Son, Moon Yoon, Nakyung Park, Sanghun Abbas, Ather Cho, Kyung Hwa 2024-01-19T10:31:50Z 2024-01-19T10:31:50Z 2022-11-16 2023-01 0048-9697 https://pubs.kist.re.kr/handle/201004/114183 To effectively evaluate the performance of capacitive deionization (CDI), an electrochemical ion separation technol-ogy, it is necessary to accurately estimate the number of ions removed (effluent concentration) according to energy consumption. Herein, we propose 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. English Elsevier BV An open-source deep learning model for predicting effluent concentration in capacitive deionization Article 10.1016/j.scitotenv.2022.159158 1 Science of the Total Environment, v.856 Science of the Total Environment 856 N scie scopus 000875282000009 Environmental Sciences Environmental Sciences & Ecology Article ENERGY-CONSUMPTION DESALINATION CDI Deep learning Neural networks Python Capacitive deionization Effluent conductivity