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

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Abstract

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.

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Abbreviations

SW:

Solid waste

ML:

Machine learning

WoS:

Web of Science

TP:

The total number of publications

TC:

The total number of times literature cited in the WoS

SP:

The number of single countries publications

CP:

The number of international collaborative publications

FP:

The number of first country publications

ANN:

Artificial neural network

SVM:

Support vector machine

RF:

Random forest

GA:

Genetic algorithm

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Acknowledgement

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

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Authors and Affiliations

  1. School of Environmental Science and Engineering, Tianjin University, Tianjin, 300350, China

    Rui Liang, Chao Chen, Akash Kumar & Beibei Yan

  2. School of Mechanical Engineering, Tianjin University of Commerce, Tianjin, 300134, China

    Junyu Tao, Yan Kang, Dong Han, Xianjia Jiang, Pei Tang & Guanyi Chen

  3. Tianjin Key Laboratory of Biomass Wastes Utilization/Tianjin Engineering Research Center of Bio Gas/Oil Technology, Tianjin, 300072, China

    Beibei Yan

  4. School of Science, Tibet University, Lhasa, 850012, China

    Guanyi Chen

Authors
  1. Rui Liang
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  2. Chao Chen
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  3. Akash Kumar
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  4. Junyu Tao
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  5. Yan Kang
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  6. Dong Han
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  8. Pei Tang
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  9. Beibei Yan
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  10. Guanyi Chen
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Corresponding author

Correspondence to Junyu Tao.

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Highlights

• 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.

Special Issue—Artificial Intelligence/Machine Learning on Environmental Science & Engineering (Responsible Editors: Yongsheng Chen, Xiaonan Wang, Joe F. Bozeman III & Shouliang Yi)

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Liang, R., Chen, C., Kumar, A. et al. State-of-the-art applications of machine learning in the life cycle of solid waste management. Front. Environ. Sci. Eng. 17, 44 (2023). https://doi.org/10.1007/s11783-023-1644-x

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  • DOI: https://doi.org/10.1007/s11783-023-1644-x

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