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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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Interpretable machine learning assisted spectroscopy for fast characterization of biomass and waste

Interpretable machine learning assisted spectroscopy for fas...

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2023年有机固废处理与资源化利用大会

作者： Liang Rui Chen Chao Tao Junyu Yan Beibei Chen Guanyi School of Environmental Science and engineering Tianjin University School of Mechanical engineering Tianjin University of Commerce tianjin key Lab of biomass wastes utilization/tianjin engineering research center of bio gas/oil technology School of Science Tibet University

Background,Aims and Scope.The characterization of biomass and waste(BW) is important to its downstream treatment and utilization.In order to overcome the long duration and complex procedure problems of traditional BW characterization methods,the spectroscopy and machine learning based BW fast characterization method has been developed recently,and presented satisfactory performance.However,this fast characterization process is lack of interpretability towards its chemical insights,leading to less satisfactory recognition for its reliability and difficulty in expanding the further application.Accordingly,this study proposed a hypothesis to the inner working mechanism of the spectroscopy and machine learning based BW fast characterization method:Infrared spectra have close qualitative and quantitative correlation with the functional groups' species and content.Each functional group has its specific element composition and heating value characteristics.Hence,the element composition and heating value results are obtained by processing the functional group information behind the infrared spectra.this paper aimed to verify the hypothesis.A novel dimensionality reduction method with physicochemical meanings was proposed to explain the chemical insights of the machine learning model.The high loading spectral peaks of BW,which contain the important information of the functional groups of raw materials,were selected as the results of dimensionality reduction.Then,combined with functional groups attribution of these spectral peaks,the machine learning model could provide clearer logic explanation of the input spectra and output characteristics,furthermore,provide support to the above hypothesis.This paper discussed the results of the dimensionality reduction,evaluated the characterization performance between the newly dimensionality reduction method and PCA,moreover,explored the theoretical fundamentals of the input spectra on the output characteristics.It is hoped to provide mechanism explanation and theoretical basis for

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