CAS OpenIR
Performance prediction of ZVI-based anaerobic digestion reactor using machine learning algorithms
Xu, Weichao1,2,3; Long, Fei1; Zhao, He3; Zhang, Yaobin4; Liang, Dawei1,5; Wang, Luguang1; Lesnik, Keaton Larson1; Cao, Hongbin3; Zhang, Yuxiu2; Liu, Hong1
2021-02-15
Source PublicationWASTE MANAGEMENT
ISSN0956-053X
Volume121Pages:59-66
AbstractThe 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.
KeywordAnaerobic digestion Zero-valent iron Machine learning Methane production Prediction
DOI10.1016/j.wasman.2020.12.003
Language英语
Funding ProjectYouth 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 Research AreaEngineering ; Environmental Sciences & Ecology
WOS SubjectEngineering, Environmental ; Environmental Sciences
Funding OrganizationYouth 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)
WOS IDWOS:000614606600008
PublisherPERGAMON-ELSEVIER SCIENCE LTD
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ipe.ac.cn/handle/122111/51382
Collection中国科学院过程工程研究所
Corresponding AuthorZhao, He; Zhang, Yuxiu; Liu, Hong
Affiliation1.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
Recommended Citation
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.
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