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Open AccessArticle

Moisture Estimation in Cabinet Dryers with Thin-Layer Relationships Using a Genetic Algorithm and Neural Network

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Energy Institute of Higher Education, Saveh 39177-67746, Iran
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Department of Renewable Energy and Environmental Engineering, University of Tehran, Tehran 1417466191, Iran
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Faculty of Mechanical Engineering, Shahrood University of Technology, Shahrood 3616713455, Iran
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Department of Chemistry, Payame Noor University (PNU), P.O. Box, Tehran 19395-3697, Iran
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Department for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City, Vietnam
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Faculty of Information Technology, Ton Duc Thang University, Ho Chi Minh City, Vietnam
*
Author to whom correspondence should be addressed.
Mathematics 2019, 7(11), 1042; https://doi.org/10.3390/math7111042
Received: 21 June 2019 / Revised: 25 July 2019 / Accepted: 25 July 2019 / Published: 3 November 2019
Nowadays, industrial dryers are used instead of traditional methods for drying. When designing dryers suitable for controlling the process of drying and reaching a high-quality product, it is necessary to predict the gradual moisture loss during drying. Few studies have been conducted to compare thin-layer models and artificial neural network models on the kinetics of pistachio drying in a cabinet dryer. For this purpose, ten mathematical-experimental models with a neural network model based on the kinetic data of pistachio drying were studied. The data obtained was from a cabinet dryer evaluated at four temperatures of inlet air and different air velocities. The pistachio seeds were placed in a thin layer on an aluminum sheet on a drying tray and weighed by a scale attached to the computer at different times. In the neural network, data was divided into three parts: Educational (60%), validation (20%) and testing (20%). Finally, the best mathematical-experimental model using a genetic algorithm and the best neural network structure for predicting instantaneous moisture were selected based on the least squared error and the highest correlation coefficient. View Full-Text
Keywords: cabinet dryer; genetic algorithm; neural network; temperature; air velocity; moisture cabinet dryer; genetic algorithm; neural network; temperature; air velocity; moisture
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MDPI and ACS Style

Maleki, B.; Ghazvini, M.; Ahmadi, M.H.; Maddah, H.; Shamshirband, S. Moisture Estimation in Cabinet Dryers with Thin-Layer Relationships Using a Genetic Algorithm and Neural Network. Mathematics 2019, 7, 1042.

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