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Performance prediction of ZVI-based anaerobic digestion reactor using machine learning algorithms

文献类型：期刊论文

作者Xu, Weichao1,2,3; Long, Fei1; Zhao, He3; Zhang, Yaobin4; Liang, Dawei1,5; Wang, Luguang1; Lesnik, Keaton Larson1; Cao, Hongbin3; Zhang, Yuxiu2; Liu, Hong1
刊名WASTE MANAGEMENT
出版日期2021-02-15
卷号121页码:59-66
关键词Anaerobic digestion Zero-valent iron Machine learning Methane production Prediction
ISSN号0956-053X
DOI10.1016/j.wasman.2020.12.003
英文摘要The use of zero-valent iron (ZVI) to enhance anaerobic digestion (AD) systems is widely advocated as it improves methane production and system stability. Accurate modeling of ZVI-based AD reactor is conducive to predicting methane production potential, optimizing operational strategy, and gathering reference information for industrial design in place of time-consuming and laborious tests. In this study, three machine learning (ML) algorithms, namely random forest (RF), extreme gradient boosting (XGBoost), and deep learning (DL), were evaluated for their feasibility of predicting the performance of ZVI-based AD reactors based on the operating parameters collected in 9 published articles. XGBoost demonstrated the highest accuracy in predicting total methane production, with a root mean squared error (RMSE) of 21.09, compared to 26.03 and 27.35 of RF and DL, respectively. The accuracy represented by mean absolute percentage error also showed the same trend, with 14.26%, 15.14% and 17.82% for XGBoost, RF and DL, respectively. Through the feature importance generated by XGBoost, the parameters of total solid of feedstock (TSf), sCOD, ZVI dosage and particle size were identified as the dominant parameters that affect the methane production, with feature importance weights of 0.339, 0.238, 0.158, and 0.116, respectively. The digestion time was further introduced into the above-established model to predict the cumulative methane production. With the expansion of training dataset, DL outperformed XGBoost and RF to show the lowest RMSEs of 11.83 and 5.82 in the control and ZVI-added reactors, respectively. This study demonstrates the potential of using ML algorithms to model ZVI-based AD reactors. (C) 2020 Elsevier Ltd. All rights reserved.
资助项目Youth Innovation Promotion Association, CAS[2014037] ; National Key Research and Development Program of China[2017YFC0504400] ; Fundamental Research Funds for the Central Universities of China University of Mining and Technology (Beijing)[2020YJSHH06]
WOS研究方向Engineering ; Environmental Sciences & Ecology
语种英语
WOS记录号WOS:000614606600008
出版者PERGAMON-ELSEVIER SCIENCE LTD
资助机构Youth Innovation Promotion Association, CAS ; National Key Research and Development Program of China ; Fundamental Research Funds for the Central Universities of China University of Mining and Technology (Beijing)
源URL[http://ir.ipe.ac.cn/handle/122111/51381]  
专题中国科学院过程工程研究所
通讯作者Zhao, He; Zhang, Yuxiu; Liu, Hong
作者单位1.Oregon State Univ, Dept Biol & Ecol Engn, Corvallis, OR 97333 USA
2.China Univ Min & Technol Beijing, Sch Chem & Environm Engn, Beijing 100083, Peoples R China
3.Chinese Acad Sci, Innovat Acad Green Manufacture, Beijing Engn Res Ctr Proc Pollut Control, Inst Proc Engn,Natl Key Lab Biochem Engn, Beijing 100190, Peoples R China
4.Dalian Univ Technol, Key Lab Ind Ecol & Environm Engn, Sch Environm Sci & Technol, Minist Educ, Dalian 116024, Peoples R China
5.Beihang Univ, Sch Space & Environm, Beijing Key Lab Bioinspired Energy Mat & Devices, Beijing 102206, Peoples R China
推荐引用方式
GB/T 7714		 Xu, Weichao,Long, Fei,Zhao, He,et al. Performance prediction of ZVI-based anaerobic digestion reactor using machine learning algorithms[J]. WASTE MANAGEMENT,2021,121:59-66.
APA Xu, Weichao.,Long, Fei.,Zhao, He.,Zhang, Yaobin.,Liang, Dawei.,...&Liu, Hong.(2021).Performance prediction of ZVI-based anaerobic digestion reactor using machine learning algorithms.WASTE MANAGEMENT,121,59-66.
MLA Xu, Weichao,et al."Performance prediction of ZVI-based anaerobic digestion reactor using machine learning algorithms".WASTE MANAGEMENT 121(2021):59-66.

入库方式： OAI收割

来源：过程工程研究所

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