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

文献类型： 外文期刊

第一作者： Wu, Yan

作者： Wu, Yan;Sheng, Sheng;Wang, Jun;Wu, Fu-an;Wu, Yan;Zhang, Hang;Li, Meng-shan;Wu, Yan;Sheng, Sheng;Wang, Jun;Wu, Fu-an

作者机构：

关键词： dissolution behaviour; evolutionary computation; particle dynamics; computational model

期刊名称：ROYAL SOCIETY OPEN SCIENCE （ 影响因子：3.653； 五年影响因子：3.853 ）

ISSN： 2054-5703

年卷期： 2022 年 9 卷 1 期

页码：

收录情况： SCI

摘要： Solubility of gas in polymers is an important physico-chemical property of foam materials and widely used in the preparation and modification of new materials. Under the conditions of high temperature and high pressure, the dissolution process is a nonlinear, non-equilibrium and dynamic process, so it is difficult to establish an accurate solubility calculation model. Inspired by particle dynamics and evolutionary algorithm, this paper proposes a hybrid model based on chaotic self-adaptive particle dynamics evolutionary algorithm (CSA-PD-EA), which can use the iterative process of particles in evolutionary algorithms at the dynamic level to simulate the mutual diffusion process of molecules during dissolution. The predicted solubility of supercritical CO2 in poly(d,l-lactide-co-glycolide), poly(l-lactide) and poly(vinyl acetate) indicated that the comprehensive prediction performance of the CSA-PD-EA model was high. The calculation error and correlation coefficient were, respectively, 0.3842 and 0.9187. The CSA-PD-EA model showed prominent advantages in accuracy, efficiency and correlation over other computational models, and its calculation time was 4.144-15.012% of that of other dynamic models. The CSA-PD-EA model has wide application prospects in the computation of physical and chemical properties and can provide the basis for the theoretical calculation of multi-scale complex systems in chemistry, materials, biology and physics.

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