Machine Learning in Electrochemical Oxidation Process: A Mini-Review

In recent years, machine learning (ML) techniques have demonstrated a strong ability to solve highly complex and non-linear problems by analyzing large datasets and learning their intrinsic patterns and relationships. Particularly in chemical engineering and materials science, ML can be used to discover microstructural composition, optimize chemical processes, and create novel synthetic pathways. Electrochemical processes offer the advantages of precise process control, environmental friendliness, high energy conversion efficiency and low cost. This review article provides the first systematic summary of ML in the application of electrochemical oxidation, including pollutant removal, battery remediation, substance synthesis and material characterization prediction. Hot trends at the intersection of ML and electrochemical oxidation were analyzed through bibliometrics. Common ML models were outlined. The role of ML in improving removal efficiency, optimizing experimental conditions, aiding battery diagnosis and predictive maintenance, and revealing material characterization was highlighted. In addition, current issues and future perspectives were presented in relation to the strengths and weaknesses of ML algorithms applied to electrochemical oxidation. In order to further support the sustainable growth of electrochemistry from basic research to useful applications, this review attempts to make it easier to integrate ML into electrochemical oxidation.