Machine learning for CO2 capture and conversion: A review

Abstract

Coupled electrochemical systems for the direct capture and conversion of CO${}_2$ have garnered significant attention owing to their potential to enhance energy- and cost-efficiency by circumventing the amine regeneration step. However, optimizing the coupled system is more challenging than handling separated systems because of its complexity, caused by the incorporation of solvent and heterogeneous catalysts. Nevertheless, the deployment of machine learning can be immensely beneficial, reducing both time and cost owing to its ability to simulate and describe complex systems with numerous parameters involved. In this review, we summarized the machine learning techniques employed in the development of CO${}_2$ capture solvents such as amine and ionic liquids, as well as electrochemical CO${}_2$ conversion catalysts. To optimize a coupled electrochemical system, these two separately developed systems will need to be combined via machine learning techniques in the future.