Improve the energy efficiency of the fruit freeze-drying through the predictive analysis

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dc.contributor.authorÖztuna Taner, Öznur
dc.contributor.authorÇolak, Andaç Batur
dc.date.accessioned2025-04-15T05:38:16Z
dc.date.available2025-04-15T05:38:16Z
dc.date.issued2024
dc.departmentRektörlük
dc.description.abstractThe considerable energy expenditure involved in the freeze-drying of foods justifies the development of innovative engineering techniques. Artificial intelligence will facilitate mass balance and energy efficiency in future food freeze-drying processes. This study assessed the energy and manufacturing efficiency of the freeze-drying facility through the application of artificial intelligence. Two distinct artificial neural network models were created utilizing real-time data from a factory located in an industrial zone that processed freeze-dried vegetables and kiwi fruit. Analyzing energy efficiency values and production was done using network models constructed from 20 experimental data sets. The Levenberg-Marquardt approach was employed to train neural networks with a multilayer perceptron architecture. The neural network models' prediction values were compared with the experimentally acquired data, and their performance was examined using several performance criteria. The evaluations carried out for 20 different scenarios revealed overall energy efficiency rates ranging from 25.8 % to 64.5%. The considerable energy expenditure involved in the freeze-drying of foods justifies the development of innovative engineering techniques. Artificial intelligence will facilitate mass balance and energy efficiency in future food freeze-drying processes
dc.identifier.doi10.1016/j.fbp.2024.11.028
dc.identifier.issn0960-3085 / 1744-3571
dc.identifier.urihttps://dx.doi.org/10.1016/j.fbp.2024.11.028
dc.identifier.urihttps://hdl.handle.net/20.500.12451/13035
dc.identifier.volume149
dc.identifier.wos001374219400001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.institutionauthorÖztuna Taner, Öznur
dc.language.isoen
dc.publisherElsevier
dc.relation.ispartofFood and Bioproducts Processing
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectArtificial Intelligence
dc.subjectArtificial Neural Network
dc.subjectEnergy Efficiency
dc.subjectFreeze Drying
dc.subjectFood Industry
dc.titleImprove the energy efficiency of the fruit freeze-drying through the predictive analysis
dc.typeArticle

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