Prediction of Carbon Dioxide Solubility in Polymers Based on Adaptive Particle Swarm Optimization and Least Squares Support Vector Machine

Abstract

AbstractSolubility is a significant physical and chemical property. The solubility of carbon dioxide(CO2) in polymers is an important application of green chemistry. Aimed at the problem of insufficient precision of the existing prediction model, a solubility prediction model based on the adaptive particle swarm optimization algorithm and the least‐squares support vector machine(APSO‐LSSVM) is proposed. Different from the traditional particle swarm algorithm, APSO algorithm improves the problem of easily falling into local optimal solution. The regularization parameters and kernel function tuning parameters of the LSSVM were optimized by APSO and then use this model to predict the solubility of CO2 in eight polymers within a wide range of temperatures and pressures. APSO‐LSSVM model has great prediction ability, with a good correlation between prediction data and experimental data, high prediction accuracy, short calculation time and strong stability. Compared with back propagation artificial neural network(BP ANN), back propagation‐particle swarm optimization algorithm artificial neural network(BP‐PSO ANN) and LSSVM, APSO‐LSSVM has better comprehensive performance. The average absolute relative deviation(AARD), root mean square error(RMSE), determination coefficient(R2) were respectively 0.2130, 0.0120, 0.9853. In addition, the model has good expansibility and can be applied to other fields such as chemistry and medicine.