Journal
BIORESOURCE TECHNOLOGY
Volume 101, Issue 8, Pages 2884-2887Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.biortech.2009.09.093
Keywords
Single-factor-at-a-time; Design expert; Neural network modeling; Genetic algorithm
Funding
- Department of Biotechnology (DBT), Government of India [BT/PR-6827/AAQ/03/263/2005]
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A nonlinear model describing the relationship between the biosurfactant concentration as a process output and the critical medium components as the independent variables was developed by artificial neural network modeling. The model was optimized for the maximum biosurfactant production by using genetic algorithm. Based on a single-factor-at-a-time optimization strategy, the critical medium components were found to be glucose, urea, SrCl2 and MgSO4. The experimental results obtained from a statistical experimental design were used for the modeling and optimization by linking an artificial neural network (ANN) model with genetic algorithm (GA) in MATLAB. Using the optimized concentration of critical elements, the biosurfactant yield showed close agreement with the model prediction. An enhancement in biosurfactant production by approximately 70% was achieved by this optimization procedure. (C) 2009 Elsevier Ltd. All rights reserved.
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