Modeling biohydrogen production using different data driven approaches

Yixiao Wang

doi:10.1016/j.ijhydene.2021.06.122

Modeling biohydrogen production using different data driven approaches

2021-08-01

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DOI：10.1016/j.ijhydene.2021.06.122

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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.

Keywords:

Biohydrogen

Multilayer perceptron artificial neural network

Levenberg-Marquardt algorithm

Response surface methodology

Modeling biohydrogen production using different data driven approaches

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