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Ionic Liquids (ILs) continue to emerge as innovative green solvents with significant importance from academic, industrial, and environmental perspectives (Zhang et al. in J Clean Prod 415, 2023). Their distinct properties, such as high selectivity and low volatility, make them invaluable in complex applications including gas sweetening and reducing sour and acid gases. However, the oil and gas industry faces persistent challenges in removing toxic and corrosive hydrogen sulfide (H₂S) efficiently. Conventional methods (e.g., amine scrubbing) are energy-intensive and environmentally taxing (Wang et al. in Environ Sci Technol 58:1456–1467, 2024), while experimental screening of ILs for optimal H₂S solubility remains costly and time-consuming. This study leverages the tunability of ILs to address these limitations by employing advanced artificial intelligence techniques to develop machine learning models—including Decision Trees (DT), Random Forest (RF), XGBoost, CatBoost, and LightGBM—using an extensive database of 1521 measurements of H₂S solubility in 43 ILs. The results indicate that the CatBoost model, achieving a determination coefficient (R2) of 0.9947, provides the most accurate predictions, followed closely by XGBoost (R2 = 0.9921). By correlating IL structural features with H₂S solubility through ML, this study provides a roadmap for synthesizing tailored ILs, reducing development time for scalable gas sweetening technologies. The models developed here allow researchers to prioritize IL candidates with high predicted H₂S uptake, cutting R&D costs and accelerating deployment in gas processing plants. This work enables rapid identification of optimal ILs for H₂S capture by eliminating the need for trial-and-error experiments, thus streamlining solvent design for industrial-scale gas treatment. The findings underscore the potential of ILs as effective solvents for natural gas and petroleum products, highlighting the pivotal role of AI in optimizing industrial gas sweetening.

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Prediction of H2S Solubility in Ionic Liquids Using Ensemble Machine Learning Models

  • 会议论文

  • 发表日期：

    2026年

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  • A. Belkhadria,
  • S. Benraad,
  • A. Aouanouk,
  • R. Absi

摘要

Ionic Liquids (ILs) continue to emerge as innovative green solvents with significant importance from academic, industrial, and environmental perspectives (Zhang et al. in J Clean Prod 415, 2023). Their distinct properties, such as high selectivity and low volatility, make them invaluable in complex applications including gas sweetening and reducing sour and acid gases. However, the oil and gas industry faces persistent challenges in removing toxic and corrosive hydrogen sulfide (H₂S) efficiently. Conventional methods (e.g., amine scrubbing) are energy-intensive and environmentally taxing (Wang et al. in Environ Sci Technol 58:1456–1467, 2024), while experimental screening of ILs for optimal H₂S solubility remains costly and time-consuming. This study leverages the tunability of ILs to address these limitations by employing advanced artificial intelligence techniques to develop machine learning models—including Decision Trees (DT), Random Forest (RF), XGBoost, CatBoost, and LightGBM—using an extensive database of 1521 measurements of H₂S solubility in 43 ILs. The results indicate that the CatBoost model, achieving a determination coefficient (R2) of 0.9947, provides the most accurate predictions, followed closely by XGBoost (R2 = 0.9921). By correlating IL structural features with H₂S solubility through ML, this study provides a roadmap for synthesizing tailored ILs, reducing development time for scalable gas sweetening technologies. The models developed here allow researchers to prioritize IL candidates with high predicted H₂S uptake, cutting R&D costs and accelerating deployment in gas processing plants. This work enables rapid identification of optimal ILs for H₂S capture by eliminating the need for trial-and-error experiments, thus streamlining solvent design for industrial-scale gas treatment. The findings underscore the potential of ILs as effective solvents for natural gas and petroleum products, highlighting the pivotal role of AI in optimizing industrial gas sweetening.