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State-of-the-art applications of machine learning in the life cycle of solid waste management

Frontiers of Environmental Science & Engineering 2023年第4期17卷 53-69页

作者： Rui Liang Chao Chen Akash Kumar Junyu Tao Yan Kang Dong Han Xianjia Jiang Pei Tang Beibei Yan Guanyi Chen School of Environmental Science and Engineering Tianjin UniversityTianjin 300350China School of Mechanical Engineering Tianjin University of CommerceTianjin 300134China Tianjin Key Laboratory of Biomass Wastes Utilization/Tianjin Engineering Research Center of Bio Gas/Oil Technology Tianjin 300072China School of Science Tibet UniversityLhasa 850012China

Due to the superiority of machine learning(ML)data processing,it is widely used in research of solid waste(sw).This study analyzed the research and developmental progress of the applications of ML in the life cycle of sw.Statistical analyses were undertaken on the literature published between 1985 and 2021 in the Science Citation Index Expanded and Social Sciences Citation Index to provide an overview of the progress.Based on the articles considered,a rapid upward trend from 1985 to 2021 was found and international cooperatives were found to have strengthened.The three topics of ML,namely,sw categories,ML algorithms,and specific applications,as applied to the life cycle of sw were discussed.ML has been applied during the entire sw process,thereby affecting its life cycle.ML was used to predict the generation and characteristics of sw,optimize its collection and transportation,and model the processing of its energy utilization.Finally,the current challenges of applying ML to sw and future perspectives were discussed.The goal is to achieve high economic and environmental benefits and carbon reduction during the life cycle of sw.ML plays an important role in the modernization and intellectualization of sw management.It is hoped that this work would be helpful to provide a constructive overview towards the state-of-the-art development of sw disposal.

关键词： Machine learning(ML) Solid waste(sw) Bibliometrics sw management Energy utilization Life cycle

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