Abstract
A metaheuristic is a general algorithmic framework which can be applied to different optimization problems with relative few modifications to make them adapted to a specific problem. This work describes a dynamic mathematical model for Bacterial growth bioprocess containing 9 unknown parameters, which were calibrated using particle swarm optimization and genetic algorithms through the minimization of an evaluation function. Two kinetic expressions, the Monod and Haldane equations, commonly employed to describe microbial growth were tested in the model simulations. The identification problem is formulated as a multi-modal numerical optimization problem with high dimension. The performances of the two methods are analyzed by numerical simulations.
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