Modeling And Optimization For The Fermentation Process Of Penicillin

With the quick development of the biological technology,the microorganism ferment hasbecome more and more important in national economy.Because of the strong nonlinear characteristic,time-variation,uncertainty and some essential biologic variables are difficult to be measured directly in fermentative process.It makes the optimization control hard to realizein fermentative process.So modeling and forecasting for the fermentative process by softsensor measuring technology is one of the methods to overcome the difficulty.Penicillin ferment is the typical one of all microorganism ferment.Therefore,modeling and optimization control of penicillin ferment has important actual value.This thesis based on the Penicillin fermentation processes and dynamics differential equation models of Paul to design the random evolution rules of cellular automata models and establishes the probability models of Penicillin fermentation processes.And a two-dimensional cellular automatamodel was set up.The simulation results prove that the CA models can approximate the process and predict the state variables exactly,which is able to realize the optimization and control of the process.The modified CACO (Continuous Ant Colony algorithm) algorithm, which is selected for the Penicillin fermentation process optimization.Reason is the highly nonlinear characteristic of Penicillin fermentation process. Taking the yield of final product asoptimization goal, the feed rate, temperature and pH as well as dissolved oxygen concentration (DO) as the optimization variables. Using MATLAB to simulate the optimization process, we get the satisfactory results, and the final biomass concentration of Penicillin is increased through optimization calculation of modified CACO.