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Changes in the Properties of Hazelnut Shells Due to Conduction Drying

Author

Listed:
  • Ana Matin

    (Faculty of Agriculture, University of Zagreb, Svetošimunska Cesta 25, 10000 Zagreb, Croatia)

  • Ivan Brandić

    (Faculty of Agriculture, University of Zagreb, Svetošimunska Cesta 25, 10000 Zagreb, Croatia)

  • Neven Voća

    (Faculty of Agriculture, University of Zagreb, Svetošimunska Cesta 25, 10000 Zagreb, Croatia)

  • Nikola Bilandžija

    (Faculty of Agriculture, University of Zagreb, Svetošimunska Cesta 25, 10000 Zagreb, Croatia)

  • Božidar Matin

    (Faculty of Forestry and Wood Technology, University of Zagreb, Svetošimunska Cesta 23, 10000 Zagreb, Croatia)

  • Vanja Jurišić

    (Faculty of Agriculture, University of Zagreb, Svetošimunska Cesta 25, 10000 Zagreb, Croatia)

  • Alan Antonović

    (Faculty of Forestry and Wood Technology, University of Zagreb, Svetošimunska Cesta 23, 10000 Zagreb, Croatia)

  • Tajana Krička

    (Faculty of Agriculture, University of Zagreb, Svetošimunska Cesta 25, 10000 Zagreb, Croatia)

Abstract

In this study, the physical properties of two hazelnut species were investigated before and after drying at different temperatures and durations. The results showed that the physical properties of the hazelnut samples, including size, volume, density, weight, kernel mass, and shell mass, were significantly affected by temperature, duration, and their interactions. In addition, the moisture content of the samples decreased with increasing temperature and drying duration. The lowest value for the Istarski duguljasti variety was 5.36% (160 °C and 45 min), while the lowest value for Rimski okrugli was measured at 160 °C and 60 min (5.02%). Ash content was affected by both temperature and time, with the Istarski duguljasti variety having a minimum value of 0.84% at 120 °C and 60 min and Rimski okrugli a maximum value of 1.24% at 100 °C and 30 min. The variables of the ultimate analysis, such as nitrogen, carbon, sulfur, and hydrogen, increased with increasing temperature and time. The oxygen content and the higher heating value decreased with increasing temperature. Energy optimization in the drying process is crucial to reduce costs and save time. Effective energy optimization measures can lead to significant cost savings and improved operational efficiency in the drying process.

Suggested Citation

  • Ana Matin & Ivan Brandić & Neven Voća & Nikola Bilandžija & Božidar Matin & Vanja Jurišić & Alan Antonović & Tajana Krička, 2023. "Changes in the Properties of Hazelnut Shells Due to Conduction Drying," Agriculture, MDPI, vol. 13(3), pages 1-15, February.
  • Handle: RePEc:gam:jagris:v:13:y:2023:i:3:p:589-:d:1083508
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    References listed on IDEAS

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    1. Charles R. Harris & K. Jarrod Millman & Stéfan J. Walt & Ralf Gommers & Pauli Virtanen & David Cournapeau & Eric Wieser & Julian Taylor & Sebastian Berg & Nathaniel J. Smith & Robert Kern & Matti Picu, 2020. "Array programming with NumPy," Nature, Nature, vol. 585(7825), pages 357-362, September.
    2. Vera Marcantonio & Enrico Bocci & Danilo Monarca, 2019. "Development of a Chemical Quasi-Equilibrium Model of Biomass Waste Gasification in a Fluidized-Bed Reactor by Using Aspen Plus," Energies, MDPI, vol. 13(1), pages 1-15, December.
    3. Zahra Parhizi & Hamed Karami & Iman Golpour & Mohammad Kaveh & Mariusz Szymanek & Ana M. Blanco-Marigorta & José Daniel Marcos & Esmail Khalife & Stanisław Skowron & Nashwan Adnan Othman & Yousef Darv, 2022. "Modeling and Optimization of Energy and Exergy Parameters of a Hybrid-Solar Dryer for Basil Leaf Drying Using RSM," Sustainability, MDPI, vol. 14(14), pages 1-27, July.
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    Cited by:

    1. Anna Borkowska & Kamila E. Klimek & Grzegorz Maj & Magdalena Kapłan, 2024. "Analysis of the Energy–Carbon Potential of the Pericarp Cover of Selected Hazelnut Varieties," Energies, MDPI, vol. 17(16), pages 1-16, August.

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