Hybrid differential evolution for problems of kinetic parameter estimationand dynamic optimization of an ethanol fermentation process

Hybrid differential evolution (HDE) is applied to estimate the kinetic mode l parameters of batch fermentation for ethanol and glycerol production usin g Saccharomyces diastaticus LORRE 316. In this study, we considered two kin etic models for describing the dynamic behaviors of S. diastaticus LORRE 31 6. In the parameter estimation problem, we used the worst observed error fo r all experiments as an objective function so that the parameter estimation problem becomes the min-max estimation problem. Several numerical methods have been employed to solve the min-max estimation problem for comparison. HDE could use a small population size to obtain a more satisfied solution a s compared from these computations. To validate the two kinetic models, we respectively used the two kinetic models to determine the optimal feed rate s for the fedbatch optimization problem. Because the fedbatch optimization problem was a constrained dynamic optimization problem, we introduced the H DE with a multiplier updating method including adaptive penalty parameters to obtain the feasible feed rates. Such feed rates are then applied for fed batch experiments in a 5 L fermenter for model validation, From the compari son of batch and fedbatch experiments, we observed that the proposed kineti c model was more adequate than Monod's model.