Review of life-cycle environmental consequences of waste-to-energy solutions on the municipal solid waste management system

Istrate, Ioan-Robert; Iribarren, Diego; Gálvez‐Martos, José‐Luis; Dufour, Javier

doi:10.1016/j.resconrec.2020.104778

Cited by 131 publications

(36 citation statements)

References 50 publications

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“…About 74% of the MSW generated in Japan is incinerated, 3% of the waste is disposed off in landfills after recycling 17% of waste ( Mollica et al, 2020 ). Nations like Norway, Finland, Denmark and Sweden use energy recovery systems to incinerate more than 50% of their MSW ( Istrate et al, 2020 ). MSW is maintained in Austria by sending less than 9% of the waste to landfills while 40% goes to incinerating energy recovery facilities and 32% of the waste generated to composting ( Kyriakis et al, 2019 ).…”

Section: Pre and Post Pandemic Waste Management Practices: Global Scenariomentioning

confidence: 99%

“…The most frequently used option for WtE is incineration in densely populated countries, such as China ( Kumar and Samadder, 2017 ). China's MSW incineration capacity was estimated by Istrate et al (2020) and reported that it increased to 231,600 tons/day by 2015 from 15,000 tons/day in 2003, but substantial portions of MSW are still disposed off in landfills. Table 1 presents population, total cases; waste amount generated pre- and post- COVID pandemic and management strategies of different countries.…”

Section: Pre and Post Pandemic Waste Management Practices: Global Scenariomentioning

confidence: 99%

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Solid waste management during COVID-19 pandemic: Recovery techniques and responses

et al. 2022

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Solid waste management (SWM) is a service of public health that is often understated in its significance. If a public health emergency like the COVID-19 outbreak exacerbates the SWM problem, its true importance as an imperative service becomes more apparent. The crisis triggered by the COVID-19 pandemic has changed the dynamics of waste generation globally in nearly every sector and has therefore raised the need for special attention. The unpredictable variations in the quantity and composition of waste also pressurize policymakers to react dynamically. This review highlights the major problems faced during the pandemic by SWM sector and the underlying possibilities to fill the gaps in the existing system. The review focuses on particular areas that have been the most important cause of concern throughout the crisis in the process of waste management. In addition, the mixing of virus infected biomedical waste with the stream of normal solid waste and lack of active involvement of the citizen and cooperation presents the major negative safety and health concerns for the workers involved in the sanitation process. Apart from presenting innovative solutions to tackle current waste management issues, this study also proposes several key potential guidelines to holistically mitigate possible future pandemics, if any. This article can also be of great implication for creation of a specific strategy towards preventing/controlling any potential pandemic of similar kind in the near future.

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Section: Pre and Post Pandemic Waste Management Practices: Global Scenariomentioning

confidence: 99%

Section: Pre and Post Pandemic Waste Management Practices: Global Scenariomentioning

confidence: 99%

Solid waste management during COVID-19 pandemic: Recovery techniques and responses

et al. 2022

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“…However, a reduction in economic activity would not be attractive [16]. With that, EPA has recognized MSW recycling as the second most environmentally sound strategy for dealing with urban waste [17].…”

Section: Introductionmentioning

confidence: 99%

Precision Measurement for Industry 4.0 Standards towards Solid Waste Classification through Enhanced Imaging Sensors and Deep Learning Model

Qin

Ahmad

Ali

et al. 2021

Wireless Communications and Mobile Computing

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Achievement of precision measurement is highly desired in a current industrial revolution where a significant increase in living standards increased municipal solid waste. The current industry 4.0 standards require accurate and efficient edge computing sensors towards solid waste classification. Thus, if waste is not managed properly, it would bring about an adverse impact on health, the economy, and the global environment. All stakeholders need to realize their roles and responsibilities for solid waste generation and recycling. To ensure recycling can be successful, the waste should be correctly and efficiently separated. The performance of edge computing devices is directly proportional to computational complexity in the context of nonorganic waste classification. Existing research on waste classification was done using CNN architecture, e.g., AlexNet, which contains about 62,378,344 parameters, and over 729 million floating operations (FLOPs) are required to classify a single image. As a result, it is too heavy and not suitable for computing applications that require inexpensive computational complexities. This research proposes an enhanced lightweight deep learning model for solid waste classification developed using MobileNetV2, efficient for lightweight applications including edge computing devices and other mobile applications. The proposed model outperforms the existing similar models achieving an accuracy of 82.48% and 83.46% with Softmax and support vector machine (SVM) classifiers, respectively. Although MobileNetV2 may provide a lower accuracy if compared to CNN architecture which is larger and heavier, the accuracy is still comparable, and it is more practical for edge computing devices and mobile applications.

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“…The increasing number of the world populations in recent years has accelerated concern over the sustainable management of municipal solid waste (MSW). MSW commonly refers to solid waste from residential, commercial, institutional sources, and hazardous waste [1][2][3][4][5]. The management of MSW generally includes collecting, transporting, processing, and disposing of waste [6] which requires a large amount of management costs.…”

Section: Introductionmentioning

confidence: 99%

Optimization of a Capacitated Vehicle Routing Problem for Municipal Solid Waste Collection Using an Intelligence Hybrid Harmony Search Algorithm

Wongsinlatam

Thanasate-angkool

2021

Preprint

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The study of sustainable management of municipal solid waste (MSW) collection has been increasing in recent years. However, the focus areas of research are mostly in the economics and environmental dimensions. This paper adds social aspects of MSW into considerations to accelerate more comprehensive decision making. The consideration on the capacity and fixed costs of vehicles, the distances from depot to the disposal facilities are determined in this paper. The environmental issues relating to fuel consumption, carbon emissions, and the evaluation of social impact from the penalty costs of imbalanced trip assignments are also determined to adopt a comprehensive solution to the capacitated vehicle routing problem (CVRP) model. Then, the optimization model of MSW collection is proposed to minimize management costs which compose of the fixed costs of vehicles, fuel consumption costs, carbon emissions costs, and penalty costs. In this paper, two metaheuristic techniques are used to optimization the CVRP model in the MSW collection system. A new metaheuristic algorithm, called an intelligence hybrid harmony search algorithm (IHHS), is proposed in comparison with the standard harmony search (SHS) algorithm. The result shows that the IHHS algorithm can obtain the global optimal solution and can minimize the total objective function for the CVRP model.

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Review of life-cycle environmental consequences of waste-to-energy solutions on the municipal solid waste management system

Cited by 131 publications

References 50 publications

Solid waste management during COVID-19 pandemic: Recovery techniques and responses

Solid waste management during COVID-19 pandemic: Recovery techniques and responses

Precision Measurement for Industry 4.0 Standards towards Solid Waste Classification through Enhanced Imaging Sensors and Deep Learning Model

Optimization of a Capacitated Vehicle Routing Problem for Municipal Solid Waste Collection Using an Intelligence Hybrid Harmony Search Algorithm

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