Due to industrial development, the volume of carbon dioxide (CO₂) is rapidly increasing.. Several techniques have been used to eliminate CO₂ from the output gas mixtures. One of these methods is CO₂ capturing by ionic liquids (ILs). Computational models for estimating the CO₂ solubility in ILS is of utmost importance. In this research, a white box model in the form of a mathematical correlation using the largest data bank in literature is presented by the group method of data handling (GMDH). This research investigates the application of GMDH intelligent method as a powerful computational approach for predicting CO₂ solubility in different ionic liquids with temperature lower and upper than 324 K. In this regard, 4726 data points including the solubility of CO₂ in 60 ILs were used for model development Moreover, seven different ionic liquids were selected to perform the external test. To evaluate the validity and efficiency of the suggested model, regression analysis was implemented on the actual and estimated target values. As a result, a proper fit between the experimental and predicted data was obtained and presented by various figures and statistical parameters. It is also worth noting that the predicted negative values in the proposed models are considered zero. Also, the results of the established correlation were compared to other proposed models exist in the literature of ionic liquids. The terminal form of the models suggested by GMDH approach and obtained based on temperature are two simple mathematical correlations by exerting input parameters of temperature (T), pressure (P), critical temperature (T_c ), critical pressure (P_c ) and, acentric factor (ω) which does not suffer from the black box property of other neural network algorithms. The model suggested in this work, would be a promising one which can act as an efficient predictor for CO₂ solubility estimation in ILs and is capable of being used in different industries.

由于工业的发展，二氧化碳（CO ₂）的体积正在迅速增加。已经使用了几种技术来从输出气体混合物中消除CO ₂。这些方法之一是通过离子液体（IL）捕获CO ₂。估算ILS中CO ₂溶解度的计算模型至关重要。在这项研究中，通过数据处理的分组方法（GMDH）提出了使用文献中最大的数据库以数学相关性形式的白盒模型。这项研究探讨了GMDH智能方法作为预测CO ₂的强大计算方法的应用在高于和低于324 K的温度下在不同离子液体中的溶解度。在这一点上，包括CO ₂溶解度在内的4726个数据点在60种IL中，有60种离子液体用于模型开发。此外，选择了7种不同的离子液体进行外部测试。为了评估建议模型的有效性和效率，对实际和估计目标值进行了回归分析。结果，获得了实验数据和预测数据之间的适当拟合，并由各种数字和统计参数表示。还值得注意的是，在建议的模型中预测的负值被视为零。此外，已建立的相关性的结果与离子液体文献中存在的其他建议的模型进行了比较。GMDH方法提出并基于温度获得的模型的终端形式是通过施加温度（T），压力（P），临界温度（T _c ），临界压力（P _c ）以及不遭受其他神经网络算法黑盒特性影响的无心因素（ω）。在这项工作中建议的模型将是一个有前途的模型，可以用作IL中CO ₂溶解度估计的有效预测器，并且可以在不同行业中使用。