Molecular insights on the solvent screening for the benzene extraction from fuels using ionic liquids via QSPR method

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<sup>2</sup> = 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<sup>2</sup> = 0.927) and ANN model (R<sup>2</sup> = 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.

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Publication Details

Journal
Scientific Reports
Published
2024-12-28
DOI
https://doi.org/10.1038/s41598-024-79639-x
Citations
4
Primary Topic
Ionic liquids properties and applications
Type
article
Field-Weighted Citation Impact
0.43

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article

Molecular insights on the solvent screening for the benzene extraction from fuels using ionic liquids via QSPR method

Mahdieh Amereh, Mohammad Amin Sobati, Ali Ebrahimpoor Gorji
4 citations
Scientific Reports
Ionic liquids properties and applications
0.43
article

Molecular insights on the solvent screening for the benzene extraction from fuels using ionic liquids via QSPR method

Mahdieh Amereh, Mohammad Amin Sobati, Ali Ebrahimpoor Gorji
article en
4 citations

Abstract

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<sup>2</sup> = 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<sup>2</sup> = 0.927) and ANN model (R<sup>2</sup> = 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.

Scientific ReportsVol. 14(1)
Iran University of Science and Technology (IR)
Università degli Studi dell'Insubria
Openalex Percentile: Top 46%
Ionic liquids properties and applications
0.43
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