Clustering hybrid regression: a novel computational approach to study and model biohydrogen production through dark fermentation

Systematic approach to assess biohydrogen potential of anaerobic sludge and soil rhizobia as biocatalysts: Influence of crucial factors affecting acidogenic fermentation

A systematic protocol was designed to enumerate the variation in biohydrogen production with two different biocatalysts (sludge and soil) under different pH and organic loads. Both the biocatalysts showed cumulatively higher H2 production under acidogenic condition (pH 6) than at neutral pH condition. The cumulative hydrogen production was non-linearly fitted with modified Gompertz model and statistically validated. Pretreated soil biocatalyst showed relatively higher H2 production (OLR II, 142±5ml) than pretreated sludge (OLR I, 123±5ml); which was evidenced by substrate linked dehydrogenase activity and bio-electrochemical analysis. Experimental results revealed agricultural soil as a better biocatalyst than anaerobic sludge for all the operated process conditions. The voltammogram profiles and Tafel slopes revealed dominance of reductive catalytic activity of the pretreated inoculums substantiating dark-fermentation. Soil consortia showed low polarization resistance (2.24kΩ) and high reductive electron transfer efficiency (1.17 Vdec(-1)) at a high organic load; thus, rebating high H2 production.