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Application of machine learning methods for the prediction of organic solid waste treatment and recycling processes: A review

文献类型：期刊论文

作者Guo, Hao-nan3,4; Wu, Shu-biao2; Tian, Ying-jie1; Zhang, Jun6; Liu, Hong-tao4,5
刊名BIORESOURCE TECHNOLOGY
出版日期2021
卷号319页码:13
关键词Machine learning Organic solid waste Modeling Prediction
ISSN号0960-8524
DOI10.1016/j.biortech.2020.124114
英文摘要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.
WOS关键词ARTIFICIAL NEURAL-NETWORK ; SUPPORT VECTOR MACHINE ; ANAEROBIC CO-DIGESTION ; HIGHER HEATING VALUE ; BIOGAS PRODUCTION ; LEAST-SQUARES ; LIGNOCELLULOSIC BIOMASS ; CLASSIFICATION-SYSTEM ; PROCESS PARAMETERS ; METHANE EMISSIONS
资助项目Strategic 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]
WOS研究方向Agriculture ; Biotechnology & Applied Microbiology ; Energy & Fuels
语种英语
WOS记录号WOS:000593734700005
出版者ELSEVIER SCI LTD
资助机构Strategic 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
源URL[http://ir.igsnrr.ac.cn/handle/311030/137064]  
专题黄河三角洲现代农业工程实验室_外文论文
作者单位1.CAS Res Ctr Fictitious Econ & Data Sci, Beijing 100190, Peoples R China
2.Aarhus Univ, Aarhus Inst Adv Studies, DK-8000 Aarhus C, Denmark
3.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China
4.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China
5.Chinese Acad Sci, Engn Lab Yellow River Delta Modern Agr, Beijing 100101, Peoples R China
6.Guilin Univ Technol, Guangxi Key Lab Environm Pollut Control Theory &, Guilin 541004, Peoples R China
推荐引用方式
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.

入库方式： OAI收割

来源：地理科学与资源研究所

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