A Multi-scale Modeling Of Propylene Polymerization

Developing a mathematic model of industrial polymerization process, which can be successfully applied to predict both the process kinetics mechanics and the process industrial performance, is not only helpful to master the inherence of the polymerization process, but also the basis of optimizing the production process and enhancing the process benefit. Due to its complexity of the polymerization kinetics mechanism and the practical industrial polymerization processes, modeling of a polymerization process becomes a major branch of polymer reaction engineering, and it is also an unsolved and challenging problem.In this paper, the propylene polymerization process is considered. The detailed microscale kinetic reaction, mesoscale transport modeling and macroscale reactor's modeling of propylene polymerization have been presented using multiscale modeling approcach in preparation for the control and optimization of polypropylene reactors.At microscale level, the primary reaction equations of propylene polymerization using Ziegler-Natta catalysts are introduced and the kinetic parameters are given. After that, we proposed a modified polymeric multigrain model to describe kinetic behavior, molecular weight distribution, monomer concentration, the degree of polymerization and polydispersity index. This modified mesocale model gives a more valid mathematical description by accounting for the monomer diffusion phenomena at two levels and can accurately explain experimental data. It's parameters have definite physical meanings and the solving procedure is quite quick. Special attention is also paid in this paper to discuss the efficiency of clubbed shells algrithm. The significantly computational time saving without sacrificing the accuracy of results has been achieved.A steady state model of industrial polypropylene reactors using vapor-liquid phase balance equation, the relation between molecular weight and melt index, reaction kinetic equations and mass balance equations is developed, which can predict the important process variable, such as MI, slurry density etc, employing the necessary parameter from mesoscale model and through data identification.