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BioMed Research International
Volume 2013 (2013), Article ID 265618, 9 pages
http://dx.doi.org/10.1155/2013/265618
Research Article

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

1Pollution Control Research Laboratory, Department of Chemical Engineering, Annamalai University, Annamalai Nagar, Tamil Nadu 608 002, India
2Core Group Pollution Prevention and Resource Recovery, Department of Environmental Engineering and Water Technology, UNESCO-IHE, P.O. Box 3015, 2601 DA, Delft, The Netherlands

Received 11 September 2013; Accepted 11 October 2013

Academic Editor: Kannan Pakshirajan

Copyright © 2013 P. Mullai et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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.