Application of machine learning approaches for estimating carbon dioxide absorption capacity of a variety of blended imidazolium-based ionic liquids.

Ionic liquids (ILs) have gained attention in recent times as potentially effective absorbents for CO emissions owing to the number of their notable attributes, including reduced volatility, enhanced thermal consistency etc. Due to the number of challenges of thermodynamic models in forecasting CO solubility in ILs under a variety of operating conditions, machine learning (ML) approaches have been developed as a result of the necessity for an alternate solution. Nevertheless, there are currently quite a few of forecasting techniques available for evaluating the solubility of CO, specifically in combinations of imidazolium-based ILs. For this reason, the present study focuses on the utilization of molecular structure-based descriptors as an alternative chemistry concept for predicting the CO solubility in an imidazolium-based ILs mixture. This research utilized and contrasted 6 sophisticated machine learning models (AdaBoost-SVR, Extra trees, DT, CatBoost, LightGBM, XGBoost) to determine the most effective method for target parameter estimation. The study employed an exclusive and all-encompassing databank consisting of 43 imidazolium-based ILs, 26 input variables, and 4397 experimental data points in total. The remarkable 90 % overall accuracy consistently surpassed by all models serves as evidence of the ML methodologies' robustness and efficacy. The highest-performing approaches, XGBoost, exhibited a remarkable precision level of R being equal to 0.999 and RMSE of 0.0077. A comprehensive trend analysis was performed to assess the XGBoost model's performance across different operational scenarios such as molecular weight, temperature, water content, and pressure. The developed model proved to be capable of accurately detecting patterns in various operating conditions. By employing sensitivity analysis with SHAP values, it was observed that pressure, temperature, and molecular weight were the most impactful factors influencing the XGBoost model's predictions.