Amereh Mahdieh, Gorji Ali Ebrahimpoor, Sobati Mohammad Amin
School of Chemical Engineering, Iran University of Science and Technology (IUST), Tehran, Iran.
Sci Rep. 2024 Dec 28;14(1):30718. doi: 10.1038/s41598-024-79639-x.
Benzene separation from hydrocarbon mixtures is a challenge in the refining and petrochemical industries. The application of liquid-liquid extraction process using ionic liquids (I.Ls) is an option for this separation. The selection of the most appropriate I.L. for this application is a challenging task due to the variety of anion and cation structures. In the current study, the benzene distribution between the aliphatic hydrocarbon-rich and I.L.-rich phases has been evaluated using the Quantitative Structure-Property Relationship (QSPR) method. A dataset comprising of 112 ternary systems (namely, I.L., benzene, and aliphatic hydrocarbon) was compiled after an extensive review of literature. The primary dataset consists of 17 anions, 20 cations, and 12 aliphatic hydrocarbons. Therefore, the impact of the structure of anion, cation, or aliphatic hydrocarbon on the benzene distribution between the aliphatic hydrocarbon-rich and I.L.-rich phases has been investigated. The linear QSPR models were constructed using Multiple Linear Regression (MLR). The statistical evaluation of the final linear model showed that the constructed model (R = 0.900) has an acceptable capability to predict the mole fraction of benzene in the I.L.-rich phase. Additionally, non-linear QSPR models were developed using Genetic Programming (GP) and Artificial Neural Network (ANN) machine learning methods. The statistical evaluation of the GP model (R = 0.927) and ANN model (R = 0.939) showed that non-linear models had slightly higher prediction accuracy compared to the linear model. The final QSPR model was developed using the BELe3 cation descriptor which is a 2D Burden eigenvalues descriptor and HTm anion descriptor which is a 3D GETAWAY descriptor. After model construction, the selected molecular descriptors of anion and cation structures has been interpreted. The results showed that the size and the electronegativity of the atoms in the anion and cation structure are probably important parameters that affect the benzene distribution between the aliphatic hydrocarbon-rich and I.L.-rich phases. Additionally, the anion shape can be considered as an effective parameter in the benzene extraction process.
从烃类混合物中分离苯是炼油和石化行业面临的一项挑战。使用离子液体(I.Ls)的液 - 液萃取工艺是实现这种分离的一种选择。由于阴离子和阳离子结构的多样性,为该应用选择最合适的离子液体是一项具有挑战性的任务。在当前研究中,已使用定量结构 - 性质关系(QSPR)方法评估了苯在富含脂肪烃相和富含离子液体相之间的分布。在广泛查阅文献后,编制了一个包含112个三元体系(即离子液体、苯和脂肪烃)的数据集。原始数据集由17种阴离子、20种阳离子和12种脂肪烃组成。因此,研究了阴离子、阳离子或脂肪烃的结构对苯在富含脂肪烃相和富含离子液体相之间分布的影响。使用多元线性回归(MLR)构建了线性QSPR模型。对最终线性模型的统计评估表明,构建的模型(R = 0.900)具有可接受的能力来预测富含离子液体相中苯的摩尔分数。此外,使用遗传编程(GP)和人工神经网络(ANN)机器学习方法开发了非线性QSPR模型。对GP模型(R = 0.927)和ANN模型(R = 0.939)的统计评估表明,非线性模型的预测准确性略高于线性模型。最终的QSPR模型是使用BELe3阳离子描述符(一种二维Burden特征值描述符)和HTm阴离子描述符(一种三维GETAWAY描述符)开发的。在模型构建之后,对所选的阴离子和阳离子结构分子描述符进行了解释。结果表明,阴离子和阳离子结构中原子的大小和电负性可能是影响苯在富含脂肪烃相和富含离子液体相之间分布的重要参数。此外,阴离子形状可被视为苯萃取过程中的一个有效参数。