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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.

Suggested Citation

  • 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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    1. Mohamed Ismail, Harun & Ng, Hoon Kiat & Queck, Cheen Wei & Gan, Suyin, 2012. "Artificial neural networks modelling of engine-out responses for a light-duty diesel engine fuelled with biodiesel blends," Applied Energy, Elsevier, vol. 92(C), pages 769-777.
    2. Ribau, João P. & Silva, Carla M. & Sousa, João M.C., 2014. "Efficiency, cost and life cycle CO2 optimization of fuel cell hybrid and plug-in hybrid urban buses," Applied Energy, Elsevier, vol. 129(C), pages 320-335.
    3. He, Kebin & Huo, Hong & Zhang, Qiang & He, Dongquan & An, Feng & Wang, Michael & Walsh, Michael P., 2005. "Oil consumption and CO2 emissions in China's road transport: current status, future trends, and policy implications," Energy Policy, Elsevier, vol. 33(12), pages 1499-1507, August.
    4. Kara Togun, Necla & Baysec, Sedat, 2010. "Prediction of torque and specific fuel consumption of a gasoline engine by using artificial neural networks," Applied Energy, Elsevier, vol. 87(1), pages 349-355, January.
    5. Yap, Wai Kean & Karri, Vishy, 2011. "ANN virtual sensors for emissions prediction and control," Applied Energy, Elsevier, vol. 88(12), pages 4505-4516.
    6. Zamboni, Giorgio & Malfettani, Stefano & André, Michel & Carraro, Chiara & Marelli, Silvia & Capobianco, Massimo, 2013. "Assessment of heavy-duty vehicle activities, fuel consumption and exhaust emissions in port areas," Applied Energy, Elsevier, vol. 111(C), pages 921-929.
    7. Sahoo, Lalit Kumar & Bandyopadhyay, Santanu & Banerjee, Rangan, 2014. "Benchmarking energy consumption for dump trucks in mines," Applied Energy, Elsevier, vol. 113(C), pages 1382-1396.
    8. Lutsey, Nicholas & Brodrick, Christie-Joy & Lipman, Timothy, 2007. "Analysis of potential fuel consumption and emissions reductions from fuel cell auxiliary power units (APUs) in long-haul trucks," Energy, Elsevier, vol. 32(12), pages 2428-2438.
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    Cited by:

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    4. Patterson, S.R. & Kozan, E. & Hyland, P., 2017. "Energy efficient scheduling of open-pit coal mine trucks," European Journal of Operational Research, Elsevier, vol. 262(2), pages 759-770.
    5. Przemysław Bodziony & Michał Patyk, 2024. "The Influence of the Mining Operation Environment on the Energy Consumption and Technical Availability of Truck Haulage Operations in Surface Mines," Energies, MDPI, vol. 17(11), pages 1-18, May.
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    9. Kshirsagar, Charudatta M. & Anand, Ramanathan, 2017. "Artificial neural network applied forecast on a parametric study of Calophyllum inophyllum methyl ester-diesel engine out responses," Applied Energy, Elsevier, vol. 189(C), pages 555-567.
    10. Gun Il Kim & Beakcheol Jang, 2023. "Petroleum Price Prediction with CNN-LSTM and CNN-GRU Using Skip-Connection," Mathematics, MDPI, vol. 11(3), pages 1-16, January.
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    13. 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.
    14. 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).
    15. Dindarloo, Saeid R. & Siami-Irdemoosa, Elnaz, 2016. "Determinants of fuel consumption in mining trucks," Energy, Elsevier, vol. 112(C), pages 232-240.
    16. 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.
    17. 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. Lulu Gao & Dongyue Wang & Chun Jin & Tong Yi, 2022. "Modelling and Performance Analysis of Cyclic Hydro-Pneumatic Energy Storage System Considering the Thermodynamic Characteristics," Energies, MDPI, vol. 15(18), pages 1-19, September.
    20. 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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