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

作     者：Rui Liang Chao Chen Akash Kumar Junyu Tao Yan Kang Dong Han Xianjia Jiang Pei Tang Beibei Yan Guanyi Chen Rui Liang;Chao Chen;Akash Kumar;Junyu Tao;Yan Kang;Dong Han;Xianjia Jiang;Pei Tang;Beibei Yan;Guanyi Chen

作者机构：School of Environmental Science and EngineeringTianjin UniversityTianjin 300350China School of Mechanical EngineeringTianjin University of CommerceTianjin 300134China Tianjin Key Laboratory of Biomass Wastes Utilization/Tianjin Engineering Research Center of Bio Gas/Oil TechnologyTianjin 300072China School of ScienceTibet UniversityLhasa 850012China 

出 版 物：《Frontiers of Environmental Science & Engineering》 (环境科学与工程前沿（英文）)

年 卷 期：2023年第17卷第4期

页      面：53-69页

核心收录：

学科分类：0830[工学-环境科学与工程（可授工学、理学、农学学位）] 08[工学] 081104[工学-模式识别与智能系统] 0811[工学-控制科学与工程] 

基　　金：This research was supported by the National Natural Science Foundation of China(No.52100157). 

主　　题：Machine learning(ML) Solid waste(SW) Bibliometrics SW management Energy utilization Life cycle 

摘      要：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.