AccScience Publishing / AJWEP / Volume 9 / Issue 3 / DOI: 10.3233/AJW-2012-9_3_04
RESEARCH ARTICLE

Kinetic Modelling of Phenol Biodegradation by Mixed Microbial Culture in Static Batch Mode

Hemant Kumar1 Kaustubha Mohanty1*
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1 Department of Chemical Engineeringn Institute of Technology Guwahati Guwahati, Assam - 781039, India
AJWEP 2012, 9(3), 19–24; https://doi.org/10.3233/AJW-2012-9_3_04
Submitted: 25 February 2011 | Accepted: 11 April 2012 | Published: 1 January 2012
© 2012 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution -Noncommercial 4.0 International License (CC-by the license) ( https://creativecommons.org/licenses/by-nc/4.0/ )
Abstract

In the present study, mixed microbial culture isolated from the sludge of effluent treatment plant of a refinery was tested for its phenol biodegradation potential under static batch condition. The result showed that, after acclimatization, the culture could biodegrade upto 750 mg L-1 of phenol. 100% phenol degradation was achieved for the various concentrations studied. Kinetic study showed that specific growth rate of microorganisms and specific substrate degradation rate increased up to 300 mg L-1 of initial phenol concentration and then started decreasing. The biodegradation kinetics was fitted to different substrate inhibition models by using optimization software tool (solver) in Microsoft office 2007. Among all models, Aiba model (µmax = 0.3187 h-1, KI = 400, R2 = 0.915) and Edward Model (µmax = 0.0011 h-1, KI = 210 mg L-1, R2 = 0.942) were fitted the best. Growth kinetics was also fitted well to the classical Haldane model. The values of inhibition constant, KI from Yano model indicated that this culture may well degrade phenol beyond 750 mg L-1.

Keywords
Phenol
mixed culture
batch mode
growth kinetics
inhibition model
Conflict of interest
The authors declare they have no competing interests.
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Asian Journal of Water, Environment and Pollution, Electronic ISSN: 1875-8568 Print ISSN: 0972-9860, Published by AccScience Publishing