Machine learning for CO2 capture and conversion: A review
Sung Eun Jerng,
Yang Jeong Park,
Ju Li
Affiliations
Sung Eun Jerng
Department of Environmental and Energy Engineering, The University of Suwon, 17, Wauan-gil, Bongdam-eup, Hwaseong-si, 18323, Gyeonggi-do, Republic of Korea
Yang Jeong Park
Department of Materials Science and Engineering, Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, 02139, MA, United States of America; Department of Nuclear Science and Engineering, Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, 02139, MA, United States of America; MIT-IBM Watson AI Lab, 75 Binney Street, Cambridge, 02142, MA, United States of America
Ju Li
Department of Materials Science and Engineering, Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, 02139, MA, United States of America; Department of Nuclear Science and Engineering, Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, 02139, MA, United States of America; MIT-IBM Watson AI Lab, 75 Binney Street, Cambridge, 02142, MA, United States of America; Corresponding author at: Department of Materials Science and Engineering, Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, 02139, MA, United States of America.
Coupled electrochemical systems for the direct capture and conversion of CO2 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 CO2 capture solvents such as amine and ionic liquids, as well as electrochemical CO2 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.
