{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,20]],"date-time":"2026-10-20T21:48:50Z","timestamp":1792532930617,"version":"4.5.0"},"reference-count":42,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100014188","name":"Korea Ministry of Science and ICT","doi-asserted-by":"publisher","award":["RS-2024-00406500"],"award-info":[{"award-number":["RS-2024-00406500"]}],"id":[{"id":"10.13039\/501100014188","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100014188","name":"Korea Ministry of Science and ICT","doi-asserted-by":"publisher","award":["RS-2023-00252518"],"award-info":[{"award-number":["RS-2023-00252518"]}],"id":[{"id":"10.13039\/501100014188","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Water Research"],"published-print":{"date-parts":[[2026,7]]},"DOI":"10.1016\/j.watres.2026.125855","type":"journal-article","created":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T06:56:56Z","timestamp":1775026616000},"page":"125855","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":2,"special_numbering":"C","title":["Adaptive reinforcement learning for energy-efficient high-recovery closed-circuit reverse osmosis"],"prefix":"10.1016","volume":"299","author":[{"given":"Jeongwoo","family":"Moon","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-7107-9913","authenticated-orcid":false,"given":"Byeongchan","family":"Yun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8303-0461","authenticated-orcid":false,"given":"Kiho","family":"Park","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Seong-Su","family":"Kim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Youngjoo","family":"Lee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1739-1274","authenticated-orcid":false,"given":"Kwanho","family":"Jeong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3157-0295","authenticated-orcid":false,"given":"Kyung Hwa","family":"Cho","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.watres.2026.125855_bib0001","doi-asserted-by":"crossref","DOI":"10.1016\/j.desal.2021.115443","article-title":"An efficient deep reinforcement machine learning-based control reverse osmosis system for water desalination","volume":"522","author":"Bonny","year":"2022","journal-title":"Desalination"},{"issue":"3","key":"10.1016\/j.watres.2026.125855_bib0002","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1080\/00031305.1981.10479327","article-title":"Rank transformations as a bridge between parametric and nonparametric statistics","volume":"35","author":"Conover","year":"1981","journal-title":"Am. Stat."},{"key":"10.1016\/j.watres.2026.125855_bib0003","doi-asserted-by":"crossref","DOI":"10.1016\/j.watres.2024.122179","article-title":"Unified control of diverse actions in a wastewater treatment activated sludge system using reinforcement learning for multi-objective optimization","volume":"263","author":"Croll","year":"2024","journal-title":"Water Res."},{"key":"10.1016\/j.watres.2026.125855_bib0004","unstructured":"Dewey, D. 2014 Reinforcement learning and the reward engineering principle."},{"issue":"1","key":"10.1016\/j.watres.2026.125855_bib0005","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1016\/0771-050X(80)90013-3","article-title":"A family of embedded Runge-Kutta formulae","volume":"6","author":"Dormand","year":"1980","journal-title":"J. Comput. Appl. Math."},{"key":"10.1016\/j.watres.2026.125855_bib0006","doi-asserted-by":"crossref","first-page":"364","DOI":"10.1016\/j.watres.2016.10.016","article-title":"Leachate from microplastics impairs larval development in brown mussels","volume":"106","author":"e Silva","year":"2016","journal-title":"Water Res."},{"key":"10.1016\/j.watres.2026.125855_bib0007","doi-asserted-by":"crossref","DOI":"10.1016\/j.watres.2025.123885","article-title":"A dimensionless approach to minimizing energy consumption in a single-pass reverse osmosis desalination system","author":"Elbassoussi","year":"2025","journal-title":"Water Res."},{"issue":"6043","key":"10.1016\/j.watres.2026.125855_bib0008","doi-asserted-by":"crossref","first-page":"712","DOI":"10.1126\/science.1200488","article-title":"The future of seawater desalination: energy, technology, and the environment","volume":"333","author":"Elimelech","year":"2011","journal-title":"Science"},{"key":"10.1016\/j.watres.2026.125855_bib0009","series-title":"Breakthroughs in Statistics: methodology and Distribution","first-page":"66","author":"Fisher","year":"1970"},{"issue":"200","key":"10.1016\/j.watres.2026.125855_bib0010","doi-asserted-by":"crossref","first-page":"675","DOI":"10.1080\/01621459.1937.10503522","article-title":"The use of ranks to avoid the assumption of normality implicit in the analysis of variance","volume":"32","author":"Friedman","year":"1937","journal-title":"J. Am. Stat. Assoc."},{"key":"10.1016\/j.watres.2026.125855_bib0011","doi-asserted-by":"crossref","DOI":"10.1016\/j.desal.2021.115300","article-title":"Operational optimization of closed-circuit reverse osmosis (CCRO) pilot to recover concentrate at an advanced water purification facility for potable reuse","volume":"518","author":"Gu","year":"2021","journal-title":"Desalination"},{"key":"10.1016\/j.watres.2026.125855_bib0012","series-title":"Soft Actor-Critic: off-policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor","first-page":"1861","author":"Haarnoja","year":"2018"},{"key":"10.1016\/j.watres.2026.125855_bib0013","doi-asserted-by":"crossref","DOI":"10.1016\/j.desal.2021.115289","article-title":"Modeling and evaluating performance of full-scale reverse osmosis system in industrial water treatment plant","volume":"518","author":"Jeong","year":"2021","journal-title":"Desalination"},{"key":"10.1016\/j.watres.2026.125855_bib0014","doi-asserted-by":"crossref","DOI":"10.1016\/j.apenergy.2019.113652","article-title":"A comprehensive review of energy consumption of seawater reverse osmosis desalination plants","volume":"254","author":"Kim","year":"2019","journal-title":"Appl. Energy"},{"key":"10.1016\/j.watres.2026.125855_bib0015","doi-asserted-by":"crossref","DOI":"10.1016\/j.desal.2021.115229","article-title":"A spatiotemporal model for dynamic RO simulations","volume":"516","author":"Li","year":"2021","journal-title":"Desalination"},{"key":"10.1016\/j.watres.2026.125855_bib0016","doi-asserted-by":"crossref","DOI":"10.1016\/j.desal.2022.116149","article-title":"Cyclic simulation and energy assessment of closed-circuit RO (CCRO) of brackish water","volume":"545","author":"Li","year":"2023","journal-title":"Desalination"},{"key":"10.1016\/j.watres.2026.125855_bib0017","doi-asserted-by":"crossref","DOI":"10.1016\/j.desal.2023.116519","article-title":"An improved closed-circuit RO (CCRO) system: design and cyclic simulation","volume":"554","author":"Li","year":"2023","journal-title":"Desalination"},{"key":"10.1016\/j.watres.2026.125855_bib0018","first-page":"30","article-title":"A unified approach to interpreting model predictions","author":"Lundberg","year":"2017","journal-title":"Advanc. Neural. Informat. Process. Syst"},{"issue":"7540","key":"10.1016\/j.watres.2026.125855_bib0019","doi-asserted-by":"crossref","first-page":"529","DOI":"10.1038\/nature14236","article-title":"Human-level control through deep reinforcement learning","volume":"518","author":"Mnih","year":"2015","journal-title":"Nature"},{"key":"10.1016\/j.watres.2026.125855_bib0020","doi-asserted-by":"crossref","DOI":"10.1016\/j.desal.2025.119406","article-title":"Analysing practical inefficiencies of closed-circuit reverse osmosis (CCRO) seawater desalination via finite-difference time-dependent model","author":"Mo","year":"2025","journal-title":"Desalination"},{"key":"10.1016\/j.watres.2026.125855_bib0021","doi-asserted-by":"crossref","DOI":"10.1016\/j.desal.2025.118678","article-title":"Robust deep learning model combined with missing input data estimation: application in a 1000 m3\/day high-salinity SWRO plant","volume":"603","author":"Moon","year":"2025","journal-title":"Desalination"},{"key":"10.1016\/j.watres.2026.125855_bib0022","doi-asserted-by":"crossref","DOI":"10.1016\/j.desal.2024.117531","article-title":"Cost-based optimization, feasibility study, and sensitivity analysis of forward osmosis\/crystallization\/reverse osmosis with high-temperature operation for high-salinity seawater desalination","volume":"580","author":"Moon","year":"2024","journal-title":"Desalination"},{"key":"10.1016\/j.watres.2026.125855_bib0023","doi-asserted-by":"crossref","DOI":"10.1016\/j.desal.2025.118811","article-title":"Critical challenges in high-salinity seawater reverse osmosis systems: technical, energy, and environmental reviews","author":"Moon","year":"2025","journal-title":"Desalination"},{"key":"10.1016\/j.watres.2026.125855_bib0024","first-page":"504","article-title":"A compact hybrid batch\/semi-batch reverse osmosis (HBSRO) system for high-recovery, low-energy desalination","author":"Park","year":"2021","journal-title":"Desalination"},{"key":"10.1016\/j.watres.2026.125855_bib0025","doi-asserted-by":"crossref","DOI":"10.1016\/j.chemosphere.2022.136364","article-title":"Deep reinforcement learning in an ultrafiltration system: optimizing operating pressure and chemical cleaning conditions","volume":"308","author":"Park","year":"2022","journal-title":"Chemosphere"},{"key":"10.1016\/j.watres.2026.125855_bib0026","doi-asserted-by":"crossref","DOI":"10.1016\/j.watres.2023.120791","article-title":"Enhancing urban flood forecasting in drainage systems using dynamic ensemble-based data mining","volume":"247","author":"Piadeh","year":"2023","journal-title":"Water Res."},{"key":"10.1016\/j.watres.2026.125855_bib0027","series-title":"arXiv preprint","article-title":"Proximal policy optimization algorithms","author":"Schulman","year":"2017"},{"issue":"3","key":"10.1016\/j.watres.2026.125855_bib0028","doi-asserted-by":"crossref","first-page":"591","DOI":"10.1093\/biomet\/52.3-4.591","article-title":"An analysis of variance test for normality","volume":"52","author":"Shaphiro","year":"1965","journal-title":"Biometrika"},{"key":"10.1016\/j.watres.2026.125855_bib0029","doi-asserted-by":"crossref","DOI":"10.1016\/j.desal.2025.119193","article-title":"Optimizing membrane cleaning strategy of industrial reverse osmosis process using long sequence time-series forecasting","author":"Shim","year":"2025","journal-title":"Desalination"},{"key":"10.1016\/j.watres.2026.125855_bib0030","doi-asserted-by":"crossref","DOI":"10.1016\/j.watres.2024.122086","article-title":"Autonomous real-time control for membrane capacitive deionization","volume":"262","author":"Shim","year":"2024","journal-title":"Water Res."},{"key":"10.1016\/j.watres.2026.125855_bib0031","doi-asserted-by":"crossref","DOI":"10.1016\/j.watres.2025.123772","article-title":"Data-driven differentiable model for dynamic prediction and control in wastewater treatment","author":"Song","year":"2025","journal-title":"Water Res."},{"key":"10.1016\/j.watres.2026.125855_bib0032","doi-asserted-by":"crossref","DOI":"10.1016\/j.seppur.2021.118421","article-title":"Analytical study of optimum operating conditions in semi-batch closed-circuit reverse osmosis (CCRO)","volume":"264","author":"Sutariya","year":"2021","journal-title":"Sep. Purif. Technol."},{"key":"10.1016\/j.watres.2026.125855_bib0033","series-title":"Reinforcement learning: an introduction","author":"Sutton","year":"1998"},{"key":"10.1016\/j.watres.2026.125855_bib0034","doi-asserted-by":"crossref","DOI":"10.1016\/j.watres.2024.122363","article-title":"Machine learning models for predicting the rejection of organic pollutants by forward osmosis and reverse osmosis membranes and unveiling the rejection mechanisms","volume":"266","author":"Tayara","year":"2024","journal-title":"Water Res."},{"key":"10.1016\/j.watres.2026.125855_bib0035","series-title":"arXiv preprint","article-title":"Gymnasium: a standard interface for reinforcement learning environments","author":"Towers","year":"2024"},{"key":"10.1016\/j.watres.2026.125855_bib0036","doi-asserted-by":"crossref","first-page":"99","DOI":"10.2307\/3001913","article-title":"Comparing individual means in the analysis of variance","author":"Tukey","year":"1949","journal-title":"Biometrics"},{"key":"10.1016\/j.watres.2026.125855_bib0037","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1016\/j.desal.2014.03.035","article-title":"Treatment of model inland brackish groundwater reverse osmosis concentrate with electrodialysis\u2014part I: sensitivity to superficial velocity","volume":"344","author":"Walker","year":"2014","journal-title":"Desalination"},{"key":"10.1016\/j.watres.2026.125855_bib0038","doi-asserted-by":"crossref","first-page":"384","DOI":"10.1016\/j.watres.2018.01.060","article-title":"Inorganic fouling mitigation by salinity cycling in batch reverse osmosis","volume":"137","author":"Warsinger","year":"2018","journal-title":"Water Res."},{"key":"10.1016\/j.watres.2026.125855_bib0039","doi-asserted-by":"crossref","first-page":"272","DOI":"10.1016\/j.watres.2016.09.029","article-title":"Energy efficiency of batch and semi-batch (CCRO) reverse osmosis desalination","volume":"106","author":"Warsinger","year":"2016","journal-title":"Water Res."},{"issue":"6","key":"10.1016\/j.watres.2026.125855_bib0040","doi-asserted-by":"crossref","first-page":"80","DOI":"10.2307\/3001968","article-title":"Individual comparisons by ranking methods","volume":"1","author":"Wilcoxon","year":"1945","journal-title":"Biometrics. Bullet."},{"key":"10.1016\/j.watres.2026.125855_bib0041","doi-asserted-by":"crossref","DOI":"10.1016\/j.watres.2022.119337","article-title":"Automation of membrane capacitive deionization process using reinforcement learning","volume":"227","author":"Yoon","year":"2022","journal-title":"Water Res."},{"key":"10.1016\/j.watres.2026.125855_bib0042","doi-asserted-by":"crossref","DOI":"10.1016\/j.desal.2025.118870","article-title":"Towards autonomous operation of two-stage reverse osmosis water treatment system with multi-agent reinforcement learning","volume":"609","author":"Yun","year":"2025","journal-title":"Desalination"}],"container-title":["Water Research"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0043135426005373?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0043135426005373?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,5,16]],"date-time":"2026-05-16T06:56:15Z","timestamp":1778914575000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0043135426005373"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7]]},"references-count":42,"alternative-id":["S0043135426005373"],"URL":"https:\/\/doi.org\/10.1016\/j.watres.2026.125855","relation":{},"ISSN":["0043-1354"],"issn-type":[{"value":"0043-1354","type":"print"}],"subject":[],"published":{"date-parts":[[2026,7]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Adaptive reinforcement learning for energy-efficient high-recovery closed-circuit reverse osmosis","name":"articletitle","label":"Article Title"},{"value":"Water Research","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.watres.2026.125855","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"125855"}}