IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v17y2024i13p3199-d1425409.html
   My bibliography  Save this article

Modeling the Drying Process of Onion Slices Using Artificial Neural Networks

Author

Listed:
  • Sławomir Francik

    (Department of Mechanical Engineering and Agrophysics, Faculty of Production Engineering and Energetics, University of Agriculture in Krakow, Al. Mickiewicza 21, 31-120 Kraków, Poland)

  • Bogusława Łapczyńska-Kordon

    (Department of Mechanical Engineering and Agrophysics, Faculty of Production Engineering and Energetics, University of Agriculture in Krakow, Al. Mickiewicza 21, 31-120 Kraków, Poland)

  • Michał Hajos

    (Department of Mechanical Engineering and Agrophysics, Faculty of Production Engineering and Energetics, University of Agriculture in Krakow, Al. Mickiewicza 21, 31-120 Kraków, Poland)

  • Grzegorz Basista

    (Department of Mechanical Engineering and Agrophysics, Faculty of Production Engineering and Energetics, University of Agriculture in Krakow, Al. Mickiewicza 21, 31-120 Kraków, Poland)

  • Agnieszka Zawiślak

    (Department of Biotechnology and General Technology of Food, Faculty of Food Technology, University of Agriculture in Krakow, Al. Mickiewicza 21, 31-120 Kraków, Poland)

  • Renata Francik

    (Faculty of Medicine and Health Sciences, University of Applied Sciences in Nowy Sącz, Kościuszki 2G, 33-300 Nowy Sącz, Poland)

Abstract

One of the food preservation technologies is the drying process, which requires heat and is significantly energy-intensive, resulting in high costs. This caused the search for new design solutions for dryers, which requires continuous experimental research and the creation of new decision-supporting models for the optimization of drying processes. In this work, four models of the kinetics of convective onion drying were developed using Artificial Neural Networks (ANNs), taking into account pre-treatment before drying and the different temperatures of the drying agent. The moisture content in the dried material at a specific moment in time was taken as the dependent variable (ANN output). The following were accepted as independent variables (ANN inputs): drying temperature, initial sample thickness, initial moisture content, initial mass of the sample, time of drying, and material pre-treatment (no pre-treatment—blanching–osmotic dehydration). Four semantic models were formulated, the general Ann1 model taking into account all input variables and three detailed Ann2 models for individual types of pre-treatment. For the best Ann1, the MAPE values were 5.88–7.02% (for different data: Training, Test, Validation). For the detailed Ann2 models, the error values were more than twice lower. The MAPE values ranged from 1.14% to 3.12%.

Suggested Citation

  • Sławomir Francik & Bogusława Łapczyńska-Kordon & Michał Hajos & Grzegorz Basista & Agnieszka Zawiślak & Renata Francik, 2024. "Modeling the Drying Process of Onion Slices Using Artificial Neural Networks," Energies, MDPI, vol. 17(13), pages 1-24, June.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:13:p:3199-:d:1425409
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/17/13/3199/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/17/13/3199/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Tugce Ozsan Kilic & Ismail Boyar & Cuneyt Dincer & Can Ertekin & Ahmet Naci Onus, 2023. "Effects of Different Osmotic Pre-Treatments on the Drying Characteristics, Modeling and Physicochemical Properties of Momordica charantia L. Slices," Agriculture, MDPI, vol. 13(10), pages 1-21, September.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Anna Sadowska & Katarzyna Najman & Franciszek Świderski, 2024. "Research Progress of the Functional Properties of Fruit and Vegetables and Their Preserves," Agriculture, MDPI, vol. 14(5), pages 1-3, April.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:17:y:2024:i:13:p:3199-:d:1425409. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.