Skip to main content

Advertisement

SpringerLink
Log in

A method for data-driven evaluation of operator impact on energy efficiency of digging machines

  • Original Article
  • Published:
Energy Efficiency Aims and scope Submit manuscript

Abstract

Material handling (including digging) is one of the most energy-intensive processes in mining. Operators’ skills and practices are known to be some of the major factors that affect energy efficiency of digging operations. Improving operators’ skills through training is an inexpensive and effective method to improve energy efficiency. The method proposed in this work uses data collected by monitoring systems on digging equipment to detect the monitored parameters that lead to differences in energy efficiency of operators (responsible parameters). After data extraction, removing the outliers, and identifying the operators with sufficient working hours, correlation analysis can be used to find parameters that are correlated with energy efficiency. Regression analysis on pairs of operators is then used to detect responsible parameters. Random sampling is used to overcome missing data issues in the analysis. This statistics-based method is simple and adequately accounts for the high variability in data collected from these monitoring systems. The proposed method was illustrated using data collected on five operators working on a 64-m3 (85 yd3) Bucyrus-Erie 1570w dragline. The case study results show that dump height and engagement/disengagement position of the bucket are the most likely parameters to cause differences between energy efficiency of these operators. On the other hand, cycle time, payload, and swing in time are least likely to influence differences in operator energy efficiency.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  • Abdi Oskouei, M. (2013). Methods for evaluating effect of operators on dragline energy efficiency. Missouri University of Science and Technology.

  • Abdi Oskouei, M., & Awuah-Offei, K. (2014). Statistical methods for evaluating the effect of operators on energy efficiency of mining machines. Mining Technology, 123(4), 175–182. doi:10.1179/1743286314Y.0000000067.

    Article  Google Scholar 

  • Awuah-Offei, K. (2005). Dynamic simulation of cable shovel-formation interactions for efficient oil sands excavation. Missouri University of Science and Technology.

  • Awuah-Offei, K., Osei, B., & Askari-Nasab, H. (2011). Modeling truck / shovel energy efficiency under uncertainty. Transactions of the Society for Mining, Metallurgy, and Exploration, 330, 573–584.

    Google Scholar 

  • Bernold, L. E. (2007). Quantitative assessment of backhoe operator skill. Journal of Construction Engineering and Management, 133(November), 889–899. doi:10.1061/(ASCE)0733-9364(2007)133:11(889).

    Article  Google Scholar 

  • Biau, D. J. (2011). In brief: standard deviation and standard error. Clinical Orthopaedics and Related Research, 469(9), 2661–2664. doi:10.1007/s11999-011-1908-9.

    Article  Google Scholar 

  • Bogunovic, D. (2008). Integrated data environment for analysis and control of energy consumption (IDE-ACE) in surface coal mining. Ph.D. diss., The Pennsylvania State University.

  • Bogunovic, D., & Kecojevic, V. (2011). Impact of bucket fill factor on dragline production rate and energy consumption. Mining Engineering, 63(8), 48–53.

    Google Scholar 

  • DOE, U. S. (2007). Mining industry energy bandwidth study. Washington, D.C.: U.S. Department of Energy.

    Google Scholar 

  • DOE/EIA. (2013). International energy outlook 2013 (pp. 1–300).

  • Erdem, B., & Düzgün, H. Ş. B. (2005). Dragline cycle time analysis. Journal of Scientific & Industrial Research, 64(January), 19–29.

    Google Scholar 

  • Hettinger, D., & Lumley, G. (1999). Using data analysis to improve dragline productivity. Coal Age, 104(9), 64–66.

    Google Scholar 

  • Humphrey, J. D. (1990). The fundamentals of the dragline (pp. 1–28).

  • Kizil, M. (2010). Improving dragline productivity using a diggability index as an indicator. In: Society for Mining, Metallurgy & Exploration, Inc. (SME) (pp. 134–141). Phoenix, Arizona, USA.

  • Komljenovic, D., Bogunovic, D., & Kecojevic, V. (2010). Dragline operator performance indicator. International Journal of Mining, Reclamation and Environmet, 24(1), 34–43.

    Article  Google Scholar 

  • Little, R. J. A. (1992). Regression with missing X’s : a review. Journal of American Statistical Association, 87(420), 1227–1237.

    Google Scholar 

  • Lumley, G. (2005). Reducing the variability in dragline operator performance reducing the variability in dragline operator performance. In: Coal operaotrs’ conference (pp. 97–106). Wollongong, Australia.

  • Mielli, F. (2011). Three major mining challenges and their technological solutions. Mining Engineering, (september), 69–72.

  • National Mining Association (NMA). (2013). The economic contributions of U.S. mining (2011).

  • Norgate, T., & Haque, N. (2010). Energy and greenhouse gas impacts of mining and mineral processing operations. Journal of Cleaner Production, 18(3), 266–274. doi:10.1016/j.jclepro.2009.09.020.

    Article  Google Scholar 

  • Norman, S. (2011). Variability reduction in dragline operator performance. In: SME Annual Meeting (preprint 11-095). Denver, CO.

  • Patnayak, S., Tannant, D. D., Parsons, I., Del Valle, V., & Wong, J. (2007). Operator and dipper tooth influence on electric shovel performance during oil sands mining. International Journal of Mining, Reclamation and Environment, 1–26. doi:10.1080/17480930701482961.

  • Rai, P., Ratnesh, T., & Nath, R. (2000). Cycle time and idle time analysis of draglines for increased productivity–a case study. Indian Journal of Engineering & Material Sciences, 7, 77–81.

    Google Scholar 

  • Schafer, J. L., & Graham, J. W. (2002). Missing data: our view of the state of the art. Psychological Methods, 7(2), 147–177. doi:10.1037//1082-989X.7.2.147.

    Article  Google Scholar 

  • Torrance, A., & Baldwin, G. (1990). Blast performance assessment using a dragline monitor. In: Third International Symposium on Rock Fragmentation by Blasting (pp. 219–224). Brisbane, Australia.

  • Vukotic, I., & Kecojevic, V. (2014). Evaluation of rope shovel operators in surface coal mining using a Multi-Attribute Decision-Making model. International Journal of Mining Science and Technology, 24(2), 259–268. doi:10.1016/j.ijmst.2014.01.019.

    Article  Google Scholar 

  • Williams, G. (2005). Achievements through the dragline improvement group (DIG) in Anglo coal. In: Institute of quarrying southern Africa (36th conference & exhibition). Boksburg, South Africa.

  • Zhu, Y. Q., & Yin, Z. D. (2008). A new evaluation system for energy saving based on energy efficiency and loss ratio. In: IEEE International Conference on Sustainable Energy Technologies (ICSET) (pp. 121–124). Singapore.

Download references

Author information

Authors and Affiliations

  1. University of Iowa, 401 IATL, Iowa City, IA, 52246, USA

    Maryam Abdi Oskouei

  2. Missouri University of Science & Technology, 326 McNutt Hall, Rolla, MO, 65409, USA

    Kwame Awuah-Offei

Authors
  1. Maryam Abdi Oskouei
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kwame Awuah-Offei
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Maryam Abdi Oskouei.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Abdi Oskouei, M., Awuah-Offei, K. A method for data-driven evaluation of operator impact on energy efficiency of digging machines. Energy Efficiency 9, 129–140 (2016). https://doi.org/10.1007/s12053-015-9353-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12053-015-9353-3

Keywords

Search

Navigation