IDEAS home Printed from https://ideas.repec.org/a/taf/nmcmxx/v26y2020i5p453-480.html
   My bibliography  Save this article

First principles versus artificial neural network modelling of a solar desalination system with experimental validation

Author

Listed:
  • Ali Bagheri
  • Nadia Esfandiari
  • Bizhan Honarvar
  • Amin Azdarpour

Abstract

The present study mainly focuses on enhancing the performance of solar still unit using solar energy through cylindrical parabolic collector and solar panels. A 300 W solar panel is used to heat saline water by thermal elements outside the solar still unit. Solar panels are cooled during the hot hours of the day; thus, reducing their temperature may lead to an increase in solar panel efficiency followed by an increase in the efficiency of the solar still unit. The maximum amount of freshwater used in the experiment was 2.132 kg/day. The experiments were modelled using ANNs. Based on neural network simulation results, there is a significant correlation between experimental data and neural network modelling. This paper compares experimental data with data obtained from mathematical modelling and ANNs. As a conclusion, the artificial neural network prediction has been more accurate than the simplified first principles model presented.

Suggested Citation

  • Ali Bagheri & Nadia Esfandiari & Bizhan Honarvar & Amin Azdarpour, 2020. "First principles versus artificial neural network modelling of a solar desalination system with experimental validation," Mathematical and Computer Modelling of Dynamical Systems, Taylor & Francis Journals, vol. 26(5), pages 453-480, September.
    • Handle: RePEc:taf:nmcmxx:v:26:y:2020:i:5:p:453-480
    DOI: 10.1080/13873954.2020.1788609
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/13873954.2020.1788609
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/13873954.2020.1788609?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    More about this item

    Statistics

    Access and download statistics

    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:taf:nmcmxx:v:26:y:2020:i:5:p:453-480. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/NMCM20 .

    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.