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Exergy and Energy Analyses of Microwave Dryer for Cantaloupe Slice and Prediction of Thermodynamic Parameters Using ANN and ANFIS Algorithms

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  • Safoura Zadhossein

    (Department of Biosystems Engineering, College of Agriculture and Natural Resources, University of Mohaghegh Ardabili, Ardabil 56199-11367, Iran)

  • Yousef Abbaspour-Gilandeh

    (Department of Biosystems Engineering, College of Agriculture and Natural Resources, University of Mohaghegh Ardabili, Ardabil 56199-11367, Iran)

  • Mohammad Kaveh

    (Department of Biosystems Engineering, College of Agriculture and Natural Resources, University of Mohaghegh Ardabili, Ardabil 56199-11367, Iran)

  • Mariusz Szymanek

    (Department of Agricultural, Forest and Transport Machinery, University of Life Sciences in Lublin, Głęboka 28, 20-612 Lublin, Poland)

  • Esmail Khalife

    (Department of Civil Engineering, Cihan University-Erbil, Kurdistan Region, Erbil 44001, Iraq)

  • Olusegun D. Samuel

    (Department of Mechanical Engineering, Federal University of Petroleum Resources, Effurun P.M.B. 1221, Delta State, Nigeria
    Department of Mechanical Engineering, University of South Africa, Florida 1709, South Africa)

  • Milad Amiri

    (Faculty of Mechanical Engineering and Ship Technology, Institute of Energy, Gdańsk University of Technology, Gdańsk, Narutowicza 11/12, 80-233 Gdańsk, Poland)

  • Jacek Dziwulski

    (Department of Strategy and Business Planning, Faculty of Management, Lublin University of Technology, ul. Nadbystrzycka 38D, 20-618 Lublin, Poland)

Abstract

The study targeted towards drying of cantaloupe slices with various thicknesses in a microwave dryer. The experiments were carried out at three microwave powers of 180, 360, and 540 W and three thicknesses of 2, 4, and 6 mm for cantaloupe drying, and the weight variations were determined. Artificial neural networks (ANN) and adaptive neuro-fuzzy inference systems (ANFIS) were exploited to investigate energy and exergy indices of cantaloupe drying using various afore-mentioned input parameters. The results indicated that a rise in microwave power and a decline in sample thickness can significantly decrease the specific energy consumption (SEC), energy loss, exergy loss, and improvement potential (probability level of 5%). The mean SEC, energy efficiency, energy loss, thermal efficiency, dryer efficiency, exergy efficiency, exergy loss, improvement potential, and sustainability index ranged in 10.48–25.92 MJ/kg water, 16.11–47.24%, 2.65–11.24 MJ/kg water, 7.02–36.46%, 12.36–42.70%, 11.25–38.89%, 3–12.2 MJ/kg water, 1.88–10.83 MJ/kg water, and 1.12–1.63, respectively. Based on the results, the use of higher microwave powers for drying thinner samples can improve the thermodynamic performance of the process. The ANFIS model offers a more accurate forecast of energy and exergy indices of cantaloupe drying compare to ANN model.

Suggested Citation

  • Safoura Zadhossein & Yousef Abbaspour-Gilandeh & Mohammad Kaveh & Mariusz Szymanek & Esmail Khalife & Olusegun D. Samuel & Milad Amiri & Jacek Dziwulski, 2021. "Exergy and Energy Analyses of Microwave Dryer for Cantaloupe Slice and Prediction of Thermodynamic Parameters Using ANN and ANFIS Algorithms," Energies, MDPI, vol. 14(16), pages 1-19, August.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:16:p:4838-:d:610747
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    References listed on IDEAS

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    1. Jafari, Hassan & Kalantari, Davood & Azadbakht, Mohsen, 2017. "Semi-industrial continuous band microwave dryer for energy and exergy analyses, mathematical modeling of paddy drying and it's qualitative study," Energy, Elsevier, vol. 138(C), pages 1016-1029.
    2. Azadbakht, Mohsen & Torshizi, Mohammad Vahedi & Noshad, Fatemeh & Rokhbin, Arash, 2018. "Application of artificial neural network method for prediction of osmotic pretreatment based on the energy and exergy analyses in microwave drying of orange slices," Energy, Elsevier, vol. 165(PB), pages 836-845.
    3. Beigi, Mohsen & Tohidi, Mojtaba & Torki-Harchegani, Mehdi, 2017. "Exergetic analysis of deep-bed drying of rough rice in a convective dryer," Energy, Elsevier, vol. 140(P1), pages 374-382.
    4. Jafari, Hassan & Kalantari, Davood & Azadbakht, Mohsen, 2018. "Energy consumption and qualitative evaluation of a continuous band microwave dryer for rice paddy drying," Energy, Elsevier, vol. 142(C), pages 647-654.
    5. Azadbakht, Mohsen & Aghili, Hajar & Ziaratban, Armin & Torshizi, Mohammad Vahedi, 2017. "Application of artificial neural network method to exergy and energy analyses of fluidized bed dryer for potato cubes," Energy, Elsevier, vol. 120(C), pages 947-958.
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