| 2024-08 | Artificial neural network modeling for the oxidation kinetics of divalent manganese ions during chlorination and the role of arsenite ions in the binary/ternary systems | Liao, Ziqiao; Choi, Kungwon; Ullah, Zahid; Son, Moon; Ahn, Yongtae; Khan, Moonis Ali; Prabhu, Subbaiah Muthu; Jeon, Byong-Hun |
| 2023-07 | Comparison of tree-based model with deep learning model in predicting effluent pH and concentration by capacitive deionization | Ullah, Zahid; Yoon, Nakyung; Tarus, Bethwel Kipchirchir; Park, Sanghun; Son, Moon |
| 2024-09 | Decoupling ion concentrations from effluent conductivity profiles in capacitive and battery electrode deionizations using an artificial intelligence model | Kim, Hoo Hugo; Choi, Byeongwook; Ullah, Zahid; Jeong, Nahyeon; Cho, Kyung Hwa; Park, Sanghun; Baek, Sang-Soo; Son, Moon |
| 2024-10 | Desalination performance in versatile capacitive/battery deionization configurations using a cation intercalating electrode | Tarus, Bethwel Kipchirchir; Ullah, Zahid; Jande, Yusufu A. C.; Njau, Karoli N.; Byun, Jeehye; Son, Moon |
| 2023-10 | Energy storage capability of seawater batteries for intermittent power generation systems: Conceptualization and modeling | Park, Sanghun; Yoon, Nakyung; Ullah, Zahid; Tarus, Bethwel Kipchirchir; Choi, Byeongwook; Kim, Hoo Hugo; Son, Moon |
| 2024-11 | Reinforcement Learning and Machine Learning Controllers for Enhancing Water Quality and Process Efficiency in Electrochemical Desalination | Ullah, Zahid; Yun, Nakyeong; Rossi, Ruggero; Son, Moon |
