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<p>Ionic liquids (ILs) as eco-friendly solvents have gained significant attention across various fields of science, including the petroleum industry. Among the different ILs families, imidazolium-based ILs have been the subject of many research studies. However, not enough experimental studies have been conducted to determine the viscosity of this family of ILs, making accurate viscosity prediction crucial for their practical applications. This study aims to predict the viscosity of imidazolium-based ILs and their mixtures using critical properties as input parameters. Machine learning (ML) models have been implemented, and their performance in viscosity prediction for IL mixtures was compared with a molecular-based model, ePC-SAFT-FVT (ePC-FVT-MB), and an ion-based model, ePC-SAFT-FVT (ePC-FVT-MB). Graphical and statistical analyses revealed that the RF model offered the lowest error for viscosity prediction of pure ILs, while CatBoost performed the best for IL mixtures. In addition, sensitivity analysis showed that viscosity decreased with temperature and increased with pressure. The proposed models exhibit high accuracy under varying conditions. Outlier detection using the Leverage method indicated that 95.11% of pure IL viscosity data and 94.92% of mixed ILs viscosity data are statistically valid.</p>

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New insight into viscosity prediction of imidazolium-based ionic liquids and their mixtures with machine learning models

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年7月2日

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  • Amir Hossein Sheikhshoaei,
  • Ali Sanati

摘要

Ionic liquids (ILs) as eco-friendly solvents have gained significant attention across various fields of science, including the petroleum industry. Among the different ILs families, imidazolium-based ILs have been the subject of many research studies. However, not enough experimental studies have been conducted to determine the viscosity of this family of ILs, making accurate viscosity prediction crucial for their practical applications. This study aims to predict the viscosity of imidazolium-based ILs and their mixtures using critical properties as input parameters. Machine learning (ML) models have been implemented, and their performance in viscosity prediction for IL mixtures was compared with a molecular-based model, ePC-SAFT-FVT (ePC-FVT-MB), and an ion-based model, ePC-SAFT-FVT (ePC-FVT-MB). Graphical and statistical analyses revealed that the RF model offered the lowest error for viscosity prediction of pure ILs, while CatBoost performed the best for IL mixtures. In addition, sensitivity analysis showed that viscosity decreased with temperature and increased with pressure. The proposed models exhibit high accuracy under varying conditions. Outlier detection using the Leverage method indicated that 95.11% of pure IL viscosity data and 94.92% of mixed ILs viscosity data are statistically valid.