Energy Efficiency in Cable Shovel Operations

  • Kwame Awuah-OffeiEmail author
Part of the Green Energy and Technology book series (GREEN)


This chapter seeks to establish the current knowledge on energy efficiency of cable shovel operations. Additionally, the chapter uses a review of the literature to make recommendations for industrial best practices and for future research to address identified gaps in the literature. The chapter first presents the fundamentals of cable shovel operations and the factors that affect the energy efficiency of shovel operations. Subsequently, the chapter presents an overview of the latest research on cable shovel energy efficiency, which is used as the basis for the recommendations. The chapter recommends that industry practitioners should use the right drive systems for their cable shovels, use data analytics to understand shovel energy efficiency, and carefully evaluate the costs and benefits of energy efficiency initiatives. The chapter also recommends that future research on shovel energy efficiency should: (i) establish theoretical benchmarks for cable shovel operations; (ii) account for human factors in the design of operator guidance systems to assist operators during shovel operations; and (iii) evaluate how effective operator training programs are in improving shovel energy efficiency.


Cable shovel Energy efficiency Data analytics Human factors 



The author is grateful to Dr. Nuray Demirel who reviewed the original manuscript and made valuable suggestions that improved this manuscript tremendously.


  1. 1.
    Houley L, Alahakoon S (2012) Impact assessment of AC and DC electric rope shovels on coal mine power distribution system. In: University power engineering conference (AUPEC), 2012 22nd Australas, pp 1–6Google Scholar
  2. 2.
    Hustrulid W, Kuchta M, Martin R (2013) Open pit mine planning and design, 3rd edn. CRC Press, New YorkGoogle Scholar
  3. 3.
    Patnayak S, Tannant DD (2005) Performance monitoring of electric cable shovels. Int J Surf Min Reclam Environ 19:276–294. doi: 10.1080/13895260500327912 CrossRefGoogle Scholar
  4. 4.
    Patnayak S, Tannant DD, Parsons I (2008) Operator and dipper tooth influence on electric shovel performance during oil sands mining. Int J Min Reclam Environ 22:120–145. doi: 10.1080/17480930701482961 CrossRefGoogle Scholar
  5. 5.
    Babaei Khorzoughi M, Hall R (2016) A study of digging productivity of an electric rope shovel for different operators. Minerals 6:48CrossRefGoogle Scholar
  6. 6.
    Oskouei MA, 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.
    Hendricks C, Scoble MJ, Peck J (1989) Performance monitoring of electric mining shovels. Inst Min Metall Trans Sect A Min Ind 98:A151–A159Google Scholar
  8. 8.
    Acaroglu O, Ozdemir L, Asbury B (2008) A fuzzy logic model to predict specific energy requirement for TBM performance prediction. Tunn Undergr Sp Technol 23:600–608. doi: 10.1016/j.tust.2007.11.003 CrossRefGoogle Scholar
  9. 9.
    Iai M, Gertsch L (2013) Excavation of lunar regolith with large grains by rippers for improved excavation efficiency. J Aerosp Eng 26:97–104. doi: 10.1061/(ASCE)AS.1943-5525.0000221 CrossRefGoogle Scholar
  10. 10.
    Muro T, Tsuchiya K, Kohno K (2002) Experimental considerations for steady state edge excavation under a constant cutting depth for a mortar specimen using a disk cutter bit. J Terramech 39:143–159. doi: 10.1016/S0022-4898(02)00021-6 CrossRefGoogle Scholar
  11. 11.
    Scoble MJ, Muftuoglu YV (1984) Derivation of a diggability index for surface mine equipment selection. Min Sci Technol 1:305–322. doi: 10.1016/S0167-9031(84)90349-9 CrossRefGoogle Scholar
  12. 12.
    Hadjigeorgiou J, Poulin R (1998) Assessment of ease of excavation of surface mines. J Terramech 35:137–153. doi: 10.1016/S0022-4898(98)00018-4 CrossRefGoogle Scholar
  13. 13.
    Awuah-Offei K, Frimpong S (2007) Cable shovel digging optimization for energy efficiency. Mech Mach Theory 42:995–1006. doi: 10.1016/j.mechmachtheory.2006.07.008 CrossRefzbMATHGoogle Scholar
  14. 14.
    Frimpong S, Hu Y, Awuah-Offei K (2005) Mechanics of cable shovel-formation interactions in surface mining excavations. J Terramech 42:15–33. doi: 10.1016/j.jterra.2004.06.002 CrossRefGoogle Scholar
  15. 15.
    Guzmán MV, Valenzuela MA, Member S (2015) Integrated mechanical–electrical modeling of an ac electric mining shovel and evaluation of power requirements during a truck loading. Cycle 51:2590–2599Google Scholar
  16. 16.
    Awuah-Offei K (2016) Energy efficiency in mining: a review with emphasis on the role of operators in loading and hauling operations. J Clean Prod 117:89–97. doi: 10.1016/j.jclepro.2016.01.035 CrossRefGoogle Scholar
  17. 17.
    Awuah-Offei K, Frimpong S (2011) Efficient cable shovel excavation in surface mines. Geotech Geol Eng 29:19–26. doi:
  18. 18.
    Brown GM, Elbacher BJ, Koellner WG (2000) Increased productivity with AC drives for mining excavators and haul trucks. In: Conference record of the 2000 IEEE industry applications conference. Thirty-fifth IAS annual meeting and world conference on industrial applications of electrical energy, vol 1, pp P28–P37. doi: 10.1109/IAS.2000.880962
  19. 19.
    Awuah-Offei K, Osei B, Askari-Nasab H (2011) Modeling truck/shovel energy efficiency under uncertainty. Trans Soc Min Metall Explor 330:573–584Google Scholar
  20. 20.
    Abdi Oskouei M, Awuah-Offei K (2015) A method for data-driven evaluation of operator impact on energy efficiency of digging machines. Energy Effic 1–12. doi: 10.1007/s12053-015-9353-3
  21. 21.
    Karpuz C, Ceylanoğlu A, Paşamehmetoğlu AG (1992) An investigation on the influence of depth of cut and blasting on shovel digging performance. Int J Surf Min Reclam Environ 6:161–167. doi: 10.1080/09208119208944331 CrossRefGoogle Scholar
  22. 22.
    Singh SP, Narendrula R (2006) Factors affecting the productivity of loaders in surface mines. Int J Surf Min Reclam Environ 20:20–32. doi: 10.1080/13895260500261574 CrossRefGoogle Scholar
  23. 23.
    Rasimarzabadi R, Joseph TG (2016) Particle flow mechanism into cable shovel dippers. J Terramech 64:10–22. doi: 10.1016/j.jterra.2015.12.003 CrossRefGoogle Scholar
  24. 24.
    Abdel-Baqi OJ, Onsager MG, Miller PJ (2016) The effect of available short-circuit capacity and trail cable length on substation voltage amplification in surface excavation industry. IEEE Trans Ind Appl 52:3518–3526. doi: 10.1109/TIA.2016.2535162 CrossRefGoogle Scholar
  25. 25.
    Jahromi MG, Mirzaeva G, Mitchell SD, Gay D (2015) Advanced fault tolerance strategy for DC microgrids in mining excavators. IEEE Int Symp Ind Electron 1502–1507. doi: 10.1109/ISIE.2015.7281696
  26. 26.
    Wei B, Gao F (2012) Digging trajectory optimization for a new excavating mechanism. In: Proceedings of ASME 2012 international design engineering technical conferences and computers and information in engineering conference IDETC/CIE 2012. Chicago, IL, USA, pp 1033–1039Google Scholar
  27. 27.
    Raza MA, Frimpong S (2017) Mechanics of electric rope shovel performance and reliability in formation excavation. In: Canbolat H (ed) Lagrangian mechanics, pp 107–133Google Scholar
  28. 28.
    Valenzuela M, Valenzuela A (2016) Payload estimation in AC electric mining shovels using drive signals. In: IEEE transactions on industry application, pp 4470–4479Google Scholar
  29. 29.
    Rasuli A, Tafazoli S, Dunford WG (2014) Dynamic modeling, parameter identification, and payload estimation of mining cable shovels. In: 2014 IEEE Industry Applications Society annual meeting, IAS, Vancouver, BC, Canada, part no. 6978451Google Scholar
  30. 30.
    Onal E, Craddock C, Endsley MR, Chapman A (2013) From theory to practice: how designing for situation awareness can transform confusing, overloaded shovel operator interfaces, reduce costs, and increase safety. In: ISARC 2013–30th International symposium on automation and robotics for the construction, mining, held conjunction with 23rd world mining congress, 30066Google Scholar
  31. 31.
    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
  32. 32.
    Blackwell GH (2013) Remote and semi-automated operation of an electric cable shovel. In: ISARC 2013–30th international symposium on automation and robotics in construction, pp 1526–1541Google Scholar
  33. 33.
    Cloete S, Horberry T (2013) Collision avoidance and semi-automation in electric rope shovel operation. In: 49th Annual human factors ergonomics society Australia conference 2013, HFESA 2013. Perth, WA, Australia, Article number 026Google Scholar
  34. 34.
    Hendricks C, Daneshmend L, Wu S, Scoble M (1993) Design of a simulator for productivity analysis of electric mining shovels. In: Proceedings 2nd international symposium on mine mechanics and automation, pp 329–336Google Scholar
  35. 35.
    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

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Mining & Nuclear EngineeringMissouri University of Science & TechnologyRollaUSA

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