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Please use this identifier to cite or link to this item: http://repository.iitr.ac.in/handle/123456789/1882
Title: Optimization of polyhydroxybutyrate (PHB) production by Azohydromonas lata MTCC 2311 by using genetic algorithm based on artificial neural network and response surface methodology
Authors: Zafar M.
Kumar S.
Kumar S.
Dhiman A.K.
Published in: Biocatalysis and Agricultural Biotechnology
Abstract: In the present study, the maximum biomass and polyhydroxybutyrate productions were studied and optimized using suitable carbon and nitrogen sources by bacterial strain Azohydromonas lata MTCC 2311. Among three carbon sources namely, sucrose, fructose, and glucose and four nitrogen sources namely, (NH 4) 2SO 4, NH 4Cl, urea, and NH 4NO 3 studied in shake flask experiments, sucrose and urea were found to be the best carbon and nitrogen sources, respectively. Further, response surface methodology (RSM) and artificial neural network models (ANN) were applied to navigate the experimental data obtained in accordance with the central composite design. The effects of sucrose (3.2-36.82g/L), urea (0.16-1.84g/L), and TE solution (0.32-3.68ml/L) on biomass and PHB concentrations were investigated. The modeling and optimization ability of hybrid ANN-GA had shown higher accuracy in finding optimum concentrations of medium variables than hybrid RSM-GA. Hybrid ANN-GA predicted the maximum biomass concentration (12.25g/L) at the optimum level of medium variables: sucrose, 35.27g/L; urea, 1.55g/L; and TE solution, 0.42ml/L. Whereas, the maximum predicted PHB concentration (5.95g/L) was reported at: sucrose, 35.20g/L; urea, 1.58g/L; and TE solution, 0.36ml/L. The validation with additional set of data shows that the predictive errors (%) in biomass and PHB concentrations were 3.67 and 2.52, respectively for shake flask experiments, whereas, the predictive errors (%) were 13.80 and 14.28, respectively, for bioreactor experiments. © 2011 Elsevier Ltd.
Citation: Biocatalysis and Agricultural Biotechnology (2012), 1(1): 70-79
URI: https://doi.org/10.1016/j.bcab.2011.08.012
http://repository.iitr.ac.in/handle/123456789/1882
Issue Date: 2012
Keywords: Artificial Neural Network
Azohydromonas lata
Genetic algorithm
Optimization
Polyhydroxybutyrate
Response surface methodology
ISSN: 18788181
Author Scopus IDs: 57197240378
57209548014
55500513200
8548369300
Author Affiliations: Zafar, M., Department of Chemical Engineering, Indian Institute of Technology Roorkee, Roorkee 247 667, Uttarakhand, India
Kumar, S., Department of Chemical Engineering, Indian Institute of Technology Roorkee, Roorkee 247 667, Uttarakhand, India
Kumar, S., Department of Chemical Engineering, Indian Institute of Technology Roorkee, Roorkee 247 667, Uttarakhand, India
Dhiman, A.K., Department of Chemical Engineering, Indian Institute of Technology Roorkee, Roorkee 247 667, Uttarakhand, India
Corresponding Author: Kumar, S.; Department of Chemical Engineering, Indian Institute of Technology Roorkee, Roorkee 247 667, Uttarakhand, India; email: skumar@iitr.ernet.in
Appears in Collections:Journal Publications [CH]

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