Research On Modeling And Optimization Of Nosiheptide Fermentation Process

As a novel un-assimilated feed additive, the Nosihepide has a flourishing prospect in the market. As a novel un-assimilated feed additive, the Nosihepide has a flourishing prospect in the market. The present paper studies the process Modeling and Optimization of Nosiheptide Fermentation based on the theory of neural and intelligent optimization methods.For the on-line measurement of biological parameters, the principium-model which is simplified by the actual process can show the change of parameters, but difficult to ensure the accuracy of on-line measurement; while black box model which is modeled for the sample datas can fit the parameters accurately, but difficult to improve the generalization ability for the dependency of the sample data. The paper proposes a hybrid modeling method based on the improved RBF neural network and principium-model. The simulation in MATLAB proves that the model based on the hybrid modeling method makes the biological parameters more accurate and has strong generalization ability.Artificial Fish Swarm Algorithm (AFSA) is an intelligent optimization method based on swarm intelligence. AFSA has distributed parallel searching ability. After analyzing of the disadvantage of AFSA, this paper presents one self-adaption Artificial Fish Swarm Algorithm, which has the ability of famine and variation. After given more artificial intelligence, the proposed algorithm can greatly improve the ability of seeking the global excellentresult, convergence property and accuracy.Based on the feature of Nosihepide fermentation process, a method to optimize growth phase and product phase is presentd using improved ASFA. In growth phase the quantity of biomass is taken as the target and temperature, DO and pH are taken as three decision variables. In product phase the quantity of product is taken as the target and the speed of feed is taken as decision variable. The best operating condition of each step are different, so the optimization of step needs to be sought respectively to gain the optimal trajectories by simulating. The simulation result indicated that the method can improve the output of product.