MULTI-OBJECTIVE OPTIMIZATION OF SYNDIOTACTIC POLYMERIZATION OF STYRENE USING GENETIC ALGORITHM TECHNIQUE

Chemical

Abstract

The optimal control policies for the syndiotactic polymerization of styrene over silica supported metallocene catalyst, have been determined using a multiobjective optimization technique. Kinetics model (KM) and genetic algorithms (GA) were tested as tools for modeling and optimization of syndiotactic polystyrene (sPS) synthesis process. The dependence between the main parameters of the process and working conditions were modeled by using KM. To verify the KM, syndiotactic polymerization of styrene over silica supported metallocene catalyst was conducted. The validation results show that the KM predicts best polymerization reactor performance with an average absolute error less than 15%. The KM is then included into an optimizing control scheme, which uses a genetic algorithm solving technique and a multiobjective function in a scalar form. Genetic algorithms based methodology provides accurate results, computing optimal values of decision variables, which lead to the maximum rate of polymerization and the desired value for molecular weight. The validation results in these optimum values are valid and the average absolute error less than 5 % of all responses