Physics-informed Data-driven control of Electrochemical Separation Processes

Optimizing the operational conditions of electrochemical separation systems to achieve higher separation efficiency remains a complex challenge due to their nonlinear and dynamic nature. In this work, we proposed a Reinforcement Learning (RL)-based control framework to address this challenge. By applying various RL algorithms, we trained an RL-based controller that adapts to different system configurations and conditions. Also, the trained model learns the optimality between the removal efficiency and energy consumption. Overall, this approach autonomously learns the optimal operational parameters, significantly improving ion removal efficiency. The proposed RL-based control system enhances the performance of electrochemical system, providing a versatile and adaptive solution for optimizing separation across multiple electrochemical technologies. This work demonstrates the potential of RL in advancing the design and control of sustainable water purification systems.