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This paper focuses on a comprehensive solution for dry municipal solid waste (MSW) management with describes the scenario of the gasification process. For this purpose, the potential of implementing the MSW gasification process in Southeast Asia (Iran) with three agents of air, steam, and air-steam is investigated. In order to manage the MSW gasification process as accurately as possible in the study area, a thermodynamic, exergy, economic, environmental, carbon footprint, water footprint, ecological emergy, and risk assessment for the three mentioned gasification agents were carried out in the most populated cities of Iran. In the capital of Iran (Tehran) with the largest population and as a result of largest MSW production, the potential of the gasification process has been investigated at different times (10 consecutive years and 12 months of a year). Finally, by combining artificial neural networks and genetic programming, comprehensive relationships have been established to predict the thermodynamic conditions of gasification with an air-steam agent. The general results of the survey indicate that gasification with air agents has good potential for most regions. The use of the steam agent has created worse economic, environmental, and risk conditions than the other two agents. Investigating the potential of the gasification process in Tehran shows that this city has a good potential to implement this process compared to other cities in Iran, and the most suitable location for establishing the gasification process is the 4th region of this city. [Display omitted] • Potential of municipal solid waste (MSW) gasification for all Iranian cities. • Comparison of using air, steam, and air-steam agents for MSW gasification. • The payback period of air agent MSW gasification is lower than other agents. • Machine Learning used for prediction of the behavior of MSW gasification. [ABSTRACT FROM AUTHOR]

The aim of this study is to perform a life cycle and energy flow assessments of Municipal Solid Waste (MSW). These latter are modeled using Artificial Neural Network (ANN) for integrated waste management system in Tehran Municipality, Iran. The initial data used in this study have been obtained from Waste Management Organization of Tehran Municipality, while the required data related to the background system were extracted from the EcoInvent 2.2 database. Based on the obtained results from the energy cycle analysis, energy consumptions for transportation and processing plus recycling were calculated as 227.02 and 155.95 Gigajoules (GJ), respectively for 8500 t MSW. This energy consumption has led to the production of 18,884.03 GJ energy output (recycled materials) with an energy ratio of 121.56. A high energy ratio demonstrated that recycling of municipal solid is a new gateway against energy challenges. By optimizing energy consumption, especially energy related to transportation, this ratio could increase more than before. The results obtained from Life Cycle Assessment (LCA) indicate that transportation is considered the most important hot spot in the recycling of MSW while recycling papers is the main contributor to the redaction of environmental indicators in the recycling system. More intensity is observed for indicators related to toxicity due to the lack of waste disposal and to the entry of undesirable substances in natural resources. The 5-7-7-11 structure is the best topology to predict the amount of recycled materials and the impacts of environmental emissions from MSW recycling process developed by feed-forward back-propagation ANN models which are based on Levenberg-Marquardt (LM) training algorithm. Coefficient of determination values for train are in the ranges of 0.926–0.978 and have excellent performance in predicting all the output parameters based on the input parameters. [ABSTRACT FROM AUTHOR]

As the destructive impacts of unbridled use of fossil fuels threaten the quality of life, moving towards renewable energy like solar power has precipitated. While such movements are commendable, environmental impacts of its infrastructure development should not be overlooked. In this sense, this paper addresses a multi-objective robust mathematical model to study an environmentally conscious solar photovoltaic supply chain under correlated uncertainty. The proposed model aims at minimizing the environmental impacts of included activities in the concerned supply chain in addition to minimizing the conventional cost objective. To quantify and assess the relevant environmental impacts, a life cycle assessment-based model is accomplished employing the ReCiPe 2008‏ approach embedded in the SimaPro software tool. The augmented ϵ -constraint procedure is deployed to optimize the proposed multi-objective model, which is able to attain compromise solutions from the Pareto-optimal set. Correlated uncertainty whose importance is underlined in the real-life situations of the photovoltaic industry is also tackled exploiting a robust optimization method. Ultimately, the validity of the presented model is vindicated through discussing a case study. The acquired results endorse the effectiveness and usefulness of the model and offer prominent practical and managerial insights. • An optimization model to study an environmentally conscious solar photovoltaic supply chain is presented. • To quantify the environmental impacts, a life cycle assessment-based model is performed. • The augmented ϵ -constraint procedure is deployed to optimize the proposed model. • The model is able to consider the correlated uncertainty of data. • The validity of the model is vindicated througha case study. [ABSTRACT FROM AUTHOR]

This study deals with the interface between energy consumption and its related emissions in the manufacturing of one cubic meter medium density fiberboard (MDF) in Iran and provides a comprehensive life cycle inventory (LCI) data for production process. The study was developed following the methodological guidelines of ISO 14040. Inventories data for a gate-to-gate assessment of MDF manufacturing were collected by on-site surveys. Ten impact categories including of: Abiotic depletion (AD), Acidification (AC), Eutrophication (EP), Global warming (GW), Ozone layer depletion (OLD), Fresh water aquatic ecotoxicity (FE), Marine aquatic ecotoxicity (ME), Human toxicity (HT), Terrestrial ecotoxicity (TE) and Photochemical oxidation (PO) in three subsystem of manufacturing process were assessed. The subsystem of fiber preparation was the major contributor to the environmental impacts. Urea-formaldehyde (UF) resin production was identified as the main hotspot, except in AD, AC, OLD and PO impacts that natural gas, electricity, transport and production process were the main hotspots, respectively. Moreover, Tube dryer, MDF saw and hogger were the most effective sectors in the emissions of production process. Results for substitution of natural gas showed that except in some impacts categories, use of alternative source of thermal energy such as wood residue could improve emissions from manufacturing. [ABSTRACT FROM AUTHOR]