Development of a multi-layer perceptron artificial neural network model to determine haul trucks energy consumption

Soofastaei, Ali, Aminossadati, Saiied M., Arefi, Mohammad M. and Kizil, Mehmet S. (2016) Development of a multi-layer perceptron artificial neural network model to determine haul trucks energy consumption. International Journal of Mining Science and Technology, 26 2: 285-293. doi:10.1016/j.ijmst.2015.12.015


Author Soofastaei, Ali
Aminossadati, Saiied M.
Arefi, Mohammad M.
Kizil, Mehmet S.
Title Development of a multi-layer perceptron artificial neural network model to determine haul trucks energy consumption
Journal name International Journal of Mining Science and Technology   Check publisher's open access policy
ISSN 2095-2686
2212-6066
Publication date 2016-03
Sub-type Article (original research)
DOI 10.1016/j.ijmst.2015.12.015
Open Access Status Not Open Access
Volume 26
Issue 2
Start page 285
End page 293
Total pages 9
Place of publication Amsterdam, Netherlands
Publisher Elsevier
Collection year 2017
Language eng
Abstract The mining industry annually consumes trillions of British thermal units of energy, a large part of which is saveable. Diesel fuel is a significant source of energy in surface mining operations and haul trucks are the major users of this energy source. Gross vehicle weight, truck velocity and total resistance have been recognised as the key parameters affecting the fuel consumption. In this paper, an artificial neural network model was developed to predict the fuel consumption of haul trucks in surface mines based on the gross vehicle weight, truck velocity and total resistance. The network was trained and tested using real data collected from a surface mining operation. The results indicate that the artificial neural network modelling can accurately predict haul truck fuel consumption based on the values of the haulage parameters considered in this study.
Keyword Artificial neural network
Fuel consumption
Haul truck
Surface mine
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: School of Mechanical & Mining Engineering Publications
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