Parallel differential algorithms for fermentation process

In biotechnology the estimation of the kinetic parameters needs a lot of approximation due to the non-linearity of the system and to the important number of model parameters. Therefore, the computation time increases with the complexity of the problem. We present the performances of the DE (differential evolution), which is a part of EA (evolutionary algorithms) based on GA (genetic algorithms) applied to estimate the parameters model of the fermentation bioprocess. The master-slave scheme ameliorates the time computation allowing us to know the physiological states of the yeast.