IDEAS home Printed from https://ideas.repec.org/h/ito/pchaps/245570.html
   My bibliography  Save this book chapter

Improve Energy Efficiency in Surface Mines Using Artificial Intelligence

In: Alternative Energies and Efficiency Evaluation

Author

Listed:
  • Ali Soofastaei
  • Milad Fouladgar

Abstract

This chapter demonstrates the practical application of artificial intelligence (AI) to improve energy efficiency in surface mines. The suggested AI approach has been applied in two different mine sites in Australia and Iran, and the achieved results have been promising. Mobile equipment in mine sites consumes a massive amount of energy, and the main part of this energy is provided by diesel. The critical diesel consumers in surface mines are haul trucks, the huge machines that move mine materials in the mine sites. There are many effective parameters on haul trucks' fuel consumption. AI models can help mine managers to predict and minimize haul truck energy consumption and consequently reduce the greenhouse gas emission generated by these trucks. This chapter presents a practical and validated AI approach to optimize three key parameters, including truck speed and payload and the total haul road resistance to minimize haul truck fuel consumption in surface mines. The results of the developed AI model for two mine sites have been presented in this chapter. The model increased the energy efficiency of mostly used trucks in surface mining, Caterpillar 793D and Komatsu HD785. The results show the trucks' fuel consumption reduction between 9 and 12%.

Suggested Citation

  • Ali Soofastaei & Milad Fouladgar, 2022. "Improve Energy Efficiency in Surface Mines Using Artificial Intelligence," Chapters, in: Muhammad Wakil Shahzad & Muhammad Sultan & Laurent Dala & Ben Bin Xu & Muhammad Ahmad Jamil & Nida I (ed.), Alternative Energies and Efficiency Evaluation, IntechOpen.
    • Handle: RePEc:ito:pchaps:245570
    DOI: 10.5772/intechopen.101493
    as

    Download full text from publisher

    File URL: https://www.intechopen.com/chapters/79641
    Download Restriction: no
    File URL: https://libkey.io/10.5772/intechopen.101493?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
    ---><---

    More about this item

    Keywords

    artificial intelligence; energy efficiency; fuel consumption; haul trucks; prediction; optimization; mining engineering;
    All these keywords.

    JEL classification:

    • Q40 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - General

    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:ito:pchaps:245570. 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: Slobodan Momcilovic (email available below). General contact details of provider: http://www.intechopen.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.