Mathematical modeling of ethylene polymerization over advanced multisite catalysts: an artificial intelligence approach

Abstract

Recent developments in multisite catalysts based on metallocenes and post-metallocenes attracted the attention of researchers and industrial petrochemical companies due to the production of high-performance polymeric materials which generally are not achievable based on Ziegler–Natta catalysts. In this study, with the aim of predicting the average molecular weight of produced polyethylene and activity of ethylene polymerization using multisite catalysts, robust precise models based on artificial neural networks are developed. The average error for the prediction of the average molecular weight and activity are 3.76% and 5.89%, respectively. The Leverage method was used to check the reliability of the proposed model and the quality of experimental data which have been used for model development. The results showed that just a few data points are outside of the applicability domain of the developed models, confirming that both developed models and their predictions are statistically correct. Comparison of the artificial neural network models with other artificial intelligence approaches including support vector machine and group method of data handling type neural networks illustrates the better performance and robustness of the proposed models. The results of this study promise that neural networks can be used as reliable models with reasonable accuracy to estimate the performance of ethylene polymerization over this type of new metallocene/post-metallocene multisite catalysts.