Bayesian optimization of dynamic Ce(III) electrooxidation. Issue 12 (27th October 2022)

• Record Type: Journal Article
• Authors: Frey, Daniel; Shin, Ju Hee; Akin, Adeola; Zhang, Xinshu; Modestino, Miguel A.
• Is Part Of: AIChE journal. Volume 68:Issue 12(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-10-27
• Subjects: artificial intelligence -- diffusion (mass transfer, heat transfer) -- electrochemistry -- mathematical modeling -- optimization; Chemical engineering -- Periodicals; Génie chimique -- Périodiques; 660.28
• DOI: 10.1002/aic.17930
• Languages: English
• ISSNs: 0001-1541

Abstract

Abstract: Cerium(III/IV) is a redox couple with promising applications in redox flow batteries and redox‐mediated water electrolyzers, but due to its high oxidation potential, parasitic oxygen evolution can occur and decrease the Faradaic efficiency (FE) of the oxidation reaction. Dynamic potential dosing can help balance the transport limitations and the electrooxidation rates, allowing for control over the cerium(III) concentration near the electrode, enhancing FE. Due to the large number of possible pulsing operating conditions, Bayesian optimization (BO) was used to rapidly identify the optimal potential pulses. With an active pulse voltage of 2.5 V vs. Ag/AgCl, the maximum FE achieved was 0.91 (active pulse time = 5 ms, resting pulse time = 136 ms), underscoring the need for long inactive times for cerium(III) diffusion to the electrode. Furthermore, a multiobjective BO was performed to identify optimal trade‐offs between FE and the partial current density to achieve improvements over constant current operation.