【摘要】 CO₂在离子液体中溶解度是离子液体对CO₂进行捕集回收的重要基础数据之一,研究提出了一种半经验半机理模型,用于预测CO₂在离子液体中溶解度。首先,基于Krichevsky-Kasarnovsky方程的先验机理知识构建了预测模型,并利用亨利常数经验Valentiner式和无限稀释偏摩尔体积经验多项式修正该模型,以提高模型的应用范围;将CO₂在多种离子液体和相应压力、温度范围中的溶解度实验数据分为训练集和测试集,训练集实验数据涵盖了相应的操作条件范围,采用非线性最小二乘优化算法对模型参数进行学习和修正,以提高模型的预测精度;然后,利用测试集对所建模型的预测性能进行评测,模型评测结果表明:该预测模型可用于CO₂在多种离子液体中的溶解度预测,预测结果与相应实验数据较为吻合,模型预测性能良好。研究工作为进一步丰富和完善CO₂在离子液体中溶解度的数据信息分析,提供了一种理论方法。更多还原

【Abstract】 CO₂ solubility in ionic liquids is an important parameter for CO₂ capture and recovery by ionic liquids. A semi-empirical and semi-mechanism model was proposed for predicting CO₂ solubility in ionic liquids. A predictive model was first constructed based on Krichevsky-Kasarnovsky equation. The Henry’s constant using empirical Valentiner and infinite dilution partial molar volume using empirical polynomial were used to modify the model and extend its application. The experimental data of CO₂ solubility in various ionic liquids and associated pressures and temperature ranges were divided into training and testing sets. The training set covered the corresponding operating conditions and nonlinear least square optimization algorithm was used to learn and correct the model parameters to improve prediction accuracy. Subsequently, the prediction performance of the model was evaluated using the testing set. Evaluation results show that this prediction model is successful in the prediction of CO₂ solubility in various ionic liquids. The prediction results match the experimental data. In conclusion, this work provides a theoretical method for data analysis of CO₂ solubility in ionic liquids.更多还原