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Cho, Kyung Hwa
Water-Environmental Informatics Lab (WEIL)
Research Interests
  • Water Quality Monitoring and Modeling, Water Treatment Process Modeling

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

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Title
An open-source deep learning model for predicting effluent concentration in capacitive deionization
Author
Son, MoonYoon, NakyungPark, SanghunAbbas, AtherCho, Kyung Hwa
Issue Date
2023-01
Publisher
ELSEVIER
Citation
SCIENCE OF THE TOTAL ENVIRONMENT, v.856, no.2, pp.159158
Abstract
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.
URI
https://scholarworks.unist.ac.kr/handle/201301/60031
DOI
10.1016/j.scitotenv.2022.159158
ISSN
0048-9697
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UEE_Journal Papers
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