Hybrid Differential Evolution for Problems of Kinetic Parameter Estimation and Dynamic Optimization of an Ethanol Fermentation Process

Abstract

Hybrid differential evolution (HDE) is applied to estimate the kinetic model parameters of batch fermentation for ethanol and glycerol production using Saccharomyces diastaticus LORRE 316. In this study, we considered two kinetic models for describing the dynamic behaviors of S. diastaticus LORRE 316. In the parameter estimation problem, we used the worst observed error for 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 as compared from these computations. To validate the two kinetic models, we respectively used the two kinetic models to determine the optimal feed rates for the fedbatch optimization problem. Because the fedbatch optimization problem was a constrained dynamic optimization problem, we introduced the HDE with a multiplier updating method includin...