The DOI matches its Crossref record; 81 of the 81 references it cites were found in a registry or an index.
Application of machine learning methods for the prediction of organic solid waste treatment and recycling processes: A review
Abstract
Conventional treatment and recycling methods of organic solid waste contain inherent flaws, such as low efficiency, low accuracy, high cost, and potential environmental risks. In the past decade, machine learning has gradually attracted increasing attention in solving the complex problems of organic solid waste treatment. Although significant research has been carried out, there is a lack of a systematic review of the research findings in this field. This study sorts the research studies published between 2003 and 2020, summarizes the specific application fields, characteristics, and suitability of different machine learning models, and discusses the relevant application limitations and future prospects. It can be concluded that studies mostly focused on municipal solid waste management, followed by anaerobic digestion, thermal treatment, composting, and landfill. The most widely used model is the artificial neural network, which has been successfully applied to various complicated non-linear organic solid waste related problems.
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Cortes, C., & Vapnik, V. (1995) · Machine Learning · doi:10.1007/bf00994018 · Cited by 59,421
Composting of animal manures and chemical criteria for compost maturity assessment. A review
Bernal, M. P., Alburquerque, J. A., & Moral, R. (2009) · Bioresource Technology · doi:10.1016/j.biortech.2008.11.027 · Cited by 2,519
Olden, J. D., & Jackson, D. A. (2002) · Ecological Modelling · doi:10.1016/s0304-3800(02)00064-9 · Cited by 1,223
Review of LCA studies of solid waste management systems – Part I: Lessons learned and perspectives
Laurent, A., Bakas, I., Clavreul, J., Bernstad, A., Niero, M., Gentil, E., Hauschild, M. Z., & Christensen, T. H. (2014) · Waste Management · doi:10.1016/j.wasman.2013.10.045 · Cited by 744
The evolution of waste-to-energy incineration: A review
Makarichi, L., Jutidamrongphan, W., & Techato, K.-A. (2018) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2018.04.088 · Cited by 566
Guo, X.-X., Liu, H.-T., & Wu, S.-B. (2019) · Science of The Total Environment · doi:10.1016/j.scitotenv.2019.01.137 · Cited by 562
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Artificial intelligence applications in solid waste management: A systematic research review
Abdallah, M., Abu Talib, M., Feroz, S., Nasir, Q., Abdalla, H., & Mahfood, B. (2020) · Waste Management · doi:10.1016/j.wasman.2020.04.057 · Cited by 523
Zhu, X., Li, Y., & Wang, X. (2019) · Bioresource Technology · doi:10.1016/j.biortech.2019.121527 · Cited by 431
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Adedeji, O., & Wang, Z. (2019) · Procedia Manufacturing · doi:10.1016/j.promfg.2019.05.086 · Cited by 400
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Metadata
- Authors
- Guo, H.-N., Wu, S.-B., Tian, Y.-J., Zhang, J., & Liu, H.-T.
- Venue
- Bioresource Technology
- Publisher
- Elsevier BV
- Published
- 2021-01
- Volume
- 319
- Pages
- 124114
- ISSN
- 0960-8524 (ISSN-L)
- OpenAlex
- W3084731547
- Cited by
- 375
- References
- 81
- FWCI
- 24.17
- Type
- journal article
- Language
- en
- Open access
- closed
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