The Critical Role of Artificial Intelligence in Optimizing Electrochemical Processes for Water and Wastewater Remediation: A State-of-the-Art Review

Artificial intelligence (AI) is transforming electrochemical water and wastewater treatment by enhancing efficiency, predictive accuracy, and process control. However, a comprehensive evaluation of AI models in optimizing electrochemical processes for pollutant removal is still lacking. This review addresses this gap by systematically analyzing AI applications in electrocoagulation (EC), electrooxidation (EO), electro-Fenton (EF), and electrodialysis (ED). Focusing on key advances and parameter optimization, it highlights how AI-driven models improve removal efficiency by capturing complex nonlinear interactions among variables such as current density, pH, electrode material, electrolyte composition, and pollutant concentration. Recent studies have notably shown that artificial neural networks (ANNs) and adaptive neuro-fuzzy inference systems (ANFIS) have achieved R² values above 0.99 in EC and EO processes, outperforming traditional models. Hybrid AI approaches like ANN-GA and ANFIS-ACO have further optimized catalyst dosage and ion migration in EF and ED. While AI has demonstrated remarkable potential, challenges such as limited data availability, model interpretability, and real-world implementation remain significant obstacles. Integrating AI with mechanistic modeling and real-time monitoring may overcome these barriers and enable autonomous, energy-efficient treatment systems. This Perspective offers critical insights into current progress and future opportunities, underscoring the role of intelligent optimization in advancing sustainable and scalable electrochemical water treatment technologies.