Autonomous real-time control for membrane capacitive deionization

• Published in: Water research (Oxford) Vol. 262; p. 122086
• Main Authors: Shim, Jaegyu, Lee, Suin, Yun, Nakyeong, Son, Moon, Chae, Sung Ho, Cho, Kyung Hwa
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 15.09.2024
• Subjects: Artificial Intelligence; Electric Conductivity; Membrane capacitive deionization; Membranes, Artificial; Models, Theoretical; Optimization; Process control; Real-time; Reinforcement learning; Water Purification - methods; R2; TD3; SHAP; LSTM; Real-time; Process control; PPO2; Reinforcement learning; DDPG; CDI; NRMSE; Membrane capacitive deionization; Optimization; SEC; A2C; VIF; SAC; RL; EC; MSE; MCDI
• ISSN: 0043-1354; 1879-2448; 1879-2448
• DOI: 10.1016/j.watres.2024.122086

•LSTM-based simulation models were established to simulate the actual MCDI operation.•RL agents were examined in the actual MCDI system using a telecommunication system.•A2C was the best agent, with the largest desalination goal and longer operation.•SHAP analysis explained the contribution of input parameters to the model decisions. Artificial intelligence has been employed to simulate and optimize the performance of membrane capacitive deionization (MCDI), an emerging ion separation process. However, a real-time control for optimal MCDI operation has not been investigated yet. In this study, we aimed to develop a reinforcement learning (RL)-based control model and investigate the model to find an energy-efficient MCDI operation strategy. To fulfill the objectives, we established three long-short term memory models to predict applied voltage, outflow pH, and outflow electrical conductivity. Also, four RL agents were trained to minimize outflow concentration and energy consumption simultaneously. Consequently, actor-critic (A2C) and proximal policy optimization (PPO2) achieved the ion separation goal (<0.8 mS/cm) as they determined the electrical current and pump speed to be low. Particularly, A2C kept the parameters consistent in charging MCDI, which caused lower energy consumption (0.0128 kWh/m3) than PPO2 (0.0363 kWh/m3). To understand the decision-making process of A2C, the Shapley additive explanation based on the decision tree model estimated the influence of input parameters on the control parameters. The results of this study demonstrate the feasibility of RL-based controls in MCDI operations. Thus, we expect that the RL-based control model can improve further and enhance the efficiency of water treatment technologies. [Display omitted]