Machine learning in modeling, analysis and control of electrochemical reactors: A tutorial review

Abstract

Electrochemical reactors play a critical role in various industrial sectors, including energy storage, chemical production, and environmental engineering. The complexity of these systems – arising from coupled electrochemical reactions with mass, heat and charge transport phenomena – poses significant challenges in modeling, analysis, and control. Machine learning (ML) has emerged as a promising tool for addressing these challenges by providing data-driven solutions to complex process modeling, optimization, and advanced control. This tutorial review explores the state-of-the-art applications of ML in electrochemical reactor systems, including ML-based modeling techniques and ML-based advanced control strategies, followed by the discussions of practical challenges and their solutions. An electrochemical carbon dioxide (CO₂) reduction reactor is used as a demonstration example to show the effectiveness of various modeling and control methods. In addition, an integrated data infrastructure platform is presented for the digitalization and control of the electrochemical CO₂ reduction reactor. By identifying current gaps and future opportunities, this article provides a roadmap for leveraging ML tools to improve the analysis and operation of electrochemical reactors.