Enhanced continuous biohydrogen production using dynamic membrane with conductive biofilm supporter.

The present study investigated the effect of a conductive biofilm supporter on continuous production of biohydrogen in a dynamic membrane bioreactor (DMBR). Two lab-scale DMBRs were operated: one with a nonconductive polyester mesh (DMBR I) and the other with a conductive stainless-steel mesh (DMBR II). The highest average hydrogen productivity and the yield were 16.8% greater in DMBR II than in DMBR I, with values of 51.64 ± 0.66 L/L-d and 2.01 ± 0.03 mol H2/mol hexoseconsumed, respectively. The improved hydrogen production was concurrent with a higher NADH/NAD+ ratio and a lower ORP (Oxidation-reduction potential). Metabolic flux analysis implied that the conductive supporter promoted H2-producing acetogenesis and repressed competitive NADH-consuming pathways, such as homoacetogenesis and lactate production. Microbial community analysis revealed that electroactive Clostridium sp. were the dominant H2 producers in DMBR II. Conclusively, conductive meshes may be useful as biofilm supporters of dynamic membranes during H2 production for selectively enhancing H2-producing pathways.

Machine learning in fermentative biohydrogen production: Advantages, challenges, and applications.

Hydrogen can be produced in an environmentally friendly manner through biological processes using a variety of organic waste and biomass as feedstock. However, the complexity of biological processes limits their predictability and reliability, which hinders the scale-up and dissemination. This article reviews contemporary research and perspectives on the application of machine learning in biohydrogen production technology. Several machine learning algorithems have recently been implemented for modeling the nonlinear and complex relationships among operational and performance parameters in biohydrogen production as well as predicting the process performance and microbial population dynamics. Reinforced machine learning methods exhibited precise state prediction and retrieved the underlying kinetics effectively. Machine-learning based prediction was also improved by using microbial sequencing data as input parameters. Further research on machine learning could be instrumental in designing a process control tool to maintain reliable hydrogen production performance and identify connection between the process performance and the microbial population.

Effect of bioaugmentation using Clostridium butyricum on the start-up and the performance of continuous biohydrogen production.

This study aimed to mitigate the instability in the start-up and continuous performance of dark fermentative biohydrogen production using heat-treated sludge by the addition of an exogenous H2-producing strain. Continuous fermentation augmented with Clostridium butyricum showed the highest average biohydrogen production rate (HPR) as 50.35 ± 2.56 and 58.57 ± 5.03 L/L-d with H2-producing butyric and acetic acid pathways, whereas the fermenters without bioaugmentation showed the termination of biohydrogen production in 3 days of continuous operation with non H2-producing lactic acid pathway and H2-consuming propionic acid pathway. The bioaugmentation blocked the growth of the competitors for hexose such as Streptococcus, Lactobacillus and Megasphaera, and provided H2-producer dominated microbiome with not only Clostridium butyricum, but also Clostridium puniceum and Clostridium neuense originated from heat-treated sludge. Bioaugmentation of a H2-producing strain would be a reliable dissemination strategy for dark fermentative biohydrogen production by minimizing the influence of seed sludge population.