A Framework for Optimal Parameter Selection in Electrocoagulation Wastewater Treatment Using Integrated Physics-Based and Machine Learning Models

Electrocoagulation (EC) systems are regaining attention as a promising wastewater treatment technology due to their numerous advantages, including low system and operational costs and environmental friendliness. However, the widespread adoption and further development of EC systems have been hindered by a lack of fundamental understanding, necessitating systematic research to provide essential insights for system developers. In this study, a continuous EC system with a realistic setup is analyzed using an unsteady, two-dimensional physics-based model that incorporates multiphysics. The model captures key mechanisms, such as arsenic adsorption onto flocs, electrochemical reactions at the electrodes, chemical reactions in the bulk solution, and ionic species transport via diffusion and convection. Additionally, it accounts for bulk wastewater flow circulating between the EC cell and an external storage tank. This comprehensive modeling approach enables a fundamental analysis of how operating conditions influence arsenic removal efficiency, providing crucial insights for optimizing system utilization. Furthermore, the developed model is used to generate data under various operating conditions. Seven machine learning models are trained on this data after hyperparameter optimization. These high-accuracy models are then employed to develop processing maps that identify the conditions necessary to achieve acceptable removal efficiency. This study is the first to generate processing maps by synergistically integrating physics-based and data-driven models. These maps provide clear design and operational guidelines, helping researchers and engineers optimize EC systems. This research establishes a framework for combining physics-based and data-driven modeling approaches to generate processing maps that serve as essential guidelines for wastewater treatment applications.