The Crystallization Morphology Evolution And Identification Of Crystallization Kinetics Parameters Of IPP

Crystallization, as the key factor affecting product quality, is one of the major structure evolutions in polymer fabrication. Nowadays, numerical simulation has aroused intense attention throughout the world. The reliable crystallization parameters are the premise of getting precise simulation results of crystallization morphology evolution. Thus, exploring semi-crystallization procedure and crystallization dynamics is meaningful for crystallization evolution model reorganization and optimization.iPP crystallization under quiescent isothermal and non-isothermal condition has been investigated. Based on the experimental results, a differential system of crystallization evolution has been optimized by physical analysis methods and genetic algorithms. Simulation result using the optimum parameters has a good agreement with the experimental result. The reliability of the crystallization parameters was verified. The main works and achievements are as following:1. iPP crystallization under quiescent isothermal and non-isothermal condition has been investigated. Quiescent isothermal crystallization processes under different temperature were studied, and the results indicate the crystallization rate decreases, the number of crystals reduces and the size of crystals increases with the temperature increasing. Quiescent non-isothermal crystallization experiments under different cooling rate were conducted, and the results show that the crystallization rate and the number of crystals increases while the size of crystals decreases with the cooling rate increasing.2. The processes of crystallization morphology evolution of iPP under isothermal and non-isothermal conditions were investigated based on a differential system model. The model parameters were acquired using the least square method based on the experimental results. Comparison results indicate there is a perfect agreement between simulation and analytical calculations under isothermal conditions, and simulations have consistent results with the experimental results under non-isothermal conditions. The reliability of the model parameters was verified.3. Based on the experiment and simulation result, the theories and algorithms are developed to identify the parameters of crystallization evolution model using real-code Genetic-Algorithms. The parameters of crystallization model were optimized. The optimum parameters were used in the crystallization model, and the crystallization processes were implemented. Comparison of the simulation results with the experimental results shows the simulation results have better consistency with the experiments results. The model using the optimum parameters can describe the crystallization evolution more accurately.