The Optimization Algorithm And Application Of The Identification Model Of Nonlinear Impulsive System With Time Delays

This dissertation studies the differential dynamical systems and their identification problems with the bio-dissimilation of glycerol to 1,3-propanediol by klebsiella pneumoniae in the background. The contents include the nonlinear differential dynamic systems of the microbial continuous, the nonlinear impulsive systems of fed-batch fermentation and their parameter identification models.The properties of the solution to the systems and their identifiability are discussed,and use the different optimal algorithms to do the numerical simulations. This dissertation also talks the development,properties and theorems of the nonlinear impulsive dynamic system with time-delay. This research is supported by National Natural Science Foundation of China " Optimality theory and algorithm in nonlinear piecewise smooth dynamic system" (Grant No. 10471014) and the tenth five-year plan of science and technology of China "Microbial production of 1, 3-propanediol" (Project No. 2001BA708B01-04). The main results can provide certain reference for industrialization of 1,3-propanediol production by fermentation.So this study is very interesting in both theory and practice.The main results in this dissertation may be summarized as follows:1. Many researches are based on the microbial batch and continuous cultures before, and do little research on microbial fed-batch fermentation and it hasn't had the literature to consider the identification model by introducing time-delay into the system. According to a given nonlinear impulsive system with time delays in microbial fed-batch fermentation, the properties of existence, uniqueness and continuous dependence of solutions with respect to initial function and initial impulsive point are studied.2. We construct the identification model on time delays for this nonlinear impulsive system, prove its identifiability and use the improved simulated annealing algorithm to solve it. The simulation results show that the identification model and algorithm we used are effective.3. According to the characteristic of microbial continuous fermentation, we construct the identification model of continuous fermentation on the base of the research be-fore,discuss the existence of the solution of the identification model, advance the conception of parameter infection, do the selection analysis to the parameters.construct the optimization algorithm to get the optimum parameters and do the numerical example.Gao Caixia had done this work before[37] ,but the identification parame-ters,models and optimal algorithm are different from this dissertation. It is easy tosee from the numerical result that the extent of the error reduced is much more than the one in [37],which means that it's reasonable to do the parameter infection analysis and the optimum algorithm is feasible.