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Modeling biohydrogen production using different data driven approaches
- Source :
- International Journal of Hydrogen Energy. 46:29822-29833
- Publication Year :
- 2021
- Publisher :
- Elsevier BV, 2021.
-
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.
- Subjects :
- Artificial neural network
Renewable Energy, Sustainability and the Environment
Energy Engineering and Power Technology
Experimental data
Dark fermentation
Condensed Matter Physics
Data-driven
Levenberg–Marquardt algorithm
Fuel Technology
Multilayer perceptron
Biohydrogen
Biochemical engineering
Response surface methodology
Mathematics
Subjects
Details
- ISSN :
- 03603199
- Volume :
- 46
- Database :
- OpenAIRE
- Journal :
- International Journal of Hydrogen Energy
- Accession number :
- edsair.doi...........5bbd1c70e2daa6dcc247d6a0e10a7b84