Artificial intelligence-enhanced solubility predictions of greenhouse gases in ionic liquids: A review

Abstract

Greenhouse gas emissions from human activities pose a significant threat to the ecosystem, causing climate change and ecological disruptions. Ionic liquids (ILs) show promise for gas separation and carbon capture, but predicting gas solubility in ILs is challenging due to limited data and complex thermodynamics. Artificial intelligence (AI) offers an innovative approach to improve the efficiency and accuracy of solubility predictions. This review analyzes recent advancements in AI-enabled solubility predictions, focusing on methodologies, models, and applications in gas separation and carbon capture. It examines artificial neural networks, deep learning models, and support vector machines for predicting solubility in ILs, and presents valuable results demonstrating the potential of these techniques. The study highlights AI's transformative power in understanding gas-IL interactions and inspiring environmentally friendly separation processes. It also discusses integrating AI-driven predictions with process modeling tools like Aspen Hysys and Aspen Plus, aiming to stimulate further research in gas separation technologies and pave the way for practical implementation.