A Review of Biohydrogen Productions from Lignocellulosic Precursor via Dark Fermentation: Perspective on Hydrolysate Composition and Electron-Equivalent Balance

Yiyang Liu, Jingluo Min, Xingyu Feng, Yue He, Jinze Liu, Yixiao Wang, Jun He, Hainam Do, Valérie Sage, Gang Yang and Yong Sun
Additional contact information
Yiyang Liu: Department of Chemical and Environmental Engineering, University of Nottingham Ningbo China, 199 Taikang E Rd, Ningbo 315100, China
Jingluo Min: Department of Chemical and Environmental Engineering, University of Nottingham Ningbo China, 199 Taikang E Rd, Ningbo 315100, China
Xingyu Feng: Department of Chemical and Environmental Engineering, University of Nottingham Ningbo China, 199 Taikang E Rd, Ningbo 315100, China
Yue He: Department of Chemical and Environmental Engineering, University of Nottingham Ningbo China, 199 Taikang E Rd, Ningbo 315100, China
Jinze Liu: Department of Chemical and Environmental Engineering, University of Nottingham Ningbo China, 199 Taikang E Rd, Ningbo 315100, China
Yixiao Wang: Department of Chemical and Environmental Engineering, University of Nottingham Ningbo China, 199 Taikang E Rd, Ningbo 315100, China
Jun He: Department of Chemical and Environmental Engineering, University of Nottingham Ningbo China, 199 Taikang E Rd, Ningbo 315100, China
Hainam Do: Department of Chemical and Environmental Engineering, University of Nottingham Ningbo China, 199 Taikang E Rd, Ningbo 315100, China
Valérie Sage: The Commonwealth Scientific and Industrial Research Organization (CSIRO), Australian Resources Research Centre 26 Dick Perry Avenue, Kensington Energy Business Unit, Perth, WA 6155, Australia
Gang Yang: State Key Laboratory of Biochemical Engineering, Institute of Process Engineering, Chinese Academy of Sciences, Beijing 100190, China
Yong Sun: Department of Chemical and Environmental Engineering, University of Nottingham Ningbo China, 199 Taikang E Rd, Ningbo 315100, China

Energies, 2020, vol. 13, issue 10, 1-27

Abstract: This paper reviews the current technological development of bio-hydrogen (BioH 2 ) generation, focusing on using lignocellulosic feedstock via dark fermentation (DF). Using the collected reference reports as the training data set, supervised machine learning via the constructed artificial neuron networks (ANNs) imbedded with feed backward propagation and one cross-out validation approach was deployed to establish correlations between the carbon sources (glucose and xylose) together with the inhibitors (acetate and other inhibitors, such as furfural and aromatic compounds), hydrogen yield (HY), and hydrogen evolution rate (HER) from reported works. Through the statistical analysis, the concentrations variations of glucose (F-value = 0.0027) and acetate (F-value = 0.0028) were found to be statistically significant among the investigated parameters to HY and HER. Manipulating the ratio of glucose to acetate at an optimal range (approximate in 14:1) will effectively improve the BioH 2 generation (HY and HER) regardless of microbial strains inoculated. Comparative studies were also carried out on the evolutions of electron equivalent balances using lignocellulosic biomass as substrates for BioH 2 production across different reported works. The larger electron sinks in the acetate is found to be appreciably related to the higher HY and HER. To maintain a relative higher level of the BioH 2 production, the biosynthesis needs to be kept over 30% in batch cultivation, while the biosynthesis can be kept at a low level (2%) in the continuous operation among the investigated reports. Among available solutions for the enhancement of BioH 2 production, the selection of microbial strains with higher capacity in hydrogen productions is still one of the most phenomenal approaches in enhancing BioH 2 production. Other process intensifications using continuous operation compounded with synergistic chemical additions could deliver additional enhancement for BioH 2 productions during dark fermentation.

Keywords: biohydrogen; lignocellulosic precursor; artificial neuron networks; electron-equivalent balance; process intensification; review (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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