Nonlinear Model Predictive Control Algorithm Using In Chemical Process

To solve the problems which exist in chemical processes, such as, nonlinear, numerous variables and numerous constraints etc., nonlinear model predictive control strategies are proposed for several kinds of nonlinear systems in this paper. The main research works are as follows:A survey of current nonlinear model predictive control on both theory and application is introduced, including the algorithms,stability condition,robust theory and future needs for development. Model predictive control based on the local linearization state-space model is introduced in detail.A new nonlinear model predictive control algorithm based on sensitivities equations is presented to the problem of nonlinear systems in the chemical process. By analyzing the sensitivities equations of the nonlinear system, a linear approximation of the derivatives information used in on-line optimization is obtained, and then the Levenberg-Marquardt algorithm is used to solve the optimal problem in every sample time. The on-line computation is reduced greatly. The simulation result for a nonlinear continuous stirred tank reactor (CSTR) shows the validity and feasibility of the proposed algorithm.A new nonlinear model predictive control algorithm using velocity-based linearization is presented. The model used in the derivation of the control algorithm is obtained by using a velocity-based linearization method, and has a linear structure with variable parameters. The model parameters are then determined by the operating conditions of the system. It is shown that the linearized model approximates well the original nonlinear one. The model predictive control algorithm presented in this paper is based on Levenberg-Marquardt algorithm, which is efficient in computation and provides a general framework for model predictive control design. A simulation study on a nonlinear continuous stirred tank reactor (CSTR) shows that the proposed control algorithm is effective and applicable to many nonlinear industrial systems.A dynamic model of propylene polymerization was developed based on the kinetics of polymerization. The melt index inferential model's structure was derived from first principle method, while the model unknown parameters were validated by process data. Considering the particular structure of dual-loop reactor, a approach based on the temperature and density of the two reactors was given to regulate the polymerization rate and jacket exchanged rate, after that the industry practical data was used to estimate these unknown parameters. the proposed model was applied to investigate the effects of the step changes in feed flow rate of materials, including propylene, catalyst and hydrogen, the simulation results confirm the validity of the proposed model. Based on the reduced dynamic model, nonlinear model predictive controller using velocity-based linearization is used to simulate the start up of the polymerization reactor.