Energy Efficiency

, Volume 9, Issue 1, pp 129–140 | Cite as

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

  • Maryam Abdi OskoueiEmail author
  • Kwame Awuah-Offei
Original Article


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.


Operators’ skills and practice Operators’ performance Energy efficiency Mining and digging equipment Regression analysis 


  1. Abdi Oskouei, M. (2013). Methods for evaluating effect of operators on dragline energy efficiency. Missouri University of Science and Technology.Google Scholar
  2. 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.CrossRefGoogle Scholar
  3. Awuah-Offei, K. (2005). Dynamic simulation of cable shovel-formation interactions for efficient oil sands excavation. Missouri University of Science and Technology.Google Scholar
  4. 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
  5. 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).CrossRefGoogle Scholar
  6. 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.CrossRefGoogle Scholar
  7. 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.Google Scholar
  8. 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
  9. DOE, U. S. (2007). Mining industry energy bandwidth study. Washington, D.C.: U.S. Department of Energy.Google Scholar
  10. DOE/EIA. (2013). International energy outlook 2013 (pp. 1–300).Google Scholar
  11. Erdem, B., & Düzgün, H. Ş. B. (2005). Dragline cycle time analysis. Journal of Scientific & Industrial Research, 64(January), 19–29.Google Scholar
  12. Hettinger, D., & Lumley, G. (1999). Using data analysis to improve dragline productivity. Coal Age, 104(9), 64–66.Google Scholar
  13. Humphrey, J. D. (1990). The fundamentals of the dragline (pp. 1–28).Google Scholar
  14. 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.Google Scholar
  15. Komljenovic, D., Bogunovic, D., & Kecojevic, V. (2010). Dragline operator performance indicator. International Journal of Mining, Reclamation and Environmet, 24(1), 34–43.CrossRefGoogle Scholar
  16. Little, R. J. A. (1992). Regression with missing X’s : a review. Journal of American Statistical Association, 87(420), 1227–1237.Google Scholar
  17. 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.Google Scholar
  18. Mielli, F. (2011). Three major mining challenges and their technological solutions. Mining Engineering, (september), 69–72.Google Scholar
  19. National Mining Association (NMA). (2013). The economic contributions of U.S. mining (2011).Google Scholar
  20. 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.CrossRefGoogle Scholar
  21. Norman, S. (2011). Variability reduction in dragline operator performance. In: SME Annual Meeting (preprint 11-095). Denver, CO.Google Scholar
  22. 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.
  23. 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
  24. 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.CrossRefGoogle Scholar
  25. 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.Google Scholar
  26. 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.CrossRefGoogle Scholar
  27. 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.Google Scholar
  28. 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.Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2015

Authors and Affiliations

  1. 1.University of IowaIowa CityUSA
  2. 2.Missouri University of Science & TechnologyRollaUSA

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