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Training of load haul dump (LHD) machine operators: a case study at LKAB’s Kiirunavaara mine
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Mining and Geotechnical Engineering.ORCID iD: 0000-0002-6133-3357
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Mining and Geotechnical Engineering.ORCID iD: 0000-0002-5347-0853
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Mining and Geotechnical Engineering.ORCID iD: 0009-0009-0076-4661
2023 (English)In: Mining Technology, ISSN 2572-6668, Vol. 132, no 4, p. 237-252Article in journal (Refereed) Published
Abstract [en]

Mining is a high-risk industry, so efficiency and safety are key priorities. Technological advancements, such as digitisation, digitalisation, and automation have made mines safer. These developments have also highlighted the need for operators with updated skills and improved education programs. This study analysed the training of semi-autonomous and manual Load Haul Dump (LHD) operators’ at LKAB’s Kiirunavaara mine, focusing on operators’ training, perspective and integration of more recent tool such as simulator training. The survey questionnaire was sent to all 120 LHD operators. 86 answers were received, giving response rate of 70%. Results showed that operators generally were satisfied with how the training was structured, organised, and delivered. However, they wanted to add more topics, including practical loading, spending time with departments of other sub-processes, etc. In addition, 36% of the operators, including 20% of those operating semi-autonomous LHDs, and 80% of those operating manual LHDs, found simulator training difficult.

Place, publisher, year, edition, pages
Taylor & Francis, 2023. Vol. 132, no 4, p. 237-252
Keywords [en]
LHD, Mining education, Operator training, Simulators, Training, Training method, Underground, Underground mining equipment
National Category
Other Civil Engineering
Research subject
Mining and Rock Engineering
Identifiers
URN: urn:nbn:se:ltu:diva-98585DOI: 10.1080/25726668.2023.2217669ISI: 001000867300001Scopus ID: 2-s2.0-85161500912OAI: oai:DiVA.org:ltu-98585DiVA, id: diva2:1770517
Funder
EU, Horizon 2020, 101003591
Note

Validerad;2023;Nivå 2;2023-11-07 (sofila);

Funder: Luossavaara-Kiirunavaara AB, Sweden

Available from: 2023-06-19 Created: 2023-06-19 Last updated: 2025-04-09Bibliographically approved
In thesis
1. LHD operations in sublevel caving mines: a productivity perspective
Open this publication in new window or tab >>LHD operations in sublevel caving mines: a productivity perspective
2024 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

Mining is a high-risk industry, so efficiency and safety are key priorities. As mines continue to go deeper and exploit low-grade deposits, bulk mining methods, such as sublevel caving (SLC), have become increasingly important. SLC is suitable for massive steeply dipping ore bodies and is known for its high degree of mechanisation, productivity, and low operational cost. Moreover, technological developments and mechanisation have allowed these methods to be applied at greater depths. In modern mechanised mines Load haul dump (LHD) machines are central to achieving the desired productivity. Therefore, automation of LHDs and their increasing use in mines make it crucial to understand the performance of these machines in actual mining environments. The aim of this research was to understand the differences in the productivity of semiautonomous and manual LHDs and identify how external factors impact the performance of these machines in SLC operations. The research also investigated how LHD operator training could improve the loading efficiency.

Performance data for semi-autonomous and manual LHDs were collected from LKAB’s Kiirunavaara mine’s central database, GIRON. These data were used to compare cycle times and payloads of semi-autonomous and manual LHDs. The data were filtered and sorted so that only data where both machine types were operating in the same area (crosscut, ring, and ore pass) were used. To understand the impact of external factors, data on the occurrence of boulders were collected from LKAB’s Malmberget mine by recording videos of LHD buckets, while the data on operator training were obtained by performing baseline mapping and conducting a questionnaire study with the LHD operators at LKAB’s Kiirunavaara mine.

The results of the comparative analysis of manual and semi-autonomous LHDs showed the mean payload was 0.34 tonnes higher for manual LHD machines. However, the differences were not consistent across different areas of the mine. Similarly, when comparing the cycle times, in 57% of the studied area, manual LHDs had lower cycle time, while the opposite was true in the remaining 43% of the areas. Therefore, the differences in cycle time and payload due to mode of operation are not conclusive, meaning that one machine type does not completely outperform the other. This highlights the importance of understanding the external factors that cause such differences. Moreover, the findings emphasize the need to upgrade LHD operator training based on pedagogical principles and the inclusion of new technologies to enhance loading efficiency and increase overall productivity.

Place, publisher, year, edition, pages
Luleå: Luleå tekniska universitet, 2024
Series
Licentiate thesis / Luleå University of Technology, ISSN 1402-1757
Keywords
Underground Mining, Load Haul Dump machine (LHD), Automation, Training, Stochastic Simulation
National Category
Other Civil Engineering
Research subject
Mining and Rock Engineering
Identifiers
urn:nbn:se:ltu:diva-105420 (URN)978-91-8048-574-6 (ISBN)978-91-8048-575-3 (ISBN)
Presentation
2024-06-18, A109, Luleå University of Technology, Luleå, 10:00 (English)
Opponent
Supervisors
Available from: 2024-05-08 Created: 2024-05-08 Last updated: 2024-05-28Bibliographically approved

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Tariq, MuhammadGustafson, AnnaSchunnesson, Håkan

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