Huang Xingyuan

Predictive calculation of carbon dioxide solubility in polymers

The solubility of carbon dioxide in polymers has attracted great attention from scientists because it is an important application of green chemistry, and it is widely applied in extraction, separation and the preparation of new materials. In this work, a new solubility prediction model with both good accuracy and efficiency, called CEAPSO KHM RBF ANN, is developed. In the CEAPSO KHM RBF ANN model, an accelerated particle swarm optimization (APSO) algorithm with chaotic disturbance is employed to trim the radial basis function artificial neural network (RBF ANN) connection weights and biases in order to reduce the premature convergence problem, and the K-harmonic means (KHM) clustering method is used to tune the hidden centers and spreads of the radial basis function. The proposed model is employed to investigate the solubility of CO2 in polymers including polypropylene, polystyrene, poly(vinyl acetate), carboxylated polyesters and poly(butylene succinate-co-adipate). The results indicate that the proposed model is an effective method for solubility prediction with better performance and higher efficiency compared with the other methods, and should contribute to the understanding of the phase behaviour of the gas/polymer system and for the design and optimization of processing techniques.

Solubility prediction of gases in polymers based on an artificial neural network: a review

As an important physical chemistry property, solubility is still a popular research topic. Its theoretical calculation method has developed rapidly. In particular, the artificial neural network (ANN) has attracted the attention of researchers because of its unique nonlinear processing ability. This review provides a brief explanation of the ANN approaches that are most commonly applied to predict gas solubility in polymers, and states the implementation principle, progress, and performance analysis of hybrid ANNs based on the intelligence algorithm. The prospect of solubility prediction based on current research trends is then proposed. This review attempts to analyze the solubility calculation method and provides an insight into and reference for the application of the artificial intelligence method in chemistry and material fields, and can expand in the future because of the increasing number of solubility prediction approaches being introduced.

Prediction of supercritical carbon dioxide solubility in polymers based on hybrid artificial intelligence method integrated with the diffusion theory

Solubility is one of important research hotspots of physical chemistry properties and is widely utilized in the modification, synthesis and preparation of a lot of materials. To avoid the defects of traditional thermodynamic dissolution forecasting methods, according to the mass transfer features of a two-phase system, the dissolution process is simulated. In this paper, the diffusion theory is integrated into the improvement of particle swarm optimization (PSO) so that the particles in the algorithm evolve along with the diffusion energy. In this way, the improved PSO of dual-population diffusion is obtained and used to train the parameters of the radial basis function artificial neural network. Then, a prediction model for supercritical carbon dioxide solubility in polymers is proposed. The solution experiments of 8 polymers indicate that the predicted values with the model are consistent with the experimental results. The prediction accuracy is higher and the correlation is significant. The average relative error, mean square error and square correlation coefficient are respectively 0.0043, 0.0161, and 0.9954. The prediction model has a high comprehensive performance and provides the basis for the prediction, analysis and optimization of other physical and chemical fields.