Identification And Control Of Nonlinear System In A Class Of Microbial Fed-Batch And Continuous Culture

This dissertation investigates the optimal control problems and parameter identification problem in fed-batch fermentation and continuous fermentation of glycerol bioconversion to 1,3-propanediol by Klebsiella pneumoniae. The research can increase the productivity of prod-uct and provide certain reference for commercial process of 1,3-propanediol by fermentation. Also it has important reference value in the mechanism of intracellular metabolism. Therefore, it is very interesting both in theory and in practice. The main contributions obtained in this dissertation are summarized as follows.1. To maximize the concentration of 1,3-propanediol at the terminal time in fed-batch cul-ture, an impulsive optimal control problem is presented. The feeding volumes of glycerol and the the feeding time points are taken as decision variables. Based on the penalty function and barrier function method and a multi-group particle swarm optimization algorithm, a optimization algo-rithm is constructed to seek the optimal solution. Numerical results show that the computational optimal feeding strategy can imcrease the concentration of the 1,3-propanediol at the terminal time.2. An impulsive optimal control problem involving the system with some variable param-eters is considered. The final 1,3-PD yield and it's sensitivity value the respect to uncertain system parameters are considered as the cost function. The feeding volumes of glycerol and the the feeding time points are taken as decision variables. Continuous state inequality constraints are imposed to ensure that the concentrations of biomass, glycerol, and reaction products lie within specified limits. By introducing an auxiliary dynamic system to calculate the sensitivity value, we obtain an equivalent impulsive optimal control problem in standard form. A hybrid algorithm by integrating a particle swarm optimization with gradient-based optimization algo-rithm is proposed to seek the optimal feeding strategy. Numerical results show that the validity of the model and the computational approach.3. An optimal control problem for a state-dependent switching system in fed-batch culture is considered. The ration function of feeding velocity of glycerol to alkali is taken as control function. The gradient of the objective function and constraints are computed by using the sen-sitivity function method. Controlled random search and gradient-based optimization algorithm are used to seek the optimal solution. Numerical results show that the obtained optimal strategy can control the product and substrate concentration.4. A nonlinear enzyme-catalytic switching system is established to describe the fed-batch culture. Due to the lack of the intracellular experimental data, we quantitatively formulate the robustness value of the metabolic system to evaluate the reliability of the switching system. With the robustness value and the relative error between the experimental data and the computational values of the extracellular substance as the performance index, a parameter identification prob-lem of the enzyme-catalytic switching system is proposed. Since a large number of numerical computations of differential equations are needed in solving the problem, an improved parallel genetic optimization algorithm is constructed to find the optimal parameters. Numerical result-s show that the optimal parameters and the corresponding switching system can describe the fed-batch culture reasonably.5. A nonlinear dynamical system with genetic regulation of dha regulon to describe the microbial continuous culture. We consider parameter identification in the complex metabolic network. The parameters to be estimated include pathway variables and system parameters. The existence, uniqueness and continuity of solutions are discussed. The state vector include extracellular and intracellular part. Taking into account the difficulty in accurately measuring the concentration of intracellular substances for parameter identification, we quantitatively define biological robustness of the intracellular substance concentrations at steady state. Taking the robustness index of the intracellular state as a cost function, a parameter identification problem is proposed. The relative error between the experimental data and the computational values of the extracellular substance be in an acceptable range. Since many times of numerical computations of differential equations are needed in solving the problem, a improved parallel particle swarm optimization algorithm is constructed to find the optimal parameters.