Artificial neural network modeling and genetic algorithm based medium optimization for the improved production of marine biosurfactant.
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, SrCl(2) and MgSO(4). 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.
Sivapathasekaran
C
C
Department of Biotechnology, Indian Institute of Technology Kharagpur, West Bengal-721302, India.
Mukherjee
Soumen
S
Ray
Arja
A
Gupta
Ashish
A
Sen
Ramkrishna
R
D000465:Algorithms; D001407:Bacillus / Q000378:metabolism*; D001709:Biotechnology / Q000379:methods*; D005947:Glucose / Q000378:metabolism; D008278:Magnesium Sulfate / Q000378:metabolism; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D013324:Strontium / Q000378:metabolism; D013501:Surface-Active Agents / Q000378:metabolism*; D014508:Urea / Q000378:metabolism
D013501:Surface-Active Agents; D008278:Magnesium Sulfate; D014508:Urea; C025700:strontium chloride; D005947:Glucose; D013324:Strontium