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ABSTRACT
Traditional strategies designed to determine the best combination of physico-chemical parameters to optimally drive a fermentation process suffer from large computational burden, or are unable to account for the interaction effects of the various process parameters. In this paper, we report a web-enabled engine for fermentation optimization using Genetic Algorithm. The setpoint values of the process parameters are concatenated into strings, called chromosomes and represent potential solution states. A population of such strings is formed. These chromosomes are evaluated for their fitness using an objective function or through experimentation. The best-fitted chromosomes are selected and undergo mutations and crossovers to produce the next generation. This procedure is repeated until the fitness value becomes constant and the best chromosome emerges. The system considers the interactive effects of all the process parameters. The effectiveness of this engine has been evaluated on the feeding strategy for the production of Saccharomyces cerevisiae DS2155, optimization of yogurt acidification process using Lactobacillus bulgaricus and Streptococcus thermophilus. Data revealed a feeding strategy with a biomass yield improvement of 0.53g/l on S. cerevisiae, and an acidification slope of 0.06117 compared to an initial of 0.0342, reducing the yogurt acidification time from six to two hours. This system will reduce the cost for fermentation process development.