The Modeling And Control Of The Penicillin Fermentation Process Based On The Radial Basis Function Neural Network

In this thesis, the modeling and control of the penicillin fed-batchfermentation process were studied. Penicillin fermentation process is a complex biochemical and nonlinearsystem. The efficiency of penicillin fermentation is decided by the states of thefermentation process, so adjusting and controlling the states is very important.Thus the study about the modeling and control has practical effect for penicillinfermentation process. Neural networks are effective in solving nonlinear problems.Therefore, it's promising to apply neural networks to the modeling and control ofthe penicillin fermentation process. This thesis uses Radial Basis Function neuralnetwork (RBF neural network) to describe and control the penicillin fermentationprocess. The main contributions of this thesis are as follows: Firstly, an identification model for penicillin fermentation process isdesigned based on Radial Basis Function network. The training and test data getfrom mechanism model. Using this model, we can know in advance the tendencyof the states change of the fermentation process under given conditions. This isvery useful in practice. Secondly, based on RBF neural network's advantage of linearity in parameter,an RBF network based nonlinear internal model controller is designed.Simulations results of applying this method to the controlling of penicillinfed-batch fermentation process shows that the proposed controller is efficient. Parts of research results have been published on "Proceeding of the 22ndChinese Control Conference" and "Journal of Central South University ofTechnology (nature science)". The "Model building in the penicillin fermentationprocess based on RBF neural networks for identification" paper published on"Journal of Central South University of Technology (nature science)" is indexedby EI. The Nature Science Foundation of China under grant 60274060 supportsthis project.