Solubility is as a key chemical and physical property. Solubility prediction methods are applied in diverse fields including preparation synthesis and modifications of materials. To overcome the shortcomings of existing solubility prediction methods, taking the mass transfer of two-phase system as an example, a solubility prediction model based on the diffusion theory and hybrid artificial intelligence method was proposed in this paper. An improved double-population chaotic accelerated particle swarm optimization (APSO) algorithm combined diffusion theory was developed according to the particle evolution utilizing diffusion energy. The developed algorithm was applied in the training of parameters of the radial basis function artificial neural network and then a model for predicting solubility was developed. The experimental results of supercritical carbon dioxide solubility in 8 polymers were consistent with the predicted values by the model, indicating the high prediction accuracy. The average relative deviation, squared correlation coefficient, and root mean square error were respectively 0.0036, 0.9970, and 0.0152, displaying its higher comprehensive performance. The model may also be applied in other physicochemical fields.

溶解度是关键的化学和物理性质。溶解度预测方法应用于包括制备合成和材料改性在内的多种领域。为了克服现有溶解度预测方法的不足，以两相体系的传质为例，提出了一种基于扩散理论和混合人工智能方法的溶解度预测模型。根据粒子利用扩散能的演化，提出了一种结合扩散理论的改进的双种群混沌加速粒子群优化算法。将所开发的算法应用于径向基函数人工神经网络的参数训练，然后建立了溶解度预测模型。超临界二氧化碳在8种聚合物中的溶解度实验结果与模型预测值一致，表明预测精度高。平均相对偏差，平方相关系数和均方根误差分别为0.0036、0.9970和0.0152，显示出较高的综合性能。该模型也可以应用于其他物理化学领域。