Tunnel and Underground Space (터널과지하공간)

Korean Society for Rock Mechanics and Rock Engineering (한국암반공학회)
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Performance Evaluation of Truck Haulage Operations in an Underground Mine using GMG's Time Usage Model and Key Performance Indicators

GMG 시간 사용 모델 및 핵심성과지표를 이용한 지하 광산 트럭 운반 작업 성능 평가

  • Park, Sebeom (Department of Energy Resources Engineering, Pukyong National University) ;
  • Choi, Yosoon (Department of Energy Resources Engineering, Pukyong National University)
  • 박세범 (부경대학교 에너지자원공학과) ;
  • 최요순 (부경대학교 에너지자원공학과)
  • Received : 2022.07.21
  • Accepted : 2022.08.04
  • Published : 2022.08.31
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Abstract

The performance of truck haulage operations in an underground mine was evaluated using the time usage model and key performance indicators (KPIs) proposed by Global Mining Guidelines Group (GMG). An underground mine that mainly produces iron and titanium iron was selected as a study area, and truck haulage data were collected using Bluetooth beacons and tablet PCs. The collected data were analyzed to identify unit operations, activities, events, and required time of truck haulage operations, and time categories were classified based on the time usage model. The performance of the haulage operations was evaluated using nine indicators in terms of availability, utilization, and effectiveness. As a result, in terms of availability, uptime was 33.9%, physical availability was 95.7%, and mechanical availability was 94.9%. In the case of utilization, use of availability was 83.1%, asset utilization was 28.1%, and operating and effective utilization were 79.6% and 77.7%, respectively. Also, in terms of efficiency, operating efficiency was high at 97.6%, and production effectiveness was found to be 49%.

본 연구에서는 GMG (Global Mining Guidelines Group)에서 제안한 시간 사용 모델과 핵심성과지표(KPIs)들을 이용하여 트럭 운반작업에 대한 성과를 평가한 결과를 제시하였다. 이를 위해 철과 티탄 철을 주로 생산하는 국내 지하광산을 연구지역으로 선정한 다음 블루투스 비콘과 태블릿 PC를 이용하여 트럭 운반작업 데이터를 수집하였다. 수집된 데이터를 분석하여 트럭 운반작업의 단위작업, 활동 및 이벤트, 소요시간을 식별하였으며, 시간 사용 모델을 기반으로 시간 범주를 분류하였다. 트럭 운반작업의 성과는 가용률, 이용률, 효율성 측면의 9개 성과지표를 이용하여 평가하였다. 그 결과, 가용률 측면에서는 가동시간이 33.9%, 물리적 가용률은 95.7%, 기계적 가용률은 94.9%로 나타났다. 이용률의 경우, 가용장비 이용률은 83.1%, 자산 이용률은 28.1%, 운영 및 유효 이용률은 각각 79.6%, 77.7%로 나타났다. 또한, 효율성 측면에서는 운영 효율성이 97.6%로 높게 나타났으며, 생산 효율성은 49%로 나타났다.

Keywords

Acknowledgement

본 연구는 2022년도 정부(산업통상자원부)의 재원으로 한국에너지기술평가원 신산업 맞춤형 핵심광물개발활용기술개발사업의 지원을 받아 수행되었다(과제명: 음극재용 흑연광 스마트 탐사/개발 및 원료화 기술개발, 과제번호: 20227A10100040).

References

  1. Arputharaj, M.M., 2015, Studies on availability and utilisation of mining equipment-an overview. International Journal of Advanced Research in Engineering and Technology, 6(3), 14-21.
  2. Baek, J., Choi, Y., Lee, C., Suh, J., and Lee, S., 2017, BBUNS: Bluetooth beacon-based underground navigation system to support mine haulage operations. Minerals, 7(12), 228-243. https://doi.org/10.3390/min7110228
  3. Baek, J., Suh, J., and Choi, Y., 2018, Analysis of reviewed signal strength index from bluetooth beacons to develop proximity warning systems for underground mines. Journal of the Korean Society of Mineral and Energy Resources Engineers, 55(6), 604-613. https://doi.org/10.32390/ksmer.2018.55.6.604
  4. Bellanca, J.L., Orr, T.J., Helfrich, W.J., Macdonald, B., Navoyski, J., and Demich, B., 2019, Developing a Virtual Reality Environment for Mining Research. Mining, Metallurgy & Exploration, 36(4), 597-606. https://doi.org/10.1007/s42461-018-0046-2
  5. Choi, Y. and Yi, H., 2020, Trend of mineral resource development technology using artificial intelligence. IITP Weekly ICT Trends, 1935, 13-24.
  6. Choi, Y., 2018, Analysis of patent trend for ICT-based underground mine safety management technology. Journal of the Korean Society of Mineral and Energy Resources Engineers, 55(2), 159-164. https://doi.org/10.12972/ksmer.2018.55.2.159
  7. Gackowiec, P., Podobinska-Staniec, M., Brzychczy, E., Kuhlbach, C., and Ozver, T., 2020, Review of key performance indicators for process monitoring in the mining industry. Energies, 13(19), 5169.
  8. Global Mining Guidelines Group, 2020, A Standardized Time Classification Framework for Mobile Equipment in Surface Mining: Operational Definitions, Time Usage Model, and Key Performance Indicators, Retrieved April 5, 2022, from https://gmggroup.org/guidelines-and-publications/.
  9. Hague, K.P., 2021, Strategic Implementation of Digital Technologies to Optimize Operations in an Underground Mine, Doctoral Dissertation, The University of Arizona.
  10. Jung, J. and Choi, Y., 2017, Measuring transport time of mine equipment in an underground mine using a bluetooth beacon system. Minerals, 7(1), 1.
  11. Jung, J., Baek, J., and Choi, Y., 2016, Analysis of Features and Applications of Bluetooth Beacon Technology for Utilization in the Mining and Construction Industries. Tunnel and Underground Space, 26(3), 143-153. https://doi.org/10.7474/TUS.2016.26.3.143
  12. Kennedy, B., 1990, Surface Mining, Littleton, Colorado: Society for Mining, Metallurgy, and Exploration. (2nd Edition).
  13. Kim, H.T., 2017, The future values of natural resources. EMRD, 14, 20-23.
  14. Kim, S.S., Son J.Y., and Park, J.H., 2013, IT convergence technology trends based on big data analytics in the digital oil field. ETRI, 28(4), 1-9.
  15. Kim, Y., Baek, J., and Choi, Y., 2021, Smart Helmet-Based Personnel Proximity Warning System for Improving Underground Mine Safety. Applied Sciences, 11(10), 4342.
  16. Lim, J.T., Park, H.W., and Lim, J.S., 2014, Classification and application of digtal oil field system. Journal of the Korean Society of Mineral and Energy Resources Engineers, 51(5), 750-756. https://doi.org/10.12972/ksmer.2014.51.5.750
  17. Park, S. and Choi, Y., 2021a, Bluetooth Beacon-Based Mine Production Management Application to Support Ore Haulage Operations in Underground Mines. Sustainability, 13(4), 2281.
  18. Park, S. and Choi, Y., 2021b, Analysis and Diagnosis of Truck Transport Routes in Underground Mines Using Transport Time Data Collected through Bluetooth Beacons and Tablet Computers. Applied Sciences, 11(10), 4525.
  19. Park, S. and Choi, Y., 2022a, Development of Applications for Recording Ore Production Data and Writing Daily Work Report of Dump Truck in Mining Sites. Tunnel and Underground Space, 32(2), 93-106. https://doi.org/10.7474/TUS.2022.32.2.093
  20. Park, S. and Choi, Y., 2022b, Current State of Digital Information Gap in the Korean Mining Industry and its Mitigation Strategy: A Case of Daily Work Report App Development. Journal of the Korean Society of Mineral and Energy Resources Engineers, 59(2), 173-181. https://doi.org/10.32390/ksmer.2022.59.2.173
  21. Park, S. and Choi, Y., 2022c, Analysis of International Standardization Trends of Smart Mining Technology: Focusing on GMG Guidelines. Tunnel and Underground Space, 32(3), 173-193. https://doi.org/10.7474/TUS.2022.32.3.173
  22. Pfleider, E., 1968, Surface Mining (1st Edition), New York: American Institute of Mining, Metallurgical, and Petroleum Engineers.
  23. Ralston, J., Reid, D., Hargrave, C., and Hainsworth, D., 2014, Sensing for advancing mining automation capability: A review of underground automation technology development. International Journal of Mining Science and Technology, 24(3), 305-310. https://doi.org/10.1016/j.ijmst.2014.03.003
  24. Sganzerla, C., Seixas, C., and Conti, A., 2016, Disruptive innovation in digital mining. Procedia Engineering, 138, 64-71. https://doi.org/10.1016/j.proeng.2016.02.057
  25. Singh, A., Kumar, D., and Hotzel, J., 2018, IoT Based information and communication system for enhancing underground mines safety and productivity: Genesis, taxonomy and open issues. Ad Hoc Networks, 78, 115-129. https://doi.org/10.1016/j.adhoc.2018.06.008
  26. Yoon, D.W., Kim, S.M., Kim, J.S., Park, K.S., Park, H.W., Byun, J.M., Suh, J.W., Lee, C.W., Jang, I.S., Cho, S.J., and Choi, Y., 2018, Introduction to machine learning for natural resources development. CIR, Korea.
  27. Zhou, C., Damiano, N., Whisner, B., and Reyes, M., 2017, Industrial Internet of Things:(IIoT) applications in underground coal mines. Mining Engineering, 69(12), 50.