Bayesian optimization of dynamic Ce(III) electrooxidation

Daniel Frey, Ju Hee Shin, Adeola Akin, Xinshu Zhang, Miguel A. Modestino

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Article numbere17930
JournalAIChE Journal
Volume68
Issue number12
DOIs
StatePublished - Dec 2022

Keywords

  • artificial intelligence
  • diffusion (mass transfer, heat transfer)
  • electrochemistry
  • mathematical modeling
  • optimization

ASJC Scopus subject areas

  • Biotechnology
  • Environmental Engineering
  • Chemical Engineering(all)

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