Multiple linear regression and artificial neural networks for highly selective cationic β‐diimine‐methallyl nickel (II) catalyst for styrene dimerization reaction to 1,3‐diphenyl‐1‐butene

Abstract

The objective of the present study is to develop predictive models to input and output parameters for linear styrene dimerization reactions. These reactions involve the conversion of two styrene molecules into 1,3‐diphenyl‐1‐butene, which is a commonly used intermediate in the production of various industrial chemicals. Multiple linear regression (MLR) and artificial neural network based on multilayer perceptron (MLP) and radial basis function were used to model 1,3‐diphenyl‐1‐butne dimerization process in order to evaluate its performance. The neural network has been trained and tested by experimental data. The effect of various parameters (such as complex concentration) on styrene conversion (Conv%) and turnover of frequency (TOF) has been investigated in the proposed work. The results found by the proposed predictive models were analyzed and compared with the experimental results. Based on the comparison of the results, the radial basis function neural network (RBFNN) model outperformed the other models (MLR and MLP) with a correlation coefficient of (0.987 for Conv% and 0.947 for TOF) and lower root mean square errors for the output parameters. This result demonstrates that the RBFNN is an efficient technique to predict the styrene conversion and turnover frequency of the dimerization reaction. It was exposed that the control strategies learned are robust and can be transferred to similar dimerization reaction configurations.