Estimation of moisture ratio for apple drying by convective and microwave methods using artificial neural network modeling

Abstract Two different drying methods were applied for dehydration of apple, i.e., convective drying (CD) and microwave drying (MD). The process of convective drying through divergent temperatures; 50, 60 and 70 °C at 1.0 m/s air velocity and three different levels of microwave power (90, 180, and 3...

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Autores principales:	Vali Rasooli Sharabiani, Mohammad Kaveh, Roozbeh Abdi, Mariusz Szymanek, Wojciech Tanaś
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/6493c8b470ce4201806e12d4631abc40
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spelling	oai:doaj.org-article:6493c8b470ce4201806e12d4631abc402021-12-02T17:16:05ZEstimation of moisture ratio for apple drying by convective and microwave methods using artificial neural network modeling10.1038/s41598-021-88270-z2045-2322https://doaj.org/article/6493c8b470ce4201806e12d4631abc402021-04-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-88270-zhttps://doaj.org/toc/2045-2322Abstract Two different drying methods were applied for dehydration of apple, i.e., convective drying (CD) and microwave drying (MD). The process of convective drying through divergent temperatures; 50, 60 and 70 °C at 1.0 m/s air velocity and three different levels of microwave power (90, 180, and 360 W) were studied. In the analysis of the performance of our approach on moisture ratio (MR) of apple slices, artificial neural networks (ANNs) was used to provide with a background for further discussion and evaluation. In order to evaluate the models mentioned in the literature, the Midilli et al. model was proper for dehydrating of apple slices in both MD and CD. The MD drying technology enhanced the drying rate when compared with CD drying significantly. Effective diffusivity (Deff) of moisture in CD drying (1.95 × 10−7–4.09 × 10−7 m2/s) was found to be lower than that observed in MD (2.94 × 10−7–8.21 × 10−7 m2/s). The activation energy (Ea) values of CD drying and MD drying were 122.28–125 kJ/mol and 14.01–15.03 W/g respectively. The MD had the lowest specific energy consumption (SEC) as compared to CD drying methods. According to ANN results, the best R2 values for prediction of MR in CD and MD were 0.9993 and 0.9991, respectively.Vali Rasooli SharabianiMohammad KavehRoozbeh AbdiMariusz SzymanekWojciech TanaśNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Vali Rasooli Sharabiani Mohammad Kaveh Roozbeh Abdi Mariusz Szymanek Wojciech Tanaś Estimation of moisture ratio for apple drying by convective and microwave methods using artificial neural network modeling
description	Abstract Two different drying methods were applied for dehydration of apple, i.e., convective drying (CD) and microwave drying (MD). The process of convective drying through divergent temperatures; 50, 60 and 70 °C at 1.0 m/s air velocity and three different levels of microwave power (90, 180, and 360 W) were studied. In the analysis of the performance of our approach on moisture ratio (MR) of apple slices, artificial neural networks (ANNs) was used to provide with a background for further discussion and evaluation. In order to evaluate the models mentioned in the literature, the Midilli et al. model was proper for dehydrating of apple slices in both MD and CD. The MD drying technology enhanced the drying rate when compared with CD drying significantly. Effective diffusivity (Deff) of moisture in CD drying (1.95 × 10−7–4.09 × 10−7 m2/s) was found to be lower than that observed in MD (2.94 × 10−7–8.21 × 10−7 m2/s). The activation energy (Ea) values of CD drying and MD drying were 122.28–125 kJ/mol and 14.01–15.03 W/g respectively. The MD had the lowest specific energy consumption (SEC) as compared to CD drying methods. According to ANN results, the best R2 values for prediction of MR in CD and MD were 0.9993 and 0.9991, respectively.
format	article
author	Vali Rasooli Sharabiani Mohammad Kaveh Roozbeh Abdi Mariusz Szymanek Wojciech Tanaś
author_facet	Vali Rasooli Sharabiani Mohammad Kaveh Roozbeh Abdi Mariusz Szymanek Wojciech Tanaś
author_sort	Vali Rasooli Sharabiani
title	Estimation of moisture ratio for apple drying by convective and microwave methods using artificial neural network modeling
title_short	Estimation of moisture ratio for apple drying by convective and microwave methods using artificial neural network modeling
title_full	Estimation of moisture ratio for apple drying by convective and microwave methods using artificial neural network modeling
title_fullStr	Estimation of moisture ratio for apple drying by convective and microwave methods using artificial neural network modeling
title_full_unstemmed	Estimation of moisture ratio for apple drying by convective and microwave methods using artificial neural network modeling
title_sort	estimation of moisture ratio for apple drying by convective and microwave methods using artificial neural network modeling
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/6493c8b470ce4201806e12d4631abc40
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Estimation of moisture ratio for apple drying by convective and microwave methods using artificial neural network modeling

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