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Modeling biohydrogen production using different data driven approaches

文献类型：期刊论文

作者Wang, Yixiao6; Tang, Mingzhu5; Ling, Jiangang4; Wang, Yunshan5; Liu, Yiyang1; Jin, Huan3; He, Jun3; Sun, Yong2,6
刊名INTERNATIONAL JOURNAL OF HYDROGEN ENERGY
出版日期2021-08-23
卷号46期号:58页码:29822-29833
关键词Biohydrogen Multilayer perceptron artificial neural network Levenberg-Marquardt algorithm Response surface methodology
ISSN号0360-3199
DOI10.1016/j.ijhydene.2021.06.122
英文摘要Three modeling techniques namely multilayer perceptron artificial neural network (MLPANN), microbial kinetic with Levenberg-Marquardt algorithm (MKLMA) developed from microbial growth, and the response surface methodology (RSM) were used to investigate the biohydrogen (BioH2) process. The MLPANN and MKLMA were used to model the kinetics of major metabolites during the dark fermentation (DF). The MLPANN and RSM were deployed to model the electron-equivalent balance (EEB) from the cumulative data (after 24 h fermentation) during the DF. With the additional experimental results of kinetic data (20 x 10) and cumulative data (18 x 9), the uncertainties of different models were compared. A new effective strategy for modeling the complex BioH2 process during the DF is proposed: MLPANN and MKLMA are used for the investigation of kinetics of the major metabolites from the limited numbers of experimental data set, and the MLPANN and RSM are used for statistical analysis of the investigated operational parameters upon the major metabolites through EEB perspective. The proposed strategy is a useful and practical paradigm in modeling and optimizing the BioH2 production during the dark fermentation. (c) 2021 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved. Superscript/Subscript Available
WOS关键词ARTIFICIAL NEURAL-NETWORK ; FISCHER-TROPSCH SYNTHESIS ; HYDROGEN-PRODUCTION ; MICROCHANNEL REACTOR ; SLURRY REACTOR ; OPTIMIZATION ; PERFORMANCE ; PREDICTION ; FERMENTATION ; CATALYST
资助项目Provincial Key Laboratory Programme[2020E10018] ; Ningbo Commonweal fund for juice flavor identification project[2019C10104] ; Ningbo Commonweal fund for juice flavor identification project[2019C10033] ; Faculty Inspiration Grant of University of Nottingham (FIG 2019) ; Qianjiang Talent Scheme[QJD1803014] ; National Key R&D Program of China[2018YFC1903500] ; UNNC FoSE New Re-searchers Grant 2020[I01210100011]
WOS研究方向Chemistry ; Electrochemistry ; Energy & Fuels
语种英语
WOS记录号WOS:000684999100006
出版者PERGAMON-ELSEVIER SCIENCE LTD
资助机构Provincial Key Laboratory Programme ; Ningbo Commonweal fund for juice flavor identification project ; Faculty Inspiration Grant of University of Nottingham (FIG 2019) ; Qianjiang Talent Scheme ; National Key R&D Program of China ; UNNC FoSE New Re-searchers Grant 2020
源URL[http://ir.ipe.ac.cn/handle/122111/49895]  
专题中国科学院过程工程研究所
通讯作者Jin, Huan; Sun, Yong
作者单位1.Univ Coll London UCL, Dept Chem, 20 Gordon St, London WC1H 0AJ, England
2.Edith Cowan Univ, Sch Engn, 270 Joondalup Dr, Joondalup, WA 6027, Australia
3.Univ Nottingham Ningbo, Sch Comp Sci, Ningbo 315100, Peoples R China
4.Zhejiang Univ, Natl Local Joint Engn Lab Intelligent Food Techno, Coll Biosyst Engn & Food Sci, Hangzhou 310058, Peoples R China
5.Chinese Acad Sci, Inst Proc Engn, Natl Engn Lab Cleaner Hydromet Prod Technol, Beijing 100190, Peoples R China
6.Univ Nottingham Ningbo, Key Lab Carbonaceous Wastes Proc & Proc Intensifi, Ningbo 315100, Peoples R China
推荐引用方式
GB/T 7714		 Wang, Yixiao,Tang, Mingzhu,Ling, Jiangang,et al. Modeling biohydrogen production using different data driven approaches[J]. INTERNATIONAL JOURNAL OF HYDROGEN ENERGY,2021,46(58):29822-29833.
APA Wang, Yixiao.,Tang, Mingzhu.,Ling, Jiangang.,Wang, Yunshan.,Liu, Yiyang.,...&Sun, Yong.(2021).Modeling biohydrogen production using different data driven approaches.INTERNATIONAL JOURNAL OF HYDROGEN ENERGY,46(58),29822-29833.
MLA Wang, Yixiao,et al."Modeling biohydrogen production using different data driven approaches".INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 46.58(2021):29822-29833.

入库方式： OAI收割

来源：过程工程研究所

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