Modeling,Control And Optimization Of Loop Reactor Propylene Bulk Polymerization Process

Polypropylene products are closely related to people’s production and life, many experts and scholars have already focused on the improvements of propylene polymerization process, such as polypropylene reaction mechanism, process and catalyst development, reaction device and so on. However, on the other hand, published fruits about modeling, control and optimization of advanced control/optimization algorithms and strategies for polypropylene production process are relatively few, especially in China, which can also contribute to the industrial polypropylene production in this respect. Therefore, on the basis of previous studies, this thesis pays more attention on modeling, optimization, and control strategies for double-loop propylene bulk polymerization process. The main research work is summarized as follows:1. Dynamic mechanism mathematical model and quality properties model of non-ideal loop reactor propylene bulk polymerization process are constructed by the introduction of the non-ideal characteristics, as well as referred to the actual process operating data and process parameters.Then, dynamic and steady state characteristics are analyzed based on this model. The model developed may provide a reliable benchmark model and guiding role for modeling of quality indicators of propylene polymerization process, designing of nonlinear model predictive control scheme and product grade transition optimization.2. Melt index inferential model plays an important role in the control and optimization of polypropylene production, a novel multiple-priori-knowledge based neural network (MPKNN) inferential model for melt index prediction is developed. The prior knowledge from the industrial propylene polymerization process is fully exploited and embedded into the construction of multi-layer perceptron neural network in the form of nonlinear constraints. Meanwhile, an adaptive PSO-SQP (Particle Swarm Optimization-Sequential Quadratics Programming) is proposed to optimize the network weights. The proposed MPKNN model has good fitting and prediction ability. Meanwhile, it can avoid unwanted zero value and wrong signal of the model gains. By embedding priori knowledge, the model ensures the safety in the quality control of Melt Index. In addition, a hybrid model combining the MPKNN model with a simplified mechanism model is proposed to enhance the extrapolation capability. A normalized mutual information method is employed to estimate the delay between independent variables and dependent variables.3. A nonlinear model predictive control algorithm based on MSSARX-PWL (wiener type) model is proposed for loop polypropylene production process with multivariale, coupled and unstable nonlinear characteristics. The MSSARX-PWL model structure, in which linear state space model under the closed-loop conditions is identified by the improved closed-loop subspace identification method (MSSARX), combined with the nonlinear steady-state model identified by the multivariate PWL method, is established for the nonlinear predictive model of double loop propylene polymerization process. Furthermore, the non-linear model can be inversed to linear model that without Non-linear Programming methods (NLP) solver but only the linear Quadratic Programming (QP) optimization controller is needed. The algorithm proposed can not only guarantee the accuracy of model and control, but also improve the computational efficiency.4. A multi-grade transition trajectory optimization model and transition strategy for the grade transition of double loop polypropylene bulk polymerization production process is developed. The trajectory optimization model took into account the economic benefits and plant stability during the grade transition process. Meanwhile, a variant time scale based control vector parametric methods (VTS-CVP) and (Interior-Point Optimization, IPOPT)algorithm, which can optimize the control parameters and time node together, are both used for solving dynamic optimization problem, that can greatly reduce the grade transition time and material consuming. 5. A two-layer hierarchical structure and strategy of optimization and control for polypropylene grade transition is raised to overcome process uncertainties and disturbances that lead to the deviation between the open-loop reference trajectory and the actual process. In the upper layer, the variant time scale based control vector parametric methods (VTS-CVP) is used for dynamic optimization of transition trajectory, while tracking controller based on MSSARX-PWL (wiener-type) model predictive controller in the lower layer is tracking fast to the reference trajectory from the upper layer and overcome high-frequency disturbances. Besides, a mechanism about trajectory deviation detection and optimal trajectory updating online are introduced to ensure a smooth transition for the entire process.