Vibro-Fluidized Bed Drying of Pumpkin Seeds: Assessment of Mathematical and Artificial Neural Network Models for Drying Kinetics

Pumpkin seeds were dried in a vibro-fluidized bed dryer (VFBD) at different temperatures at optimized vibration intensity of 4.26 and 4 m/s air velocity. The drying characteristics were mapped employing semiempirical models and Artificial Neural Network (ANN). Prediction of drying behavior of pumpki...

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Autores principales:	Priyanka Dhurve, Ayon Tarafdar, Vinkel Kumar Arora
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi-Wiley 2021
Materias:	Nutrition. Foods and food supply TX341-641
Acceso en línea:	https://doaj.org/article/351b0737d403492880c1358e2d20868b
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spelling	oai:doaj.org-article:351b0737d403492880c1358e2d20868b2021-11-22T01:09:52ZVibro-Fluidized Bed Drying of Pumpkin Seeds: Assessment of Mathematical and Artificial Neural Network Models for Drying Kinetics1745-455710.1155/2021/7739732https://doaj.org/article/351b0737d403492880c1358e2d20868b2021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/7739732https://doaj.org/toc/1745-4557Pumpkin seeds were dried in a vibro-fluidized bed dryer (VFBD) at different temperatures at optimized vibration intensity of 4.26 and 4 m/s air velocity. The drying characteristics were mapped employing semiempirical models and Artificial Neural Network (ANN). Prediction of drying behavior of pumpkin seeds was done using semiempirical models, of which, one was preferred as it indicated the best statistical indicators. Two-term model showed the best fit of data with R2 − 0.999, and lowest χ2 − 1.03 × 10−4 and MSE 7.55 × 10−5. A feedforward backpropagation ANN model was trained by the Levenberg–Marquardt training algorithm using a TANSIGMOID activation function with 2-10-2 topology. Performance assessment of ANN showed better prediction of drying behavior with R2 = 0.9967 and MSE = 5.21 × 10−5 for moisture content, and R2 = 0.9963 and MSE = 2.42 × 10−5 for moisture ratio than mathematical models. In general, the prediction of drying kinetics and other drying parameters was more precise in the ANN technique as compared to semiempirical models. The diffusion coefficient, Biot number, and hm increased from 1.12 × 10−9 ± 3.62 × 10−10 to 1.98 × 10−9 ± 4.61 × 10−10 m2/s, 0.51 ± 0.01 to 0.60 ± 0.01, and 1.49 × 10−7 ± 4.89 × 10−8 to 3.10 × 10−7 ± 7.13 × 10−8 m/s, respectively, as temperature elevated from 40 to 60°C. Arrhenius’s equation was used to the obtain the activation energy of 32.71 ± 1.05 kJ/mol.Priyanka DhurveAyon TarafdarVinkel Kumar AroraHindawi-WileyarticleNutrition. Foods and food supplyTX341-641ENJournal of Food Quality, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Nutrition. Foods and food supply TX341-641
spellingShingle	Nutrition. Foods and food supply TX341-641 Priyanka Dhurve Ayon Tarafdar Vinkel Kumar Arora Vibro-Fluidized Bed Drying of Pumpkin Seeds: Assessment of Mathematical and Artificial Neural Network Models for Drying Kinetics
description	Pumpkin seeds were dried in a vibro-fluidized bed dryer (VFBD) at different temperatures at optimized vibration intensity of 4.26 and 4 m/s air velocity. The drying characteristics were mapped employing semiempirical models and Artificial Neural Network (ANN). Prediction of drying behavior of pumpkin seeds was done using semiempirical models, of which, one was preferred as it indicated the best statistical indicators. Two-term model showed the best fit of data with R2 − 0.999, and lowest χ2 − 1.03 × 10−4 and MSE 7.55 × 10−5. A feedforward backpropagation ANN model was trained by the Levenberg–Marquardt training algorithm using a TANSIGMOID activation function with 2-10-2 topology. Performance assessment of ANN showed better prediction of drying behavior with R2 = 0.9967 and MSE = 5.21 × 10−5 for moisture content, and R2 = 0.9963 and MSE = 2.42 × 10−5 for moisture ratio than mathematical models. In general, the prediction of drying kinetics and other drying parameters was more precise in the ANN technique as compared to semiempirical models. The diffusion coefficient, Biot number, and hm increased from 1.12 × 10−9 ± 3.62 × 10−10 to 1.98 × 10−9 ± 4.61 × 10−10 m2/s, 0.51 ± 0.01 to 0.60 ± 0.01, and 1.49 × 10−7 ± 4.89 × 10−8 to 3.10 × 10−7 ± 7.13 × 10−8 m/s, respectively, as temperature elevated from 40 to 60°C. Arrhenius’s equation was used to the obtain the activation energy of 32.71 ± 1.05 kJ/mol.
format	article
author	Priyanka Dhurve Ayon Tarafdar Vinkel Kumar Arora
author_facet	Priyanka Dhurve Ayon Tarafdar Vinkel Kumar Arora
author_sort	Priyanka Dhurve
title	Vibro-Fluidized Bed Drying of Pumpkin Seeds: Assessment of Mathematical and Artificial Neural Network Models for Drying Kinetics
title_short	Vibro-Fluidized Bed Drying of Pumpkin Seeds: Assessment of Mathematical and Artificial Neural Network Models for Drying Kinetics
title_full	Vibro-Fluidized Bed Drying of Pumpkin Seeds: Assessment of Mathematical and Artificial Neural Network Models for Drying Kinetics
title_fullStr	Vibro-Fluidized Bed Drying of Pumpkin Seeds: Assessment of Mathematical and Artificial Neural Network Models for Drying Kinetics
title_full_unstemmed	Vibro-Fluidized Bed Drying of Pumpkin Seeds: Assessment of Mathematical and Artificial Neural Network Models for Drying Kinetics
title_sort	vibro-fluidized bed drying of pumpkin seeds: assessment of mathematical and artificial neural network models for drying kinetics
publisher	Hindawi-Wiley
publishDate	2021
url	https://doaj.org/article/351b0737d403492880c1358e2d20868b
work_keys_str_mv	AT priyankadhurve vibrofluidizedbeddryingofpumpkinseedsassessmentofmathematicalandartificialneuralnetworkmodelsfordryingkinetics AT ayontarafdar vibrofluidizedbeddryingofpumpkinseedsassessmentofmathematicalandartificialneuralnetworkmodelsfordryingkinetics AT vinkelkumararora vibrofluidizedbeddryingofpumpkinseedsassessmentofmathematicalandartificialneuralnetworkmodelsfordryingkinetics
_version_	1718418392028807168

Vibro-Fluidized Bed Drying of Pumpkin Seeds: Assessment of Mathematical and Artificial Neural Network Models for Drying Kinetics

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