Fed-batch optimization of α-amylase and protease-producing Bacillus subtilis using genetic algorithm and particle swarm optimization

• Main Authors: Wanwisa Skolpap, Somboon Nuchprayoon, Jeno M. Scharer, Nurak Grisdanurak, Peter L. Douglas, Murray Moo-Young
• Format: Journal
• Published: 2018
• Subjects: Chemical Engineering; Chemistry; Engineering; Mathematics
• Online Access: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=47849093348&origin=inward; http://cmuir.cmu.ac.th/jspui/handle/6653943832/60221
• Institution: Chiang Mai University

Genetic algorithm (GA) and particle swarm optimization (PSO) were implemented to select sets of decision variables for optimal feeding profiles of fed-batch culture of recombinant Bacillus subtilis ATCC 6051a. Both GA and PSO were employed to optimize the volumetric production of recombinant extracellular α-amylases as desirable products and native proteases as undesirable products. The model contains higher-order model equations (14 state variables). The optimization methodology for the dual-enzyme system was coupling Pontryagin's optimum principle with the Luedeking-Piret equation reflecting experimental observations. The optimal solutions attained by using GA and PSO were comparable. Specifically, the maximum specific α-amylase productivity was 18% and 3.5% higher than that of the experimental results and a simplified Markov chain Monte Carlo (MCMC) method, respectively. Nevertheless, GA consumed computational time approximately 17% lower than in case of PSO. © 2008 Elsevier Ltd. All rights reserved.