Application of artificial intelligence techniques for heat exchanger predictions in food industry

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dc.contributor.authorÖztuna Taner, Öznur
dc.contributor.authorMercan, Hatice
dc.contributor.authorÇolak, Andaç Batur
dc.contributor.authorRadulovic, Jovana
dc.contributor.authorTaner, Tolga
dc.contributor.authorDalkılıç, Ahmet Selim
dc.date.accessioned2024-07-23T13:18:43Z
dc.date.available2024-07-23T13:18:43Z
dc.date.issued2024en_US
dc.departmentRektörlük, Bilişim Teknolojileri Uygulama ve Araştırma Merkezien_US
dc.description.abstractHeat exchangers (HEXs) are deployed in diverse engineering applications, such as cooling and refrigeration systems; power plants; and automotive, chemical, textile, and food industries. Understanding the principles and fluid-to-fluid heat exchange geometry can be complex. Researchers usually apply the first and second laws of thermodynamics to conduct numerical, analytical, and experimental techniques on HEXs. Experimental approaches tend to be costlier due to setup expenses, while theoretical and numerical analyses rely heavily on assumptions and complex equations. To address these challenges, artificial intelligence (AI) models have emerged as a promising solution for modeling, optimization, and performance estimation of thermal systems employing HEXs. In the last 30 years, AI-based approaches have gained widespread adoption in thermal analysis of HEXs, building upon past research. Three main types of thermal analysis have been reported: single-phase flow, two-phase flow, and machine learning-based physical property evaluation. AI approaches have proven effective in estimating crucial HEX parameters like pressure drop (?P), heat transfer coefficient (h), friction factor (f), and Nusselt number (Nu). They have also demonstrated success in assessing phase change characteristics during fluid boiling and condensation processes, as well as identifying two-phase flows. Despite these advancements, it is emphasized that more work remains to fully harness AI’s potential for thermal analysis of HEXs. As AI gains traction, it presents itself as a valuable technology for enhancing the study of HEXs with satisfactory results.en_US
dc.identifier.doi10.1016/B978-0-443-21574-2.00003-4en_US
dc.identifier.endpage325en_US
dc.identifier.scopus2-s2.0-85198607248en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage269en_US
dc.identifier.urihttps://hdl.handle.net/11467/7375
dc.identifier.urihttps://doi.org/10.1016/B978-0-443-21574-2.00003-4
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.ispartofAdvanced Materials based Thermally Enhanced Phase Change Materials: Fundamentals and Applicationsen_US
dc.relation.publicationcategoryKitap Bölümü - Uluslararası - Başka Kurum Yazarıen_US
dc.rightsinfo:eu-repo/semantics/restrictedAccessen_US
dc.subjectArtificial intelligence (AI); Artificial neural network (ANN); Food industry; Genetic algorithm (GA); Heat exchanger; Nanoemulsions; Nanoparticlesen_US
dc.titleApplication of artificial intelligence techniques for heat exchanger predictions in food industryen_US
dc.typeBook Chapteren_US

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