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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Online Access: | https://doi.org/10.1002/fsn3.1212 |
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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 |
work_keys_str_mv |
AT ebrahimsadeghi mathematicalmodellingofinfrareddriedkiwifruitslicesundernaturalandforcedconvection AT alihaghighiasl mathematicalmodellingofinfrareddriedkiwifruitslicesundernaturalandforcedconvection AT kamyarmovagharnejad mathematicalmodellingofinfrareddriedkiwifruitslicesundernaturalandforcedconvection |
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