ETD Collection

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2016 - 20185

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Subject: search.filters.subject.Platinum mines and mining--South Africa

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    The application of mechanised technology to South Africa's bushveld UG2 tabular orebody
    (2018) Tendaupenyu, Peter Adam
    Platinum is important to South Africa. For South Africa to remain a relevant global player, mechanisation is the future. Mechanisation in South Africa’s Bushveld narrow, tabular UG2 orebody has not always delivered as per expectations. This has retarded modernisation of the platinum mining industry. Mining companies have considered reverting back to conventional mining methods. This report investigates, through a series of interviews with key industry players the reasons for the failures. By comparing practice with the recommendations in seven areas: - orebody characteristics, mine design and layout, mine logistics, mining machine size, machine utilisation, labour force structure and change management issues, gaps holding back mechanisation are identified. These are;  Applied technology and Orebody – the ore body and its characteristics are not always compatible with the mining method and technology applied.  Mine design & layout and Mining – Mining layout is informed by extraction strategy and should support safe, effective and economic mining. This has not always been the case.  Move and Mining – Movement of ore and people has not always been seamless. This is a logistical issue which if not addressed adequately in the design phase will cripple a mechanised operation.  Maintain and Mining – Plant and machinery must be available in - shift to support mining activities. Effectiveness of planned maintenance must be applied, measured and quantified. This is not always the case.  Skills and Mining - Selection, training and placement of personnel is essential in the successful operation of a machanised operation. This is true for the entire hierarchy of the organisation. A trackless operation cannot be successfull if run by people with no appreciation and respect the machines they operate and manage. iv  Health & Safety – Mining.There is a reduction in injuries when mechanised operations are compared to conventional operations. Mechanisation however brings its own sets of safety and health issues. Examples are: - o Injuries in mechanisation are more severe. o There are no current interventions to health disorders caused by machine vibrations and diesel emission.  Ethics and Mining – It is the responsibility of all to eliminate the wilful damage to plant and machinery. Machinery must work at all times that it is planned to be utilised. The culture of reporting for work when scheduled to do so is paramount. Future reseach should be directed at addressing each of these gaps.
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    Mine X (Portal C) jointed pillar numerical analysis
    (2018) Chindedza, Tafadzwa
    Mine X is a platinum mine in Southern Africa, mining Platinum Group Elements using the room and pillar mining method. Mine X is currently mining four portals that were named Portal A, Portal B, Portal C and Portal D. A pillar run was experienced at Portal B and it was found necessary to revise the original pillar design for all of Mine X’s portals. All portals at Mine X were originally designed using the Hedley and Grant (1972) pillar design formula. This research focuses on the numerical analysis on Mine X Portal C pillar design. The main objectives being to evaluate the effects of joints and pillar size on pillar strength and to evaluate the appropriateness of the original pillar design. The Universal Distinct Element Code (UDEC) software was chosen to conduct the analysis as it allows for a relatively large number of joints to be incorporated, and also permits to model tensile fractures. Propagation of tensile fractures is a key aspect of the pillar failure process in the model and reality alike. Therefore, significant effort has gone towards reproducing and calibrating this process based primarily on results of laboratory tests conducted on actual rock specimens collated at Portal C. Since the type of modelling carried out in this project is relatively new in rock engineering, a review of the literature was deemed important. A study of existing approaches towards room and pillar designs was conducted so as to understand the mine’s expectation from its original pillar design. Similar work previously done by others was studied and an optimum approach to follow was decided upon. Data was collected from the mine that included test results on specimens, mapping data and pictures showing existing underground conditions. Previous work done on Mine X was also reviewed from which core logging data was obtained. All the collected raw data was processed to come up with information that could be used as inputs into the numerical models. It was decided to model the micro particles of the rock as voronoi tessellation and the cementation between these particles were modelled as voronoi contacts. Voronoi tessellation was essential to model tensile fracturing. Calibration against laboratory results was carried out for the purpose of obtaining voronoi properties that could be used in the model. It was decided to represent the joint network using Discrete Fracture Network (DFN - a statistical description of fractures where a set of statistical parameters are defined and a joint set is generated based on those statistics) instead of explicitly modelling the mapped structures. The modelling process conducted in UDEC required some sensitivity analyses to be done to evaluate the effect of parameters such as velocity and mechanical damping. Three sets of models were run, each set run on three different pillar sizes (2 m, 4 m and 6 m pillar widths). The first set was modelled as an intact pillar and the other two sets were modelled as jointed pillars. Each of the last two sets had a jointing network representing one of the two different geotechnical domains at the portal. The results from these models were compared. The modelling results showed that pillar strength increases with increase in pillar size. Stiffness also increase as pillar width increases. However, a discrepancy was observed on the intact pillars where the 2 m pillar proved to be stiffer than the 4 m pillar. The existence of joints reduces intact pillar strength by 70% to 80%. The existence of the low angle joint sets translates into less stiff, more flexible and more ductile pillars. Mine X is currently mining 4 m square pillars. According to the numerical modelling carried out, these pillars are too small with strengths ranging between 55 and 65 MPa. From the Hedley and Grant (1972) fomula used for the original pillar design, the mine is expecting pillars with average pillar strength of at least 95 MPa from the 4 m pillars. There is need for revising the design criteria and adjusting the mined pillar sizes to about 8 m wide pillars.
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    Implementation of a GHH roof bolter machine at Merensky shaft (Booysendal Platinum Mine): a case study
    (2018) Kekana, Wonderboy Orchard
    Mining Merensky reef successfully at Booysendal Mine will require a machine which enhances team efficiencies delivering results on specified operational and quality parameters. The operational objective of the Booysendal Mine trial of the 3108 ADE Roof bolter was to prove that this machine can mine at 1900 mm stoping width and to set a base for KPIs. The purpose of the upgraded 3108 ADE machine was to achieve to drill and install a bolt in 8 minutes translating in 3 bords supported per shift. This machine had to deliver 235m, 1900 m² per month at stoping width of 1900mm translating to 2.55g/ton in terms of head grade. The objective of this study is to assess the use of industrial engineering techniques to expedite the implementation of a roofbolter in the challenging environment at Booysendal Mine. The process of creating a learning environment is one of the important goals in many business improvement frameworks, and underlies the work done here. To deliver on the above mentioned objectives, this research presents a case study in the use of several industrial engineering techniques:  Lean Manufacturing tools were used to draw up effective communication channels for Operators engagement, visible performance data gathering and performance monitoring.  Observational research methods were used to assess and evaluate the impact of 3108a modifications in achieving the set KPIs per shift in a real underground situation.  The A3 report process was used as the framework to communicate the process of the 3108a machine Roll-out to people on the team. Theory suggests that the method of implementation presented here will create a learning environment, which will in turn meet production targets. The implementation team had set itself a number of production targets, primarily a demand for 42 bolts per shift to be installed and stoping width to be controlled to 1900mm. The targets were based both on benchmarks elsewhere and on economic demands at the mine. iii The study shows that through a step-to-step improvement approach the Operators improved from installing less than 19 bolts per shift to move to 19 – 34 bolts per shift. The 1900mm stoping width target was not consistently achieved. In this study, the author played the role of Project Champion, actioning the knowledge acquired when attending the CMMS courses and applied tools/tactics learned from Trackless Mining and Operations Management courses and tools such as Lean Manufacturing, A3 reports and physical observations. The main thrust of this study was to answer this central question: How well will a set of industrial engineering tools work to improve the modified GHH bolter performance to enable the Merensky shaft to efficiently mine at a stoping width of 1900mm consistently? The research shows that detailed improvement approach delivered more bolts drilled and installed per shift. The report goes further and gives practical on-site implementation team actions taken to ensure a machine delivers 3 bords per shift. Where the implementation team identified challenges outside the scope of this study for addressing the stoping width, the team gave recommendations to relevant technical team on-mine. The tools performed well:  The most successful tool was the use of visual indicators and other elements of communication from Lean to engage Operators and encourage the use of gathering data for process improvement because key feature of Lean is its ability to manage a large number and variety of issues simultaneously using visual prompts to assist in communication of issues.  Lean promotes “going to the Gemba” – managers need to see exactly how things really work. The observational tools used in this implementation showed the value of physical observations because even if 8 minutes per installed bolts was not achieved the implementation team knew exactly where the constraints were and how to tackle it.  The A3 methodology was an effective way of structuring and managing the improvement process in the implementation of the Roof bolter. Through the stepped approach in this case study we managed to deliver exceptional production results six months ahead of planned timeframe.
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    Quantification of sampling uncertainties at grade control decision points for a platinum mine
    (2017) Woollam, Mandi
    The mining industry has been challenged with rising costs and lower ore grades mined, thereby squeezing profit margins. Furthermore, with decreasing ore grades being mined, the PGE grade of interest is edging closer to the capabilities of the available analytical techniques. This is placing more pressure on maximizing the precision and accuracy of data and the importance of the quality of sampling has therefore become elevated. The quality of sampling is affected by all contributing effects that make up the sampling chain. Grade control sampling assay data is used routinely in conjunction with the geology of the ore to assign a destination for the ore (either to a stockpile or to the mill), based on its grade classification for the platinum mine. The classification of the grade for a particular mining block is made using four key elements (4E). This grade determination is often found to be different to that generated from the exploration sampling process. These differences between assay data obtained using different sampling techniques are due to errors in sampling. These errors were investigated in depth in this study. In order to understand the possible errors in the assignment of grade to a mining block, the errors at the classification thresholds (referred to as grade control decision points (GCDP)) have been investigated. These errors, comprised of both random and systematic errors and were determined independently in this study. Components that generate these errors include the heterogeneity of the ore, the action of taking a sample, sample preparation and the analytical technique used to analyse the sample. The random error associated with the assignment of grade to a particular mining block is highly dependent on the analytical technique, grade of the ore, as well as the number of assays used. The application of the central limit theorem in calculating the average grade of a block (with n=30 samples analysed in duplicate) reduces the random error by a factor of 7.75. Although this factor reduces the random error sufficiently (< 10 % at all grades) to enhance confidence, the large errors (>> 10 %) assigned to each of the individual thirty samples (at grades less than 1.7 g/t 4E) still present a risk of incorrect classification of the ore below 1.7 g/t 4E. The effect of systematic errors are additive and cannot be reduced by averaging more assays (unlike random error). These errors have their origin in the “design” of the sampling protocol. This study highlighted that, in general, the grade calculated for a mine area will be lower when calculated using grade control assay data when compared with the grade calculated if exploration assay data were used. This systematic error was also found to change in magnitude and sign depending on the 4E grade of the ore. The grade at which mining is viable (pay-limit of the mine) is defined in the 2015 annual report as 2.5 g/t 4E. This study, quantified the smallest systematic error (< ± 2 %) between the two sampling techniques at ore grades equal to 1.7 g/t 4E. Both these grades (2.5 and 1.7 g/t) are significant in that it is the upper and lower grade limit for the very low grade ore (VLGO) category. Ore that is assigned to grade categories higher than 2.5 g/t 4E, will be marked as destined for the milling process in mining. It is at this grade that the error should be minimised so as to minimise financial loss; such loss will occur when ore is stockpiled but should have been processed or when ore is diluted with sub-standard grades, which should have rather been stockpiled. This study quantified the errors associated with grade control into all its contributing parts; the largest error (- 8 %) was assigned to the sample preparation component of the sampling protocol. All other components were quantified as an average error and this was done at the mine’s different ore grades. Sampling design (quality assurance) components that present an opportunity to reduce the systematic errors were identified as: • Increased sub-sampling, sample mass • Re-design, sub-sampling sample cups • Protect the integrity of the analytical sample; through the introduction of a third sample cup to obtain the sample required for geological logging • Optimize the drilling speed of the reverse circulation drill • Optimize the rotational speed of the rota-port cone splitter • Centre the cone splitter perfectly below the falling sample stream • Increase the analytical sample mass (to a maximum) • Modification of sample preparation (eliminating the crushing stage) • Minimise loss of fines during sampling and sample preparation Additional quality control (QC) activities were recommended; these will reveal “out of (statistical) control”, sampling components. The cost of incorrect classification of ore is difficult to quantify but when mining tonnages are considered, it is certain to be significant. Using the average realised basket price ZAR / Pt (oz) for 2016, the value of ore (at 2.5 g/t 4E) from a three hundred tonne truck, is calculated to be around R287,666. Thus if a truck is incorrectly assigned to stockpile instead of processing this value will be lost. Resources invested in the resolution of these systematic errors will be resources well spent. Management should be commended for investing in heterogeneity and twin-hole test work, both of which provide information that quantify and identify sampling errors. Measuring these errors is the first step towards reduction or elimination of error.
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