Key Performance Indicators for Electricity Conservation in Open Pit Mining

Qinglin Yu; Long Wen; Craig Haight; Alex Russell-Jones

doi:doi:10.11648/j.sjams.20160402.20

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Key Performance Indicators for Electricity Conservation in Open Pit Mining

Qinglin Yu, Long Wen, Craig Haight, Alex Russell-Jones

Published in Science Journal of Applied Mathematics and Statistics (Volume 4, Issue 2)

Received: 12 April 2016 Published: 13 April 2016

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Abstract

In mining operation, blasts are used to fracture the in-situ rock mass and prepare it for excavation, crushing and grinding. The High-energy blasting, which uses increased amount of explosive material per tonne of rock, is considered to be one of most effective ways to reduce the consumption of energy in the milling process, resulting production saving as well as reduction in dust (PM5) and tailing. In this article, the main focus is to investigate the electrical intensity of the five grinding lines in the mill, as they accounted for the majority of site electricity consumption, in relations to other operational procedures, in particular the high-energy blasting. Several regression models were established, the data points were fitted within 10% of the actual values, and the majority within 5%. The models provide management better ways to predict and target electrical consumption and environmental impact.

Published in	Science Journal of Applied Mathematics and Statistics (Volume 4, Issue 2)
DOI	10.11648/j.sjams.20160402.20
Page(s)	81-87
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2016. Published by Science Publishing Group

Keywords

Key Performance Indicator (KPI), Energy Conservation, Electricity Intensity, Open-Pit Mining, High-Energy Blasting, Powder Factor

References

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[2]	W. J. Braun, and D. J. Murdoch, A First Course in Statistical Programming with R, Cambridge University Press, 2007.
[3]	B. Burger, K. McCaffery, I. McGaffin, A. Jankovic, W. Valery and D. La Rosa, Batu Hijau model for throughput forecast, mining and milling optimisation, and expansion studies, Advances in Comminution, edited by S. Komar Kawatra, SME publication, March 2006, 461–479.
[4]	A. Ebrahimi, The importance of dilution factor for open pit mining projects, SRK Consulting, 2012.
[5]	Energy Efficiency Opportunities, Analyses of diesel use for mine haul and transport operations, Department of Resources, Energy and Tourism, Australia, 2010.
[6]	Energy Efficiency Opportunities, The downer energy and emissions measure, Department of Resources, Energy and Tourism, Australia, 2011.
[7]	C. J. Geyer, Model Selection in R (unpublished manuscript), 2003.
[8]	F. Joseph, Toward a sustainable cement industry substudy 5: Key Performance Indicators, World Business Council for Sustainable Development, 2002.
[9]	S. R. Khandker, G. B. Koolwal and H. A. Samad, Using key performance indicators to manage energy costs, World Bank Publications, 2010.
[10]	T. Rorke, Blasting process improved, African Mining, 2012, 47–50.
[11]	W. Valery, A. Jankovic, and B. Sonmez, New methodology to improve productivity of mining operations, Balkan Congress, Turkey, 2011.
[12]	J. C. Van Gorp, Using key performance indicators to manage energy costs, Proceedings of the Twenty-Seventh Industrial Energy Technology Conference, New Orleans, LA, May 10-13, 2005, 9–25.

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APA Style

Qinglin Yu, Long Wen, Craig Haight, Alex Russell-Jones. (2016). Key Performance Indicators for Electricity Conservation in Open Pit Mining. Science Journal of Applied Mathematics and Statistics, 4(2), 81-87. https://doi.org/10.11648/j.sjams.20160402.20

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ACS Style

Qinglin Yu; Long Wen; Craig Haight; Alex Russell-Jones. Key Performance Indicators for Electricity Conservation in Open Pit Mining. Sci. J. Appl. Math. Stat. 2016, 4(2), 81-87. doi: 10.11648/j.sjams.20160402.20

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AMA Style

Qinglin Yu, Long Wen, Craig Haight, Alex Russell-Jones. Key Performance Indicators for Electricity Conservation in Open Pit Mining. Sci J Appl Math Stat. 2016;4(2):81-87. doi: 10.11648/j.sjams.20160402.20

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@article{10.11648/j.sjams.20160402.20,
  author = {Qinglin Yu and Long Wen and Craig Haight and Alex Russell-Jones},
  title = {Key Performance Indicators for Electricity Conservation in Open Pit Mining},
  journal = {Science Journal of Applied Mathematics and Statistics},
  volume = {4},
  number = {2},
  pages = {81-87},
  doi = {10.11648/j.sjams.20160402.20},
  url = {https://doi.org/10.11648/j.sjams.20160402.20},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.sjams.20160402.20},
  abstract = {In mining operation, blasts are used to fracture the in-situ rock mass and prepare it for excavation, crushing and grinding. The High-energy blasting, which uses increased amount of explosive material per tonne of rock, is considered to be one of most effective ways to reduce the consumption of energy in the milling process, resulting production saving as well as reduction in dust (PM5) and tailing. In this article, the main focus is to investigate the electrical intensity of the five grinding lines in the mill, as they accounted for the majority of site electricity consumption, in relations to other operational procedures, in particular the high-energy blasting. Several regression models were established, the data points were fitted within 10% of the actual values, and the majority within 5%. The models provide management better ways to predict and target electrical consumption and environmental impact.},
 year = {2016}
}

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TY  - JOUR
T1  - Key Performance Indicators for Electricity Conservation in Open Pit Mining
AU  - Qinglin Yu
AU  - Long Wen
AU  - Craig Haight
AU  - Alex Russell-Jones
Y1  - 2016/04/13
PY  - 2016
N1  - https://doi.org/10.11648/j.sjams.20160402.20
DO  - 10.11648/j.sjams.20160402.20
T2  - Science Journal of Applied Mathematics and Statistics
JF  - Science Journal of Applied Mathematics and Statistics
JO  - Science Journal of Applied Mathematics and Statistics
SP  - 81
EP  - 87
PB  - Science Publishing Group
SN  - 2376-9513
UR  - https://doi.org/10.11648/j.sjams.20160402.20
AB  - In mining operation, blasts are used to fracture the in-situ rock mass and prepare it for excavation, crushing and grinding. The High-energy blasting, which uses increased amount of explosive material per tonne of rock, is considered to be one of most effective ways to reduce the consumption of energy in the milling process, resulting production saving as well as reduction in dust (PM5) and tailing. In this article, the main focus is to investigate the electrical intensity of the five grinding lines in the mill, as they accounted for the majority of site electricity consumption, in relations to other operational procedures, in particular the high-energy blasting. Several regression models were established, the data points were fitted within 10% of the actual values, and the majority within 5%. The models provide management better ways to predict and target electrical consumption and environmental impact.
VL  - 4
IS  - 2
ER  -

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Author Information

Qinglin Yu

Centre for Optimization and Decision Science (CODS), Thompson Rivers University, Kamloops, BC, Canada
Long Wen

Centre for Optimization and Decision Science (CODS), Thompson Rivers University, Kamloops, BC, Canada
Craig Haight

Energy Management Group, Teck Highland Valley Copper Partnership, Logan Lake, BC, Canada
Alex Russell-Jones

Energy Management Group, Teck Highland Valley Copper Partnership, Logan Lake, BC, Canada

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Plain Text BibTeX RIS

APA Style

Qinglin Yu, Long Wen, Craig Haight, Alex Russell-Jones. (2016). Key Performance Indicators for Electricity Conservation in Open Pit Mining. Science Journal of Applied Mathematics and Statistics, 4(2), 81-87. https://doi.org/10.11648/j.sjams.20160402.20

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ACS Style

Qinglin Yu; Long Wen; Craig Haight; Alex Russell-Jones. Key Performance Indicators for Electricity Conservation in Open Pit Mining. Sci. J. Appl. Math. Stat. 2016, 4(2), 81-87. doi: 10.11648/j.sjams.20160402.20

Copy | Download

AMA Style

Qinglin Yu, Long Wen, Craig Haight, Alex Russell-Jones. Key Performance Indicators for Electricity Conservation in Open Pit Mining. Sci J Appl Math Stat. 2016;4(2):81-87. doi: 10.11648/j.sjams.20160402.20

Copy | Download

@article{10.11648/j.sjams.20160402.20,
  author = {Qinglin Yu and Long Wen and Craig Haight and Alex Russell-Jones},
  title = {Key Performance Indicators for Electricity Conservation in Open Pit Mining},
  journal = {Science Journal of Applied Mathematics and Statistics},
  volume = {4},
  number = {2},
  pages = {81-87},
  doi = {10.11648/j.sjams.20160402.20},
  url = {https://doi.org/10.11648/j.sjams.20160402.20},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.sjams.20160402.20},
  abstract = {In mining operation, blasts are used to fracture the in-situ rock mass and prepare it for excavation, crushing and grinding. The High-energy blasting, which uses increased amount of explosive material per tonne of rock, is considered to be one of most effective ways to reduce the consumption of energy in the milling process, resulting production saving as well as reduction in dust (PM5) and tailing. In this article, the main focus is to investigate the electrical intensity of the five grinding lines in the mill, as they accounted for the majority of site electricity consumption, in relations to other operational procedures, in particular the high-energy blasting. Several regression models were established, the data points were fitted within 10% of the actual values, and the majority within 5%. The models provide management better ways to predict and target electrical consumption and environmental impact.},
 year = {2016}
}

Copy | Download

TY  - JOUR
T1  - Key Performance Indicators for Electricity Conservation in Open Pit Mining
AU  - Qinglin Yu
AU  - Long Wen
AU  - Craig Haight
AU  - Alex Russell-Jones
Y1  - 2016/04/13
PY  - 2016
N1  - https://doi.org/10.11648/j.sjams.20160402.20
DO  - 10.11648/j.sjams.20160402.20
T2  - Science Journal of Applied Mathematics and Statistics
JF  - Science Journal of Applied Mathematics and Statistics
JO  - Science Journal of Applied Mathematics and Statistics
SP  - 81
EP  - 87
PB  - Science Publishing Group
SN  - 2376-9513
UR  - https://doi.org/10.11648/j.sjams.20160402.20
AB  - In mining operation, blasts are used to fracture the in-situ rock mass and prepare it for excavation, crushing and grinding. The High-energy blasting, which uses increased amount of explosive material per tonne of rock, is considered to be one of most effective ways to reduce the consumption of energy in the milling process, resulting production saving as well as reduction in dust (PM5) and tailing. In this article, the main focus is to investigate the electrical intensity of the five grinding lines in the mill, as they accounted for the majority of site electricity consumption, in relations to other operational procedures, in particular the high-energy blasting. Several regression models were established, the data points were fitted within 10% of the actual values, and the majority within 5%. The models provide management better ways to predict and target electrical consumption and environmental impact.
VL  - 4
IS  - 2
ER  -

Copy | Download