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Adaptive reinforcement learning for energy-efficient high-recovery closed-circuit reverse osmosis.

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Title: Adaptive reinforcement learning for energy-efficient high-recovery closed-circuit reverse osmosis.
Authors: Moon J; Future and Fusion Lab of Architectural, Civil, and Environmental Engineering, Korea University, Seoul 02841, Republic of Korea., Yun B; School of Civil, Environmental, and Architectural Engineering, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul 02841, Republic of Korea., Park K; Department of Chemical Engineering, Hanyang University, 222 Wangsimni-ro, Seongdong-gu, Seoul 04763, Republic of Korea., Kim SS; Water and Wastewater Research Center, Korea Water Resources Corporation, 125 Yuseong-dearo, 1689beon-gil, Yuseong-gu, Daejeon 34045, Republic of Korea., Lee Y; Water and Wastewater Research Center, Korea Water Resources Corporation, 125 Yuseong-dearo, 1689beon-gil, Yuseong-gu, Daejeon 34045, Republic of Korea., Jeong K; Department of Environmental Engineering, Chosun University, Gwangju 61452, Republic of Korea. Electronic address: khjeong@chosun.ac.kr., Cho KH; School of Civil, Environmental, and Architectural Engineering, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul 02841, Republic of Korea. Electronic address: khcho80@korea.ac.kr.
Source: Water research [Water Res] 2026 Jul 01; Vol. 299, pp. 125855. Date of Electronic Publication: 2026 Apr 01.
Publication Type: Journal Article
Journal Info: Publisher: Pergamon Press Country of Publication: England NLM ID: 0105072 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1879-2448 (Electronic) Linking ISSN: 00431354 NLM ISO Abbreviation: Water Res Subsets: MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:1879-2448
DOI:10.1016/j.watres.2026.125855