Metadata and feature importance analyses of membrane capacitive deionization models: Is a water treatment artificial intelligence panacea possible?

Abstract

Membrane capacitive deionization (MCDI) is an emerging technology for water purification and desalination. Artificial intelligence (AI) techniques using machine learning (ML) and deep learning (DL) have been widely applied to overcome the drawbacks of conventional numerical models in predicting effluent concentrations, an important parameter to estimate the energy consumption and degree of desalination of the MCDI process. However, despite the remarkable progress of these ML/DL models, relevant studies have only been conducted with limited datasets, leading to a substantial controversy regarding model accuracy. Therefore, in this study, we comprehensively evaluated the performances and applicability of multiple ML/DL models for MCDI using metadata (i.e., performance indices) and feature importance analyses. One MCDI dataset containing both constant-current and constant-voltage operations was reconfigured uniformly or unevenly depending on operation modes. Subsequently, six ML models and one DL model were used to execute the analyses using performance indices. Collectively, this study suggests that a perfect ML/DL model for the water treatment process may not exist, and understanding data types is as important as selecting appropriate models to build a data-driven AI model when process performance is greatly affected by specific input variables (i.e., current and voltage).