Modeling And Optimization For The Fermentation Process Of Penicillin

With the quick development of the biological technology, the microorganism ferment has become 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 realize in fermentative process. So modeling and forecasting for the fermentative process by soft sensor 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.Based on the penicillin fermentation processes and dynamics differential equation models, this thesis designs a hybrid model with least square support vector machine, and establishes the probability models of Penicillin fermentation processes. The simulation results prove that the least square support vector machine model can approximate the process and predict the state variables exactly, which is able to realize the optimization and control of the process.Recording to the highly nonlinear characteristic of Penicillin fermentation process, the quantum PSO (Particle Swarm Optimization) algorithm is selected for the penicillin fermentation process optimization. Take the yield of final product as optimization 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 quantum PSO.