Biohydrogen Production and Kinetic Modeling Using Sediment Microorganisms of Pichavaram Mangroves, India

Abstract

Mangrove sediments host rich assemblages of microorganisms, predominantly mixed bacterial cultures, which can be efficiently used for biohydrogen production through anaerobic dark fermentation. The influence of process parameters such as effect of initial glucose concentration, initial medium pH, and trace metal (Fe2+) concentration was investigated in this study. A maximum hydrogen yield of 2.34, 2.3, and 2.6 mol H2 mol−1 glucose, respectively, was obtained under the following set of optimal conditions: initial substrate concentration—10,000 mg L−1, initial pH—6.0, and ferrous sulphate concentration—100 mg L−1, respectively. The addition of trace metal to the medium (100 mg L−1 FeSO4 ·7H2O) enhanced the biohydrogen yield from 2.3 mol H2 mol−1 glucose to 2.6 mol H2 mol−1 glucose. Furthermore, the experimental data was subjected to kinetic analysis and the kinetic constants were estimated with the help of well-known kinetic models available in the literature, namely, Monod model, logistic model and Luedeking-Piret model. The model fitting was found to be in good agreement with the experimental observations, for all the models, with regression coefficient values >0.92.

Biohydrogen production: A new controllability criterion for analyzing the impacts of dark fermentation conditions

“…One of the possible reasons for the decrease in controllability is substrate inhibition at a high substrate concentration. Such an inhibitory effect of high substrate concentration is reported in [37,38] where the hydrogen yield decreased significantly as the hexose concentration increased from 10 g/L to 50 g/L. Bear in mind that some soluble by-products released during fermentation, e.g., ethanol can also inhibit the biohydrogen productivity and yield as reported in [39].…”

Reliable determination of the growth and hydrogen production parameters of the photosynthetic bacterium Rhodobacter capsulatus in fed batch culture using a combination of the Gompertz function and the Luedeking-Piret model

“… Biomass α value (ml g −1 VSS a ) β value (ml H 2 g −1 dw h −1 ) Reactor type substrate Fermentation/LP model Ref. R. palustris 6.85 b 0.41 b batch glycerol Light/modified [ 25 ] R. capsulatus B10 IR3 4.5 c 36.6 c 17 c 72 c Quasi-continuous Lactate Light/modified This study Mangrove sediments 11.04 0 batch glucose Dark/original [ 35 ] Local sewage sludge 224 0 continuous sucrose Dark/ e r H2 = DαX [ 36 ] Anaerobic sludge 759 0 batch glucose Dark/original [ 37 ] Anaerobic sludge 793 0 batch glucose Dark/original [ 38 ] Enterobacter cloacae 166 d 0 batch glucose Dark/original [ 39 ] Clostridium pasteuranium 918 876 0 0 batch xylose ...…”

Kinetic Modelling of Oxalic Acid Production from Cassava Whey by Aspergillus niger

“…Ravi et al [29] were able to use Haldane's Model to predict growth and process parameters in monocultures using substrate utilization constants in a similar investigation. Mullai et al, [30] claimed that a large fraction of substrate in a solid-state fermentation can limit microbial performance and product creation rate. The time it takes for most Ascomycota and Zygomycotabased inocula to sprout and prosper in a substrate differs from one substrate to the next, necessitating substrate research prior to growth kinetics, which is consistent with the current work.…”