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

Author

Listed:
  • Behzad Maleki

    (Energy Institute of Higher Education, Saveh 39177-67746, Iran)

  • Mahyar Ghazvini

    (Department of Renewable Energy and Environmental Engineering, University of Tehran, Tehran 1417466191, Iran)

  • Mohammad Hossein Ahmadi

    (Faculty of Mechanical Engineering, Shahrood University of Technology, Shahrood 3616713455, Iran)

  • Heydar Maddah

    (Department of Chemistry, Payame Noor University (PNU), P.O. Box, Tehran 19395-3697, Iran)

  • Shahaboddin Shamshirband

    (Department for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City, Vietnam
    Faculty of Information Technology, Ton Duc Thang University, Ho Chi Minh City, Vietnam)

Abstract

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.

Suggested Citation

  • Behzad Maleki & Mahyar Ghazvini & Mohammad Hossein Ahmadi & Heydar Maddah & Shahaboddin Shamshirband, 2019. "Moisture Estimation in Cabinet Dryers with Thin-Layer Relationships Using a Genetic Algorithm and Neural Network," Mathematics, MDPI, vol. 7(11), pages 1-12, November.
    • Handle: RePEc:gam:jmathe:v:7:y:2019:i:11:p:1042-:d:283126
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    References listed on IDEAS

    as
    1. Yilun Shang, 2018. "Resilient Multiscale Coordination Control against Adversarial Nodes," Energies, MDPI, vol. 11(7), pages 1-17, July.
    2. Mohammad Hossein Rezaei & Milad Sadeghzadeh & Mohammad Alhuyi Nazari & Mohammad Hossein Ahmadi & Fatemeh Razi Astaraei, 2018. "Applying GMDH artificial neural network in modeling CO2 emissions in four nordic countries," International Journal of Low-Carbon Technologies, Oxford University Press, vol. 13(3), pages 266-271.
    3. Reza Aghayari & Heydar Maddah & Mohammad Hossein Ahmadi & Wei-Mon Yan & Nahid Ghasemi, 2018. "Measurement and Artificial Neural Network Modeling of Electrical Conductivity of CuO/Glycerol Nanofluids at Various Thermal and Concentration Conditions," Energies, MDPI, vol. 11(5), pages 1-16, May.
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