ETD Collection
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Item Resource classification criteria for hydrothermally enriched manganese ore bodies(2019) Zulu, NokukhanyaDue to regulatory developments over the years, various international and local reporting codes have been consolidated to ensure clear and unambiguous reporting of Mineral Resources globally. The Committee for Mineral Reserves International Reporting Standards (CRIRSCO) incorporates in the International Reporting Template, the minimum standards for the Public Reporting of Exploration Results, Mineral Resources and Mineral Reserves, and also provides recommendations and interpretive guidelines for the countries represented on the CRIRSCO committee. This template is advisory only and in South Africa the South African Code for the Reporting of Exploration Results, Mineral Resources and Mineral Reserves (SAMREC); developed along the CRIRSCO guidelines; is relevant. SAMREC provides the definition for Mineral Resources and subdivides Mineral Resources in order of increasing confidence into Inferred, Measured and Indicated categories based on the confidence and quality of geoscientific evidence. Whilst there are numerous publications on Mineral Resource classification, few publications exist on the application of Mineral Resource classification techniques applied specifically to manganese deposits. This has led to manganese resource geologists adopting classification methodologies applied to other commodities, and in some cases merging and adapting different methodologies, which might be inappropriate and not suited to the specific manganese ore bodies being investigated. This study set out to develop a defendable guideline for the Mineral Resource classification of hydrothermally enriched manganese ore bodies by considering confidence in both the geology and geostatistical estimation. Wessels mine was presented as a case study. The literature review conducted, formed the foundation of this research report wherein various Mineral Resource classification techniques were investigated. Estimation parameters were identified to assess confidence in the estimate and to confirm the key geological considerations affecting confidence in the estimate. Statistical and geostatistical analyses of the geoscientific data were combined with geological knowledge to develop a guideline for the Mineral Resource classification of hydrothermally enriched manganese ore bodies. The research report shows that using a purely mathematical approach to Mineral Resource classification is an over-simplification and not suited to the manganese ore body, particularly when applied to the skew and non-stationary data of the case study. The absence of an assessment of geological risk in the current classification was found to be a gross oversight. A scorecard method for Mineral Resource classification is proposed, as an improvement over the current methodology. This proposed scorecard is designed to balance three crucial elements: confidence in data integrity, confidence in the geology, and confidence in the mathematical estimation technique. The fundamental research questions have been answered, thereby achieving the objectives of this research study. It is envisaged that this research will contribute to a published body of work that will lead to improved classification of hydrothermally enriched manganese ore bodies.Item The effects of cut-off grade and block sizes on the net present value for an iron ore deposit(2019) Malisa, Moore TheresaOptimisation of the Net Present Value (NPV) needs to consider cut-off grade as well as the block model size. This study considered the impact of these on the optimisation of the NPV. The orebody could be mined sub-optimally due to the misunderstanding of the relationship between cut-off grade and block model size. The research was based on an iron ore deposit mined through open-pit mining method in South Africa. The main objectives of the study were: to understand the importance of cut-off grade; to determine the effect of block model sizes on the average grade; to determine the effects of the block sizes and cut-off grades on the NPV, and to determine which block sizes and cut-off grade maximise the NPV. It was found that there were different cut-off grades at different levels of the exploitation of the iron ore deposit. These differences can lead to the deposit not being mined optimally. Therefore, it was important to understand the importance of cut-off grades, hence the need to investigate the effects of cut-off grades. The effect of block sizes on the NPV was included because there was insufficient research on the topic. From the literature review, the cut-off grade was defined as the boundary that separates material that is discarded from the material that is taken further for treatment. The cut-off grade determines whether the material will be considered as waste or ore. If the cut-off grade is too high, more material will be discarded as waste while a lower cut-off grade increases the entire mining capacity. The lower the cut-off grade, the higher the Mineral Reserve. It was shown from the literature that the determination of the cut-off grade is determined by factors such as the price of the commodity, production costs, grade distribution, environmental factors and other factors. The literature review highlighted that a block model is a representation of orebody characteristics, whereby a single cube will have sizes (x, y and z). The single cube will be allocated with grades, volumes, rock types, densities and many more attributes assigned to it depending on what information is required. The block model dimensions should represent the minimum block that could be selectively mined, that is, the smallest selective mining unit. The block model sizes are selected at the initial stages of creating the block model. The block size is also dependent on the sample spacing. The block size should be one-half or one-fourth of the sample spacing. When the selective mining unit is selected it should take into account the excavator that will be used to load the material. The selective mining unit is important since it determines the amount of dilution that will be encountered during mining. The larger the selective mining unit, the more the dilution, which decreases the grades. The methodology that was used to analyse the effects of cut-off grade and block sizes on the NPV was through the use of grade-tonnage curves and the DCF for different block sizes and cut-off grades. NPV is the sum of the DCF’s and the NPV assists in projecting the future revenues in terms of mines that are already in production. The DCF’s for this report were done only for 10 years because it was enough to create data of a high level of confidence. The cut-off grades that were used were 53%,60%,63% and 64% Fe as they covered the definition of the ore for the iron ore deposit. The base block model size 6.25m x 6.25m x10m.The base block-model was re-blocked into sizes: 12.5m x 12.5m x 10m; 25m x 25m x 10m and 50m x 50m x10m. Grade-tonnage curves were created for each block model size including the base 6.25m x 6.25m x 10m block model. The obtained tonnes and an average grade above certain cut-off grade were used to create the DCF in Excel to obtain the NPV. The results showed that an increase in the cut-off grade decreases the tonnes above the cut-off grade while increasing the average grade. The larger the block size, the lower the average grade due to increased dilution. The larger block sizes result in a lower NPV if the effects of mining selectively are not considered. However, if the effects of selective mining are considered, larger block sizes result in an optimised NPV. Some of the conclusions were that small block sizes result in an optimised NPV only if the effects of selective mining are not considered while larger block sizes result in an optimised NPV when the effects of selective mining are considered. The 25m x 25m x 10m which is a larger block model size is not affected by selective mining and it resulted in a higher NPV when compared to the 12.5m x 12.5m x 10m,therefore,it is better to work with larger block model sizes to avoid selective mining. It was recommended that a 60% Fe cut-off grade paired with a 12.5m x 12.5m x 10m block size to be used when the effects of selective mining are not considered since it increases the tonnes above the cut-off grade, thus increasing the LOM and the NPV is optimised. A 60% Fe cut-off grade paired with a 12.5m x 12.5m x 10m block size was also recommended to be used when the effects of selective mining are considered as this optimises the NPV. A 60% Fe cut-off grade paired with a 25m x 25m x 10m block size is recommended since it does not require to be mined selectively.Item Comparison of the genetic algorithm and linear programming approaches to optimally locate orepasses in the Venetia underground project(2019) Dabula, GangathaIn the process of optimisation and the need to get the best value from a mining operation, reducing hauling costs is a key requirement. The positioning of orepasses at Venetia Mine, which uses a sub-level caving underground mining method, will be of great importance. This will impact on the tramming distances that Load Haul Dumpers (LHDs) will have to travel from drawpoints to orepasses. This will also impact on the hauling distances for trucks from underneath the orepass to the underground crushers. In the case of Venetia Mine, tramming is planned to require diesel, tyres and other consumables. This also uses up the operational hours of machinery during its commissioned life. This study aims at analysing the current positioning of orepasses in the Venetia Underground Mine design. The current position of orepasses at Venetia Mine is decided based on experience and geotechnical constraints. This study compares this positioning to positions optimised using a linear programming approach and the genetic algorithm approach. Other optimisation techniques were available but, these two were considered because these are the best-suited optimisation techniques for solving the orepass location problem and some research on optimal facility location has been previously done using these techniques. A Microsoft Excel model was produced to calculate the equivalent cost per tonne metre for each orepass using the total tramming distance from the loading point to the orepass. This model was also able to determine the ideal orepass for each loading point by selecting the orepass with the shortest loading distance. The Microsoft Excel model had been developed in house at De Beers to test whether the orepasses had been positioned in the correct positions in the design and to also determine what the total tonnage from each orepass was during the life of mine. This excel model was then optimised using Palisade Evolver ® software. The OptQuest ® tool was used to solve the linear programming solution as one of the model constraints was not a linear function. The constraint in question is that the orepasses are not to be closer than 40m from the orebody based on geotechnical recommendations. The optimisation results showed that the genetic algorithm optimisation resulted in a 12% improvement in the total tonne metre cost for the orepasses on the K01 orebody. The linear programming solution resulted in an improvement of 10.5% on the total cost per tonne metre in the model from a base value of R 20 941 per tonne metre. The results indicated that optimisation could bring about an improvement in operating cost. However, there needs to be future work done to consider geotechnical and geological constraints which will be encountered in a real-world mine design scenario.Item Conceptualization of a mining information model (MIM) using real time information for smart decisio making: a smart economics approach(2018) Javaid, FaiqSouth Africa is a mineral resource-rich country with the largest concentrations of gold and platinum in the world. Yet the South African mining industry is facing an economic crisis. Some of the reasons for this crisis are: Low commodity prices, escalating production costs, depletion of economically ore grades, volatile currency, volatile exchange rates, difficulty to compete in the international markets, increased concerns of deteriorating relationships with mine-worker unions and the South African Government’s Department of Mineral Resources (DMR) etc. Thus there is a critical need to develop an information and decision-making system that will cater for modern-era needs. Such a system would need to optimize production cost, while properly linking it to current and expected market conditions to enable synchronised and timely decision-making. This can only be done via a framework that is supported by relevant and timely information. This will need to include the following mine and market data (in both current- and anticipated-forms): Production rates; assessment of what is going on underground; incident reporting; scheduling; costing; market updates; inventory management; life cycle management. And such a system, named Building Information Modelling (BIM), was developed for the construction industry. This indicates that development of a Mining Information Modelling (MIM) may also address above-mentioned aspects, allowing maximum production, optimal cost, less uncertainty and more efficiency – something that is difficult to attain via existing mining-software. The purpose of this research was to investigate and present a conceptual framework for the development of MIM and a smart, real-time decision-making tool. The study reveals that MIM can be achieved via the combination of software from a number of providers, together with some additions. Ideally, the combination should cover the entire mining value chain. With MIM, a decision-making tool (with the support of MIM) can either be developed as a separate software or can be integrated with MIM. Such system will cater for modern-era needs, thereby enhancing mining capabilities without affecting job-creation.Item Short-term geological modelling for improved mine planning at Isibonelo Colliery(2018) Katuruza, MeakerIsibonelo Colliery is an opencast operation employing the strip mining method, and for short-term planning, up-to-date geological information is required. The colliery currently depends on the annually updated geology resource model information for input into the three planning windows (short, medium and long term). To improve and optimise on short-term mine planning, short-term geological model information has become a requirement. This research project was undertaken to fulfil the Anglo-American Coal South Africa’s Operation Management System (OMS) requirements. This research report focussed on closing the gap between OMS requirements and current practice, by creating a short-term modelling process to fulfil the requirements for both mine planning and rock engineering disciplines. Highwall mapping techniques such as digital photogrammetry from drone highwall flyover, as well as use of total station surveys to map lithological contacts for input into the model were investigated and tested. The study area focussed mainly in the far south portion of Isibonelo South pit where most of the data was collected to build the process of short-term modelling as a test case. Short-term planning requirements using latest geological information was achieved and mine designs started to improve. Subsequent strip reconciliations showed improved correlation between planned and actual, especially in the dragline volumes from 3% to 0.5% over two mining cuts in the South pit. Coal recovery improved by 2.1% between October 2017 and April 2018. There was good coal seam correlation between the short-term, survey and resource model. The softs(weathered) horizon still need some further work to close the gap between planned and actual thicknesses. The author recommended the use of drones for highwall mapping and down the hole wireline logging of selected pre-split holes to be adopted as methods of acquiring data for short-term geological modelling, and optimise on short-term planning.Item Enhance South deep variography by including flat inclined boreholes in the local direct estimation methodology(2018) Mutobvu, Tyson RendaniThe research project presented relates to the Mineral Resource evaluation of South Deep Gold Mine in Westonaria, South Africa. The aim of the project is to establish the impact of the inclusion of the samples from flatly inclined boreholes (FIBs) in the variography and Mineral Resource estimation of the individual Elsburg top conglomerate reef (ECT). The samples from FIB boreholes are traditionally excluded from the estimation process to reduce the possibility of smearing grade as stated in the Mine’s Code of Practice. These are boreholes with a dip of greater than -55° or less than 55o. These boreholes provide the highest resolution into the orebody and thus the highest level of de-risking of the orebody and are therefore used for geological modelling. Although the addition of the samples from FIBs in brings a substantial increase in the number of samples in some geostatistical domains they do not introduce outliers. Adding the FIBs resulted in improved variogram models. Simple Kriging models considered are one using the Au (g/t) samples from the steeply inclined holes only and the other using the combined dataset. These Kriging models were post-processed through Local Direct Conditioning (LDC) and the results were compared. Reconciliation indicates that the model remains stable with 1% change at Mineral Resource and Mineral Reserve cut-off of 3.2g/t Au following the addition of Au (g/t) samples from FIBs in the mineral resource estimation. It is therefore concluded that adding the flatly inclined boreholes in the mineral resource estimation increases the confidence in Kriging and improves variogram modelsItem Designing a feasible methodology for selecting permanent areal support for varying environments in underground mines(2018) Mulenga, Prince AjawaDetailed evaluations of ten permanent areal support systems in different mining environments were carried out including comprehensive photographic records, of the support performance and installation. The data obtained at these sites was used to develop a methodology for selecting areal support systems in different mining environments. This methodology includes the evaluation of support performance, practicality and installed cost. Support performance combines with the support capacity, in terms of initial stiffness, peak load and yield, and performance factors (installation quality, equipment damage, blast damage and corrosion). Practical aspects of transport and installation can be assessed using the methodology and the installed support cost can be determined. The methodology provides a comprehensive, practical approach to assessing permanent areal support systems. The mining environment plays a major role in the support performance and practicality of support transportation and installation.Item The application of geostatistics in coal estimation and classification(2018) Nengovhela, Avhurengwi ColbertThis study set out to assess a multiplicity of related questions regarding the applicability of geostatistical principles, practices and techniques to the estimation, classification and reporting of Coal Resources. Two cases, i.e. Case A and B were selected for the study. Both areas are in the Witbank Coalfield. A few exercises were undertaken to investigate whether a technique such as Ordinary Kriging (OK) could be better suited. The second part of the problem statement is to evaluate whether the current drill hole spacing recommended by the SANS 10320:2004 standard is appropriate for the considered cases. In terms of drill spacing, the South African National Standard (SANS 10320:2004) provides that for a Measured, Indicated and Inferred classification, samples should be spaced at 200 m (minimum of 8 samples), 282 m (minimum of 4 samples) and 564 m (minimum of 1 sample) respectively. By quantifying the precision associated with estimating the two cases at different drill grids, it was shown that for both Cases A and B, a Measured Resource can be classified by using drill holes that are spaced approximately 1000 m apart. It was established that precision results associated with the global estimation variance are only applicable to the area in which the study was undertaken i.e. the findings are not globally applicable although rough approximations can be deduced. For short-term mine planning purposes, further drilling may and is usually required. The guidelines provided in the SANS standard for separation distances are evidently too stringent for both Cases A and B. Therefore, a drill spacing of 500 m, 1000 m and 4000 m should be considered as being more appropriate than the current overly tight spacing. With regard to the use of OK, the findings of this study clearly show that the current Growth Algorithm (GA) technique commonly used by South Africa coal estimators is more appropriate than other alternatives as it outperforms both OK and Inverse Distance Weighting (IDW) whether on a global or local scale. The current estimation method used for these cases is therefore appropriate. The current drill grids are too small for global estimation and reporting and thus there is possible overspending if the required estimation precision is between 5 and 10 %. At the current drill spacing, precision is around 2 % within ‘Measured’ areas, which is more than what is required to produce predictable long-term plans.Item The impact of supply and demand drivers on the iron ore price and cycle(2018) Nortje, Petrus GerhardusIron ore prices rallied from USD15/DMT during 2004 and experienced a significant drop from USD 140/DMT during the latter part of 2013. The purpose of the work is to identify the key drivers impacting on iron ore demand globally. Understanding the supply and demand balance and impact on price, is key to informed decision making relating to the iron ore business. The research methodology applied largely followed a quantitative methodology. Key drivers of iron ore demand, supply and demand balance and the impact on price were evaluated. The method applied consisted of gathering data from secondary sources and a detailed quantitative analysis on GDP, stage of economic development, steel consumption, supply and demand of iron ore and intensity of use. Approximately 98% of all iron ore is used for steel making and on that basis steel consumption is the primary driver for iron ore demand. Steel is mostly used for construction and manufacturing and is driven by emerging economies of which China is currently the largest contributor. Global GDP growth correlates well with steel consumption and is primarily driven by emerging economies. Urbanisation was and still is a key driver for construction in China, to provide housing and related infrastructure for transportation and services. Scrap steel recycling, currently at 15%, affect the demand for new steel and indirectly iron ore. Iron ore is abundant and can easily meet the demand. The significant growth from 2004/5 to 2013/14 and the unprecedented demand for steel resulted in elevated iron ore prices, introducing high cost iron ore, predominantly from Chinese State owned companies. From late 2013, the iron ore prices reduced significantly. This was mainly due to the steel consumption in China slowing down; delivering of large scale, low cost iron ore projects in Australia and Brazil and a significant reduction in oil prices. The key drivers impacting iron ore demand is: global GDP growth, industrialisation and urbanisation of emerging economies, recycling of steel, supply and demand balance of iron ore, the cost of production and the price of global iron ore. For the medium term outlook, the iron ore market will be structurally over-supplied and, as a result, the demand could be met at significantly lower cost of production levels than that seen during the period leading up to the price collapse in 2013. This is primarily because of the increase in low-cost supply from the major suppliers displacing higher cost producers. China will continue to grow and drive the global demand for steel and iron ore during the medium term albeit at much lower rates when compared to the last decade. The demand for steel will increase until 2020 according to various analyst views. The iron ore prices are expected to trade between USD50/DMT to USD70/DMT from 2016 to 2020 mainly because of the over-supply situation and demand being mostly met by large scale, low-cost producers. The decision around the continuation of high cost, state owned Chinese iron ore producers, new large-scale low cost production and the oil price will impact on the price outlook.Item Alternative methods for coal resource classification of the geologically complex Witbank Coalfield(2017) Magnus, Elaine ElizabethThe Australasian code for Reporting of Exploration Results, Mineral Resources and Ore Reserves, of the Joint Ore Reserves Committee (JORC) sets out minimum standards, recommendations and guidelines for Public Reporting in Australasia. (JORC, (2012)). The Committee for Mineral Reserve International Reporting Standards (CRIRSCO) created a set of standard international definitions for reporting Mineral Resources and Mineral Reserves based on the evolving JORC code’s definitions (CRIRSCO, (2013)). CRIRSCO’s members are National Reporting Organisations (NRO’s) which are responsible for developing mineral reporting codes for Australia (JORC), Canada (CIM Standing Committee on Reserve Definitions), Chile (National Committee), Europe (PERC), Russia (NAEN), South Africa (SAMCODES) and USA (SME) (JORC, (2012)). The NRO’s for; South Africa (SAMREC), Australia (JORC) and Canada (CIM Standing Committee on Reserve Definitions) published supporting standards for Coal Resource and Reserve Classification and Reporting namely, South African National Standard: South African Guide to the Systematic Evaluation of Coal Resources and Coal Reserves (SANS10320:2004), the Australian Guidelines for the Estimation and Classification of Coal Resources (Australian Guidelines (2014)) and the GSC Paper 88-21: A Standardized Coal Resource/Reserve Reporting System for Canada (Hughes, et al., (1989)). With the objective to identify the most appropriate Coal Resource Classification approach for the Witbank Coalfields in South Africa, Coal Resource Classification methods applied elsewhere in the world were investigated, these countries include Canada and Australia. SANS10320:2004 relies on a minimum drillhole spacing dependant on two different coal seam deposit types, whereas the Australian Guideline for the Estimation and Classification of Coal Resources (2014) provide a guide as to which geological aspects need to be considered when classifying a coal deposit into the appropriate confidence category, and no fixed drillhole spacing is recommended. The Canadian Standardized Coal Resource/Reserve Reporting System (1989) differs from the afore mentioned standards in that it is a prescriptive method based on specific levels of geological complexity, governed by specific fixed parameters. None of the other Coal Reporting codes/standards use a broad sweeping fixed drillhole spacing to classify Coal Resources as in South Africa. It is noted from experience as well as by Coal Resource Classification methods used elsewhere in the world that the use of proposed fixed drillhole spacing, such as currently in use in SANS10320:2004, is an unsatisfactory method for assessing the uncertainty and variability associated with coal deposits. The Coal Resource Classification methodologies utilised on a local scale in South Africa, were investigated to establish how mining houses manage and assess the variability in their Coal Resources. Fourteen mines operating throughout the Witbank coalfield were compared, it was found that although the Coal Resource Classification of the governing code requires a 350m drillhole spacing for highest level of confidence, the mines drill to a much smaller grid for increased confidence. Despite this, the mines still report on the SANS10320:2004 minimum standard in the public domain. A map was created based on the average drillhole spacing drilled per mine. From this it was deduced that there are zones of higher coal seam variability which required a closer spaced drilling grid to derive sufficient geological confidence in the estimates. Based on these deductions four zones of comparable continuity/variability, were identified. The zones identified by means of geological investigation and those identified by differences in variability as perceived by the Competent Person (CP) correlate. The highest variability and smallest drillhole spacing is located toward the western portion of the coalfield whereas the lowest variability with the largest drillhole spacing is located toward the east. The geologically complex Witbank coalfield was divided into four geo-zones/domains based on the depositional environment, basement rocks and post depositional influences. It is evident that a suitable Coal Resource Classification approach; which considers the characteristics of the geozones are followed. The question of which other classification methods are appropriate if not a predetermined drillhole spacing is addressed by this research. Statistics on relevant variables can provide a measure of uncertainty and therefore reliability in the estimates, for this reason three methods of uncertainty and probability characterisation were investigated. Of the three, namely; Non-linear estimation approach, conditional simulation (CS) and global estimation variance (GEV), the latter was deemed the most appropriate. GEV forms the basis of Drillhole Spacing Analysis (DHSA) and was applied to a mid-sized coal mine within the western portion of the Witbank coalfield. The analysis did not result in robust Coal Resource classification of estimates but rather provided more insight into the variability of the deposit. The results of DHSA are easily manipulated and are open for interpretation, it is therefore suggested as a valuable exercise/tool for understanding and assessing coal seam variability and to be used as a guide in Coal Resource classification. Onsite practical geological information should not be underestimated and geostatistics should always confirm the geology. A purely mathematical approach to Coal Resource classification would be a gross oversight, a combination of geological factors in association with statistical inferences is suggested. A scorecard method with associated weights is proposed to improve the confidence in the Coal Resource classification.