In this study, the solubility of CO₂ and supercritical (SC) CO₂ in 20 ionic liquids (ILs) of different chemical families over a wide range of pressure (0.25–100.12 MPa) and temperature (278.15–450.49 K) were predicted, using a robust machine learning method of multi-layer perceptron neural network (MLP-NN). The developed model with the R² of 0.9987, MSE of 0.6293 and AARD% of 1.8416 showed a great accuracy in predicting experimental values. In another approach for predicting the CO₂ solubility, an empirical correlation with several constants was developed. With the R² of 0.9922, MSE of 3.7874 and AARD% of 3.5078 the empirical correlation showed acceptable results; nevertheless weak compared to the ANN. The significance of this correlation is that it needs no physical property of the ILs or their mixture, and for its estimation, even a simple calculator is sufficient. A comprehensive statistical assessment conducted to assure the robustness and generality of the model. In addition, the applicability of the model and quality of experimental data was fully investigated by Leverage approach.

在这项研究中，预测了CO ₂和超临界（SC）CO ₂在宽压力（0.25–100.12 MPa）和温度（278.15–450.49 K）范围内的不同化学族的20种离子液体（IL）中的溶解度，使用多层感知器神经网络（MLP-NN）的鲁棒机器学习方法。R ²为0.9987，MSE为0.6293，AARD％为1.8416的已开发模型在预测实验值方面显示出很高的准确性。在预测CO ₂溶解度的另一种方法中，开发了与几个常数的经验相关性。与R ²经验相关性为0.9922，MSE为3.7874，AARD％为3.5078，结果显示可接受。但是，与人工神经网络相比，它的功能较弱。这种相关性的意义在于，它不需要IL或其混合物的物理特性，并且对于其估计，即使是简单的计算器也已足够。进行了全面的统计评估，以确保模型的鲁棒性和通用性。此外，通过杠杆方法对模型的适用性和实验数据的质量进行了充分研究。