Artificial intelligence-driven optimization of closed-loop CO2 capture and conversion

Abstract

Reactive carbon capture couples CO₂ capture with electrochemical CO₂ upgrading. A major advantage of reactive carbon capture is that CO₂ can be released from a liquid sorbent without the need for heat or vacuum. However, it is challenging to find conditions capable of both capturing and upgrading CO₂ effectively. The capture of CO₂ requires the sorbent to be at a high pH, while CO₂ electrolysis is more effective at a lower pH. In this study, we used an artificial intelligence (AI)-driven strategy to optimize several operating variables for reactive carbon capture. The optimization yielded increases in CO₂ capture efficiency from 30% to 83% and faradaic efficiency for CO (FE_CO) from 30% to 42%. These new benchmarks lead to a CO breakeven price of <$1 per kilogram for reactive carbon capture, a value that represents one of the lowest cost pathways for converting air into fuel.