Closed-loop, machine learning–driven optimization of reactor yields in reactive carbon electrolyzers

Reactive carbon capture combines CO2 capture and conversion in a single system. Reactive carbon electrolyzers receive a liquid eluent from a CO2 capture unit containing a sorbent that has captured CO2. This electrolyzer releases CO2 electrochemically and converts it into a value-added product like CO. The effectiveness of this system depends on high CO2 utilization and high product formation rates. We define their product as “reactor yield.” Here, we used a closed-loop, automated workflow with Bayesian optimization to maximize reactor yield in an electrolyzer operating with alkaline CO2 capture solutions. We explored a six-dimensional parameter space and found that a bicarbonate concentration of 1.5 M and carbonate concentration of 0.75 M achieved the highest reactor yield (44 mA cm^-2). Interestingly, this optimum occurred at non-maximum values of CO partial current density (54 vs. 87 mA cm^-2) and CO2 utilization (81% vs. 100%), highlighting the need for joint optimization of both factors.