Exploration of the reverse osmosis desalination process by explainable machine learning to support sustainable development goal 6: Clean water and sanitation

Basit Öğe Kaydı
dc.authorid0000-0003-1282-718X
dc.authorid0000-0002-7434-0994
dc.contributor.authorAydin, Menekse
dc.contributor.authorTunc, K. M. Murat
dc.contributor.authorGunay, M. Erdem
dc.date.accessioned2026-04-04T18:55:35Z
dc.date.available2026-04-04T18:55:35Z
dc.date.issued2025
dc.departmentİstanbul Bilgi Üniversitesi
dc.description.abstractSustainable Development Goal (SDG) 6 calls for safe water, sanitation, and hygiene to safeguard health. Aligned with these goals, this study explores the reverse osmosis desalination process through machine learning, aiming to enhance the production of drinking water from brackish water and seawater in an energy efficient way. A database of 838 experimental entries from 29 studies was compiled, covering three targets: water recovery, specific energy consumption (SEC), and permeate salinity. Neural-network regressors achieved high predictive accuracy, providing a strong basis for interpretability for both water types. For example, for seawater, SHapley Additive exPlanations (SHAP) analysis revealed that pretreatment and energy recovery devices (ERDs) reduced SEC while lower flowrate and multi-stage configurations improved recovery. Decision tree models further identified practical operational routes, achieving testing accuracies of 85-93 %. For example, high seawater recovery was reliably obtained with pressures above 47 bar, feed salinity below 40,000 ppm, and multi-stage membrane configurations. On the other hand, low SEC required an ERD, feed salinity <43,400 ppm, and pH 7.9. Overall, the results indicate that machine learning is a powerful tool for detecting performance trends predictive drivers in reverse osmosis desalination, thereby supporting research and practice in sustainable freshwater production.
dc.identifier.doi10.1016/j.jclepro.2025.146979
dc.identifier.doi10.1016/j.jclepro.2025.146979
dc.identifier.issn0959-6526
dc.identifier.issn1879-1786
dc.identifier.scopus2-s2.0-105021131442
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1016/j.jclepro.2025.146979
dc.identifier.urihttps://hdl.handle.net/11411/10457
dc.identifier.volume533
dc.identifier.wosWOS:001618506800001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherElsevier Sci Ltd
dc.relation.ispartofJournal of Cleaner Production
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WoS_20260402
dc.snmzKA_Scopus_20260402
dc.subjectShap
dc.subjectDecision Trees
dc.subjectClassification Trees
dc.subjectReverse Osmosis
dc.subjectDesalination
dc.subjectWater
dc.titleExploration of the reverse osmosis desalination process by explainable machine learning to support sustainable development goal 6: Clean water and sanitation
dc.typeArticle

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