Using genetic algorithms for dynamic optimization: an industrial fermentation case

The research deals with the dynamic optimization of the beer batch fermentation, obtaining a temperature profile to drive the process along an optimal trajectory. We discretize the temperature profiles in a form of chromosomes, and genetic algorithms are applied to search for the best solution. An objective function is established, and employed as fitting function along the evolutionary optimization. Satisfactory results are obtained requiring not much computation effort, and a fine discretization of the solution achieved.