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Experimental and ANN modeling investigations of energy traits for rough rice drying

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  • Beigi, Mohsen
  • Torki-Harchegani, Mehdi
  • Tohidi, Mojtaba

Abstract

In the present work, the kinetics of convective deep-bed drying of rough rice was investigated at different conditions. The energy performance of the process was investigated in terms of specific energy consumption as well as energy, drying and thermal efficiencies. Different well-known artificial neural networks (ANNs) were used to predict the traits and the best topologies, transfer functions and training algorithms were determined. Increasing drying air temperature and velocity decreased drying duration while higher relative humidity increased the process time. The results showed that applying higher temperatures together with lower levels of flow rate and relative humidity of drying air improved the energy indices. Artificial neural network modeling technique can be used, as a powerful tool, to predict and determine the energy efficient drying conditions.

Suggested Citation

  • Beigi, Mohsen & Torki-Harchegani, Mehdi & Tohidi, Mojtaba, 2017. "Experimental and ANN modeling investigations of energy traits for rough rice drying," Energy, Elsevier, vol. 141(C), pages 2196-2205.
    • Handle: RePEc:eee:energy:v:141:y:2017:i:c:p:2196-2205
    DOI: 10.1016/j.energy.2017.12.004
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    References listed on IDEAS

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    1. Tohidi, Mojtaba & Sadeghi, Morteza & Torki-Harchegani, Mehdi, 2017. "Energy and quality aspects for fixed deep bed drying of paddy," Renewable and Sustainable Energy Reviews, Elsevier, vol. 70(C), pages 519-528.
    2. Torki-Harchegani, Mehdi & Ghanbarian, Davoud & Ghasemi Pirbalouti, Abdollah & Sadeghi, Morteza, 2016. "Dehydration behaviour, mathematical modelling, energy efficiency and essential oil yield of peppermint leaves undergoing microwave and hot air treatments," Renewable and Sustainable Energy Reviews, Elsevier, vol. 58(C), pages 407-418.
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    2. Prabhakaran, SP Sathiya & Swaminathan, Ganapathiraman & Joshi, Viraj V., 2022. "Combustion and pyrolysis kinetics of Australian lignite coal and validation by artificial neural networks," Energy, Elsevier, vol. 242(C).
    3. Panda, Brajesh Kumar & Mishra, Gayatri & Panigrahi, Shubham Subrot & Shrivastava, Shanker Lal, 2021. "Microwave-assisted parboiling of high moisture paddy: A comparative study based on energy utilization, process economy and grain quality with conventional parboiling," Energy, Elsevier, vol. 232(C).
    4. Mohsen Beigi & Hossein Beigi Harchegani & Mehdi Torki & Mohammad Kaveh & Mariusz Szymanek & Esmail Khalife & Jacek Dziwulski, 2022. "Forecasting of Power Output of a PVPS Based on Meteorological Data Using RNN Approaches," Sustainability, MDPI, vol. 14(5), pages 1-12, March.
    5. Damir Đaković & Miroslav Kljajić & Nikola Milivojević & Đorđije Doder & Aleksandar S. Anđelković, 2023. "Review of Energy-Related Machine Learning Applications in Drying Processes," Energies, MDPI, vol. 17(1), pages 1-38, December.
    6. Zhang, Lihong & Wang, Jun & Wang, Bin, 2020. "Energy market prediction with novel long short-term memory network: Case study of energy futures index volatility," Energy, Elsevier, vol. 211(C).
    7. Ahmet Beyzade Demirpolat, 2019. "Investigation of Mass Transfer with Different Models in a Solar Energy Food-Drying System," Energies, MDPI, vol. 12(18), pages 1-14, September.

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