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Application of machine learning methods for the prediction of organic solid waste treatment and recycling processes: A review
Guo, Hao-nan1,2; Wu, Shu-biao3; Tian, Ying-jie4; Zhang, Jun5; Liu, Hong-tao1,6
2021
Source PublicationBIORESOURCE TECHNOLOGY
ISSN0960-8524
Volume319Pages:13
Corresponding AuthorLiu, Hong-tao(liuht@igsnrr.ac.cn)
AbstractConventional 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.
KeywordMachine learning Organic solid waste Modeling Prediction
DOI10.1016/j.biortech.2020.124114
WOS KeywordARTIFICIAL NEURAL-NETWORK ; SUPPORT VECTOR MACHINE ; ANAEROBIC CO-DIGESTION ; HIGHER HEATING VALUE ; BIOGAS PRODUCTION ; LEAST-SQUARES ; LIGNOCELLULOSIC BIOMASS ; CLASSIFICATION-SYSTEM ; PROCESS PARAMETERS ; METHANE EMISSIONS
Indexed BySCI
Language英语
Funding ProjectStrategic Priority Research Program of the Chinese Academy of Sciences[XDA23050103] ; National Key R&D Program of China[2018YFD0500205] ; Ko-chen Outstanding Young Scholars Program of the Institute of Geographic Sciences and Natural Resources Research, CAS[2017RC102]
Funding OrganizationStrategic Priority Research Program of the Chinese Academy of Sciences ; National Key R&D Program of China ; Ko-chen Outstanding Young Scholars Program of the Institute of Geographic Sciences and Natural Resources Research, CAS
WOS Research AreaAgriculture ; Biotechnology & Applied Microbiology ; Energy & Fuels
WOS SubjectAgricultural Engineering ; Biotechnology & Applied Microbiology ; Energy & Fuels
WOS IDWOS:000593734700005
PublisherELSEVIER SCI LTD
Citation statistics
Cited Times:5[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/137065
Collection中国科学院地理科学与资源研究所
Corresponding AuthorLiu, Hong-tao
Affiliation1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China
2.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China
3.Aarhus Univ, Aarhus Inst Adv Studies, DK-8000 Aarhus C, Denmark
4.CAS Res Ctr Fictitious Econ & Data Sci, Beijing 100190, Peoples R China
5.Guilin Univ Technol, Guangxi Key Lab Environm Pollut Control Theory &, Guilin 541004, Peoples R China
6.Chinese Acad Sci, Engn Lab Yellow River Delta Modern Agr, Beijing 100101, Peoples R China
Recommended Citation
GB/T 7714
Guo, Hao-nan,Wu, Shu-biao,Tian, Ying-jie,et al. Application of machine learning methods for the prediction of organic solid waste treatment and recycling processes: A review[J]. BIORESOURCE TECHNOLOGY,2021,319:13.
APA Guo, Hao-nan,Wu, Shu-biao,Tian, Ying-jie,Zhang, Jun,&Liu, Hong-tao.(2021).Application of machine learning methods for the prediction of organic solid waste treatment and recycling processes: A review.BIORESOURCE TECHNOLOGY,319,13.
MLA Guo, Hao-nan,et al."Application of machine learning methods for the prediction of organic solid waste treatment and recycling processes: A review".BIORESOURCE TECHNOLOGY 319(2021):13.
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