Hydrogen production from lignocellulosic hydrolysate in an up-scaled microbial electrolysis cell with stacked bio-electrodes. (January 2021)

• Record Type: Journal Article
• Authors: Wang, Luguang; Long, Fei; Liang, Dawei; Xiao, Xiang; Liu, Hong
• Is Part Of: Bioresource technology. Volume 320: Part A(2021)
• Publication Date: 2021-01
• Subjects: Hydrogen production -- Microbial electrolysis cell -- Lignocellulosic hydrolysate -- Scaling up -- Biocathode; Biomass -- Periodicals; Biomass energy -- Periodicals; Bioremediation -- Periodicals; Agricultural wastes -- Periodicals; Factory and trade waste -- Periodicals; Organic wastes -- Periodicals; Bioénergie -- Périodiques; Déchets agricoles -- Périodiques; Déchets industriels -- Périodiques; Déchets organiques -- Périodiques; Déchets (Combustible) -- Périodiques; 662.88
• DOI: 10.1016/j.biortech.2020.124314
• Languages: English
• ISSNs: 0960-8524

Abstract

Graphical abstract: Highlights: High yield (91%) was demonstrated using lignocellulosic hydrolysate. Biocathode was used in the scaled-up MEC with stacked electrodes. Enterococcus spp. were the electroactive bacteria on cathodes and anodes. Machine learning was used to link community data with current density. Abstract: Hydrogen production from renewable resources via microbial electrolysis cells (MECs) is a promising approach for sustainable energy production. Yet high hydrogen yield from real feedstocks has not been demonstrated in up-scaled MECs. In this study, a 10-L single chamber MEC with a high electrode surface area to volume ratio (66 m 2 /m 3 ) was constructed and electroactive cathodic biofilms were enriched for hydrogen evolution reaction. A high hydrogen yield of 91% was achieved using lignocellulosic hydrolysate with a hydrogen production rate of 0.71 L/L/D at an organic loading rate of 0.4 g/D. The anodic and cathodic microbial communities, with Enterococcus spp. as the known electroactive bacteria, were capable of achieving current densities of 13.7 A/m 2 and 16.5 A/m 2, respectively. A machine learning algorithm was used to investigate the correlation between community data and electrochemical performance, and the critical genera on determining current density were identified.