Insights on features' contribution to desalination dynamics and capacity of capacitive deionization through machine learning study

Parameter optimization in designing a rational capacitive deionization (CDI) process is usually performed to achieve both high electrosorption capacity and speed. This necessitates a clear understanding of system behavior and discriminating the features' role on desalination capacity from its dynamic. Machine learning (ML) modeling is widely employed for understanding various systems' behavior as an alternative for physics-based extrapolation...