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

doi:10.1007/s11783-023-1644-x

Front. Environ. Sci. Eng.

2023, Vol. 17

Issue (4) : 44 https://doi.org/10.1007/s11783-023-1644-x

REVIEW ARTICLE

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

Rui Liang¹, Chao Chen¹, Akash Kumar¹, Junyu Tao²(

), Yan Kang², Dong Han², Xianjia Jiang², Pei Tang², Beibei Yan^1,³, Guanyi Chen^2,⁴

¹. School of Environmental Science and Engineering, Tianjin University, Tianjin 300350, China
². School of Mechanical Engineering, Tianjin University of Commerce, Tianjin 300134, China
³. Tianjin Key Laboratory of Biomass Wastes Utilization/Tianjin Engineering Research Center of Bio Gas/Oil Technology, Tianjin 300072, China
⁴. School of Science, Tibet University, Lhasa 850012, China

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Abstract

● State-of-the-art applications of machine learning (ML) in solid waste (SW) is presented.

● Changes of research field over time, space, and hot topics were analyzed.

● Detailed application seniors of ML on the life cycle of SW were summarized.

● Perspectives towards future development of ML in the field of SW were discussed.

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.

Keywords Machine learning (ML) Solid waste (SW) Bibliometrics SW management Energy utilization Life cycle

Corresponding Author(s): Junyu Tao

About author:

*These authors equally shared correspondence to this manuscript.

Issue Date: 27 October 2022

Cite this article:

Rui Liang,Chao Chen,Akash Kumar, et al. State-of-the-art applications of machine learning in the life cycle of solid waste management[J]. Front. Environ. Sci. Eng., 2023, 17(4): 44.

URL:

https://academic.hep.com.cn/fese/EN/10.1007/s11783-023-1644-x
https://academic.hep.com.cn/fese/EN/Y2023/V17/I4/44

Fig.1 Research route of the article (a) and progress of author keyword analysis in VOSviewer (b).

Fig.2 TP and TC changes from 2001 to 2021.

Fig.3 Comparisons of the top 8 productive countries (a) and TP changes by year in the top 5 productive countries (b) during 2001–2021.

Fig.4 Top 8 productive institutions for ML applied to SW, during 2001–2021.

Fig.5 Author keywords network of 151 journal articles in VOSviewer.

Fig.6 Overly visualization of author keyword co-occurrence (> 3).

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