An open-source deep learning model for predicting effluent concentration in capacitive deionization
Published in Science of the Total Environment, 2023
Recommended citation: Son, M., Yoon, N., Park, S., Abbas, A., & Cho, K. H. (2023). An open-source deep learning model for predicting effluent concentration in capacitive deionization. Science of The Total Environment, 856, 159158. https://doi.org/10.1016/j.scitotenv.2022.159158
In this study, we proposed and evaluate a deep learning model for predicting the effluent concentration of a CDI process. The developed deep learning model exhibited excellent prediction accuracy for both constant current and constant voltage modes (R2 ≥ 0.968), and the accuracy increased with the data size. This model was based on the open-source language, Python, and the code has since been distributed with proper instructions for general use. Owing to the nature of the data-oriented deep learning model, the findings of this study are not only applicable to conventional CDI but also to various types of CDI (membrane CDI, flow CDI, faradaic CDI, etc.). Therefore, by referring to the examples shown in this study, we hope that this open-source deep learning code will be widely used in CDI research.