An open-source deep learning model for predicting effluent concentration in capacitive deionization
Cited 0 times inCited 0 times in
- An open-source deep learning model for predicting effluent concentration in capacitive deionization
- Son, Moon; Yoon, Nakyung; Park, Sanghun; Abbas, Ather; Cho, Kyung Hwa
- Issue Date
- SCIENCE OF THE TOTAL ENVIRONMENT, v.856, no.2, pp.159158
- 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.
- Appears in Collections:
- UEE_Journal Papers
- Files in This Item:
- There are no files associated with this item.
can give you direct access to the published full text of this article. (UNISTARs only)
Show full item record
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.