TY - JOUR
T1 - An open-source deep learning model for predicting effluent concentration in capacitive deionization
AU - Son, Moon
AU - Yoon, Nakyung
AU - Park, Sanghun
AU - Abbas, Ather
AU - Cho, Kyung Hwa
N1 - 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.
PY - 2023/1/15
Y1 - 2023/1/15
N2 - 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.
AB - 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.
KW - Capacitive deionization
KW - Deep learning
KW - Effluent conductivity
KW - Neural networks
KW - Python
UR - http://www.scopus.com/inward/record.url?scp=85139338612&partnerID=8YFLogxK
U2 - 10.1016/j.scitotenv.2022.159158
DO - 10.1016/j.scitotenv.2022.159158
M3 - Article
C2 - 36191701
AN - SCOPUS:85139338612
SN - 0048-9697
VL - 856
JO - Science of The Total Environment
JF - Science of The Total Environment
M1 - 159158
ER -
