Publication:
Frost detection and defrost control: emerging trends, methods, and experimental evaluations

dc.contributor.coauthorHassan, H.
dc.contributor.coauthorMalik, A. N.
dc.contributor.coauthorNawaz, T.
dc.contributor.coauthorElahi, H.
dc.contributor.departmentDepartment of Mechanical Engineering
dc.contributor.departmentMARC (Manufacturing and Automation Research Center)
dc.contributor.kuauthorLazoğlu, İsmail
dc.contributor.kuauthorUr Rahman, Hammad
dc.contributor.kuauthorAkbar, Hassan
dc.contributor.schoolcollegeinstituteResearch Center
dc.contributor.schoolcollegeinstituteCollege of Engineering
dc.date.accessioned2026-08-14T11:23:36Z
dc.date.issued2025
dc.description.abstractFrost buildup on evaporator surfaces significantly reduces heat transfer efficiency in refrigeration systems, undermining overall performance. Effective frost detection and defrost control are therefore critical for maintaining optimal operation. This paper presents a comprehensive review of recent advancements in frost detection and defrosts control. It covers various defrosting mechanisms, including active and passive approaches, and classifies defrost control strategies into direct (sensor-based) and indirect types. Furthermore, it compares traditional sensor-based methods with emerging image processing techniques and advanced AI-driven solutions. An experimental evaluation of these methods highlights their relative strengths and limitations. Finally, the study identifies key research gaps, particularly in the integration of advanced sensing technologies for precise frost detection and defrost control.
dc.description.harvestedfromManual
dc.description.indexedbyWOS
dc.description.indexedbyScopus
dc.description.publisherscopeInternational
dc.description.readpublishN/A
dc.description.sponsoredbyTubitakEuN/A
dc.description.sponsorshipThe Department of Mechatronics Engineering at the College of Electrical and Mechanical Engineering (CEME) , National University of Sciences and Technology (NUST) , provided support for this study. Data utilized for the experimental evaluation was collected at the Manufacturing and Automation Research Center, Department of Mechanical Engineering, Koc University, Istanbul, Turkey. Furthermore, the authors would like to thank BEKO Global in Istanbul, Turkey, for providing the refrigerator for data acquisition.
dc.description.versionPublished Version
dc.identifier.ScopusPercentile82
dc.identifier.ScopusQuartileQ1
dc.identifier.WoSPercentile78
dc.identifier.WoSQuartileQ1
dc.identifier.doi10.1016/j.ijrefrig.2025.08.005
dc.identifier.eissn1879-2081
dc.identifier.embargoN/A
dc.identifier.endpage141
dc.identifier.issn0140-7007
dc.identifier.scopus2-s2.0-105012760161
dc.identifier.startpage121
dc.identifier.urihttp://doi.org/10.1016/j.ijrefrig.2025.08.005
dc.identifier.urihttps://hdl.handle.net/20.500.14288/34449
dc.identifier.volume179
dc.identifier.wos001579145400001
dc.keywordsFrost detection
dc.keywordsDefrost control
dc.keywordsArtificial intelligence
dc.keywordsDigital image processing
dc.languageeng
dc.publisherElsevier
dc.relation.affiliationKoç University
dc.relation.collectionKoç University Institutional Repository
dc.relation.ispartofInternational Journal of Refrigeration
dc.relation.openaccessN/A
dc.rightsN/A
dc.rights.uriN/A
dc.subjectThermodynamics
dc.subjectEngineering
dc.titleFrost detection and defrost control: emerging trends, methods, and experimental evaluations
dc.typeJournal Article
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