IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v112y2016icp232-240.html
   My bibliography  Save this article

Determinants of fuel consumption in mining trucks

Author

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

Abstract

Analysis of fuel consumption in a large surface mine, during more than 5000 cycles of material transportation, revealed considerable variability in the data. Truck fuel estimation based on the mining truck manufacturers' manuals/estimates is not capable of capturing this variability in the fuel consumption data. Partial least squares regression and autoregressive integrated moving average methods were employed to examine the effect of cyclic activities on fuel consumption, and to recommend relevant remedies for consumption reduction. Proper modifications of the operation can result in improved cycle times. Consequently, minimizing some cyclic activities would enhance energy efficiency. The truck “empty idle time” was a major contributor to unnecessary fuel consumption. Since the truck queues at shovels are a major component of the “empty idle time”, decisions should be reviewed to reduce the truck queues at loading points. Improved dispatching strategies, optimal muck pile shape and size distribution, and improved shovel/loader operator skills are effective preventive measures to minimize truck flow bottlenecks at loading points, and thus to improve energy efficiency at mines.

Suggested Citation

  • Dindarloo, Saeid R. & Siami-Irdemoosa, Elnaz, 2016. "Determinants of fuel consumption in mining trucks," Energy, Elsevier, vol. 112(C), pages 232-240.
    • Handle: RePEc:eee:energy:v:112:y:2016:i:c:p:232-240
    DOI: 10.1016/j.energy.2016.06.085
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544216308593
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2016.06.085?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.

    References listed on IDEAS

    as
    1. Mevik, Björn-Helge & Wehrens, Ron, 2007. "The pls Package: Principal Component and Partial Least Squares Regression in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 18(i02).
    2. Muratori, Matteo & Moran, Michael J. & Serra, Emmanuele & Rizzoni, Giorgio, 2013. "Highly-resolved modeling of personal transportation energy consumption in the United States," Energy, Elsevier, vol. 58(C), pages 168-177.
    3. Khayyam, Hamid & Bab-Hadiashar, Alireza, 2014. "Adaptive intelligent energy management system of plug-in hybrid electric vehicle," Energy, Elsevier, vol. 69(C), pages 319-335.
    4. Hao, Han & Liu, Zongwei & Zhao, Fuquan & Li, Weiqi & Hang, Wen, 2015. "Scenario analysis of energy consumption and greenhouse gas emissions from China's passenger vehicles," Energy, Elsevier, vol. 91(C), pages 151-159.
    5. Fontaras, Georgios & Grigoratos, Theodoros & Savvidis, Dimitrios & Anagnostopoulos, Konstantinos & Luz, Raphael & Rexeis, Martin & Hausberger, Stefan, 2016. "An experimental evaluation of the methodology proposed for the monitoring and certification of CO2 emissions from heavy-duty vehicles in Europe," Energy, Elsevier, vol. 102(C), pages 354-364.
    6. 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.
    7. Du, J.D. & Han, W.J. & Peng, Y.H. & Gu, C.C., 2010. "Potential for reducing GHG emissions and energy consumption from implementing the aluminum intensive vehicle fleet in China," Energy, Elsevier, vol. 35(12), pages 4671-4678.
    8. Pai, Ping-Feng & Lin, Chih-Sheng, 2005. "A hybrid ARIMA and support vector machines model in stock price forecasting," Omega, Elsevier, vol. 33(6), pages 497-505, December.
    9. Whyte, Kieran & Daly, Hannah E. & Ó Gallachóir, Brian P., 2013. "Modelling HGV freight transport energy demand in Ireland and the impacts of the property construction bubble," Energy, Elsevier, vol. 50(C), pages 245-251.
    10. 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.
    11. 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.
    12. Zhang, Ming & Mu, Hailin & Li, Gang & Ning, Yadong, 2009. "Forecasting the transport energy demand based on PLSR method in China," Energy, Elsevier, vol. 34(9), pages 1396-1400.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Natallia Pashkevich & Darek Haftor & Mikael Karlsson & Soumitra Chowdhury, 2019. "Sustainability through the Digitalization of Industrial Machines: Complementary Factors of Fuel Consumption and Productivity for Forklifts with Sensors," Sustainability, MDPI, vol. 11(23), pages 1-21, November.
    2. Suh, Dong Hee, 2021. "Exploring the U.S. mining industry's demand system for production factors: Implications for economic sustainability," Resources Policy, Elsevier, vol. 74(C).
    3. Ji, Shaobo & Chen, Qiulin & Shu, Minglei & Tian, Guohong & Liao, Baoliang & Lv, Chengju & Li, Meng & Lan, Xin & Cheng, Yong, 2020. "Influence of operation management on fuel consumption of coach fleet," Energy, Elsevier, vol. 203(C).
    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. Alessandro Brusa & Enrico Corti & Alessandro Rossi & Davide Moro, 2023. "Enhancement of Heavy-Duty Engines Performance and Reliability Using Cylinder Pressure Information," Energies, MDPI, vol. 16(3), pages 1-21, January.
    6. Wang, Qian & Gu, Qinghua & Li, Xuexian & Xiong, Naixue, 2024. "Comprehensive overview: Fleet management drives green and climate-smart open pit mine," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PA).
    7. Yaghoub Pourasad & Amirhossein Ghanati & Mehrdad Khosravi, 2017. "Optimal design of aerodynamic force supplementary devices for the improvement of fuel consumption and emissions," Energy & Environment, , vol. 28(3), pages 263-282, May.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ji, Shaobo & Chen, Qiulin & Shu, Minglei & Tian, Guohong & Liao, Baoliang & Lv, Chengju & Li, Meng & Lan, Xin & Cheng, Yong, 2020. "Influence of operation management on fuel consumption of coach fleet," Energy, Elsevier, vol. 203(C).
    2. Wu, Tian & Shen, Qu & Xu, Ming & Peng, Tianduo & Ou, Xunmin, 2018. "Development and application of an energy use and CO2 emissions reduction evaluation model for China's online car hailing services," Energy, Elsevier, vol. 154(C), pages 298-307.
    3. 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.
    4. Natallia Pashkevich & Darek Haftor & Mikael Karlsson & Soumitra Chowdhury, 2019. "Sustainability through the Digitalization of Industrial Machines: Complementary Factors of Fuel Consumption and Productivity for Forklifts with Sensors," Sustainability, MDPI, vol. 11(23), pages 1-21, November.
    5. 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.
    6. Sahraei, Mohammad Ali & Duman, Hakan & Çodur, Muhammed Yasin & Eyduran, Ecevit, 2021. "Prediction of transportation energy demand: Multivariate Adaptive Regression Splines," Energy, Elsevier, vol. 224(C).
    7. Qodri Febrilian Erahman & Nadhilah Reyseliani & Widodo Wahyu Purwanto & Mahmud Sudibandriyo, 2019. "Modeling Future Energy Demand and CO 2 Emissions of Passenger Cars in Indonesia at the Provincial Level," Energies, MDPI, vol. 12(16), pages 1-25, August.
    8. Nikita V. Martyushev & Boris V. Malozyomov & Olga A. Filina & Svetlana N. Sorokova & Egor A. Efremenkov & Denis V. Valuev & Mengxu Qi, 2023. "Stochastic Models and Processing Probabilistic Data for Solving the Problem of Improving the Electric Freight Transport Reliability," Mathematics, MDPI, vol. 11(23), pages 1-19, November.
    9. Ku, Donggyun & Choi, Minje & Yoo, Nakyoung & Shin, Seungheon & Lee, Seungjae, 2021. "A new algorithm for eco-friendly path guidance focused on electric vehicles," Energy, Elsevier, vol. 233(C).
    10. Kamaladdin Fataliyev & Aneesh Chivukula & Mukesh Prasad & Wei Liu, 2021. "Stock Market Analysis with Text Data: A Review," Papers 2106.12985, arXiv.org, revised Jul 2021.
    11. Franco-Sepúlveda, Giovanni & Del Rio-Cuervo, Juan Camilo & Pachón-Hernández, María Angélica, 2019. "State of the art about metaheuristics and artificial neural networks applied to open pit mining," Resources Policy, Elsevier, vol. 60(C), pages 125-133.
    12. Xingping Zhang & Rao Rao & Jian Xie & Yanni Liang, 2014. "The Current Dilemma and Future Path of China’s Electric Vehicles," Sustainability, MDPI, vol. 6(3), pages 1-27, March.
    13. Fan, Jianqing & Jiang, Bai & Sun, Qiang, 2022. "Bayesian factor-adjusted sparse regression," Journal of Econometrics, Elsevier, vol. 230(1), pages 3-19.
    14. Lim, Juin Yau & Safder, Usman & How, Bing Shen & Ifaei, Pouya & Yoo, Chang Kyoo, 2021. "Nationwide sustainable renewable energy and Power-to-X deployment planning in South Korea assisted with forecasting model," Applied Energy, Elsevier, vol. 283(C).
    15. Zhang, Yue & Zhang, Qi & Farnoosh, Arash & Chen, Siyuan & Li, Yan, 2019. "GIS-Based Multi-Objective Particle Swarm Optimization of charging stations for electric vehicles," Energy, Elsevier, vol. 169(C), pages 844-853.
    16. S.R. Patterson & E. Kozan & P. Hyland, 2016. "An integrated model of an open-pit coal mine: improving energy efficiency decisions," International Journal of Production Research, Taylor & Francis Journals, vol. 54(14), pages 4213-4227, July.
    17. Atul Anand & L Suganthi, 2018. "Hybrid GA-PSO Optimization of Artificial Neural Network for Forecasting Electricity Demand," Energies, MDPI, vol. 11(4), pages 1-15, March.
    18. Wei Sun & Yujun He & Hong Chang, 2015. "Forecasting Fossil Fuel Energy Consumption for Power Generation Using QHSA-Based LSSVM Model," Energies, MDPI, vol. 8(2), pages 1-21, January.
    19. repec:jss:jstsof:23:i12 is not listed on IDEAS
    20. Dimitrios Kartsonakis Mademlis & Nikolaos Dritsakis, 2021. "Volatility Forecasting using Hybrid GARCH Neural Network Models: The Case of the Italian Stock Market," International Journal of Economics and Financial Issues, Econjournals, vol. 11(1), pages 49-60.
    21. Mao, Anjia & Yu, Tiantian & Ding, Zhaohao & Fang, Sidun & Guo, Jinran & Sheng, Qianqian, 2022. "Optimal scheduling for seaport integrated energy system considering flexible berth allocation," Applied Energy, Elsevier, vol. 308(C).

    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:eee:energy:v:112:y:2016:i:c:p:232-240. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    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.