Modeling biohydrogen production using different data driven approaches

Abstract

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 × 10) and cumulative data (18 × 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.

Document Type

Journal Article

Date of Publication

2021

Volume

46

Issue

58

Publication Title

International Journal of Hydrogen Energy

Publisher

Elsevier

School

School of Engineering

RAS ID

39590

Funders

Provincial Key Laboratory Programme Ningbo Commonweal fund University of Nottingham UNNC FoSE New Researchers Grant Qianjiang Talent Scheme National Key R&D Program of China

Comments

Wang, Y., Tang, M., Ling, J., Wang, Y., Liu, Y., Jin, H., . . . Sun, Y. (2021). Modeling biohydrogen production using different data driven approaches. International Journal of Hydrogen Energy, 46(58), 29822-29833. https://doi.org/10.1016/j.ijhydene.2021.06.122

Copyright

subscription content

First Page

29822

Last Page

29833

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Link to publisher version (DOI)

10.1016/j.ijhydene.2021.06.122