Mathematical modelling of infrared‐dried kiwifruit slices under natural and forced convection

Abstract In this work, the effect of the radiation intensity, slice thickness, and the distance between slices and infrared lamps under natural drying air and the effect of slice thickness and air velocity under forced drying air on the moisture diffusion characteristics and the drying rate of kiwif...

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Main Authors: Ebrahim Sadeghi, Ali Haghighi Asl, Kamyar Movagharnejad
Format: Article
Language:English
Published: Wiley 2019-11-01
Series:Food Science & Nutrition
Subjects:
Online Access:https://doi.org/10.1002/fsn3.1212
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spelling doaj-150ee733b7554b99b06c1d682cdef4452020-11-24T21:33:39ZengWileyFood Science & Nutrition2048-71772019-11-017113589360610.1002/fsn3.1212Mathematical modelling of infrared‐dried kiwifruit slices under natural and forced convectionEbrahim Sadeghi0Ali Haghighi Asl1Kamyar Movagharnejad2Faculty of Chemical, Petroleum and Gas Engineering Semnan University Semnan IranFaculty of Chemical, Petroleum and Gas Engineering Semnan University Semnan IranFaculty of Chemical Engineering Babol Noshirvani University of Technology Babol IranAbstract In this work, the effect of the radiation intensity, slice thickness, and the distance between slices and infrared lamps under natural drying air and the effect of slice thickness and air velocity under forced drying air on the moisture diffusion characteristics and the drying rate of kiwifruit slices during infrared drying were investigated. The drying of kiwifruit happened in the falling rate period, and no constant‐rate period was observed in the drying curves. One hundred models were fitted to the drying data. Among the models, the exponential dsecay function model and modified two‐term exponential‐V model and the artificial neural networks with 4‐5‐7‐1 and 3‐5‐5‐1 topologies, hyperbolic tangent sigmoid transfer function, and Levenberg‐Marquardt training algorithm presented the best results and showed the goodness of fit with the experimental data for the former and latter systems, respectively. The diffusivities varied between 1.216 × 10−10–8.997 × 10−10 m2⁄s and 2.567 × 10−10–10.335 × 10−10 m2⁄s for natural and forced drying air systems, respectively.https://doi.org/10.1002/fsn3.1212diffusivityinfrared dryerkiwifruitthin‐layer mathematical modeling
collection DOAJ
language English
format Article
sources DOAJ
author Ebrahim Sadeghi
Ali Haghighi Asl
Kamyar Movagharnejad
spellingShingle Ebrahim Sadeghi
Ali Haghighi Asl
Kamyar Movagharnejad
Mathematical modelling of infrared‐dried kiwifruit slices under natural and forced convection
Food Science & Nutrition
diffusivity
infrared dryer
kiwifruit
thin‐layer mathematical modeling
author_facet Ebrahim Sadeghi
Ali Haghighi Asl
Kamyar Movagharnejad
author_sort Ebrahim Sadeghi
title Mathematical modelling of infrared‐dried kiwifruit slices under natural and forced convection
title_short Mathematical modelling of infrared‐dried kiwifruit slices under natural and forced convection
title_full Mathematical modelling of infrared‐dried kiwifruit slices under natural and forced convection
title_fullStr Mathematical modelling of infrared‐dried kiwifruit slices under natural and forced convection
title_full_unstemmed Mathematical modelling of infrared‐dried kiwifruit slices under natural and forced convection
title_sort mathematical modelling of infrared‐dried kiwifruit slices under natural and forced convection
publisher Wiley
series Food Science & Nutrition
issn 2048-7177
publishDate 2019-11-01
description Abstract In this work, the effect of the radiation intensity, slice thickness, and the distance between slices and infrared lamps under natural drying air and the effect of slice thickness and air velocity under forced drying air on the moisture diffusion characteristics and the drying rate of kiwifruit slices during infrared drying were investigated. The drying of kiwifruit happened in the falling rate period, and no constant‐rate period was observed in the drying curves. One hundred models were fitted to the drying data. Among the models, the exponential dsecay function model and modified two‐term exponential‐V model and the artificial neural networks with 4‐5‐7‐1 and 3‐5‐5‐1 topologies, hyperbolic tangent sigmoid transfer function, and Levenberg‐Marquardt training algorithm presented the best results and showed the goodness of fit with the experimental data for the former and latter systems, respectively. The diffusivities varied between 1.216 × 10−10–8.997 × 10−10 m2⁄s and 2.567 × 10−10–10.335 × 10−10 m2⁄s for natural and forced drying air systems, respectively.
topic diffusivity
infrared dryer
kiwifruit
thin‐layer mathematical modeling
url https://doi.org/10.1002/fsn3.1212
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