A two-stage support-vector-regression optimization model for municipal solid waste management - a case study of Beijing, China

Modelling of demolition waste generation: Application to Greek residential buildings

The construction sector in Europe is among the biggest waste generators, producing 370 million tonnes of construction and demolition waste (CDW) every year, which contain important secondary materials. Quantification of CDW is important from their circular management and environmental impact point of view. Thus, the overall objective of this study was to develop a modelling methodology for estimating demolition waste (DW) generation. The volumes (m3) of individual construction materials contained in 45 residential buildings in Greece were accurately estimated using computer-aided design (CAD) software and the materials were classified according to European List of Waste. These materials will become waste upon demolition, with a total estimated generation rate of 1590 kg m-2 of top view area and with concrete and bricks representing 74.5% of total. Linear regression models were developed to predict the total and individual amounts of 12 different building materials based on structural building characteristics. To test the accuracy of the models, the materials of two residential buildings were quantified and classified and the results were compared with the model predictions. Depending on the model used, the % differences between models' predictions and CAD estimates for total DW averaged 11.1% ± 7.4% for the first case study and 2.5% ± 1.5% for the second. The models can be used for accurate quantification of total and individual DW and their management within the framework of circular economy.

Recent advances in applications of artificial intelligence in solid waste management: A review

Efficient management of solid waste is essential to lessen its potential health and environmental impacts. However, the current solid waste management practices encounter several challenges. The development of effective waste management systems using advanced technologies is vital to overcome the challenges faced by the current approaches. Artificial Intelligence (AI) has emerged as a powerful tool for applications in various fields. Several studies also reported the applications of AI techniques in the management of solid waste. This article critically reviews the recent advancements in the applications of AI techniques for the management of solid waste. Various AI and hybrid techniques have been successfully employed to predict the performance of various methods used for the generation, segregation, storage, and treatment of solid waste. The key challenges that limit the applications of AI in solid waste are highlighted. These include the availability and selection of applicable data, poor reproducibility, and less evidence of applications in real solid waste. Based on identified gaps and challenges, recommendations for future work are provided. This review is beneficial for all stakeholders in the field of solid waste management, including policy-makers, governments, waste management organizations, municipalities, and researchers.

Artificial intelligence applications for sustainable solid waste management practices in Australia: A systematic review

Solid waste generation and its impact on human health and the environment have long been a matter of concern for governments across the world. In recent years, there has been increasing emphasis on resource recovery (reusing, recycling and extracting energy from waste) using more advanced approaches such as artificial intelligence (AI) in Australia. AI is a powerful technology that is increasingly gaining popularity and application in various fields. The adoption of AI techniques offers alternative innovative approaches to solid waste management (SWM). Although there are previous studies on AI technologies and SWM, no study has assessed the adoption of AI applications in solving the diverse SWM problems for achieving sustainable waste management in Australia. Moreover, there are inconsistencies and a lack of awareness on how AI technologies function in relation to their application to SWM. This study examines the application of AI technologies in various areas of SWM (generation, sorting, collection, vehicle routing, treatment, disposal and waste management planning) to enhance sustainable waste management practices in Australia. To achieve the aims of this study, prior studies from 2005 to 2021 from various databases are collected and analyzed. The study focuses on the adoption of AI applications on SWM, compares the performance of AI applications, explores the benefits and challenges, and provides best practice recommendations on how resource efficiency can be optimized to improve economic, environmental and social outcomes. This study found that AI-based models have better prediction abilities when compared to other models used in forecasting solid waste generation and recycling. Findings show that waste generation in Australia has been steadily increasing and requires upgraded and improved recovery infrastructure and the appropriate adoption of AI technologies to enhance sustainable SWM. Australia's adoption of AI recycling technologies would benefit from a national approach that seeks consistency across jurisdictions, while catering for regional differences. This study will benefit researchers, governments, policy-makers, municipalities and other waste management organizations to increase current recycling rates, eliminate the need for manual labor, reduce costs, maximize efficiency, and transform the way we approach the management of solid waste.

Prediction of municipal solid waste generation: an investigation of the effect of clustering techniques and parameters on ANFIS model performance

The present waste-management system in most developing countries are insufficient to combat the challenge of increasing rate of solid waste generation. Accurate prediction of waste generated through modelling approach will help to overcome the challenge of deficient-planning of sustainable waste-management. In modelling the complexity within a system, a paradigm-shift from classical-model to artificial intelligent model has been necessitated. Previous researches which used Adaptive Neuro-Fuzzy Inference System (ANFIS) for waste generation forecast did not investigate the effect of clustering-techniques and parameters on the performance of the model despite its significance in achieving accurate prediction. This study therefore investigates the impact of the parameters of three clustering-technique namely: Fuzzy c-means (FCM), Grid-Partitioning (GP) and Subtractive-Clustering (SC) on the performance of the ANFIS model in predicting waste generation using South Africa as a case study. Socio-economic and demographic provincial-data for the period 2008-2016 were used as input-variables and provincial waste quantities as output-variable. ANFIS model clustered with GP using triangular input membership-function (tri-MF) and a linear type output membership-function (ANFIS-GP1) is the optimal model with Mean Absolute Percentage Error (MAPE), Mean Absolute Deviation (MAD), Root Mean Square Error (RMSE) and Correlation Co-efficient (R²) values of 12.6727, 0.6940, 1.2372 and 0.9392 respectively. Based on the result in this study, ANFIS-GP with a triangular membership-function is recommended for modelling waste generation. The tool presented in this study can be utilized for the national repository of waste generation data by the South Africa Waste Information Centre (SAWIC) in South Africa and in other developing countries.

Optimal Process Network for Integrated Solid Waste Management in Davao City, Philippines

Municipal solid waste management (MSWM) systems in developing countries adopt practices from developed countries to reduce their environmental burdens. However, several complex issues specific to developing countries hinder the full implementation of these practices. The future of MSWM in Davao City, Philippines, is envisaged as a notable example of the combination of new infrastructure and local MSWM practices. A linear programming model was developed, following material flow analysis and life cycle assessment, to design an optimal system for Davao City. The performance of the system was evaluated in terms of greenhouse gas emissions, energy and revenue generated, and the amount of landfill waste. The results show that the proposed system positively affects the environment compared to the current system, due to additional treatment options. However, the main allocation concern transitions from organic waste in the current system to plastic waste in future scenarios. Furthermore, the mitigation of greenhouse gas emissions and the extension of landfill life will be heavily influenced by trade-offs between sorting operations and the management of incinerated wastes with high calorific values. Therefore, plastic-waste-specific treatment options will be critical for future MSWM systems. The results herein underscore the need for sustainable MSWM in the study area, considering the region-specific conditions.

Comparative performance analysis of support vector regression and artificial neural network for prediction of municipal solid waste generation

The evolution of machine learning (ML) algorithms provides researchers and engineers with state-of-the-art tools to dynamically model complex relationships. The design and operation of municipal solid waste (MSW) management systems require accurate estimation of generation rates. In this study, we applied rapid, non-linear and non-parametric data driven ML algorithms independently, multi-layer perceptron artificial neural network (MLP-ANN) and support vector regression (SVR) models to predict annual MSW generation rates in Bahrain. Models were trained and tested with MSW generation data for period of 1997-2019. The population, gross domestic product, annual tourist numbers, annual electricity consumption and total annual CO₂ emissions were selected as explanatory variables and incorporated into developed models. The zero score normalization (ZSN) and minimum maximum normalization (MMN) methods were utilized to improve the quality of data and subsequently enhances the performance of ML algorithms. Statistical metrics were employed to discriminate performance of MLP-ANN and SVR models. The linear, polynomial, radial basis function (RBF) and sigmoid kernel functions were investigated to find the optimal SVR model. Results showed that RBF-SVR model with R² value of 0.97% and 4.82% and absolute forecasting error (AFE) for the period of 2008 and 2019 exhibits superior prediction and robustness in comparison to MLP-ANN. The efficacy of MLP-ANN model was also reasonably successful with R² value of 0.94. It was shown that MMN pre-processing generated optimal MLP-ANN model while ZSN pre-processing produced optimal RBF-SVR model. This work also highlights the importance of application of ML modelling approaches to plan and implement their roadmap for waste management systems by policymakers.

Hybrid support vector regression and crow search algorithm for modeling and multiobjective optimization of microalgae-based wastewater treatment

Microalgae-based wastewater treatment (and biomass production) is an environmentally benign and energetically efficient technique as compared to traditional practices. The present study is focused on optimization of the major treatment variables such as temperature, light-dark cycle (LD), and nitrogen (N)-to-phosphate (P) ratio (N/P) for the elimination of N and P from tertiary municipal wastewater utilizing Chlorella kessleri microalgae species. In this regard, a hybrid support vector regression (SVR) technique integrated with the crow search algorithm has been applied as a novel modeling/optimization tool. The SVR models were formulated using the experimental data, which were furnished according to the response surface methodology with Box-Behnken Design. Various statistical indicators, including mean absolute percentage error, Taylor diagram, and fractional bias, confirmed the superior performance of SVR models as compared to the response surface methodology (RSM) and generalized linear model (GLM). Finally, the best SVR model was hybridized with the crow search algorithm for single/multi-objective optimizations to acquire the global optimal treatment conditions for maximum N and P removal efficiencies. The best-operating conditions were found to be 29.3°C, 24/0 h/h of LD, and 6:1 of N/P, with N and P elimination efficiencies of 99.97 and 93.48%, respectively. The optimized values were further confirmed by new experimental data.

Application of MCAT to provide multi-objective optimization model for municipal waste management system

Choosing an appropriate municipal waste management method is a very complicated environmental problem in cities. This research introduces an optimization model for waste management in the southwest region of Tehran province. It was developed by a metaheuristic algorithm that was used to minimize the economic and environmental costs. Incineration, composting, recycling and landfilling waste management methods were considered. Three scenarios were developed to determine the optimum allocation of waste to each method such to fulfill the objective of overall minimum of environmental burdens and costs. A multi-objective scenario selection model was implemented by the compromise programming method in MCAT software. Considering the budget limitation and available facilities on site, optimum allocations to recycling, composting, incineration and landfilling methods were obtained as 115,486, 132,094, 71,905 and 45,516 tons/year, respectively. The results of this study indicated that the metaheuristic algorithm in MCAT software was an efficient tool in decision making about waste management systems and thus, it was suggested to municipality managers and regional planning authorities.

A stochastic modeling approach for analyzing water resources systems

Many uncertain factors exist in the water resource systems, leading to dynamic characteristics of the water distribution process. Especially for the watershed including irrigation area with multiple water sources and water users, it is complicated that the joint risk among available water from each water source and multi-uncertainties under water resource allocation among multiple water users. In this research, an approach is developed that a copula-based interval multi-stage fuzzy stochastic programming (CIMFSP) model. This research aims at figuring out the planning of a case study for water-resources management. In the multi-sources water supply subsystem, the copula function is introduced to tackle the interaction of water availability between two water sources (i.e., A &B). Joint risk was set 0.05, 0.10, 0.15 to reflect the shortage risk of total available water at diverse levels. An interval parameter multi-stage fuzzy stochastic programming (IMFSP) model is developed for water resource distribution in a multi-users water-demand subsystem. Through this model, uncertainties presented as interval numbers and probability distributions, as well as fuzzy sets, were handled. The dynamics of the entire water resource system can be reflected by multi-stage discrete trees. A series of solutions can be generated under multiple scenarios (i.e., joint risk and α-cut levels). The modeling results will produce a series of alternatives under a battery of scenarios and help the decision-makers get an insight into the tradeoff between the system economic benefit and financial penalties under the corresponding risk level. This approach is valuable for improving the feasibility of optimal results in the watershed with irrigation region water resource management.

Predictive modelling as a tool for effective municipal waste management policy at different territorial levels

Nowadays, the European municipal waste management policy based on the circular economy paradigm demands the closing of material and financial loops at all territorial levels of public administration. The effective planning of treatment capacities (especially sorting plants, recycling, and energy recovery facilities) and municipal waste management policy requires an accurate prognosis of municipal waste generation, and therefore, the knowledge of behavioral, socio-economic, and demographic factors influencing the waste management (and recycling) behavior of households, and other municipal waste producers. To enable public bodies at different territorial levels to undertake an effective action resulting in circular economy we evaluated various factors influencing the generation of municipal waste fractions at regional, micro-regional and municipal level in the Czech Republic. Principal components were used as input for traditional models (multivariable linear regression, generalized linear model) as well as tree-based machine learning models (regression trees, random forest, gradient boosted regression trees). Study results suggest that the linear regression model usually offers a good trade-off between model accuracy and interpretability. When the most important goal of the prediction is supposed to be accuracy, the random forest is generally the best choice. The quality of developed models depends mostly on the chosen territorial level and municipal waste fraction. The performance of these models deteriorates significantly for lower territorial levels because of worse data quality and bigger variability. Only the age structure seems to be important across territorial levels and municipal waste fractions. Nevertheless, also other factors are of high significance in explaining the generation of municipal waste fractions at different territorial levels (e.g. number of economic subjects, expenditures, population density and the level of education). Therefore, there is not one single effective public policy dealing with circular economy strategy that fits all territorial levels. Public representatives should focus on policies effective at specific territorial level. However, performance of the models is poor for lower territorial levels (municipality and micro-regions). Thus, results for municipalities and micro-regions are weak and should be treated as such.

Inexact inventory-theory-based optimization of oily waste management system in shoreline spill response

The oily waste generated from the cleanup operations during shoreline spill response can result in challenging environmental and socioeconomic problems. In this study, an inexact inventory-theory-based optimization model (ITOM) for oily waste management during shoreline spill response was developed to support the spill management team. The most appropriate facilities and optimal waste allocation scheme under uncertainty can be selected to achieve minimum total system cost. To satisfy the demand of oily waste treatment, these oily waste management facilities can be selectively opened depending on the situation. In the combination with the economic order quantity model of inventory theory, the developed model can provide the optimal solutions of batch size and order cycle for treatment facilities to minimize the inventory cost. A case study was used to demonstrate the application of ITOM. The obtained solutions include the facilities selection and waste allocation for waste collection and destocking stages under different risk levels. These solutions can provide a good guideline with managers to analyze the trade-offs between system cost and constraint-violation risks. The developed model has high application potential as a job-aid tool to manage the oily waste generated from oiled shoreline cleanup operations.

A novel cooperative model in the collection of infectious waste in COVID-19 pandemic

Purpose

Production of waste has been increased exponentially due to world industrialization and urban and machine life expansion. On the other hand, the outbreak of the COVID-19 coronavirus quickly became a global crisis. This crisis has added a large amount of waste to urban waste. The purpose of this study is to create cooperation between municipal waste collector contractors.

Design/methodology/approach

Thus, a mathematical model is proposed under uncertain conditions, which includes the volume of municipal waste and infectious waste including personal protective equipment and used equipment for patients. To reduce total costs, the results are evaluated with four cooperative game theory methods such as Shapley value, t value, core center and least core. Ultimately, the saved cost by cooperation in each coalition is allocated fairly among the contractors. Finally, a comparison was made between the solution methods based on the value of the objective function and the solution time.

Findings

The results indicate that the proposed cooperative method increases cost savings and reduces the fine of residual waste. Therefore, it can be mentioned that this kind of cooperation would finally result in more incentives for contractors to form larger coalitions. Genetic algorithms were used to solve the large-scale model.

Originality/value

The proposed model boosts the current understanding of waste management in the COVID-19 pandemic. The paper adds additional value by unveiling some key future research directions. This guidance may demonstrate possible existing and unexplored gaps so that researchers can direct future research to develop new processes.

Artificial intelligence applications in solid waste management: A systematic research review

The waste management processes typically involve numerous technical, climatic, environmental, demographic, socio-economic, and legislative parameters. Such complex nonlinear processes are challenging to model, predict and optimize using conventional methods. Recently, artificial intelligence (AI) techniques have gained momentum in offering alternative computational approaches to solve solid waste management (SWM) problems. AI has been efficient at tackling ill-defined problems, learning from experience, and handling uncertainty and incomplete data. Although significant research was carried out in this domain, very few review studies have assessed the potential of AI in solving the diverse SWM problems. This systematic literature review compiled 85 research studies, published between 2004 and 2019, analyzing the application of AI in various SWM fields, including forecasting of waste characteristics, waste bin level detection, process parameters prediction, vehicle routing, and SWM planning. This review provides comprehensive analysis of the different AI models and techniques applied in SWM, application domains and reported performance parameters, as well as the software platforms used to implement such models. The challenges and insights of applying AI techniques in SWM are also discussed.

A mixed integer linear programming model for long-term planning of municipal solid waste management systems: Against restricted mass balances

Long-term planning of municipal solid waste management systems is a complex decision making problem which includes a large number of decision layers. Since all different waste treatment and disposal processes will show different responses to each municipal solid waste component, it is necessary to separately evaluate all waste components for all processes. This obligation creates an obstacle in the programming of mass balances for long-term planning of municipal solid waste management systems. The development of an ideal mixed integer linear programming model that can simultaneously respond to all essential decision layers including waste collection, process selection, waste allocation, transportation, location selection, and capacity assessment has not been made possible yet due to this important modeling obstacle. According to the current knowledge of the literature, all mixed integer linear programming studies aiming to address this obstacle so far have had to restrict many different possibilities in their mass balances. In this study, a novel mixed integer linear programming model was formulated. ALOMWASTE, the new model structure developed in this study, was built to take into consideration different process, capacity, and location possibilities that may occur in complex waste management processes at the same time. The results obtained from a case study showed the feasibility of new mixed integer linear programming model obtained in this study for the simultaneous solution of all essential decision layers in an unrestricted mass balance. The model is also able to provide significant convenience for the multi-objective optimization of financial-environmental-social costs and the solution of some uncertainty problems of decision-making tools such as life cycle assessment.

Forecasting of the unknown end-of-life tire flow for control and decision making in urban solid waste management: A case study

Efficient urban planning requires managers' experience and knowledge of reverse logistics in solid urban waste processes. Forecasting tools are needed to control, select and manage municipal solid waste. This paper presents the application of dynamic modeling approaches, namely, a linear autoregressive seasonal model, a model based on a FeedForward Artificial Neural Network and a Recurrent Neural Networks model, in order to forecast the unknown flows of end-of-life tires 12 months ahead. The models were identified using a database comprising four years of historical series related to the unknown flows of end-of-life tires. These were obtained through an exploratory analysis based on the annual sales reports of new tires issued by the Brazilian Institute of Geography and Statistics and reports related to the number of vehicles in circulation issued by Brazil's National Traffic Department. The results show that the models are able to carry out consistent forecasts over the horizon of a year ahead and the predictions are capable of identifying seasonalities and supporting decision making in urban waste management.

Optimal transportation planning of classified domestic garbage based on map distance

Domestic garbage classification is required in many cities, however, optimal transportation of classified garbage has not been widely studied. Here, the optimal transportation of the classified domestic garbage from the waste transfer center to the garbage disposal station is considered. The locations of waste transfer centers, map distances between these waste transfer centers, different types of garbage disposal stations and the quantity of waste in different areas are used as key factors for constructing a weighted graph model. This study considers Nanshan District in Shenzhen, China as a case study. Solutions were obtained by applying the proposed model to the area using data from 2011. To achieve the decision goal of identifying feasible traveling distance and time for each trash truck, the number of routes considered for recyclable waste transportation was no less than 5; for non-recyclable waste transportation was no less than 2; and for harmful waste transportation was 4. The study provides an auxiliary management tool for optimal municipal solid waste disposal that improves the sustainability of the system.

Municipal solid waste management via mathematical modeling: A case study in İstanbul, Turkey

The prominence of managing municipal solid waste (MSW) in an efficient and effective manner is increasing from day to day. In this paper, the solid waste management (SWM) system of İstanbul is analyzed by applying the techniques from mathematical programming methodology. In this manner, the solutions of the two optimization problems which aim to minimize the total cost and the environmental effects of SWM, respectively, are presented in this study. Additionally, a sensitivity analysis is performed and a multi-objective problem that combines two problems is presented. In this regard, the application of five MSW management technologies which are currently in use in İstanbul on six waste components is analyzed; and the optimal solution regarding the best mixture of these technologies is developed on a given waste composition. Besides, this optimal solution is compared with the current practice in İstanbul; and recommendations are presented about possible future investments for the policymakers. The results of the study emphasize the importance of material recovery and incineration facilities to improve profitability and to minimize environmental side effects. In particular, material recovery facility (MRF) should be expanded to be able to treat all of metal, paper and plastic from a cost management perspective. Incineration (INC) facility should also be expanded in order to treat plastics or organic waste from a Greenhouse Gas (GHG) minimization perspective. In addition to this, landfill appears to be the most prominent treatment technique according to the current problem parameters. However, regarding the waste composition, the amount of organic waste must be decreased by more than 37% for other waste streams to be treated in different facilities other than landfill. Anaerobic digestion and composting facilities need to be more cost-effective for becoming economically feasible. The methodology represented in this study can be extended and generalized to other cities around the world once the correct problem parameters are specified.

Hybrid model for the prediction of municipal solid waste generation in Hangzhou, China

Accurate prediction of municipal solid waste (MSW) generation is necessary for choosing appropriate waste treatment methods and for planning the distribution of disposal facilities. In this study, a hybrid model was established to forecast MSW generation through the combination of the ridge regression and GM(1,N) models. The hybrid model is multivariate and involves total urban population, total retail sales of social consumer goods, per capita consumption expenditure of urban areas, tourism, and college graduation. Compared with the constituent models alone, the hybrid model yields higher accuracy, with a mean absolute percentage error (MAPE) of only 2.59%. Through weight allocation and optimal treatment of residuals, the hybrid model also balances the growth trends of the individual models, making the prediction curve smoother. The model coefficients and correlation analysis show that population, economics, and educational factors are influential for waste generation. MSW output in Hangzhou will gradually increase in the future, and is expected to reach 5.12 million tons in 2021. Results can help decision makers to develop the measures and policies of waste management in the future.

Managing the uncertainty problems of municipal solid waste disposal

In waste management systems, several parameters such as the rate of waste production, disposal facility, treatment cost, and their relations may be uncertain and can influence the associated optimization processes. These uncertainty problems in waste management were addressed by using various inexact programming methods. For example, fuzzy, stochastic programming, and interval programming techniques were generally used for solving the uncertainty-related waste management problems. The analysis revealed that the efficiency of waste management system can be maximized by the proper use of these optimization techniques. In this approach, an uncertainty problem is reduced into several subproblems with sureness by using the minimax regret optimization technique. And these subproblems are focused on a calculation where the lament of not getting the goal is minimized. The analysis also revealed that the fuzzy-stochastic method was increasingly used for dealing with the waste management system uncertainty in recent times. This paper gives an overview of dealing with the uncertainty problems of waste disposal in urban areas. An indication of the solid waste disposal problems and its management in conjunction with the repercussion of the investigation is described. The rationale and setting of the uncertainty issues in proper waste management are detailed. The applications of fuzzy analysis approach and integrated waste management in dealing with the uncertainty problems are presented. The applications of these techniques in diverse case studies worldwide are discussed and finally, the conclusions of the literature analysis are summarized.

Leachate generation rate modeling using artificial intelligence algorithms aided by input optimization method for an MSW landfill

Leachate is one of the main surface water pollution sources in Selangor State (SS), Malaysia. The prediction of leachate amounts is elementary in sustainable waste management and leachate treatment processes, before discharging to surrounding environment. In developing countries, the accurate evaluation of leachate generation rates has often considered a challenge due to the lack of reliable data and high measurement costs. Leachate generation is related to several factors, including meteorological data, waste generation rates, and landfill design conditions. The high variations in these factors lead to complicating leachate modeling processes. This study aims at identifying the key elements contributing to leachate production and developing various AI-based models to predict leachate generation rates. These models included Artificial Neural Network (ANN)-Multi-linear perceptron (MLP) with single and double hidden layers, and support vector machine (SVM) regression time series algorithms. Various performance measures were applied to evaluate the developed model's accuracy. In this study, input optimization process showed that three inputs were acceptable for modeling the leachate generation rates, namely dumped waste quantity, rainfall level, and emanated gases. The initial performance analysis showed that ANN-MLP2 model-which applies two hidden layers-achieved the best performance, then followed by ANN-MLP1 model-which applies one hidden layer and three inputs-while SVM model gave the lowest performance. Ranges and frequency of relative error (RE%) also demonstrate that ANN-MLP models outperformed SVM models. Furthermore, low and peak flow criterion (LFC and PFC) assessment of leachate inflow values in ANN-MLP model with two hidden layers made more accurate values than other models. Since minimizing data collection and processing efforts as well as minimizing modeling complexity are critical in the hydrological modeling process, the applied input optimization process and the developed models in this study were able to provide a good performance in the modeling of leachate generation efficiently.

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

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.

Distributed mixed-integer fuzzy hierarchical programming for municipal solid waste management. Part I: System identification and methodology development

Due to the existence of complexities of heterogeneities, hierarchy, discreteness, and interactions in municipal solid waste management (MSWM) systems such as Beijing, China, a series of socio-economic and eco-environmental problems may emerge or worsen and result in irredeemable damages in the following decades. Meanwhile, existing studies, especially ones focusing on MSWM in Beijing, could hardly reflect these complexities in system simulations and provide reliable decision support for management practices. Thus, a framework of distributed mixed-integer fuzzy hierarchical programming (DMIFHP) is developed in this study for MSWM under these complexities. Beijing is selected as a representative case. The Beijing MSWM system is comprehensively analyzed in many aspects such as socio-economic conditions, natural conditions, spatial heterogeneities, treatment facilities, and system complexities, building a solid foundation for system simulation and optimization. Correspondingly, the MSWM system in Beijing is discretized as 235 grids to reflect spatial heterogeneity. A DMIFHP model which is a nonlinear programming problem is constructed to parameterize the Beijing MSWM system. To enable scientific solving of it, a solution algorithm is proposed based on coupling of fuzzy programming and mixed-integer linear programming. Innovations and advantages of the DMIFHP framework are discussed. The optimal MSWM schemes and mechanism revelations will be discussed in another companion paper due to length limitation.

Distributed mixed-integer fuzzy hierarchical programming for municipal solid waste management. Part II: scheme analysis and mechanism revelation

As presented in the first companion paper, distributed mixed-integer fuzzy hierarchical programming (DMIFHP) was developed for municipal solid waste management (MSWM) under complexities of heterogeneities, hierarchy, discreteness, and interactions. Beijing was selected as a representative case. This paper focuses on presenting the obtained schemes and the revealed mechanisms of the Beijing MSWM system. The optimal MSWM schemes for Beijing under various solid waste treatment policies and their differences are deliberated. The impacts of facility expansion, hierarchy, and spatial heterogeneities and potential extensions of DMIFHP are also discussed. A few of findings are revealed from the results and a series of comparisons and analyses. For instance, DMIFHP is capable of robustly reflecting these complexities in MSWM systems, especially for Beijing. The optimal MSWM schemes are of fragmented patterns due to the dominant role of the proximity principle in allocating solid waste treatment resources, and they are closely related to regulated ratios of landfilling, incineration, and composting. Communities without significant differences among distances to different types of treatment facilities are more sensitive to these ratios than others. The complexities of hierarchy and heterogeneities pose significant impacts on MSWM practices. Spatial dislocation of MSW generation rates and facility capacities caused by unreasonable planning in the past may result in insufficient utilization of treatment capacities under substantial influences of transportation costs. The problems of unreasonable MSWM planning, e.g., severe imbalance among different technologies and complete vacancy of ten facilities, should be gained deliberation of the public and the municipal or local governments in Beijing. These findings are helpful for gaining insights into MSWM systems under these complexities, mitigating key challenges in the planning of these systems, improving the related management practices, and eliminating potential socio-economic and eco-environmental issues resulting from unreasonable management.

Technologies and decision support systems to aid solid-waste management: a systematic review

Population growth associated with population migration to urban areas and industrial development have led to a consumption relation that results in environmental, social, and economic problems. With respect to the environment, a critical concern is the lack of control and the inadequate management of the solid waste generated in urban centers. Among the challenges are proper waste-collection management, treatment, and disposal, with an emphasis on sustainable management. This paper presents a systematic review on scientific publications concerning decision support systems applied to Solid Waste Management (SWM) using ICTs and OR in the period of 2010-2013. A statistical analysis of the eighty-seven most relevant publications is presented, encompassing the ICTs and OR methods adopted in SWM, the processes of solid-waste management where they were adopted, and which countries are investigating solutions for the management of solid waste. A detailed discussion on how the ICTs and OR methods have been combined in the solutions was also presented. The analysis and discussion provided aims to help researchers and managers to gather insights on technologies/methods suitable the SWM challenges they have at hand, and on gaps that can be explored regarding technologies/methods that could be useful as well as the processes in SWM that currently do not benefit from using ICTs and OR methods.

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

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.

Prediction of municipal solid waste generation using nonlinear autoregressive network

Most of the developing countries have solid waste management problems. Solid waste strategic planning requires accurate prediction of the quality and quantity of the generated waste. In developing countries, such as Malaysia, the solid waste generation rate is increasing rapidly, due to population growth and new consumption trends that characterize society. This paper proposes an artificial neural network (ANN) approach using feedforward nonlinear autoregressive network with exogenous inputs (NARX) to predict annual solid waste generation in relation to demographic and economic variables like population number, gross domestic product, electricity demand per capita and employment and unemployment numbers. In addition, variable selection procedures are also developed to select a significant explanatory variable. The model evaluation was performed using coefficient of determination (R(2)) and mean square error (MSE). The optimum model that produced the lowest testing MSE (2.46) and the highest R(2) (0.97) had three inputs (gross domestic product, population and employment), eight neurons and one lag in the hidden layer, and used Fletcher-Powell's conjugate gradient as the training algorithm.

Modeling for waste management associated with environmental-impact abatement under uncertainty

Municipal solid waste (MSW) treatment can generate significant amounts of pollutants, and thus pose a risk on human health. Besides, in MSW management, various uncertainties exist in the related costs, impact factors, and objectives, which can affect the optimization processes and the decision schemes generated. In this study, a life cycle assessment-based interval-parameter programming (LCA-IPP) method is developed for MSW management associated with environmental-impact abatement under uncertainty. The LCA-IPP can effectively examine the environmental consequences based on a number of environmental impact categories (i.e., greenhouse gas equivalent, acid gas emissions, and respiratory inorganics), through analyzing each life cycle stage and/or major contributing process related to various MSW management activities. It can also tackle uncertainties existed in the related costs, impact factors, and objectives and expressed as interval numbers. Then, the LCA-IPP method is applied to MSW management for the City of Beijing, the capital of China, where energy consumptions and six environmental parameters [i.e., CO2, CO, CH4, NOX, SO2, inhalable particle (PM10)] are used as systematic tool to quantify environmental releases in entire life cycle stage of waste collection, transportation, treatment, and disposal of. Results associated with system cost, environmental impact, and the related policy implication are generated and analyzed. Results can help identify desired alternatives for managing MSW flows, which has advantages in providing compromised schemes under an integrated consideration of economic efficiency and environmental impact under uncertainty.

An integrated simulation and optimization approach for managing human health risks of atmospheric pollutants by coal-fired power plants

This research developed a simulation-aided nonlinear programming model (SNPM). This model incorporated the consideration of pollutant dispersion modeling, and the management of coal blending and the related human health risks within a general modeling framework In SNPM, the simulation effort (i.e., California puff [CALPUFF]) was used to forecast the fate of air pollutants for quantifying the health risk under various conditions, while the optimization studies were to identify the optimal coal blending strategies from a number of alternatives. To solve the model, a surrogate-based indirect search approach was proposed, where the support vector regression (SVR) was used to create a set of easy-to-use and rapid-response surrogates for identifying the function relationships between coal-blending operating conditions and health risks. Through replacing the CALPUFF and the corresponding hazard quotient equation with the surrogates, the computation efficiency could be improved. The developed SNPM was applied to minimize the human health risk associated with air pollutants discharged from Gaojing and Shijingshan power plants in the west of Beijing. Solution results indicated that it could be used for reducing the health risk of the public in the vicinity of the two power plants, identifying desired coal blending strategies for decision makers, and considering a proper balance between coal purchase cost and human health risk.

IMPLICATIONS

A simulation-aided nonlinear programming model (SNPM) is developed. It integrates the advantages of CALPUFF and nonlinear programming model. To solve the model, a surrogate-based indirect search approach based on the combination of support vector regression and genetic algorithm is proposed. SNPM is applied to reduce the health risk caused by air pollutants discharged from Gaojing and Shijingshan power plants in the west of Beijing. Solution results indicate that it is useful for generating coal blending schemes, reducing the health risk of the public, reflecting the trade-offbetween coal purchase cost and health risk.

Optimization for municipal solid waste treatment based on energy consumption and contaminant emission

This paper analyzes the characterization of energy consumption and contaminant emissions from a municipal solid waste (MSW) treatment system that comprises transfer station, landfill site, combustion plant, composting plant, dejecta treatment station, and an integrated MSW treatment plant. The consumed energy and energy medium materials were integrated under comprehensive energy consumption (CEC) for comparison. Among typical MSW disposal methods such as combustion, composting, and landfilling, landfilling has the minimum CEC value. Installing an integrated treatment plant is the recommended MSW management method because of its lower CEC. Furthermore, this method is used to ensure process centralization. In landfill sites, a positive linear correlation was observed between the CEC and contaminant removal ratios when emitted pollutants have a certain weight coefficient. The process should utilize the minimum CEC value of 5.3702 kgce/t MSW and consider energy consumption, energy recovery, MSW components, and the equivalent of carbon dioxide emissions.