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Research on Model Identification and Nonlinear Predictive Control of Continuous Chemical Processes

Author: ZhangJianZhong
Tutor: WangQingChao
School: Harbin Institute of Technology
Course: Chemical Process Equipment
Keywords: Continuous chemical process Model Identification Nonlinear model predictive control Particle swarm optimization
CLC: TQ021
Type: PhD thesis
Year: 2010
Downloads: 447
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Abstract


Modeling and control of chemical process is a hot and difficult field of process control, chemical process is usually very time-varying and nonlinear, it is difficult to establish accurate model of the mechanism, the process temperature, concentration, average molecular weight precise control of other indicators of the impact on the yield and quality of the product, and has a strong theoretical and practical research value. Predictive control of a control method as a development in the actual production, and has been widely applied. Traditional predictive control methods are based on linear models to achieve, and for strongly nonlinear chemical process, linear model predictive control is difficult to achieve satisfactory control effect, so further research is needed based on the nonlinear model predictive control method. Typical continuous chemical production process, such as order continuous stirred tank reactor, pH and in the process, MMA polymerization reaction, biological reaction, such as an object, to describe the dynamics of the system to establish the identification model based on the process input and output data, and based on the resultant model the design nonlinear predictive controller. At the same time, the combination of intelligent optimization algorithm research control law for solving constrained nonlinear model predictive control problem. Dissertation contents mainly include the following aspects: 1. Study to analyze the model order continuous stirred tank reactor step response characteristics and stability. Identification of the mechanistic model of the CSTR LSSVM-ARX Hammerstein model structure, and compare recognition results with Hammerstein model of the structure of the polynomial function. Based on the identification of the resulting model, the corresponding nonlinear model predictive control algorithm, the predictive control law solving nonlinear aspects of the structure of the inverse model. This method is used CSTR reactant concentration control and compare the effect with the Hammerstein model predictive control based on polynomial functions as well as the PI controller simulation. Proposed an identification of Wiener model new method using the Laguerre orthogonal function describing the model linear link, using the least squares support vector machine to describe the nonlinear part. Given the structure of the model and the identification of specific steps, and the method is extended to multi-input multi-output Wiener model identification. The proposed method for SISO and MIMO simulation, and based on a polynomial function of Laguerre-Wiener model identification results. The linear part the Laguerre functions Wiener model identification and predictive control for pH neutralization process. Identification of the mechanistic model of the process for the the Laguerre-LSSVM structure of Wiener model, Laguerre-SVR structure Wiener model on Laguerre-polynomial structure of the Wiener model and linear Laguerre model, compare the effects of model identification, respectively, based on get four models of the pH neutralization process implementation constraints predictive control using SQP Algorithm control law. The MMA polymerization object, research based on Gaussian process model identification and nonlinear model predictive control. Delay dynamic analysis of Gaussian process modeling effect for different models, compare the computation time and predicted performance, choose the most appropriate delay to the establishment of the the final Gaussian model output model predicted results. Identification Gaussian process model as a predictive model, build a nonlinear model predictive control algorithm and tracking control for the number-average molecular weight of MMA polymerization process. Particle Swarm Optimization prone to the shortcomings of \multi offspring merit-based crossover and a certain probability of variation in order to get better fitness of new particles particles instead of being eliminated, thereby improving the algorithm the ability of global optimization. Hybrid genetic particle swarm algorithm for generalized predictive control of constrained linear control law solving simulation to verify its validity. On this basis, a nonlinear generalized predictive control method based on NARIMAX model constrained nonlinear generalized predictive control parameters online identification methods and hybrid genetic particle swarm optimization-based control solution steps, for open-loop unstable and the control of bioreactors.

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CLC: > Industrial Technology > Chemical Industry > General issues > Chemical processes ( physical processes and physical and chemical processes ) > Basic theory
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