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Role of the Operator in Dragline Energy Efficiency

  • Maryam Abdi-Oskouei
  • Kwame Awuah-Offei
Chapter
Part of the Green Energy and Technology book series (GREEN)

Abstract

Dragline operators, as controllers of one the most energy-intensive equipments in surface coal mines, play a significant role in dragline energy efficiency and thus mine profitability. The literature lacks work that explores monitoring system data and applies data-driven methods to gain a better understanding of dragline operation and develop more effective training approaches. This chapter provides a framework for assessing dragline energy efficiency performance using monitoring data and using such work to improve operator training. The first step in improving dragline performance is the assessment using data from dragline monitoring systems to estimate an overall performance indicator. Next, the analyst should apply a comprehensive algorithm to quantify the relationship between different operating parameters and the overall performance indicator. Finally, operators’ performance can be improved by using the results to optimize operator training.

Keywords

Energy efficiency Operator’s skills Data-driven analysis Mining equipment performance Dragline 

References

  1. 1.
    U.S. Energy Information Administration (2016) International Energy Outlook 2016-Electricity. In: International Energy Outlook. pp 81–100Google Scholar
  2. 2.
    U.S. Department of Energy (DOE) (2007) Mining Industry Energy Bandwidth StudyGoogle Scholar
  3. 3.
    Steele R, Sterling D (2011) Identifying Opportunities to reduce the consumption of energy across mining and mineral processing plants. In: SME Annual Meeting, pp 1–5Google Scholar
  4. 4.
    Williams G (2005) Achievements through the dragline improvement group (DIG) in Anglo coal. Inst Quarr South AfricaGoogle Scholar
  5. 5.
    Abdi Oskouei M, Awuah-Offei K (2015) A method for data-driven evaluation of operator impact on energy efficiency of digging machines. Energy Effic. doi: 10.1007/s12053-015-9353-3 Google Scholar
  6. 6.
    Abdi Oskouei M, Awuah-Offei K (2014) Statistical methods for evaluating the effect of operators on energy efficiency of mining machines. Min Technol 123:175–182. doi: 10.1179/1743286314Y.0000000067 CrossRefGoogle Scholar
  7. 7.
    Komljenovic D, Bogunovic D, Kecojevic V (2010) Dragline operator performance indicator. Int J Mining Reclam Environ 24:34–42. doi: 10.1080/17480930902778191 CrossRefGoogle Scholar
  8. 8.
    Bogunovic D, Kecojevic V, Lund V et al (2009) Analysis and control of energy consumption in surface coal mining. SME Annual Meeting 1–7Google Scholar
  9. 9.
    Levesque M, Millar D, Paraszczak J (2014) Energy and mining-The home truths. J Clean Prod 84:233–255. doi: 10.1016/j.jclepro.2013.12.088 CrossRefGoogle Scholar
  10. 10.
    Lumley G (2014) Mining for efficiencyGoogle Scholar
  11. 11.
    Bogunovic D, Kecojevic V (2011) Impact of fill factor on dragline production rate and energy consumption. Min EngGoogle Scholar
  12. 12.
    Rai P, Trivedi R, Nath R (2000) Cycle time and idle time analysis of draglines for increased productivity–a case study. Indian J Eng Mater Sci 7:77–81Google Scholar
  13. 13.
    Bogunovic D (2008) Integrated data environment for analysis and control of energy consumption (Ide-Ace) in surface coal mining. The Pennsylvania State UniversityGoogle Scholar
  14. 14.
    Vynne JF (2008) Innovative dragline monitoring systems and technologies. In CIM ConferenceGoogle Scholar
  15. 15.
    Rowlands C, Just GD (1992) Performance characteristics of dragline buckets. In: Third Large Open Pit Mineral Conference, pp 89–92Google Scholar
  16. 16.
    Pippenger JG (1995) Competing with the big boys: productivity and innovation at the Freedom lignite mineral, pp 3–6Google Scholar
  17. 17.
    Isokangas E (1997) Measuring dragline performance improvement initiatives. Int Congr Autom TechnolGoogle Scholar
  18. 18.
    Erdem B, Düzgün HŞB (2005) Dragline cycle time analysis. J Sci Ind Res 64:19–29Google Scholar
  19. 19.
    Mohammadi M, Rai P, Oraee SK (2015) A critical investigation of digging time segment of draglines in a large surface mine. Geotech Geol Eng 33:763–771. doi: 10.1007/s10706-015-9857-9 CrossRefGoogle Scholar
  20. 20.
    Mohammadi M, Rai P, Gupta S (2015) Performance measurement of mining equipment. Int J Emerg Technol Adv Eng 5:240–248Google Scholar
  21. 21.
    Rai P (2004) Performance assessment of draglines in opencast mines. Indian J Eng Mater Sci 11:493–498Google Scholar
  22. 22.
    Rai P, Yadav U, Kumar A (2011) Productivity analysis of draglines operating in horizontal and vertical tandem mode of operation in a coal mine-a case study. Geotech Geol Eng 29:493–504. doi: 10.1007/s10706-011-9398-9 CrossRefGoogle Scholar
  23. 23.
    Nichols ST, Barton TH, Gunthrope G (1981) Load model of a dragline. IEEE Trans Ind Appl 356–361Google Scholar
  24. 24.
    Lumley G (2004) How to increase dragline productivity without spending too much money. In: Sixth Annual AJM Open Cut Coal Mining ConferenceGoogle Scholar
  25. 25.
    Lumley G (2005) Reducing the variability in dragline operator performance. In: Coal Conference, pp 97–106Google Scholar
  26. 26.
    Mirabediny H, Baafi E V (1998) Statistical analysis of dragline monitoring data. In: Third Reg APCOM Symposium, pp 7–9Google Scholar
  27. 27.
    Matuszak RA (1982) How do you measure your dragline output?. First Int, SME-AIME Fall MeetGoogle Scholar
  28. 28.
    Torrance A, Baldwin G (1990) Blast performance assessment using a dragline monitor. In: Third International Symposium Rock Fragmentation by Blasting, pp 219–224Google Scholar
  29. 29.
    Hawkes PJ, Spathis T, Sengstock GW (1995) Monitoring equipment productivity improvements in coal mines. In: EXPLO conference, pp 4–7Google Scholar
  30. 30.
    Drives & Controls Services (2003) AccuWeigh production monitoring systemsGoogle Scholar
  31. 31.
    Mohammadi M, Rai P, Singh U, Singh SK (2016) Investigation of cycle time segments of dragline operation in surface coal mine: a statistical approach. Geotech Geol Eng. doi: 10.1007/s10706-016-9987-8 Google Scholar
  32. 32.
    Kusi-Sarpong S, Sarkis J, Wang X (2016) Assessing green supply chain practices in the Ghanaian mining industry: a framework and evaluation. Int J Prod Econ. doi: 10.1016/j.ijpe.2016.04.002 Google Scholar
  33. 33.
    Kizil M (2010) Improving dragline productivity using a diggability index as an indicator. SME Annu, MeetGoogle Scholar
  34. 34.
    Sandelowski M (1995) Focus on qualitative methods sample size in qualitative. 179–183Google Scholar
  35. 35.
    Norman S (2011) Variability reduction in dragline operator performance. In: SME Annual Meeting pp 1–2Google Scholar
  36. 36.
    Bernold L, Lloyd J, Vouk M (2003) Equipment operator training in the age of internet2. Nist special publication, pp 505–510Google Scholar
  37. 37.
    Dorey F, Knights PF (2015) Quantifying the benefits of simulator training for dragline operators. Min Technol 124:97–106. doi: 10.1179/1743286315Y.0000000007 CrossRefGoogle Scholar
  38. 38.
    Hettinger D, Lumley G (1999) Using data analysis to improve dragline productivity. Coal AgeGoogle Scholar
  39. 39.
    Morrison R, Scott A (2002) Maximising the value of “A Wealth of Information.” Value Track symposium, pp 7–8Google Scholar
  40. 40.
    Babaei Khorzoughi M, Hall R (2016) A study of digging productivity of an electric rope shovel for different operators. Minerals 6:48. doi: 10.3390/min6020048 CrossRefGoogle Scholar
  41. 41.
    Vukotic I, Kecojevic V (2014) Evaluation of rope shovel operators in surface coal mining using a Multi-attribute Decision-making model. Int J Min Sci Technol 24:259–268. doi: 10.1016/j.ijmst.2014.01.019 CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

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

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