Review and development of an explicit machine learning model for pollutant gas solubility in ionic liquids as green solvents

The increasing release of greenhouse gases (GHGs) like CO₂, CH₄, N₂O, and industrial contaminants (indirect GHGs) such as SO₂ and H₂S has prompted significant global worries due to their role in climate change, air pollution, and harm to the environment. Ionic liquids (ILs) as green solvents have emerged as promising alternatives to traditional solvents because of their minimal volatility, high thermal stability, and adjustable physicochemical characteristics. Yet, limited gas solubility data in ILs is hindering their applications in carbon capture and air pollution control. Machine learning (ML) is a powerful tool for modeling and simulating the solubility of polluting gases in ILs. This research aims to critically review recent progress in ML modeling of pollutant gas removal by ILs. More importantly, a new ML model of genetic programming (GP) was developed to generate an explicit and accurate mathematical equation to predict the solubility of SO₂, CH₄, N₂O, CO, H₂S and CO₂ in ILs, using a large dataset (3209) for different gas-IL systems. Using temperature, pressure, and structural related parameters of ILs and gases as input parameters, the model achieved a high accuracy (R² > 0.97). Finally, a simple Excel method for calculating gas solubility has been created for prediction and modeling purposes.