Development of a digital twin framework for hybrid adsorption-ultrafiltration systems in drinking water treatment

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dc.contributor.authorGumus, Dilek
dc.contributor.authorAlver, Alper
dc.contributor.authorAkbal, Feryal
dc.date.accessioned2026-04-25T14:20:01Z
dc.date.available2026-04-25T14:20:01Z
dc.date.issued2026
dc.departmentSinop Üniversitesi
dc.description.abstractA data-driven digital twin was developed to address the ongoing challenges of humic acid removal and membrane fouling in hybrid adsorption-ultrafiltration (UF) systems used for drinking water treatment. Natural organic matter, particularly humic substances, continues to pose operational challenges in membrane-based processes, leading to irreversible fouling and flux reduction. The digital twin incorporates eXtreme Gradient Boosting (XGBoost) models combined with SHapley Additive exPlanations (SHAP) to forecast key performance indicators such as dissolved organic carbon (DOC), UV254 absorbance, specific UV absorbance (SUVA), and membrane fouling percentage under various hydraulic and chemical loading conditions. The UF-only model showed the highest accuracy, with R2 values of 0.98 for DOC, 0.99 for UV254, 0.80 for SUVA, and 0.99 for fouling. The hybrid GAC-UF model also performed strongly in predicting fouling (R2 = 0.99) and UV254 (R2= 0.79), with moderate skill in DOC removal. These models are integrated into a forward-simulation framework that enables real-time scenario testing, allowing operators to assess system responses and receive guidance on filtration cycle limits and pretreatment effectiveness. The digital twin offers a reliable decision-support platform suitable for advanced treatment systems. It improves understanding of process dynamics and provides transparent, interpretable insights into operational sensitivities, supporting informed decision-making. This framework paves the way for AI-driven optimization and predictive control in modern water treatment facilities.
dc.description.sponsorshipScientific Research Project Coordination Unit of Ondokuz Mayimath;s University, Samsun, Turkiye [PYO.MUH.1901.11.006]
dc.description.sponsorshipThis study was supported by the Scientific Research Project Coordination Unit of Ondokuz May & imath;s University, Samsun, Turkiye, under grant number PYO.MUH.1901.11.006.
dc.identifier.doi10.1016/j.psep.2025.108182
dc.identifier.issn0957-5820
dc.identifier.issn1744-3598
dc.identifier.scopus2-s2.0-105022160650
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1016/j.psep.2025.108182
dc.identifier.urihttps://hdl.handle.net/11486/8324
dc.identifier.volume205
dc.identifier.wosWOS:001627473300008
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherElsevier
dc.relation.ispartofProcess Safety and Environmental Protection
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WOS_20260420
dc.subjectDigital twin
dc.subjectHumic acid
dc.subjectUltrafiltration
dc.subjectAdsorption
dc.subjectMachine learning
dc.subjectMembrane fouling
dc.titleDevelopment of a digital twin framework for hybrid adsorption-ultrafiltration systems in drinking water treatment
dc.typeArticle

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