Predicting ammonia solubility in ionic liquids using machine learning models based on critical properties

Air pollution continues to be one of the most critical environmental challenges today. Among the various contaminants released into the atmosphere, ammonia (NH3) has been drawing more attention because of its considerable effects on both the environment and human health. Its emissions contribute to the formation of secondary particulate matter, which not only poses significant health risks but also deteriorates environmental quality. Ionic liquids have emerged as promising candidates for capturing NH3, and the ability to reliably predict its solubility in ILs is essential for identifying suitable solvents and optimizing the separation process. This study uses machine learning models (CatBoost, XGBoost, LightGBM, and GPR) for predicting ammonia solubility in ionic liquids. The input parameters include temperature (T), pressure (P), critical temperature (Tc), critical pressure (Pc), critical volume (Vc), acentric factor (ω), and boiling point (Tb). Statistical errors and graphical analyses showed that the CatBoost model performed better than other models and had high reliability for predicting NH3 solubility. Among the evaluated models, CatBoost delivered the most accurate predictions, achieving a root mean square error (RMSE) of 0.0137 and an R² value of 0.9967 for all data. This model effectively captured the influence of key parameters on ammonia solubility. Notably, 97.47 % of the data points fell within the model’s applicability domain, highlighting its strong predictive reliability. These outcomes underscore the capability of the CatBoost algorithm to serve as a robust and efficient approach for estimating NH3 solubility in ionic liquids, offering valuable support for future materials design and separation process optimization.