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Distillation anomaly and fault detection based on clustering algorithms

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dc.contributor.authorMartínez García, F. M.
dc.contributor.authorMolina García, A.
dc.contributor.authorGómez de León Hijes, Félix
dc.contributor.authorAlarcón García, Mariano
dc.contributor.departmentIngeniería de la Información y las Comunicaciones
dc.contributor.otherFacultades de la UMU::Facultad de Química
dc.date.accessioned2026-01-23T15:31:08Z
dc.date.available2026-01-23T15:31:08Z
dc.date.copyright© 2025 The Authors
dc.date.issued2025-11
dc.description.abstractAnomaly detection in production processes is essential for ensuring reliability and efficiency in the industrial sector. In this way, system optimization requires advanced monitoring strategies such as predictive maintenance and intelligent fault detection. Traditional diagnostic methods rely on retrospective data analysis and deterministic cause-effect models, while machine learning approaches enable real-time monitoring and data-driven modeling to detect deviations from normal operation. This study proposes a scalable anomaly detection framework based on clustering algorithms, specifically applied to batch distillation processes—critical operations in chemical manufacturing that remain underexplored in real-world applications, particularly in multiproduct plants. The methodology was validated through an industrial case study at a chemical facility in El Palmar, Murcia (Spain), operated by a multinational corporation. Over 300,000 data points were collected over three years, focusing on critical variables governing distillation unit performance. Clustering techniques including k- means, DBSCAN, and hierarchical clustering were applied to identify deviations from standard operating conditions. Results demonstrate the effectiveness, flexibility, and scalability of the proposed approach, detecting anomalies in real time due to equipment faults, unstable conditions, or operator error. Integration of this system reduces unplanned shutdowns, improves energy efficiency, safety, and product quality, and provides operators with a real-time dashboard for decision support. Statistical evaluation of algorithms ensures adaptability across product types, while the custom application enables graphical monitoring of process deviations. Future work includes integrating performance indicators and ERP/MES connectivity. This framework serves as a reference model for deploying scalable anomaly detection systems across diverse industrial environments.
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dc.format.extent17
dc.identifier.citationF.M. Martínez-García, A. Molina García, F.C. Gómez de León, M. Alarcón. Distillation anomaly and fault detection based on clustering algorithms. Journal of Industrial Information Integration, Volume 48, 2025, 100970, ISSN 2452-414X, https://doi.org/10.1016/j.jii.2025.100970.
dc.identifier.doihttps://doi.org/10.1016/j.jii.2025.100970
dc.identifier.eissn2452-414X
dc.identifier.issn2467-964X
dc.identifier.urihttp://hdl.handle.net/10201/192169
dc.languageeng
dc.publisherElsevier
dc.relationThe work carried out and the results are part of the INNOTWIN project (Ref. 00134999/MIP-20201030), financed with European Union funds through the CDTI Misiones program (Spain).
dc.relation.publisherversionhttps://www.sciencedirect.com/science/article/pii/S2452414X25001931?via%3Dihub
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.accessRightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectAnomaly detection
dc.subjectClustering method
dc.subjectDistillation unit
dc.subjectIntelligent manufacturing systems
dc.subject.odsObjetivo 9: Infraestructura
dc.titleDistillation anomaly and fault detection based on clustering algorithms
dc.typeinfo:eu-repo/semantics/article
dc.type.versioninfo:eu-repo/semantics/publishedVersion
dspace.entity.typePublicationes
relation.isAuthorOfPublication38812f89-0f66-4792-8520-79c0f0dbeaa8
relation.isAuthorOfPublication8149bb7b-b65f-4424-8a27-6488825553d6
relation.isAuthorOfPublication.latestForDiscovery38812f89-0f66-4792-8520-79c0f0dbeaa8
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