A new parameter optimization algorithm of penicillin fermentation model

Modeling and simulation technology of biological reaction is the important method of theoretical studies and production control. However the greatest technical difficulties of biological reaction modeling is how to identify and discuss the undetermined parameters existing in the complex system by analyzing limited experimental data-to look for an intelligent optimization algorithm that can optimize multivariable and mult-extremum high dimensional nonlinear functions effectively. This thesis took the fed-batch fermentation model in the penicillin industrial production for example, based on the industrial control rolling simulation software, discussed the application characteristics and their improvement of the real model of four widely used optimization algorithms: Simplex Algorithm, Genetic Algorithm, Simulated Algorithm and Particle Swarm Optimization. Lastly, the thesis proposed a new combined optimization algorithm- Genetic-simplex Algorithm by analyzing the complementarity of SIM and GA. Proved by the results of industrial rolling simulation this algorithm has combined the advantages of Simplex Algorithm and Genetic Algorithm, possessing excellent optimization ability.