selectionmore » and application of multimedia models for sites contaminated with radioactive and mixed wastes are highlighted. This report begins with a brief introduction to the concept of multimedia modeling, followed by an overview of the three models. The remaining chapters present more technical discussions of the issues associated with each compartment and their direct application to the specific models. In these analyses, the following components are discussed: source term; air transport; ground water transport; overland flow, runoff, and surface water transport; food chain modeling; exposure assessment; dosimetry/risk assessment; uncertainty; default parameters. The report concludes with a description of evolving updates to the model; these descriptions were provided by the model developers.« less

Mathematical Model Developed for Environmental Samples: Prediction of GC/MS Dioxin TEQ from XDS-CALUX Bioassay Data

PubMed Central

Brown, David J.; Orelien, Jean; Gordon, John D.; Chu, Andrew C.; Chu, Michael D.; Nakamura, Masafumi; Handa, Hiroshi; Kayama, Fujio; Denison, Michael S.; Clark, George C.

2010-01-01

Remediation of hazardous waste sites requires efficient and cost-effective methods to assess the extent of contamination by toxic substances including dioxin-like chemicals. Traditionally, dioxin-like contamination has been assessed by gas chromatography/high-resolution mass spectrometry (GC/MS) analysis for specific polychlorinated dibenzo-p-dioxins, dibenzofurans, and biphenyl congeners. Toxic equivalency factors for these congeners are then used to estimate the overall dioxin toxic equivalency (TEQ) of complex mixtures found in samples. The XDS-CALUX bioassay estimates contamination by dioxin-like chemicals in a sample extract by measuring expression of a sensitive reporter gene in genetically engineered cells. The output of the XDS-CALUX assay is a CALUX-TEQ value, calibrated based on TCDD standards. Soil samples taken from a variety of hazardous waste sites were measured using the XDS-CALUX bioassay and GC/MS. TEQ and CALUX-TEQ from these methods were compared, and a mathematical model was developed describing the relationship between these two data sets: log(TEQ) = 0.654 × log(CALUX-TEQ) + 0.058-(log(CALUX-TEQ))2. Applying this equation to these samples showed that predicted and GC/MS measured TEQ values strongly correlate (R2 = 0.876) and that TEQ values predicted from CALUX-TEQ were on average nearly identical to the GC/MS-TEQ. The ability of XDS-CALUX bioassay data to predict GC/MS-derived TEQ data should make this procedure useful in risk assessment and management decisions. PMID:17626436

Enhancing Flood Prediction Reliability Using Bayesian Model Averaging

NASA Astrophysics Data System (ADS)

Liu, Z.; Merwade, V.

2017-12-01

Uncertainty analysis is an indispensable part of modeling the hydrology and hydrodynamics of non-idealized environmental systems. Compared to reliance on prediction from one model simulation, using on ensemble of predictions that consider uncertainty from different sources is more reliable. In this study, Bayesian model averaging (BMA) is applied to Black River watershed in Arkansas and Missouri by combining multi-model simulations to get reliable deterministic water stage and probabilistic inundation extent predictions. The simulation ensemble is generated from 81 LISFLOOD-FP subgrid model configurations that include uncertainty from channel shape, channel width, channel roughness and discharge. Model simulation outputs are trained with observed water stage data during one flood event, and BMA prediction ability is validated for another flood event. Results from this study indicate that BMA does not always outperform all members in the ensemble, but it provides relatively robust deterministic flood stage predictions across the basin. Station based BMA (BMA_S) water stage prediction has better performance than global based BMA (BMA_G) prediction which is superior to the ensemble mean prediction. Additionally, high-frequency flood inundation extent (probability greater than 60%) in BMA_G probabilistic map is more accurate than the probabilistic flood inundation extent based on equal weights.

The use of a paper/wood/plastics mixing as a model waste to study the incineration of municipal solid waste in fluidized beds

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

Desroches-Ducarne, E.; Marty, E.; Martin, G.

1997-12-31

Municipal Solid Waste (MSW) incineration in fluidized beds has recently become the subject of intense research. In this paper, the authors chose to elaborate a simple model waste prepared with 4 of the main MSW components (paper, wood, PE, and PVC). The behaviors of typical French MSW and model waste during their combustion in a fluidized bed incinerator were studied. To establish the comparison, the emissions of NO, N{sub 2}O, SO{sub 2} and HCl were investigated. Moreover, experiments were performed according to statistical experimental designs to examine the effect of various operating parameters including bed temperature, excess air, limestone feedingmore » rate and waste moisture. On a qualitative point of view, the impact of the combustion conditions on the pollutants emissions was found to be the same for the two wastes. Bed temperature and excess air have, in both cases, an important impact on NO and N{sub 2}O emissions. Nitrogen oxides production was also directly related to limestone feed content. Very high sulphur and chlorine retention was obtained when limestone was added at much larger excess to the feed. N{sub 2}O emissions are less important during the model waste combustion. The nitrogen functionality seems to give rise to such a phenomenon. In MSW, nitrogen is included in high molecules which is released for a great part as HCN or remains in char, which are the main sources of N{sub 2}O. To confirm this assumption, tests for measuring the HCN/NH{sub 3} ratio for each fuel were performed.« less

Tobacco industry responsibility for butts: a Model Tobacco Waste Act

PubMed Central

Curtis, Clifton; Novotny, Thomas E; Lee, Kelley; Freiberg, Mike; McLaughlin, Ian

2017-01-01

Cigarette butts and other postconsumer products from tobacco use are the most common waste elements picked up worldwide each year during environmental cleanups. Under the environmental principle of Extended Producer Responsibility, tobacco product manufacturers may be held responsible for collection, transport, processing and safe disposal of tobacco product waste (TPW). Legislation has been applied to other toxic and hazardous postconsumer waste products such as paints, pesticide containers and unused pharmaceuticals, to reduce, prevent and mitigate their environmental impacts. Additional product stewardship (PS) requirements may be necessary for other stakeholders and beneficiaries of tobacco product sales and use, especially suppliers, retailers and consumers, in order to ensure effective TPW reduction. This report describes how a Model Tobacco Waste Act may be adopted by national and subnational jurisdictions to address the environmental impacts of TPW. Such a law will also reduce tobacco use and its health consequences by raising attention to the environmental hazards of TPW, increasing the price of tobacco products, and reducing the number of tobacco product retailers. PMID:26931480

PREDICTING CLIMATE-INDUCED RANGE SHIFTS: MODEL DIFFERENCES AND MODEL RELIABILITY

EPA Science Inventory

Predicted changes in the global climate are likely to cause large shifts in the geographic ranges of many plant and animal species. To date, predictions of future range shifts have relied on a variety of modeling approaches with different levels of model accuracy. Using a common ...

Glass Property Models, Constraints, and Formulation Approaches for Vitrification of High-Level Nuclear Wastes at the US Hanford Site

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

Kim, Dong-Sang

2015-03-02

The legacy nuclear wastes stored in underground tanks at the US Department of Energy’s Hanford site is planned to be separated into high-level waste and low-activity waste fractions and vitrified separately. Formulating optimized glass compositions that maximize the waste loading in glass is critical for successful and economical treatment and immobilization of nuclear wastes. Glass property-composition models have been developed and applied to formulate glass compositions for various objectives for the past several decades. The property models with associated uncertainties and combined with composition and property constraints have been used to develop preliminary glass formulation algorithms designed for vitrification processmore » control and waste form qualification at the planned waste vitrification plant. This paper provides an overview of current status of glass property-composition models, constraints applicable to Hanford waste vitrification, and glass formulation approaches that have been developed for vitrification of hazardous and highly radioactive wastes stored at the Hanford site.« less

Predictive Validation of an Influenza Spread Model

PubMed Central

Hyder, Ayaz; Buckeridge, David L.; Leung, Brian

2013-01-01

Background Modeling plays a critical role in mitigating impacts of seasonal influenza epidemics. Complex simulation models are currently at the forefront of evaluating optimal mitigation strategies at multiple scales and levels of organization. Given their evaluative role, these models remain limited in their ability to predict and forecast future epidemics leading some researchers and public-health practitioners to question their usefulness. The objective of this study is to evaluate the predictive ability of an existing complex simulation model of influenza spread. Methods and Findings We used extensive data on past epidemics to demonstrate the process of predictive validation. This involved generalizing an individual-based model for influenza spread and fitting it to laboratory-confirmed influenza infection data from a single observed epidemic (1998–1999). Next, we used the fitted model and modified two of its parameters based on data on real-world perturbations (vaccination coverage by age group and strain type). Simulating epidemics under these changes allowed us to estimate the deviation/error between the expected epidemic curve under perturbation and observed epidemics taking place from 1999 to 2006. Our model was able to forecast absolute intensity and epidemic peak week several weeks earlier with reasonable reliability and depended on the method of forecasting-static or dynamic. Conclusions Good predictive ability of influenza epidemics is critical for implementing mitigation strategies in an effective and timely manner. Through the process of predictive validation applied to a current complex simulation model of influenza spread, we provided users of the model (e.g. public-health officials and policy-makers) with quantitative metrics and practical recommendations on mitigating impacts of seasonal influenza epidemics. This methodology may be applied to other models of communicable infectious diseases to test and potentially improve their predictive

Genome-Based Models to Optimize In Situ Bioremediation of Uranium and Harvesting Electrical Energy from Waste Organic Matter

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

Lovley, Derek R

2012-12-28

The goal of this research was to provide computational tools to predictively model the behavior of two microbial communities of direct relevance to Department of Energy interests: 1) the microbial community responsible for in situ bioremediation of uranium in contaminated subsurface environments; and 2) the microbial community capable of harvesting electricity from waste organic matter and renewable biomass. During this project the concept of microbial electrosynthesis, a novel form of artificial photosynthesis for the direct production of fuels and other organic commodities from carbon dioxide and water was also developed and research was expanded into this area as well.

SEM Model Medical Solid Waste Hospital Management In Medan City

NASA Astrophysics Data System (ADS)

Simarmata, Verawaty; Pandia, Setiaty; Mawengkang, Herman

2018-01-01

In daily activities, hospitals, as one of the important health care unit, generate both medical solid waste and non-medical solid waste. The occurrence of medical solid waste could be from the results of treatment activities, such as, in the treatment room for a hospital inpatient, general clinic, a dental clinic, a mother and child clinic, laboratories and pharmacies. Most of the medical solid waste contains infectious and hazardous materials. Therefore it should be managed properly, otherwise it could be a source of new infectious for the community around the hospital as well as for health workers themselves. Efforts surveillance of various environmental factors need to be applied in accordance with the principles of sanitation focuses on environmental cleanliness. One of the efforts that need to be done in improving the quality of the environment is to undertake waste management activities, because with proper waste management is the most important in order to achieve an optimal degree of human health. Health development in Indonesian aims to achieve a future in which the Indonesian people live in a healthy environment, its people behave clean and healthy, able to reach quality health services, fair and equitable, so as to have optimal health status, health development paradigm anchored to the healthy. The healthy condition of the individual and society can be influenced by the environment. Poor environmental quality is a cause of various health problems. Efforts surveillance of various environmental factors need to be applied in accordance with the principles of sanitation focuses on environmental cleanliness. This paper proposes a model for managing the medical solid waste in hospitals in Medan city, in order to create healthy environment around hospitals.

Sweat loss prediction using a multi-model approach

NASA Astrophysics Data System (ADS)

Xu, Xiaojiang; Santee, William R.

2011-07-01

A new multi-model approach (MMA) for sweat loss prediction is proposed to improve prediction accuracy. MMA was computed as the average of sweat loss predicted by two existing thermoregulation models: i.e., the rational model SCENARIO and the empirical model Heat Strain Decision Aid (HSDA). Three independent physiological datasets, a total of 44 trials, were used to compare predictions by MMA, SCENARIO, and HSDA. The observed sweat losses were collected under different combinations of uniform ensembles, environmental conditions (15-40°C, RH 25-75%), and exercise intensities (250-600 W). Root mean square deviation (RMSD), residual plots, and paired t tests were used to compare predictions with observations. Overall, MMA reduced RMSD by 30-39% in comparison with either SCENARIO or HSDA, and increased the prediction accuracy to 66% from 34% or 55%. Of the MMA predictions, 70% fell within the range of mean observed value ± SD, while only 43% of SCENARIO and 50% of HSDA predictions fell within the same range. Paired t tests showed that differences between observations and MMA predictions were not significant, but differences between observations and SCENARIO or HSDA predictions were significantly different for two datasets. Thus, MMA predicted sweat loss more accurately than either of the two single models for the three datasets used. Future work will be to evaluate MMA using additional physiological data to expand the scope of populations and conditions.

Referenceless perceptual fog density prediction model

NASA Astrophysics Data System (ADS)

Choi, Lark Kwon; You, Jaehee; Bovik, Alan C.

2014-02-01

We propose a perceptual fog density prediction model based on natural scene statistics (NSS) and "fog aware" statistical features, which can predict the visibility in a foggy scene from a single image without reference to a corresponding fogless image, without side geographical camera information, without training on human-rated judgments, and without dependency on salient objects such as lane markings or traffic signs. The proposed fog density predictor only makes use of measurable deviations from statistical regularities observed in natural foggy and fog-free images. A fog aware collection of statistical features is derived from a corpus of foggy and fog-free images by using a space domain NSS model and observed characteristics of foggy images such as low contrast, faint color, and shifted intensity. The proposed model not only predicts perceptual fog density for the entire image but also provides a local fog density index for each patch. The predicted fog density of the model correlates well with the measured visibility in a foggy scene as measured by judgments taken in a human subjective study on a large foggy image database. As one application, the proposed model accurately evaluates the performance of defog algorithms designed to enhance the visibility of foggy images.

Predictive models of forest dynamics.

PubMed

Purves, Drew; Pacala, Stephen

2008-06-13

Dynamic global vegetation models (DGVMs) have shown that forest dynamics could dramatically alter the response of the global climate system to increased atmospheric carbon dioxide over the next century. But there is little agreement between different DGVMs, making forest dynamics one of the greatest sources of uncertainty in predicting future climate. DGVM predictions could be strengthened by integrating the ecological realities of biodiversity and height-structured competition for light, facilitated by recent advances in the mathematics of forest modeling, ecological understanding of diverse forest communities, and the availability of forest inventory data.

Data-driven Climate Modeling and Prediction

NASA Astrophysics Data System (ADS)

Kondrashov, D. A.; Chekroun, M.

2016-12-01

Global climate models aim to simulate a broad range of spatio-temporal scales of climate variability with state vector having many millions of degrees of freedom. On the other hand, while detailed weather prediction out to a few days requires high numerical resolution, it is fairly clear that a major fraction of large-scale climate variability can be predicted in a much lower-dimensional phase space. Low-dimensional models can simulate and predict this fraction of climate variability, provided they are able to account for linear and nonlinear interactions between the modes representing large scales of climate dynamics, as well as their interactions with a much larger number of modes representing fast and small scales. This presentation will highlight several new applications by Multilayered Stochastic Modeling (MSM) [Kondrashov, Chekroun and Ghil, 2015] framework that has abundantly proven its efficiency in the modeling and real-time forecasting of various climate phenomena. MSM is a data-driven inverse modeling technique that aims to obtain a low-order nonlinear system of prognostic equations driven by stochastic forcing, and estimates both the dynamical operator and the properties of the driving noise from multivariate time series of observations or a high-end model's simulation. MSM leads to a system of stochastic differential equations (SDEs) involving hidden (auxiliary) variables of fast-small scales ranked by layers, which interact with the macroscopic (observed) variables of large-slow scales to model the dynamics of the latter, and thus convey memory effects. New MSM climate applications focus on development of computationally efficient low-order models by using data-adaptive decomposition methods that convey memory effects by time-embedding techniques, such as Multichannel Singular Spectrum Analysis (M-SSA) [Ghil et al. 2002] and recently developed Data-Adaptive Harmonic (DAH) decomposition method [Chekroun and Kondrashov, 2016]. In particular, new results

Quantitative structure-property relationships for predicting sorption of pharmaceuticals to sewage sludge during waste water treatment processes.

PubMed

Berthod, L; Whitley, D C; Roberts, G; Sharpe, A; Greenwood, R; Mills, G A

2017-02-01

Understanding the sorption of pharmaceuticals to sewage sludge during waste water treatment processes is important for understanding their environmental fate and in risk assessments. The degree of sorption is defined by the sludge/water partition coefficient (K d ). Experimental K d values (n=297) for active pharmaceutical ingredients (n=148) in primary and activated sludge were collected from literature. The compounds were classified by their charge at pH7.4 (44 uncharged, 60 positively and 28 negatively charged, and 16 zwitterions). Univariate models relating log K d to log K ow for each charge class showed weak correlations (maximum R 2 =0.51 for positively charged) with no overall correlation for the combined dataset (R 2 =0.04). Weaker correlations were found when relating log K d to log D ow . Three sets of molecular descriptors (Molecular Operating Environment, VolSurf and ParaSurf) encoding a range of physico-chemical properties were used to derive multivariate models using stepwise regression, partial least squares and Bayesian artificial neural networks (ANN). The best predictive performance was obtained with ANN, with R 2 =0.62-0.69 for these descriptors using the complete dataset. Use of more complex Vsurf and ParaSurf descriptors showed little improvement over Molecular Operating Environment descriptors. The most influential descriptors in the ANN models, identified by automatic relevance determination, highlighted the importance of hydrophobicity, charge and molecular shape effects in these sorbate-sorbent interactions. The heterogeneous nature of the different sewage sludges used to measure K d limited the predictability of sorption from physico-chemical properties of the pharmaceuticals alone. Standardization of test materials for the measurement of K d would improve comparability of data from different studies, in the long-term leading to better quality environmental risk assessments. Copyright © 2016 British Geological Survey, NERC. Published by

Thermal control of high energy nuclear waste, space option. [mathematical models

NASA Technical Reports Server (NTRS)

Peoples, J. A.

1979-01-01

Problems related to the temperature and packaging of nuclear waste material for disposal in space are explored. An approach is suggested for solving both problems with emphasis on high energy density waste material. A passive cooling concept is presented which utilized conduction rods that penetrate the inner core. Data are presented to illustrate the effectiveness of the rods and the limit of their capability. A computerized thermal model is discussed and developed for the cooling concept.

Modeling and Prediction of Fan Noise

NASA Technical Reports Server (NTRS)

Envia, Ed

2008-01-01

Fan noise is a significant contributor to the total noise signature of a modern high bypass ratio aircraft engine and with the advent of ultra high bypass ratio engines like the geared turbofan, it is likely to remain so in the future. As such, accurate modeling and prediction of the basic characteristics of fan noise are necessary ingredients in designing quieter aircraft engines in order to ensure compliance with ever more stringent aviation noise regulations. In this paper, results from a comprehensive study aimed at establishing the utility of current tools for modeling and predicting fan noise will be summarized. It should be emphasized that these tools exemplify present state of the practice and embody what is currently used at NASA and Industry for predicting fan noise. The ability of these tools to model and predict fan noise is assessed against a set of benchmark fan noise databases obtained for a range of representative fan cycles and operating conditions. Detailed comparisons between the predicted and measured narrowband spectral and directivity characteristics of fan nose will be presented in the full paper. General conclusions regarding the utility of current tools and recommendations for future improvements will also be given.

Finding Furfural Hydrogenation Catalysts via Predictive Modelling

PubMed Central

Strassberger, Zea; Mooijman, Maurice; Ruijter, Eelco; Alberts, Albert H; Maldonado, Ana G; Orru, Romano V A; Rothenberg, Gadi

2010-01-01

Abstract We combine multicomponent reactions, catalytic performance studies and predictive modelling to find transfer hydrogenation catalysts. An initial set of 18 ruthenium-carbene complexes were synthesized and screened in the transfer hydrogenation of furfural to furfurol with isopropyl alcohol complexes gave varied yields, from 62% up to >99.9%, with no obvious structure/activity correlations. Control experiments proved that the carbene ligand remains coordinated to the ruthenium centre throughout the reaction. Deuterium-labelling studies showed a secondary isotope effect (kH:kD=1.5). Further mechanistic studies showed that this transfer hydrogenation follows the so-called monohydride pathway. Using these data, we built a predictive model for 13 of the catalysts, based on 2D and 3D molecular descriptors. We tested and validated the model using the remaining five catalysts (cross-validation, R2=0.913). Then, with this model, the conversion and selectivity were predicted for four completely new ruthenium-carbene complexes. These four catalysts were then synthesized and tested. The results were within 3% of the model’s predictions, demonstrating the validity and value of predictive modelling in catalyst optimization. PMID:23193388

Clinical Predictive Modeling Development and Deployment through FHIR Web Services.

PubMed

Khalilia, Mohammed; Choi, Myung; Henderson, Amelia; Iyengar, Sneha; Braunstein, Mark; Sun, Jimeng

2015-01-01

Clinical predictive modeling involves two challenging tasks: model development and model deployment. In this paper we demonstrate a software architecture for developing and deploying clinical predictive models using web services via the Health Level 7 (HL7) Fast Healthcare Interoperability Resources (FHIR) standard. The services enable model development using electronic health records (EHRs) stored in OMOP CDM databases and model deployment for scoring individual patients through FHIR resources. The MIMIC2 ICU dataset and a synthetic outpatient dataset were transformed into OMOP CDM databases for predictive model development. The resulting predictive models are deployed as FHIR resources, which receive requests of patient information, perform prediction against the deployed predictive model and respond with prediction scores. To assess the practicality of this approach we evaluated the response and prediction time of the FHIR modeling web services. We found the system to be reasonably fast with one second total response time per patient prediction.

Clinical Predictive Modeling Development and Deployment through FHIR Web Services

PubMed Central

Khalilia, Mohammed; Choi, Myung; Henderson, Amelia; Iyengar, Sneha; Braunstein, Mark; Sun, Jimeng

2015-01-01

Clinical predictive modeling involves two challenging tasks: model development and model deployment. In this paper we demonstrate a software architecture for developing and deploying clinical predictive models using web services via the Health Level 7 (HL7) Fast Healthcare Interoperability Resources (FHIR) standard. The services enable model development using electronic health records (EHRs) stored in OMOP CDM databases and model deployment for scoring individual patients through FHIR resources. The MIMIC2 ICU dataset and a synthetic outpatient dataset were transformed into OMOP CDM databases for predictive model development. The resulting predictive models are deployed as FHIR resources, which receive requests of patient information, perform prediction against the deployed predictive model and respond with prediction scores. To assess the practicality of this approach we evaluated the response and prediction time of the FHIR modeling web services. We found the system to be reasonably fast with one second total response time per patient prediction. PMID:26958207

Estimation of methane emission rate changes using age-defined waste in a landfill site.

PubMed

Ishii, Kazuei; Furuichi, Toru

2013-09-01

Long term methane emissions from landfill sites are often predicted by first-order decay (FOD) models, in which the default coefficients of the methane generation potential and the methane generation rate given by the Intergovernmental Panel on Climate Change (IPCC) are usually used. However, previous studies have demonstrated the large uncertainty in these coefficients because they are derived from a calibration procedure under ideal steady-state conditions, not actual landfill site conditions. In this study, the coefficients in the FOD model were estimated by a new approach to predict more precise long term methane generation by considering region-specific conditions. In the new approach, age-defined waste samples, which had been under the actual landfill site conditions, were collected in Hokkaido, Japan (in cold region), and the time series data on the age-defined waste sample's methane generation potential was used to estimate the coefficients in the FOD model. The degradation coefficients were 0.0501/y and 0.0621/y for paper and food waste, and the methane generation potentials were 214.4 mL/g-wet waste and 126.7 mL/g-wet waste for paper and food waste, respectively. These coefficients were compared with the default coefficients given by the IPCC. Although the degradation coefficient for food waste was smaller than the default value, the other coefficients were within the range of the default coefficients. With these new coefficients to calculate methane generation, the long term methane emissions from the landfill site was estimated at 1.35×10(4)m(3)-CH(4), which corresponds to approximately 2.53% of the total carbon dioxide emissions in the city (5.34×10(5)t-CO(2)/y). Copyright © 2013 Elsevier Ltd. All rights reserved.

The Component Slope Linear Model for Calculating Intensive Partial Molar Properties: Application to Waste Glasses and Aluminate Solutions - 13099

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

Reynolds, Jacob G.

2013-07-01

Partial molar properties are the changes occurring when the fraction of one component is varied while the fractions of all other component mole fractions change proportionally. They have many practical and theoretical applications in chemical thermodynamics. Partial molar properties of chemical mixtures are difficult to measure because the component mole fractions must sum to one, so a change in fraction of one component must be offset with a change in one or more other components. Given that more than one component fraction is changing at a time, it is difficult to assign a change in measured response to a changemore » in a single component. In this study, the Component Slope Linear Model (CSLM), a model previously published in the statistics literature, is shown to have coefficients that correspond to the intensive partial molar properties. If a measured property is plotted against the mole fraction of a component while keeping the proportions of all other components constant, the slope at any given point on a graph of this curve is the partial molar property for that constituent. Actually plotting this graph has been used to determine partial molar properties for many years. The CSLM directly includes this slope in a model that predicts properties as a function of the component mole fractions. This model is demonstrated by applying it to the constant pressure heat capacity data from the NaOHNaAl(OH){sub 4}-H{sub 2}O system, a system that simplifies Hanford nuclear waste. The partial molar properties of H{sub 2}O, NaOH, and NaAl(OH){sub 4} are determined. The equivalence of the CSLM and the graphical method is verified by comparing results determined by the two methods. The CSLM model has been previously used to predict the liquidus temperature of spinel crystals precipitated from Hanford waste glass. Those model coefficients are re-interpreted here as the partial molar spinel liquidus temperature of the glass components. (authors)« less

Thermal barrier coating life prediction model development

NASA Technical Reports Server (NTRS)

Hillery, R. V.; Pilsner, B. H.; Mcknight, R. L.; Cook, T. S.; Hartle, M. S.

1988-01-01

This report describes work performed to determine the predominat modes of degradation of a plasma sprayed thermal barrier coating system and to develop and verify life prediction models accounting for these degradation modes. The primary TBC system consisted of a low pressure plasma sprayed NiCrAlY bond coat, an air plasma sprayed ZrO2-Y2O3 top coat, and a Rene' 80 substrate. The work was divided into 3 technical tasks. The primary failure mode to be addressed was loss of the zirconia layer through spalling. Experiments showed that oxidation of the bond coat is a significant contributor to coating failure. It was evident from the test results that the species of oxide scale initially formed on the bond coat plays a role in coating degradation and failure. It was also shown that elevated temperature creep of the bond coat plays a role in coating failure. An empirical model was developed for predicting the test life of specimens with selected coating, specimen, and test condition variations. In the second task, a coating life prediction model was developed based on the data from Task 1 experiments, results from thermomechanical experiments performed as part of Task 2, and finite element analyses of the TBC system during thermal cycles. The third and final task attempted to verify the validity of the model developed in Task 2. This was done by using the model to predict the test lives of several coating variations and specimen geometries, then comparing these predicted lives to experimentally determined test lives. It was found that the model correctly predicts trends, but that additional refinement is needed to accurately predict coating life.

Impact of modellers' decisions on hydrological a priori predictions

NASA Astrophysics Data System (ADS)

Holländer, H. M.; Bormann, H.; Blume, T.; Buytaert, W.; Chirico, G. B.; Exbrayat, J.-F.; Gustafsson, D.; Hölzel, H.; Krauße, T.; Kraft, P.; Stoll, S.; Blöschl, G.; Flühler, H.

2014-06-01

In practice, the catchment hydrologist is often confronted with the task of predicting discharge without having the needed records for calibration. Here, we report the discharge predictions of 10 modellers - using the model of their choice - for the man-made Chicken Creek catchment (6 ha, northeast Germany, Gerwin et al., 2009b) and we analyse how well they improved their prediction in three steps based on adding information prior to each following step. The modellers predicted the catchment's hydrological response in its initial phase without having access to the observed records. They used conceptually different physically based models and their modelling experience differed largely. Hence, they encountered two problems: (i) to simulate discharge for an ungauged catchment and (ii) using models that were developed for catchments, which are not in a state of landscape transformation. The prediction exercise was organized in three steps: (1) for the first prediction the modellers received a basic data set describing the catchment to a degree somewhat more complete than usually available for a priori predictions of ungauged catchments; they did not obtain information on stream flow, soil moisture, nor groundwater response and had therefore to guess the initial conditions; (2) before the second prediction they inspected the catchment on-site and discussed their first prediction attempt; (3) for their third prediction they were offered additional data by charging them pro forma with the costs for obtaining this additional information. Holländer et al. (2009) discussed the range of predictions obtained in step (1). Here, we detail the modeller's assumptions and decisions in accounting for the various processes. We document the prediction progress as well as the learning process resulting from the availability of added information. For the second and third steps, the progress in prediction quality is evaluated in relation to individual modelling experience and costs of

A multivariate model for predicting segmental body composition.

PubMed

Tian, Simiao; Mioche, Laurence; Denis, Jean-Baptiste; Morio, Béatrice

2013-12-01

The aims of the present study were to propose a multivariate model for predicting simultaneously body, trunk and appendicular fat and lean masses from easily measured variables and to compare its predictive capacity with that of the available univariate models that predict body fat percentage (BF%). The dual-energy X-ray absorptiometry (DXA) dataset (52% men and 48% women) with White, Black and Hispanic ethnicities (1999-2004, National Health and Nutrition Examination Survey) was randomly divided into three sub-datasets: a training dataset (TRD), a test dataset (TED); a validation dataset (VAD), comprising 3835, 1917 and 1917 subjects. For each sex, several multivariate prediction models were fitted from the TRD using age, weight, height and possibly waist circumference. The most accurate model was selected from the TED and then applied to the VAD and a French DXA dataset (French DB) (526 men and 529 women) to assess the prediction accuracy in comparison with that of five published univariate models, for which adjusted formulas were re-estimated using the TRD. Waist circumference was found to improve the prediction accuracy, especially in men. For BF%, the standard error of prediction (SEP) values were 3.26 (3.75) % for men and 3.47 (3.95)% for women in the VAD (French DB), as good as those of the adjusted univariate models. Moreover, the SEP values for the prediction of body and appendicular lean masses ranged from 1.39 to 2.75 kg for both the sexes. The prediction accuracy was best for age < 65 years, BMI < 30 kg/m2 and the Hispanic ethnicity. The application of our multivariate model to large populations could be useful to address various public health issues.

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.

Development of a decision model for the techno-economic assessment of municipal solid waste utilization pathways.

PubMed

Khan, Md Mohib-Ul-Haque; Jain, Siddharth; Vaezi, Mahdi; Kumar, Amit

2016-02-01

Economic competitiveness is one of the key factors in making decisions towards the development of waste conversion facilities and devising a sustainable waste management strategy. The goal of this study is to develop a framework, as well as to develop and demonstrate a comprehensive techno-economic model to help county and municipal decision makers in establishing waste conversion facilities. The user-friendly data-intensive model, called the FUNdamental ENgineering PrinciplEs-based ModeL for Estimation of Cost of Energy and Fuels from MSW (FUNNEL-Cost-MSW), compares nine different waste management scenarios, including landfilling and composting, in terms of economic parameters such as gate fees and return on investment. In addition, a geographic information system (GIS) model was developed to determine suitable locations for waste conversion facilities and landfill sites based on integration of environmental, social, and economic factors. Finally, a case study on Parkland County and its surrounding counties in the province of Alberta, Canada, was conducted and a sensitivity analysis was performed to assess the influence of the key technical and economic parameters on the calculated results. Copyright © 2015 Elsevier Ltd. All rights reserved.

Permanent Disposal of Nuclear Waste in Salt

NASA Astrophysics Data System (ADS)

Hansen, F. D.

2016-12-01

Salt formations hold promise for eternal removal of nuclear waste from our biosphere. Germany and the United States have ample salt formations for this purpose, ranging from flat-bedded formations to geologically mature dome structures. Both nations are revisiting nuclear waste disposal options, accompanied by extensive collaboration on applied salt repository research, design, and operation. Salt formations provide isolation while geotechnical barriers reestablish impermeability after waste is placed in the geology. Between excavation and closure, physical, mechanical, thermal, chemical, and hydrological processes ensue. Salt response over a range of stress and temperature has been characterized for decades. Research practices employ refined test techniques and controls, which improve parameter assessment for features of the constitutive models. Extraordinary computational capabilities require exacting understanding of laboratory measurements and objective interpretation of modeling results. A repository for heat-generative nuclear waste provides an engineering challenge beyond common experience. Long-term evolution of the underground setting is precluded from direct observation or measurement. Therefore, analogues and modeling predictions are necessary to establish enduring safety functions. A strong case for granular salt reconsolidation and a focused research agenda support salt repository concepts that include safety-by-design. Sandia National Laboratories is a multi-program laboratory managed and operated by Sandia Corporation, a wholly owned subsidiary of Lockheed Martin Corporation, for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-AC04-94AL85000. Author: F. D. Hansen, Sandia National Laboratories

Dynamic Simulation of Human Gait Model With Predictive Capability.

PubMed

Sun, Jinming; Wu, Shaoli; Voglewede, Philip A

2018-03-01

In this paper, it is proposed that the central nervous system (CNS) controls human gait using a predictive control approach in conjunction with classical feedback control instead of exclusive classical feedback control theory that controls based on past error. To validate this proposition, a dynamic model of human gait is developed using a novel predictive approach to investigate the principles of the CNS. The model developed includes two parts: a plant model that represents the dynamics of human gait and a controller that represents the CNS. The plant model is a seven-segment, six-joint model that has nine degrees-of-freedom (DOF). The plant model is validated using data collected from able-bodied human subjects. The proposed controller utilizes model predictive control (MPC). MPC uses an internal model to predict the output in advance, compare the predicted output to the reference, and optimize the control input so that the predicted error is minimal. To decrease the complexity of the model, two joints are controlled using a proportional-derivative (PD) controller. The developed predictive human gait model is validated by simulating able-bodied human gait. The simulation results show that the developed model is able to simulate the kinematic output close to experimental data.

Prediction of stress corrosion of carbon steel by nuclear process liquid wastes