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Prediction of fuel consumption of mining dump trucks: A neural networks approach

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  • Siami-Irdemoosa, Elnaz
  • Dindarloo, Saeid R.

Abstract

Fuel consumption of mining dump trucks accounts for about 30% of total energy use in surface mines. Moreover, a fleet of large dump trucks is the main source of greenhouse gas (GHG) generation. Modeling and prediction of fuel consumption per cycle is a valuable tool in assessing both energy costs and the resulting GHG generation. However, only a few studies have been published on fuel prediction in mining operations. In this paper, fuel consumption per cycle of operation was predicted using artificial neural networks (ANN) technique. Explanatory variables were: pay load, loading time, idled while loaded, loaded travel time, empty travel time, and idled while empty. The output variable was the amount of fuel consumed in one cycle. Mean absolute percentage error (MAPE) of 10% demonstrated applicability of ANN in prediction of the fuel consumption. The results demonstrated the considerable effect of mining trucks idle times in fuel consumption. A large portion of the unnecessary energy consumption and GHG generation, in this study, was solely due to avoidable idle times. This necessitates implementation of proper actions/remedies in form of both preventive and corrective actions.

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  • Siami-Irdemoosa, Elnaz & Dindarloo, Saeid R., 2015. "Prediction of fuel consumption of mining dump trucks: A neural networks approach," Applied Energy, Elsevier, vol. 151(C), pages 77-84.
    • Handle: RePEc:eee:appene:v:151:y:2015:i:c:p:77-84
    DOI: 10.1016/j.apenergy.2015.04.064
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    Cited by:

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    10. Feng, Yanbiao & Dong, Zuomin, 2020. "Integrated design and control optimization of fuel cell hybrid mining truck with minimized lifecycle cost," Applied Energy, Elsevier, vol. 270(C).
    11. Noriega, Roberto & Pourrahimian, Yashar, 2022. "A systematic review of artificial intelligence and data-driven approaches in strategic open-pit mine planning," Resources Policy, Elsevier, vol. 77(C).
    12. Hassanean S. H. Jassim & Weizhuo Lu & Thomas Olofsson, 2017. "Predicting Energy Consumption and CO 2 Emissions of Excavators in Earthwork Operations: An Artificial Neural Network Model," Sustainability, MDPI, vol. 9(7), pages 1-25, July.
    13. Wang, Shuaian & Yan, Ran, 2023. "Fundamental challenge and solution methods in prescriptive analytics for freight transportation," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 169(C).
    14. Dindarloo, Saeid R. & Siami-Irdemoosa, Elnaz, 2016. "Determinants of fuel consumption in mining trucks," Energy, Elsevier, vol. 112(C), pages 232-240.
    15. Wörz, Sascha & Bernhardt, Heinz, 2017. "A novel method for optimal fuel consumption estimation and planning for transportation systems," Energy, Elsevier, vol. 120(C), pages 565-572.
    16. Xiao, Jin & Li, Yuxi & Xie, Ling & Liu, Dunhu & Huang, Jing, 2018. "A hybrid model based on selective ensemble for energy consumption forecasting in China," Energy, Elsevier, vol. 159(C), pages 534-546.
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    19. Wang, Qiang & Jiang, Feng, 2019. "Integrating linear and nonlinear forecasting techniques based on grey theory and artificial intelligence to forecast shale gas monthly production in Pennsylvania and Texas of the United States," Energy, Elsevier, vol. 178(C), pages 781-803.

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