Skip to main content
SpringerLink
Log in
Book cover

Energy Efficiency in the Minerals Industry pp 181–194Cite as

Role of the Operator in Dragline Energy Efficiency

  • Chapter
  • First Online:

Part of the book series: Green Energy and Technology ((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.

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

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. U.S. Energy Information Administration (2016) International Energy Outlook 2016-Electricity. In: International Energy Outlook. pp 81–100

    Google Scholar 

  2. U.S. Department of Energy (DOE) (2007) Mining Industry Energy Bandwidth Study

    Google Scholar 

  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 1–5

    Google Scholar 

  4. Williams G (2005) Achievements through the dragline improvement group (DIG) in Anglo coal. Inst Quarr South Africa

    Google Scholar 

  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. 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

    Article  Google Scholar 

  7. Komljenovic D, Bogunovic D, Kecojevic V (2010) Dragline operator performance indicator. Int J Mining Reclam Environ 24:34–42. doi:10.1080/17480930902778191

    Article  Google Scholar 

  8. Bogunovic D, Kecojevic V, Lund V et al (2009) Analysis and control of energy consumption in surface coal mining. SME Annual Meeting 1–7

    Google Scholar 

  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

    Article  Google Scholar 

  10. Lumley G (2014) Mining for efficiency

    Google Scholar 

  11. Bogunovic D, Kecojevic V (2011) Impact of fill factor on dragline production rate and energy consumption. Min Eng

    Google Scholar 

  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 7:77–81

    Google Scholar 

  13. Bogunovic D (2008) Integrated data environment for analysis and control of energy consumption (Ide-Ace) in surface coal mining. The Pennsylvania State University

    Google Scholar 

  14. Vynne JF (2008) Innovative dragline monitoring systems and technologies. In CIM Conference

    Google Scholar 

  15. Rowlands C, Just GD (1992) Performance characteristics of dragline buckets. In: Third Large Open Pit Mineral Conference, pp 89–92

    Google Scholar 

  16. Pippenger JG (1995) Competing with the big boys: productivity and innovation at the Freedom lignite mineral, pp 3–6

    Google Scholar 

  17. Isokangas E (1997) Measuring dragline performance improvement initiatives. Int Congr Autom Technol

    Google Scholar 

  18. Erdem B, Düzgün HŞB (2005) Dragline cycle time analysis. J Sci Ind Res 64:19–29

    Google Scholar 

  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

    Article  Google Scholar 

  20. Mohammadi M, Rai P, Gupta S (2015) Performance measurement of mining equipment. Int J Emerg Technol Adv Eng 5:240–248

    Google Scholar 

  21. Rai P (2004) Performance assessment of draglines in opencast mines. Indian J Eng Mater Sci 11:493–498

    Google Scholar 

  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

    Article  Google Scholar 

  23. Nichols ST, Barton TH, Gunthrope G (1981) Load model of a dragline. IEEE Trans Ind Appl 356–361

    Google Scholar 

  24. Lumley G (2004) How to increase dragline productivity without spending too much money. In: Sixth Annual AJM Open Cut Coal Mining Conference

    Google Scholar 

  25. Lumley G (2005) Reducing the variability in dragline operator performance. In: Coal Conference, pp 97–106

    Google Scholar 

  26. Mirabediny H, Baafi E V (1998) Statistical analysis of dragline monitoring data. In: Third Reg APCOM Symposium, pp 7–9

    Google Scholar 

  27. Matuszak RA (1982) How do you measure your dragline output?. First Int, SME-AIME Fall Meet

    Google Scholar 

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

    Google Scholar 

  29. Hawkes PJ, Spathis T, Sengstock GW (1995) Monitoring equipment productivity improvements in coal mines. In: EXPLO conference, pp 4–7

    Google Scholar 

  30. Drives & Controls Services (2003) AccuWeigh production monitoring systems

    Google Scholar 

  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. 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. Kizil M (2010) Improving dragline productivity using a diggability index as an indicator. SME Annu, Meet

    Google Scholar 

  34. Sandelowski M (1995) Focus on qualitative methods sample size in qualitative. 179–183

    Google Scholar 

  35. Norman S (2011) Variability reduction in dragline operator performance. In: SME Annual Meeting pp 1–2

    Google Scholar 

  36. Bernold L, Lloyd J, Vouk M (2003) Equipment operator training in the age of internet2. Nist special publication, pp 505–510

    Google Scholar 

  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

    Article  Google Scholar 

  38. Hettinger D, Lumley G (1999) Using data analysis to improve dragline productivity. Coal Age

    Google Scholar 

  39. Morrison R, Scott A (2002) Maximising the value of “A Wealth of Information.” Value Track symposium, pp 7–8

    Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. University of Iowa, 417 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 .

Editor information

Editors and Affiliations

  1. Department of Mining and Nuclear Engineering, Missouri University Science and Technology, Rolla, Missouri, USA

    Kwame Awuah-Offei

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Abdi-Oskouei, M., Awuah-Offei, K. (2018). Role of the Operator in Dragline Energy Efficiency. In: Awuah-Offei, K. (eds) Energy Efficiency in the Minerals Industry. Green Energy and Technology. Springer, Cham. https://doi.org/10.1007/978-3-319-54199-0_10

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-54199-0_10

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-54198-3

  • Online ISBN: 978-3-319-54199-0

  • eBook Packages: EnergyEnergy (R0)

Publish with us

Policies and ethics

Search

Navigation