Moisture Estimation in Cabinet Dryers with Thin-Layer Relationships Using a Genetic Algorithm and Neural Network
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 thi...
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MDPI AG
2019-11-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/7/11/1042 |
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| author | Behzad Maleki Mahyar Ghazvini Mohammad Hossein Ahmadi Heydar Maddah Shahaboddin Shamshirband |
| author_facet | Behzad Maleki Mahyar Ghazvini Mohammad Hossein Ahmadi Heydar Maddah Shahaboddin Shamshirband |
| author_sort | Behzad Maleki |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-22T19:11:16Z |
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| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-12-22T19:11:16Z |
| publishDate | 2019-11-01 |
| publisher | MDPI AG |
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| series | Mathematics |
| spelling | doaj.art-c573f58ca78245fdbadbe40c2169d4662022-12-21T18:15:40ZengMDPI AGMathematics2227-73902019-11-01711104210.3390/math7111042math7111042Moisture Estimation in Cabinet Dryers with Thin-Layer Relationships Using a Genetic Algorithm and Neural NetworkBehzad Maleki0Mahyar Ghazvini1Mohammad Hossein Ahmadi2Heydar Maddah3Shahaboddin Shamshirband4Energy Institute of Higher Education, Saveh 39177-67746, IranDepartment of Renewable Energy and Environmental Engineering, University of Tehran, Tehran 1417466191, IranFaculty of Mechanical Engineering, Shahrood University of Technology, Shahrood 3616713455, IranDepartment of Chemistry, Payame Noor University (PNU), P.O. Box, Tehran 19395-3697, IranDepartment for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City, VietnamNowadays, 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.https://www.mdpi.com/2227-7390/7/11/1042cabinet dryergenetic algorithmneural networktemperatureair velocitymoisture |
| spellingShingle | Behzad Maleki Mahyar Ghazvini Mohammad Hossein Ahmadi Heydar Maddah Shahaboddin Shamshirband Moisture Estimation in Cabinet Dryers with Thin-Layer Relationships Using a Genetic Algorithm and Neural Network Mathematics cabinet dryer genetic algorithm neural network temperature air velocity moisture |
| title | Moisture Estimation in Cabinet Dryers with Thin-Layer Relationships Using a Genetic Algorithm and Neural Network |
| title_full | Moisture Estimation in Cabinet Dryers with Thin-Layer Relationships Using a Genetic Algorithm and Neural Network |
| title_fullStr | Moisture Estimation in Cabinet Dryers with Thin-Layer Relationships Using a Genetic Algorithm and Neural Network |
| title_full_unstemmed | Moisture Estimation in Cabinet Dryers with Thin-Layer Relationships Using a Genetic Algorithm and Neural Network |
| title_short | Moisture Estimation in Cabinet Dryers with Thin-Layer Relationships Using a Genetic Algorithm and Neural Network |
| title_sort | moisture estimation in cabinet dryers with thin layer relationships using a genetic algorithm and neural network |
| topic | cabinet dryer genetic algorithm neural network temperature air velocity moisture |
| url | https://www.mdpi.com/2227-7390/7/11/1042 |
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