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Predicting Energy Consumption and CO 2 Emissions of Excavators in Earthwork Operations: An Artificial Neural Network Model

Author

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  • Hassanean S. H. Jassim

    (Department of Civil, Environmental and Natural Resources Engineering, Luleå University of Technology, Lulea 97187, Sweden)

  • Weizhuo Lu

    (Department of Civil, Environmental and Natural Resources Engineering, Luleå University of Technology, Lulea 97187, Sweden)

  • Thomas Olofsson

    (Department of Civil, Environmental and Natural Resources Engineering, Luleå University of Technology, Lulea 97187, Sweden)

Abstract

Excavators are one of the most energy-intensive elements of earthwork operations. Predicting the energy consumption and CO 2 emissions of excavators is therefore critical in order to mitigate the environmental impact of earthwork operations. However, there is a lack of method for estimating such energy consumption and CO 2 emissions, especially during the early planning stages of these activities. This research proposes a model using an artificial neural network (ANN) to predict an excavator’s hourly energy consumption and CO 2 emissions under different site conditions. The proposed ANN model includes five input parameters: digging depth, cycle time, bucket payload, engine horsepower, and load factor. The Caterpillar handbook’s data, that included operational characteristics of twenty-five models of excavators, were used to develop the training and testing sets for the ANN model. The proposed ANN models were also designed to identify which factors from all the input parameters have the greatest impact on energy and emissions, based on partitioning weight analysis. The results showed that the proposed ANN models can provide an accurate estimating tool for the early planning stage to predict the energy consumption and CO 2 emissions of excavators. Analyses have revealed that, within all the input parameters, cycle time has the greatest impact on energy consumption and CO 2 emissions. The findings from the research enable the control of crucial factors which significantly impact on energy consumption and CO 2 emissions.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:9:y:2017:i:7:p:1257-:d:105256
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    References listed on IDEAS

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    1. Malindu Sandanayake & Guomin Zhang & Sujeeva Setunge & Chris Malcolm Thomas, 2015. "Environmental Emissions of Construction Equipment Usage in Pile Foundation Construction Process—A Case Study," Springer Books, in: Liyin Shen & Kunhui Ye & Chao Mao (ed.), Proceedings of the 19th International Symposium on Advancement of Construction Management and Real Estate, edition 127, chapter 0, pages 327-339, Springer.
    2. Seung Ok & Sunil Sinha, 2006. "Construction equipment productivity estimation using artificial neural network model," Construction Management and Economics, Taylor & Francis Journals, vol. 24(10), pages 1029-1044.
    3. 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.
    4. Zhang, Shaojun & Wu, Ye & Liu, Huan & Huang, Ruikun & Yang, Liuhanzi & Li, Zhenhua & Fu, Lixin & Hao, Jiming, 2014. "Real-world fuel consumption and CO2 emissions of urban public buses in Beijing," Applied Energy, Elsevier, vol. 113(C), pages 1645-1655.
    5. Enevoldsen, Martin K. & Ryelund, Anders V. & Andersen, Mikael Skou, 2007. "Decoupling of industrial energy consumption and CO2-emissions in energy-intensive industries in Scandinavia," Energy Economics, Elsevier, vol. 29(4), pages 665-692, July.
    6. Zhang, Guoqiang & Eddy Patuwo, B. & Y. Hu, Michael, 1998. "Forecasting with artificial neural networks:: The state of the art," International Journal of Forecasting, Elsevier, vol. 14(1), pages 35-62, March.
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