An open-source deep learning model for predicting effluent concentration in capacitive deionization

Moon Son, Nakyung Yoon, Sanghun Park, Ather Abbas, Kyung Hwa Cho*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


To effectively evaluate the performance of capacitive deionization (CDI), an electrochemical ion separation technology, 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.

Original languageEnglish (US)
Article number159158
JournalScience of The Total Environment
StatePublished - Jan 15 2023

Bibliographical note

Funding Information:
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government ( MSIT ) (No. 2021R1C1C2005643 and No. 2022R1A2C2006172 ). This work was supported by the institutional program of KIST ( 2E31932 and 2E31933 ).

Publisher Copyright:
© 2022 Elsevier B.V.


  • Capacitive deionization
  • Deep learning
  • Effluent conductivity
  • Neural networks
  • Python

ASJC Scopus subject areas

  • Environmental Engineering
  • Environmental Chemistry
  • Waste Management and Disposal
  • Pollution


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