Municipal solid waste management for low-carbon transition: A systematic review of artificial neural network applications for trend prediction.

Improper municipal solid waste (MSW) management contributes to greenhouse gas emissions, necessitating emissions reduction strategies such as waste reduction, recycling, and composting to move towards a more sustainable, low-carbon future. Machine learning models are applied for MSW-related trend prediction to provide insights on future waste generation or carbon emissions trends and assist the formulation of effective low-carbon policies. Yet, the existing machine learning models are diverse and scattered. This inconsistency poses challenges for researchers in the MSW domain who seek to identify and optimize the machine learning techniques and configurations for their applications. This systematic review focuses on MSW-related trend prediction using the most frequently applied machine learning model, artificial neural network (ANN), while addressing potential methodological improvements for reducing prediction uncertainty. Thirty-two papers published from 2013 to 2023 are included in this review, all applying ANN for MSW-related trend prediction. Observing a decrease in the size of data samples used in studies from daily to annual timescales, the summarized statistics suggest that well-performing ANN models can still be developed with approximately 33 annual data samples. This indicates promising opportunities for modeling macroscale greenhouse gas emissions in future works. Existing literature commonly used the grid search (manual) technique for hyperparameter (e.g., learning rate, number of neurons) optimization and should explore more time-efficient automated optimization techniques. Since there are no one-size-fits-all performance indicators, it is crucial to report the model's predictive performance based on more than one performance indicator and examine its uncertainty. The predictive performance of newly-developed integrated models should also be benchmarked to show performance improvement clearly and promote similar applications in future works. The review analyzed the shortcomings, best practices, and prospects of ANNs for MSW-related trend predictions, supporting the realization of practical applications of ANNs to enhance waste management practices and reduce carbon emissions.

Forecasting meteorological impacts on the environmental sustainability of a large-scale solar plant via artificial intelligence-based life cycle assessment.

Although large-scale solar (LSS) is a promising renewable energy technology, it causes adverse impacts on the ecosystem, human health, and resource depletion throughout its upstream (i.e., raw material extraction to solar panel production) and downstream (i.e., plant demolition and waste management) processes. The LSS operational performance also fluctuates due to meteorological conditions, leading to uncertainty in electricity generation and raising concerns about its overall environmental performance. Hitherto, there has been no evidence-backed study that evaluates the ecological sustainability of LSS with the consideration of meteorological uncertainties. In this study, a novel integrated Life Cycle Assessment (LCA) and Artificial Neural Network (ANN) framework is developed to forecast the meteorological impacts on LSS's electricity generation and its life cycle environmental sustainability. For LCA, 18 impact categories and three damage categories are characterised and assessed by ReCiPe 2016 via SimaPro v. 9.1. For ANN, a feedforward neural network is applied via Neural Designer 5.9.3. Taking an LSS plant in Malaysia as a case study, the photovoltaic panel production stage contributes the highest environmental impact in LSS (30 % of human health, 30 % of ecosystem quality, and 34 % of resource scarcity). Aluminium recycling reduces by 10 % for human health, 10 % for ecosystem quality, and 9 % for resource scarcity. The emissions avoided by the forecasted LSS-generated electricity offset the environmental burden for human health, ecosystem quality, and resource scarcity 12-68 times, 13-73 times, and 18-98 times, respectively. The developed ANN-LCA framework can provide LSS stakeholders with data-backed insights to effectively design an environmentally conscious LSS facility, considering meteorological influences.

An integrated life-cycle greenhouse gas protocol accounting on oil palm trunk and empty fruit bunch biofuel production.

Improper discard of oil palm trunk and empty fruit bunch renders massive greenhouse gases. Turning these palm wastes into solid biofuels could aid in carbon reduction. The embodied environmental impacts of the solid biofuel densification process are neglected in carbon emission quantification studies applying Greenhouse Gas Protocol while the significance of classifying the system's direct and indirect carbon emissions were overlooked in those utilising life cycle assessment. Despite the prospect of both methodologies to complement their limitations for carbon emissions quantification, no study integrates both methodologies to investigate direct and indirect emissions systematically from a life cycle perspective. An integrated framework of life cycle assessment and Greenhouse Gas Protocol is developed to quantify the direct and indirect carbon emissions of oil palm trunk and empty fruit bunch densification from cradle-to-gate for three pellet plants in Indonesia and Malaysia. The emissions are categorised into three emission scopes: Scope 1, Scope 2, and Scope 3 according to the Greenhouse Gas Protocol, integrated with avoided emissions which are quantified via life cycle assessment. The pellet plants generate 534.7-732.3 kg CO2-eq/tonnepellet per hour, in which Scope 1 (i.e., direct emissions) is the major emission scope due to high emissions from wastewater production and drying fuel combustion. Washing equipment (169.2-439.0 kg CO2-eq/tonnepellet per hour) and burners (87.1-214.5 kg CO2-eq/tonnepellet per hour) are the hotspots found in the pellet plants. Producing empty fruit bunch pellets could reduce 62.0-74.1 % of emissions than landfilling the empty fruit bunch. Empty fruit bunch pellet and oil palm trunk pellet are recommended to co-fire with coal to phase down coal usage in achieving COP26 pledge. This study provides data-driven insights for quantifying carbon emissions through the integrated framework and could be a reference in future life cycle carbon footprint studies of the biomass densification process.

Life cycle assessment of bioelectrochemical and integrated microbial fuel cell systems for sustainable wastewater treatment and resource recovery.

Bioelectrochemical system (BES) is an emerging technology that can treat wastewater via microbial activity while producing energy simultaneously. The system can couple with conventional systems to improve system performance. This study aims to compare the environmental performance of BES and the integrated microbial fuel cell (MFC) systems via a life cycle assessment methodology and identify the major environmental hotspots of the system. Fifteen treatment options are assessed with the ReCiPe 2016 characterization method using SimaPro 9.2 software. The results show double chamber air-cathode microbial electrolysis cell (MEC1) and membrane distillation integrated MFC (MD + MFC) treatment options present as the most environmental favourable among the BES and integrated MFC systems, respectively, due to the offset of the environmental loads from the avoided impacts contributed by their value-added by-product, which is hydrogen fuel for MEC1 and tap water for MD + MFC. Electricity consumption dominates the environmental loads of all the BES options for up to 90% of the global warming impact category. The environmental benefits from the electricity generation of BES are minor (i.e., MFC: 0.01-2% while microbial desalination cell: 0.01-7% of the total environmental impact in a system) to offset the environmental loads incurred by the system. Platinum-based cathode incurs 2.5-24 times higher environmental burdens than non-platinum configurations in MFC under the human carcinogenic toxicity impact category. In line with Sustainable Development Goals 6 and 13, this study provides scientific references to wastewater treatment stakeholders in selecting suitable BES and integrated MFC systems to improve water sanitation and address climate change simultaneously.

A systematic review of life cycle assessment of solid waste management: Methodological trends and prospects.

Solid waste disposal has led to increasing concerns over resource, health, and environmental problems. These issues have been investigated using the Life Cycle Assessment (LCA) technique which helps identify the roots of varying hazards and allows decision-makers to improve the environmental performance of waste management practices. However, there is a lack of review studies that conducted meta-analysis related to developments in critical methodological steps of LCA on solid waste management. To fill this gap, this review paper examines 15 elements comprising the preference of journals, 13 LCA method-related characteristics, and economic assessment. Insights on the limitations and current practices of LCA applications, along with trends for future research, are provided. 240 studies on the LCA of SWM from 2009 until 2020 were systematically reviewed and classified into two major year-groups (i.e., 2009-2014 and 2015-2020) to investigate the trend changes. Among the studied elements, it is found that energy-related applications are on the increase in LCA studies on solid waste management. Anaerobic digestion facilities nearly double in appearance in the second year-group (2015-2020). There is also a more frequent occurrence of certain characterization methods like ReCiPe and CML. Functional units become more diverse, but are overall mostly defined on a mass basis. A frequently identified issue of many LCAs on solid waste management is the ambiguity of data sources such as out-of-date literature or inconsistent geographical references. By addressing issues of methodological standardization, this review study provides a basis to further increase the reliability of results of future LCA studies on solid waste management.