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

Authors/Creators

• Amir Dashti
• Farid Amirkhani
• Mojtaba Raji
• John L. Zhou
• Ali Altaee
• Ali Braytee
• Brett Turner
• Hossein Ali Khonakdar
• Amir Razmjou, Edith Cowan UniversityFollow

Author Identifier (ORCID)

Amir Razmjou: https://orcid.org/0000-0002-3554-5129

Abstract

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.

Keywords

Genetic programming, global warming, Ionic liquids, machine learning, pollutant gases

Document Type

Journal Article

Date of Publication

6-27-2026

Volume

393

Publication Title

Separation and Purification Technology

Publisher

Elsevier

School

Mineral Recovery Research Centre / School of Engineering

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 License.

Comments

Dashti, A., Amirkhani, F., Raji, M., Zhou, J. L., Altaee, A., Braytee, A., Turner, B., Khonakdar, H. A., & Razmjou, A. (2026). Review and development of an explicit machine learning model for pollutant gas solubility in ionic liquids as green solvents. Separation and Purification Technology, 393, 137282. https://doi.org/10.1016/j.seppur.2026.137282