Effect of symmetrical tilt grain boundary on dislocation nucleation and growth in Niobium bi-crystal

Resources, Conservation & Recycling 151 (2019) 104503

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Solid waste management through the applications of mathematical models

T

Ajay Singh Agricultural and Food Engineering Department, Indian Institute of Technology Kharagpur, West Bengal, 721302, India

A R T I C LE I N FO

A B S T R A C T

Keywords: Environmental problems Municipal waste management Mathematical models Optimization modeling Multi-objective approach Multi-criteria decision analysis

Growing world population along with fast economic growth and increased living standards have increased the municipal waste generation making its management be a foremost global issue. The problem is even more serious in urban areas as its improper management prompts tainting of soil, water, and environment which create public health risks. These problems of waste disposal and management were usually assessed by traditional methods which require loads of data. The recent development in the new software technologies and Internet along with the introduction of gradually more compact and dependable hardware products have presented the ability to accurately deal with these procedures more easily than costly and tedious field experiments. This paper presents an outline of the utilization of different scientific models in solving the environmental problems of municipal waste disposal. The examination of past literature uncovered that usually optimization models were used to find the answer of 'what is the best' under an explicit arrangement of conditions, while, simulation models were usually helpful to get an answer to 'what if? ' due to their predictive capability. An indication of the municipal waste disposal problems and its management alongside the ramifications of the investigation is provided. The rationale and backdrop of the waste disposal issues are described. The applications of optimization modeling, multi-objective approach, multi-criteria decision analysis, and artificial neural networks in waste management are presented and applications of these modeling techniques in diverse case studies worldwide are described. And finally, the conclusions of the analysis are summarized.

1. Introduction The world population is growing progressively and is expected to touch the level of 9.8 billion in 2050 from the existing 7.6 billion (United Nations, 2017). This growth in the populace has expanded solid waste production considerably particularly in the urban areas (Anshassi et al., 2019; Mahpour, 2018). For instance, the solid waste generation has increased to 1.2 kg per capita per day globally from its ten years before value of 0.64 kg and this figure is expected to rise further (The World Bank, 2012). Growing world population along with fast economic growth and increased living standards have increased the municipal solid waste (MSW) generation making its management be a foremost global issue (Ayodele et al., 2018; Arebey et al., 2011). The issue is considerably more genuine in urban territories because its improper management prompts tainting of soil, water, and environment which create public health risks (Sharholy et al., 2008; Batool and Ch, 2009). MSW management is an interdisciplinary activity that consists of production, collection, transfer, processing, and most importantly disposal (Gallardo et al., 2015). Waste disposal and management problems are not new and it has been dealt with by the researchers globally (Jaligot and Chenal, 2018;

Meng et al., 2018; Raviv et al., 2018; Pardo et al., 2017; Philippe and Culot, 2009). Ahmed and Ali (2006) reported that solid waste is generated at a rate of 1.6 B tonnes per year globally. And unmanaged or poorly-managed waste is an ecological risk that can cause serious human medical issues (McLeod et al., 2006). The issues of unmanaged solid wastes are particularly serious in developing countries because of fast urbanization, lack of funds, technology, and governance (Schubeler, 1996). Besides, most of the developing world cities are highly populated and unplanned and have inadequate road access which further restricts the collection and transport of solid waste to disposal sites (Aremu et al., 2012; Cohen, 2006). SWM is one of the existing concerns of contemporary society. As all earlier disposal practice is no longer adequate to deal with the problems of today's world (Rada et al., 2013). The selected waste disposal site should not cause harm to the ecosystem of the adjoining area and the biophysical setting (Mummolo, 1996). Furthermore, the demographic and economic factors should be considered in waste dumping location choice (Yesilnacar and Cetin, 2007). Besides, a number of techniques have been reported in different pieces of literature for waste dumping location selection (Gemitzi et al., 2006; Sener et al., 2006; Simsek et al., 2005).

E-mail address: [email protected]. https://doi.org/10.1016/j.resconrec.2019.104503 Received 11 November 2018; Received in revised form 15 September 2019; Accepted 15 September 2019 Available online 23 September 2019 0921-3449/ © 2019 Elsevier B.V. All rights reserved.

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The aforementioned problems of waste disposal and management were unraveled by utilizing various scientific models amid the most recent couple of decades (Maalouf and El-Fadel, 2018; Broitman et al., 2018; Liu et al., 2017; Yang et al., 2017; Madadian et al., 2013). These models offer vital support for the correct management of MSW disposal problems. Wise and Townsend (2011) used 1-D gas stream models for MSW sites. The suggested models can be successfully connected amid the 'roughing-out' phase of gas accumulation frameworks. Yu et al. (2010) presented a coupled 1-D model for the forecast of settlement and gas stream in waste sites. The model was able to forecast the time evolution of settlement in addition to the spatial and temporal dispersion of gas weight inside multi-layered landfills in a range of working situations. The logical answer for the model was assessed with mathematical simulation and field estimations. To the extent the author knows, there has not been a current report concerning the use of mathematical modeling techniques for managing the environmental problems of waste disposal. This article presents an outline of the utilization of different scientific models in solving the environmental problems of municipal waste disposal. The paper is organized as indication of the solid waste disposal problems and its management alongside the ramifications of the investigation, rationale and backdrop of the waste disposal problems, applications of optimization modeling, multi-objective approach, multicriteria decision analysis, and artificial neural networks in waste management, applications of diverse mathematical modeling techniques in different case studies worldwide, and the conclusions of the analysis.

Solid Waste in Urban Areas Handling & Separation, Storage & Process at source

Reduce, Reuse & Recycle

Materials Recovery Services

Collection & Transport Biological Treatment

Thermal Treatment

Landfill

Aerobic Composting Anaerobic Composting Anaerobic Digestion

Incineration Gasification Energy from Waste

MSW Hazardous Waste Bioreactor Concept

Fig. 1. Sustainable solid waste management in urban areas.

Solid Waste Generation Data collection from various sources

Database design and input files preparation

2. Rationale

Database development

The rapid expansion in urban populace prompted a significant rise in MSW production and it has vital environmental and socioeconomic impact (Coelho and Lange, 2018; Singh, 2019a; Joseph et al., 2012; Hering, 2012; Singh, 2018, 2015). Growing waste production rates and dwindling waste-disposal capacities in addition to mounting environmental worries and changeable political and legislative stipulations are considerably affecting the waste organization practices in municipalities across the world. The aforementioned factors and processes are joined with multi-period and multi-target attributes. To deal with these intricacies, choices with sound environmental and economic efficiencies are required in handling the solid waste. Evaluating the production characteristics of the solid waste, i.e., recognizing sources of diverse components, approximating the material recovery potential, and humanizing management strategy, is a first and key step en route for waste' successful management (Thanh et al., 2010; Qu et al., 2009). In recent years, the researchers reported that over eighty percent of the total quantity of MSW originates from the domestic areas (Gu et al., 2015; Wei et al., 2000). However, this figure varies from region to region and also depends on the demographic and socioeconomic setup of the specific locations. There was no precise information about MSW collection and production during the past as solid waste management system was generally manual. It resulted in unplanned and poor management of MSW because the selection of disposal site, collection point, and recycling etc was done with no proper plan (Belien et al., 2012). Numerous ongoing investigations have suggested the optimal recycling and reuse of solid waste for dealing with the disposal issues of huge waste volumes in urban ecosystems (Fig. 1). The recent development in the new software technologies and Internet, along with the introduction of gradually more compact and dependable hardware products, have allowed the construction of capable modeling systems for the waste management (Rada et al., 2013). During the last few decades, researchers developed and applied flexible mathematical models to appraise the environmental problems (Mishra et al., 2017; Singh, 2016, 2012; Laureri et al., 2015; Ameyaw and Chan, 2015). Waste disposal is largely accountable for the continuing quality debasement of groundwater repositories. In a SWM framework, the

Database management and operation

Analysis

Output: -Summary statistics -Descriptive statistics -Data for graphs and diagrams Fig. 2. Flowchart of database analytical structures.

information is gathered from various sources and the examination of manipulated data is done through the database development and operation (Fig. 2). Papadopoulou et al. (2007) offered a model for assessing the ecological effect of waste site leachate spillage on groundwater quality. The model was applied in an area in Greece for spotting the groundwater pollution hazard because of seepage and leachate spillage beneath the city landfill. The results showed that the drinking wells in the region were under high threat of sullying. Moutavtchi et al. (2010) proposed a cost-benefit analysis-based model WAMED for the assessment of environmental-economic efficiency that may serve as a decision aid for SWM at the municipal and/or regional level. The model was connected in a hypothetical contextual investigation which represents the realistic conditions. The results showed that the proposed model imitates an integrated approach to lessening harmful effects on the human wellbeing and on the earth, as growing economic benefits. A computer-based model was developed by Tanskanen (2000) for examining the field collection systems of solid waste matters. A systems dynamic approach was reported by Dyson and Chang (2005) for forecasting waste production in rapidly rising urban regions based on 2

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management approaches dependent on expense and material recuperation (Chang and Chang, 1998). Mixed integer linear programming has been employed to resolve optimal system for waste-to-energy framework (Ng et al., 2013), to find out the ideal production network for the waste utilization (Santibanez-Aguilar et al., 2013), and to minimize the cost of waste stream provision (Dai et al., 2011). Fuzzy method programming has been used to minimize the disparity among individual e-waste items (Yeh and Xu, 2013). Stochastic programming has been employed to minimize the costs of waste flows and capacity expansion (Guo and Huang, 2009). And hybrid models have used various modeling techniques for achieving a number of objectives (Chang et al., 2012). In the past, various numerical models were proposed to deal with the issues of MSW (Sun and Huang, 2010; Xu et al., 2009; Li and Huang, 2008). These models could produce preferred waste treatment policies, but they might actually experience problems of acquiring the input data with projected forecast accuracy. Thus, an accurate forecast of waste production rate is vital for proper planning and management of municipal solid waste (Jalili and Noori, 2008). A non-linear optimization model was developed by Chang and Chang (1998) considering waste flows from landfills and incinerators. Non-linearity in the system was offered by the technical provisions of the incinerator and presence of pre-treatment plants. Goal programming-based optimization models were widely used to handle several conflicting objectives in MSW management (Galante et al., 2010). Santibanez-Aguilar et al. (2013) built up an optimization model for the best possible development of a store network for MSW management taking into account the environmental and economic features. The basic formulation and operation of a distinctive optimization model are shown in Fig. 4. In SWM frameworks, a few characteristics, for instance, the pace of waste generation, treatment budget, disposal site, and their relations may be uncertain. Also, these characteristics can impact the related optimization strategies. These uncertainty issues in SWM have been tended to by utilizing different programming strategies. Such as stochastic, fuzzy, and interval programming methods were commonly utilized for tackling the vulnerability-related waste managemnt issues (Singh, 2019b). A linear programming-based fuzzy-stochastic-interval model FSILP was developed by Li and Chen (2011) by combining normal LP with Nguyen's method for supporting MSW organization. The proposed model is prepared to successfully dealing with the interval, stochastic, and fuzzy uncertainties. The model was then applied in China to show its relevance in MSW management. As of late, an enhanced stochastic modeling approach was introduced and applied by Zhou et al. (2017) for the planning and optimization of a wastewater treatment framework thinking about the endeavors of uncertain elements. The presented approach model is skilled in managing numerous framework vulnerabilities that exist in framework destinations, impact

Solid waste data collection

Database creation

Database expansion

Input data

Waste management modeling

Analysis of output

Results Fig. 3. Common strategy used in waste management modeling.

restricted data points from an area in San Antonio. A simulation tool Stella was used for knowing diverse patterns of waste production related to different waste production models. Malczewski (2006) and Lukasheh et al. (2001) provided the reviews of the GIS-based multicriteria decision tools and their uses in handling the intricate ecological issues. Huida et al. (2012) reported that RFID (David 2010) can successfully be used for waste containers. Chang et al. (1997a) presented a GIS-based multi-objective encoding for vehicle directing and planning of MSW. They used the model for investigating the most favorable route between a specified point and target in a waste accumulation framework. MacDonald (1996) presented a multi-feature spatial decision aid for planning solid waste disposal problems. It consists of a specialist database management system to provide pertinent information to the planner to comprehend the spatial character of the issue. Chang et al. (2001) reported that the intricacy associated with these decision tools can effectively be handled by using information technology-based tools. Chambal et al. (2003) proposed a multi-target model which is capable to capture solid waste management objectives and goals. It also aids in the assessment of challenging environmental approaches. A common strategy used in waste management modeling is illustrated in Fig. 3.

Waste Disposal Optimization Model's Objective(s) & Constraints

3. Optimization modeling Programming formulation

Usually, optimization models are used to find the answer to 'what is the best' for an explicit arrangement of conditions (Singh, 2014a). A range of techniques has been employed in an optimization model for SWM with diverse focus and objectives (Tan et al., 2014). For example, linear programming has been used to minimize total system cost for waste management (Salvia et al., 2002), to maximize the economic value of energy consumers (Münster and Meibom, 2011), and to incorporate the optimal method of waste management in municipalities (Rathi, 2007). Non-linear programming has been applied to maximize profit and minimize waste (Shadiya et al., 2012) and to maximize waste

Solving the model formulation

Decision choices

Optimal Planning Fig. 4. Formulation and operation of an optimization model. 3

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(2014) presented a similar approach for creating MSW processing networks to generate energy considering environmental and financial issues. Vadenbo et al. (2014) documented a MIP-based multi-target optimization model for treating the sewage sludge. Considering the six environmental objectives, the model was applied in the Swiss region of Zurich for the thermal treatment of digested sewage sludge. Niziolek et al. (2015), Onel et al. (2004), and Ng et al. (2004) also used more or less similar modeling approach for solving the problem of municipal solid waste management. Sudhir et al. (1996) used a lexicographic goal programming-based decision-making system to deal with the problem of the waste collection system in India. The model considered various environmental, economic, and social constraints in its application such as the maximum amount of waste for landfill and budgetary restriction, etc. An integrated solid waste management model was proposed by Solano et al. (2002) to aid in spotting preferred solid waste management approaches that could deal with the energy, cost, and ecological objectives. Recently, a multi-objective optimization model was applied by Chen et al. (2015) for optimizing the process cost and effluent quality for a new cycle operating activated-sludge process. The model evaluated one open-loop and three closed-loop approaches. The analysis of the results demonstrated that trade-offs between operation cost and effluent quality of each approach can be offered.

factors, and related costs which can be enunciated as determinates. The proposed model is progressively adaptable and handy when contrasted with the regular uncertainty models. More recently, Raviv et al. (2018) built up a financially suitable optimization model for energy production from the agricultural wastes. The created model was utilized to analyze the productivity of the energy production framework as a component of uncertain market prices. The effects of the sensivity examination demonstrate that the market cost of transportation expenses has a lower influence on the framework's productivity than treatment technologies' items. The investigation reports the significance of the treatment site and the treatment technology determination in the structure of an ideal waste treatment framework for vegetative residuals. A fuzzy optimization model was used by Chang and Wang (1997) for scheduling and vehicle routing in a SWM collection framework. Later, Shih and Lin (1999) used a similar approach for fixing the collection, routing, and vehicle scheduling problems for contagious waste management. The researchers, i.e., Tatarakis and Minis (2009) and Sung et al. (2000) have also used diverse optimization techniques for solving the problems of vehicle routing and scheduling. Juul et al. (2013) presented an exhaustive survey of the current waste management models. They also displayed the vital parameters and key challenges that require to be considered when evaluating the financial execution of waste treatment options. The survey particularly focused on investigating how these models focus on the face of designing and planning for the arrangement of the waste and power segments. Recently, Broitman et al. (2018) developed a monetary optimization model which characterizes the ideal treatment method for diverse vegetative wastes for the Israeli agricultural sector. The model assessed the effect of output-product cost variations on the optimal scientific selection of the vegetative waste-management system, from risk-averse and risk-neutral views. The study also discussed some approach suggestions that take place from the examination concerning vegetative waste management and its related dangers.

5. Multi-criteria decision analysis Proper waste treatment is a key to reach the MSW management objectives such as economic expansion, environmental protection, and human health, etc. There are numerous treatment alternatives for MSW and selecting the optimal one usually entails decisions on the treatment technology and plant location and capacity (Achillas et al., 2013). And these decisions are generally taken by taking into account diverse criteria such as environmental impacts (ecology damage, human health hazards, global warming) and related economic costs and benefits (waste generation rate). Decision support frameworks are generally used to take the right decision considering various constraints (Karmperis et al., 2013). These frameworks can be classified into environment-based, cost-based, and multi-criteria-based (Dewi et al., 2010). Environment-based models assess the possible impacts on the environment and cost-based models assess monetary values-based options. While the multi-criteria dependent models consist of multi-criteria decision analysis (MCDA) approaches and these models generally offer more reliable decisions (Fig. 5). The management of MSW is an intricate practice as it consists of waste collection, transfer, treatment approach, and plant location, and energy recovery (Dewi et al., 2010). During recent decades, the MCDA models have been widely used for waste management problems (Phonphoton and Pharino, 2019; Makarichi et al., 2018; Lin et al., 2010; Sener et al., 2010; Garfi et al., 2009). Different clashing criteria can be legitimately incorporated into the management procedure in an MCDA model (Kou et al., 2011). And both the qualitative and quantitative criteria can be used in an MCDA system (Fig. 5) for the assessment of a SWM project (Linkov et al., 2006). Moreover, these systems are adaptable and there are optional classifications of assessment measures such as environmental, technical, and economic criteria that can be used (Huang et al., 2011; Linkov et al., 2009). Conversely, these models have some limitations also, for example, some MCDA models consider the effect of dangers that might be caused in the venture options (Karmperis et al., 2012). Furthermore, these models assess just the elective arrangements and don't offer any data for waste prevention and waste minimization. Multi-criteria decision making is a frequently utilized technique for solving municipal SWM issues. This method helps to pick the best option among several options by evaluation of various criteria. The environmental problems with multiple criteria can be solved by using various approaches, for example, strategy affect evaluation, outranking

4. Multi-objective approach The single-target numerical models offer a one of a kind ideal arrangement, whereas, the multi-target issues lead to several compromised arrangements (Singh, 2014b; Vince et al. 2008). Galante et al. (2010) used a multi-target mathematical model for SWM in Palermo, Italy. The model considered both introductory outlay and functioning expenses associated with carrying and transport locations. The model evaluated the two clashing targets such as the reduction of environmental impact and the reduction of the total cost. A MIP-based multiobjective model was presented by Chang et al. (1997b) for assessing solid waste management approaches. A dynamic multi-objective model was connected by Chang and Wang (1996) for waste treatment systems in Taiwan. The model assessed the cost in addition to air pollution, noise, and traffic. The model was solved utilizing the trade-off encoding method considering various situations. An interactive multi-target system was developed by Minciardi et al. (2008) to explain goals in the feasible waste organization, in which the inclination of planners was integrated all through the arrangement procedure. Recently, Jose et al. (2015) introduced a similar numerical system for the ideal development of MSW reuse in Mexico considering environmental and wellbeing norms. The investigation of results illustrates the tradeoff among the social hazard and the environmental and economic criteria. A multi-objective programming approach was used by Erkut et al. (2008) to deal with the location problem of waste management facilities. The proposed approach was employed in the region of Central Macedonia. A similar multi-objective programming approach was used by Tralhão et al. (2010) to examine the issue of finding waste containers in an area of Portugal. Later, Capon-Garcıa et al (2013) used a multi-objective optimization model for MSW management considering the ecological and financial facets. In the proposed model, waste treatment was demonstrated as a discovery display. Likewise, Tan et al. 4

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Abushammala et al. (2014); Dai et al. (2011), and Bunsan et al. (2013) have revealed that ANN is a useful tool for simulating environmental applications due to its capacity to assess the nonlinear correlations among diverse input and output parameters. The ANN-based approaches are extensively used to simulate diverse environmental applications amid the ongoing past (Noori et al., 2010). Shahabi et al. (2012) used feedforward ANN for waste prediction in Saqqez city utilizing weekly truck count records. Earlier, Noori et al. (2009a) integrated wavelet alteration with ANN for forecasting of weekly waste generation in Tehran city. A waste-load provision system was presented by Du et al. (2013) for managing the water quality of Xiangxihe River, which had serious water quality issues thanks to nonpoint and points source pollutions. The outcomes show that crop production generates a large amount of nonpoint source pollution and phosphorus releases into the waterway come largely from point sources, primarily from the phosphorus mining companies and chemical plants. Antanasijevic et al. (2013) present the improvement and assessment of an ANN-based system for municipal waste management. The model was applied in two countries, i.e., Bulgaria and Serbia, which have different levels of industrial structure and economic development. Water quality degradation on account of nonpoint and point source contaminations is a standout amongst the most crucial ecological problems at present. Each day, a huge amount of untreated or poorly treated wastes from domestic, agricultural, and industrial areas are poured into the water sources, affecting their ability to provide appropriate drinking water (Deviney et al., 2012). Several stochastic programming approaches such as chance-constrained programming (Charnes and Cooper, 1959) have been utilized for handling the uncertainty problems in water quality management frameworks (Bisel and Ravindran, 2011; Kataria et al., 2010). Recently, Mishra et al. (2015) have used combined ANN and fuzzy logic to evaluate the best input variables to forecast the haze formation in Delhi, India.

Multi-criteria Decision Analysis Identification of waste management scenarios

Defining various scenarios

Setting assessment criteria

Qualitative criteria • Expertise • Policy support • Public participation

Quantitative criteria • Financial criteria • Ecological criteria

Assigning weightage

Analysis & ranking of scenarios Fig. 5. Multi-criteria decision analysis in waste management system.

methods, and cost-benefit analysis (Huang et al., 2008; Geldermann et al., 2000). Morrissey and Browne (2004) reported that a good municipal solid waste management model ought to be socially acceptable apart from being economically reasonable and environmentally effective. A similar approach was later used by Su et al. (2007) in a decision aid that deemed community aspect investigation besides management aspects, technical issues, environmental effects, and expenses and benefits. Hung et al. (2007) evaluated methodology-based numerous models for supporting the decision-making in municipal solid waste. They reported that these models can be categorized into various types based on open investment and social elements etc.

7. Case studies Computer-based mathematical models have been widely used for waste management during the last few decades (Pardo et al., 2017; Kaushal and Nema, 2012; Filipiak et al., 2009; Chang and Wang, 1996). These models usually achieve one or more environmental and economic goals. Economopoulou et al. (2013) presented a methodology for the optimal organization of MSW in the Capital Region of Attica in Greece. The model runs under a specific set of constraints to achieve the objective of minimizing the annual operating cost and annual capital investment of all transportation, treatment, and final waste disposal. The results illustrate that ideal planning proposes considerable financial savings to the region while minimizing in the meantime the present stages of fuel utilization and air outflows in the clogged Athens basin. A multi-attribute decision-making model (Hokkanen and Salminem, 1997) was applied by Chambal et al. (2003) to the US Air Force base, considering the new ecological orders of Air Force base. The model used the value-focused thinking method for choosing the best alternative. Poor management of agricultural wastes such as animal manure has been recognized as a key reason of environmental problems at regional (particle matter formation and soil acidification etc) and at the global scale (ozone reduction and climate change etc). Agricultural waste is the leading source of atmospheric methane, ammonia, and nitrous oxide emissions (Oenema et al., 2005). Pardo et al. (2017) presented a new model SIMSWASTE-AD for the environmental evaluation of agricultural waste management strategies. The proposed model was tested against the available observed data. And there was a close match between observed and modeled values which was confirmed by high Rsquared values. It reflected the model potential to forecast biogas production and N mineralization under diverse operational conditions. Kuo and Perrings (2010) studied the main factors for domestic waste recycling and disposal in 18 cities in Japan and Taiwan to comprehend

6. Artificial neural networks Artificial neural networks (ANN) were generally utilized to model the managerial standards of the focal sensory system. The ANN does the job of the human cerebrum by acquiring information through the learning procedure, which includes the finding of an optimal solution (Singh, 2014c). During the recent past, ANNs have turned into a helpful and popular device for modeling ecological frameworks, for example, SWM (Bayar et al., 2009; Noori et al., 2009a). An ANN-based model was used by Abushammala et al. (2014) to study methane gas corrosion in waste-dumping sites. Recently, Younes et al. (2015) proposed an ANN method to forecasting MSW production utilizing the nonlinear autoregressive system. The proposed approach considers economic and demographic variables such as total national output, populace number, business figures, and per capita electricity demand. The performance evaluation of the model was done using R-squared and mean square error parameters. The ANN-based models have been widely used during the last few decades for dealing with the suitable forecast for solid waste production rates (Noori et al., 2009b; Jalili and Noori, 2008; Shu et al., 2006; Karaca and Özkaya, 2006). An ANN-based model was presented by Noori et al. (2009c) to forecast the weekly municipal solid waste production in Tehran, Iran relating to intricacy and dynamic waste management system. Using the aforesaid algorithms, Noori et al. (2010) developed and applied a suitable ANN model in Mashhad, Iran to enhance the system constraints for the week by week solid waste forecast. A large number of waste-load allocation models were successfully used for managing the quantity of waste discharged into water sources (Kirnbauer and Baetz, 2012; Murty et al., 2006; Hutcheson, 1992). 5

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experiments. In the ongoing past, researchers developed adaptable numerical models to evaluate environmental issues of urban ecosystems. These models offer vital support for the proper management of waste disposal problems. The examination of past writing uncovered that usually optimization models were used to find the answer of 'what is the best' under an explicit arrangement of conditions, while, simulation models were usually helpful to get an answer to 'what if? ' due to their predictive capability. A range of techniques has been employed in an optimization model for SWM with diverse focus and objectives. For example, linear programming has been used to reduce aggregate system expenditure and to incorporate the optimal method of waste management in municipalities while non-linear programming has been applied to maximize profit and minimize waste. Fuzzy method programming has been used to minimize the disparity among individual e-waste items and stochastic programming has been employed to minimize costs of waste flows and capacity expansion while hybrid models have used various modeling techniques for achieving a number of objectives. Goal programming-based optimization models were widely used to handle several conflicting objectives in municipal waste management. The analysis also revealed that the single-objective optimization models were used to get a novel ideal solution, whereas, the multi-objective issues lead to several compromised arrangements. Furthermore, the analysis showed that the ANN-based models have been widely used for dealing with a suitable forecast for solid waste production rate.

the impact of alternative waste management incentives. And the study reported that it depends on the effect of diverse policies on the comparative costs of the key alternative disposal methods such as landfilling and recycling etc. The study concluded that at higher waste collection frequency the recycling would be less and landfilling would be more. An LP-based optimization model was presented by Liu et al. (2008) for water quality evaluation in the Lake Qionghai watershed and to study water pollution control approaches. A comparable methodology was later utilized by Xie et al. (2011) for examining synthetic-industry enhancement in China. Later, Zhou et al. (2015) presented a theoretical model to investigate the carbon cycle of the MSW organization framework for urban metabolism. The model consists of vertical fluxes, carbon loads, and horizontal fluxes of the SWM progressions for example waste disposal and treatment and transport. The model was used in Jingmen City case study to analyze the scenarios which include the present carbon cycling of the MSW organization framework. The study suggested the measures, for example, waste reduction and reuse and landfilling, etc to improve MSW organization. Xu et al. (2013) suggested a model to predict the production of municipal solid waste in an urban area in China. The proposed model does not require other socioeconomic and demographic variables in its operation. Results illustrate that the model is adequately strong to predict seasonal and annual solid waste production at medium and long-term scales. Earlier, various specialists have utilized diverse models for the prediction of municipal solid waste production. For example, Liu and Yu (2007) and Li et al. (2003) have used time series models and Noori et al. (2009b) and Jalili and Noori (2008) applied artificial intelligence models. The system dynamics model was used by Kollikkathara et al. (2010) and Zhang et al. (2007). While the regression analysis models were proposed by Rimaityte et al. (2012) and Sokka et al. (2007). Laureri et al. (2015) suggested a model for the ideal gathering of wet waste at a metropolitan scale. The execution of the model was assessed by an examination with those possible through a broadly useful scientific encoding software. The approach was applied in a case study area in the Municipality of Genoa. Madadian et al. (2013) applied an Analytic Hierarchy Process and MCDA for the comparison of diverse waste management alternatives in the City of Tabriz Iran and suggested the best one under the specific set of conditions. The study compares four waste management approaches namely incineration and landfilling, refused derived fuel, biological and mechanical treatment, and source separation. The results show that the last approach provides the best solution for waste management which consists of compost production, landfilling, and refused derived fuel. The study also suggested diverse solutions that improve community approval and minimize social cost and environmental impacts. More recently, Mishra et al. (2017) presented a scenario-based model for the evaluation of waterway contamination in Kathmandu Valley, Nepal. The model analyzed the water quality parameters, for example, biochemical oxygen demand and dissolved oxygen to evaluate the sustainability of the surface water resources of the valley.

Declaration of Competing Interest None. Acknowledgements The author is thankful to the editors and unknown referees whose valuable remarks and careful comments have prompted broad improvement to the prior editions of the manuscript. References Abushammala, M., et al., 2014. Evaluation of methane generation rate and potential from selected landfills in Malaysia. Int. J. Environ. Sci. Technol. 1–8. Achillas, C., et al., 2013. The use of multi-criteria decision analysis to tackle waste management problems: a literature review. Waste Manag. Res. 31 (2), 115–129. Ahmed, S.A., Ali, S.M., 2006. People as partners: facilitating people’s participation in public-private partnerships for solid waste management. Habitat Int. 30 (4), 729–730. Ameyaw, E.E., Chan, A.P., 2015. Evaluation and ranking of risk factors in public-private partnership water supply projects in developing countries using fuzzy synthetic evaluation approach. Expert Syst. Appl. 42 (12), 5102–5116. Anshassi, M., Laux, S.J., Townsend, T.G., 2019. Approaches to integrate sustainable materials management into waste management planning and policy. Resour. Conserv. Recycl. 148, 55–66. Antanasijevic, D., et al., 2013. The forecasting of municipal waste generation using artificial neural networks and sustainability indicators. Sustain. Sci. 8, 37–46. Arebey, M., et al., 2011. Integrated technologies for solid waste bin monitoring system. Environ. Monit. Assess. 177, 399–408. Aremu, A.S., et al., 2012. Framework to determine the optimal spatial location and number of municipal solid waste bins in a developing world urban neighborhood. J. Environ. Eng. 138 (6), 645–653. Ayodele, T.R., Alao, M.A., Ogunjuyigbe, A.S.O., 2018. Recyclable resources from municipal solid waste: assessment of its energy, economic and environmental benefits in Nigeria. Resour. Conserv. Recycl. 134, 165–173. Batool, S.A., Ch, M.N., 2009. Municipal solid waste management in Lahore city district, Pakistan. Waste Manag. 29, 1971–1981. Bayar, S., et al., 2009. Modeling leaching behavior of solidified wastes using back-propagation neural networks. Ecotoxicol. Environ. Saf. 72, 843–850. Belien, J., et al., 2012. Municipal solid waste collection and management problems: a literature review. Transp. Sci. 48 (1), 78–102. Bisel, R.U., Ravindran, A., 2011. A multiobjective chance constrained programming model for supplier selection under uncertainty. Transp. Res. Part B 45 (8), 1284–1300. Broitman, D., et al., 2018. Designing an agricultural vegetative waste-management system under uncertain prices of treatment-technology output products. Waste Manag. https://doi.org/10.1016/j.wasman.2018.01.041. Bunsan, S., et al., 2013. Modeling the dioxin emission of a municipal solid waste incinerator using neural networks. Chemosphere 92 (3), 258–264.

8. Concluding remarks Growing world population along with fast economic growth and increased living standards have increased the municipal waste generation making its management be a foremost global issue. The problem is even more serious in urban areas as its improper management prompts tainting of soil, water, and environment which create public health risks. These problems of waste disposal and management were usually assessed by traditional methods which require loads of data. The recent development in the new software technologies and Internet along with the introduction of gradually more compact and dependable hardware products have presented the ability to accurately deal with these procedures more easily than costly and tedious field 6

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