Solubility prediction of gases in polymers using fuzzy neural network based on particle swarm optimization algorithm and clustering method

Abstract

A four-layer fuzzy neural network (FNN) model combining particle swarm optimization (PSO) algorithm and clustering method is proposed to predict the solubility of gases in polymers, hereafter called the CPSO-FNN, which combined fuzzy theory's better adaptive ability, neural network's capability of nonlinear and PSO algorithm's global search ability. In this article, the CPSO-FNN model has been employed to investigate solubility of CO 2 in polystyrene, N 2 in polystyrene, and CO 2 in polypropylene, respectively. Results obtained in this work indicate that the proposed CPSO-FNN is an effective method for the prediction of gases solubility in polymers. Meanwhile, compared with traditional FNN, this method shows a better performance on predicting gases solubility in polymers. The values of average relative deviation, squared correlation coefficient (R 2 ) and standard deviation are 0.135, 0.9936, and 0.0302, respectively. The statistical data demonstrate that the CPSO-FNN has an outstanding prediction accuracy and an excellent correlation between prediction values and experimental data.Recent years, the interdiscipline of information science and intelligent technology has a broad application perspective. 4,12,13 With the popularization of artificial neural networks (ANN), the determination of ANN structure, parameters and bias becomes the most crucial factors because the training process of ANN could be considered as a classical optimization problem. 12 Recently, researchers discovered that many intelligent algorithms such as genetic algorithm, 13 simulated annealing algorithm, 14 fuzzy logic theory, 15 gravitational search algorithm, 16 wavelet analysis, 17 ant colony optimization algorithm, 18 particle swarm optimization algorithm (PSO), 12,19-21 chaos theory, 22 and so on, can all be used for this determination. Therefore, ANN combined with intelligent optimization algorithms namely hybrid neural network has become one of the most active subject.So far as solubility of gases in polymers is concerned, it is affected by temperature, pressure, and sometimes it can also be affected by the interactions with the groups of the macromolecular chains. 23 As a result of the nonlinear relationship of these

The cell forming process of microcellular injection‐molded parts

“…It is known that the solubility of gas in the polymer melt will increase with the increasing pressure, and recently Li et al and Aboozar and Hamid successfully predicted this trend by back propagation artificial neural network based on the self‐adaptive particle swarm optimization algorithm and chaos theory. For microcellular injection molding process, in the plasticizing stage, the melt pressure is high and the solubility of the gas is high too, at this moment, the polymer is saturated with the gas blowing agent to form a single‐phase polymer–gas solution.…”

A double-population chaotic self-adaptive evolutionary dynamics model for the prediction of supercritical carbon dioxide solubility in polymers

“…The combined algorithm of PSO and back propagation (BP) algorithms showed better performance than a single algorithm [ 38 ]. Our team had also carried out related studies, including but not limited to the group evolution algorithm combined with the BP algorithm [ 39 ], clustering and diffusion theory and proposed several solubility prediction models of supercritical CO 2 in polymers under several macroscopic scales in order to improve the local search speed [ 40 , 41 ]. These models achieved better results in calculation accuracy, efficiency, correlation and comprehensive performance.…”

Evolving an Accurate Decision Tree‐Based Model for Predicting Carbon Dioxide Solubility in Polymers

“…The sorption of fluids in polymers has been the subject of much research interest due to the importance of these phenomena in many engineering applications, especially fluid‐polymer processes, such as polymer grafting and foaming processes, impregnation of additives, and gas separation with membranes , . In polymeric compounds, the solubility is of key importance since it governs the compatibility of blending systems . The solubility data of gases, especially CO 2 , in polymers is absolutely crucial to determine the appropriate required processing conditions for cost‐effective process design, which are mainly attained from experimental and prediction models.…”