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An open-source deep learning model for predicting effluent concentration in capacitive deionization.
Son, Moon; Yoon, Nakyung; Park, Sanghun; Abbas, Ather; Cho, Kyung Hwa.
Affiliation
  • Son M; Center for Water Cycle Research, Korea Institute of Science and Technology (KIST), Seoul 02792, Republic of Korea; Division of Energy and Environment Technology, KIST-School, University of Science and Technology, Seoul 02792, Republic of Korea.
  • Yoon N; Center for Water Cycle Research, Korea Institute of Science and Technology (KIST), Seoul 02792, Republic of Korea; School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology, UNIST-gil 50, Ulsan 44919, Republic of Korea.
  • Park S; Center for Water Cycle Research, Korea Institute of Science and Technology (KIST), Seoul 02792, Republic of Korea; School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology, UNIST-gil 50, Ulsan 44919, Republic of Korea.
  • Abbas A; School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology, UNIST-gil 50, Ulsan 44919, Republic of Korea.
  • Cho KH; School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology, UNIST-gil 50, Ulsan 44919, Republic of Korea. Electronic address: khcho@unist.ac.kr.
Sci Total Environ ; 856(Pt 2): 159158, 2023 Jan 15.
Article in En | MEDLINE | ID: mdl-36191701
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Water Purification / Deep Learning Language: En Journal: Sci Total Environ Year: 2023 Document type: Article
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Water Purification / Deep Learning Language: En Journal: Sci Total Environ Year: 2023 Document type: Article