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Draw control strategies in sublevel caving mines: A baseline mapping of LKAB's Malmberget and Kiirunavaara mines
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Mining and Geotechnical Engineering.ORCID iD: 0000-0001-8264-1255
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Mining and Geotechnical Engineering.ORCID iD: 0000-0002-5347-0853
Luossave-Kiirunavaara AB, Kiruna, Sweden.
Luossave-Kiirunavaara AB, Kiruna, Sweden.
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2018 (English)In: The Southern African Journal of Mining and Metallurgy, ISSN 2225-6253, E-ISSN 1543-9518, Vol. 118, no 7, p. 723-733Article in journal (Refereed) Published
Abstract [en]

The Malmberget and Kiirunavaara mines are the two largest underground iron ore operations in the world. Luossavaara-Kiirunavaara AB (LKAB) uses sublevel caving (SLC) to operate the mines while maintaining a high level of productivity and safety. The paper enumerates the loading criteria and loading constraints at the mines and outlines details of mine design, layout, and geology affecting the draw control. A study of the various draw control strategies used in sublevel caving operations globally has also been done to establish the present state-of-the-art. An analysis of the draw control and loading operations at the Malmberget and Kiirunavaara mines is summarized using information collected through interviews, internal documents, meetings, and manuals. An optimized draw control strategy is vital for improving ore recovery and reducing dilution in SLC. Based on the literature review and baseline mapping study, a set of guidelines for designing a new draw control strategy is presented. The draw control strategy at Malmberget and Kiirunavaara is guided by a bucket-weightbased drawpoint monitoring system that is part of the overall framework. Both mines employ a draw control strategy that considers the production requirements and mining constraints while regulating the loading process through an empirical method based on bucket weights and grades. However, in the present scenario of fluctuating metal prices and increasing operational costs a new draw control strategy is needed which is probabilistic in nature and can handle the uncertainties associated with caving operations.

Place, publisher, year, edition, pages
The Southern African Institute of Mining and Metallurgy , 2018. Vol. 118, no 7, p. 723-733
Keywords [en]
Sublevel caving, draw control, optimization, draw point monitoring
National Category
Engineering and Technology Other Civil Engineering
Research subject
Mining and Rock Engineering
Identifiers
URN: urn:nbn:se:ltu:diva-71770DOI: 10.17159/2411-9717/2018/v118n7a6ISI: 000442393900006Scopus ID: 2-s2.0-85053623244OAI: oai:DiVA.org:ltu-71770DiVA, id: diva2:1266200
Projects
SIP-STRIM
Funder
VINNOVA, 1832144
Note

Validerad;2018;Nivå 2;2018-12-07 (johcin)

Available from: 2018-11-27 Created: 2018-11-27 Last updated: 2020-02-04Bibliographically approved
In thesis
1. Draw control strategy for sublevel caving mines: A holistic approach
Open this publication in new window or tab >>Draw control strategy for sublevel caving mines: A holistic approach
2020 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Sublevel caving is an underground mass mining method used for extracting different types of ores from the earth crust. Mines using sublevel caving (SLC) as the primary mining method are generally highly mechanized with standardized and independent unit operations. Different unit operations (drilling, blasting, loading and transportation) are performed in isolation with each other which leads to standardized procedures and safe operation. Loading of the material from the production face in sublevel caving is facilitated by the flow of material under gravity into the production face. A large amount of material is loaded from a limited opening termed as the draw point which creates challenges for the mining method.

Material flow in SLC has been studied extensively in the past five decades and different methods have been used to simulate material flow in caving operations. Physical models of different scales has been designed for simulating material flow by using sand, gravel or rocks and studying the movement of material inside the model. Initial physical models showed an ellipsoidal zone above the draw point from which material flowed into the draw point. However, subsequent physical modelling results disagreed with this notion of material flow. Numerical modelling techniques have also been applied to simulate material flow. Currently, marker trials are being used to understand material flow in SLC. Markers (numbered steel rods, RFID enabled markers) are installed in boreholes drilled inside the burden of a production ring and based on the recovery sequence of markers, material flow is predicted. Results from physical models, numerical models and marker trials along with mine experience have been used in the past to optimize mine design and draw control for SLC operation. The results from latest marker trials highlight the chaotic and non-uniform nature of material flow and the unpredictability associated with material flow simulation.

In caving operations, draw control deals with the question of when to stop loading and regulates the loading process by providing the information on when to stop loading. The decision to stop loading a blasted ring and proceed to blasting the subsequent ring is a critical decision made in a SLC operation. If a draw point is closed early then ore is lost in the draw point which cannot be conclusively recovered at the lower levels and if delayed the mine faces greater dilution and increased mining costs. A study of the various draw control strategies used in sublevel caving operations globally has also been done to describe the present state-of-art. An analysis of the draw control and loading operations at the Malmberget and Kiirunavaara mines is summarized in the thesis using information collected through interviews, internal documents, meetings, and manuals. Based on the literature review and baseline mapping study, a set of guidelines for designing a new draw control strategy has been listed. 

A holistic approach to draw control is required which captures the uncertainty and variation associated with loading at the draw point and fulfils the sustainability and economical objectives for the mine. Two mathematical models, a probability model and an economic model, were created using five datasets: bucket weights, bucket grades, extraction ratio, mine economics parameters and production constraints. The probability model was used to generate a set of simulated bucket weights and corresponding bucket grades which acts as a ‘virtual mine’ environment.  The economic model assesses the economic impact of loading at the draw point. Two approaches to draw control is tested using the ‘virtual mine’ created by the probability model. Based on the results of the simulation tests, an optimal draw control strategy is suggested for a field test at the mine. The results highlight the importance of dynamic loading control for SLC operations. They also demonstrate the importance of continuous draw point monitoring to optimize SLC operations. The thesis offers a roadmap for mine digitization in which a ‘virtual mine’ model (probability model) is used for simulation and calibration, whilst an online application (economic model) is used at the mine in real time.

Place, publisher, year, edition, pages
Luleå: Luleå University of Technology, 2020. p. 180
Series
Doctoral thesis / Luleå University of Technology 1 jan 1997 → …, ISSN 1402-1544
Keywords
Draw control strategy, sublevel caving (SLC), loading process, draw point, caving operation, Bayesian statistics, probability models, economic models
National Category
Geotechnical Engineering Other Civil Engineering
Research subject
Mining and Rock Engineering
Identifiers
urn:nbn:se:ltu:diva-77636 (URN)978-91-7790-530-1 (ISBN)978-91-7790-531-8 (ISBN)
Public defence
2020-04-01, F1031, Luleå University of Technology, Luleå, 10:00 (English)
Opponent
Supervisors
Projects
VINNOVA
Funder
Vinnova, 1832144
Available from: 2020-02-04 Created: 2020-02-04 Last updated: 2020-02-04Bibliographically approved

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Shekhar, GurmeetGustafson, AnnaSchunnesson, Håkan

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