Scielo RSS <![CDATA[Journal of the Southern African Institute of Mining and Metallurgy]]> vol. 116 num. 7 lang. pt <![CDATA[SciELO Logo]]> <![CDATA[<b>Danie Krige Geostatistical Conference 2015</b>]]> <![CDATA[<b>Celebrating the ordinary</b>]]> <![CDATA[<b>Resource estimation for deep tabular orebodies the AngloGold Ashanti way</b>]]> The extreme depths and consequent expense of drilling and sampling the gold-bearing reefs of the Witwatersrand Basin have resulted in limited data being available for estimation of grade ahead of the mining face. There is, however, a wealth of information from mined-out areas of these deposits. This estimation challenge resulted in the development of a unique method of Mineral Resource estimation. AngloGold Ashanti (AGA) utilizes a technique termed macro cokriging (MCK), which allows for the integration of the limited advanced borehole data with the large chip sample data-sets from the previously mined-out areas by adopting a Bayesian geostatistical approach. The MCK process, in short, is the estimation of mixed support size data together with the application of four-parameter distribution models. The gold value estimation for the Carbon Leader Reef (CLR) on the AGA TauTona Mine is used in this paper as a case study of the process and to demonstrate the effectiveness of this technique through production reconciliation. The current method has been proven over the last 20 years and is now an established part of the Mineral Resource evaluation process within AGA. <![CDATA[<b>A test of the appropriateness of the LUC technique in high-nugget Birimian-style gold deposits</b>]]> The localized uniform conditioning (LUC) technique was proposed by Marat Abzalov in 2006. The technique converts conventional uniform conditioning (UC) grade-tonnage curves into single grade values attached to each smallest mining unit (SMU). This is achieved by ranking the SMUs within a panel in increasing order of their grade (based on the direct kriging of SMUs). This ranking is then used to localize the conventional UC grade-tonnage curves for each panel by dividing them into classes and computing their mean grades, which are assigned to the SMUs. The quality of this localization process will depend heavily on the validity of the grade patterns predicted by the direct kriging of the SMUs. Abzalov noted that where the distribution of data available for the direct kriging of the SMU is characterized by strong short-range variability, the advantages of using the LUC approach may be more limited. Consequently, a study was undertaken to determine how valid the predicted grade patterns of a typical Birimian-style gold deposit (with high nugget effect and strong short-range variability) might be expected to be. This was determined by comparing the direct SMU kriging ranking (based on sparse data) with the grade control model ranking (based on close-spaced data and the best available estimate of the deposit). The results showed a satisfactory correlation between these rankings and it was concluded that, although the grade patterns predicted by the direct kriging of the SMUs may be less meaningful for deposits exhibiting strong short-range continuity, there is nevertheless a convincing relationship with the actual (or best available) rankings. Therefore, the LUC technique is still considered to be useful for this style of deposit. <![CDATA[<b>The practical implementation of uniform conditioning at AngloGold Ashanti African Operations, and a case study as applied for potential underground mining at Nyankanga pit, Geita gold mine, Tanzania</b>]]> The use of uniform conditioning (UC) as an estimation technique to produce robust recoverable resource models has been implemented across various operations within the AGA Continental Africa Region. This paper outlines the relevance of using UC as an estimation technique to provide a robust estimate for use in underground mine planning and published Mineral Resource statements. The change-of-support model forms the basis on which planning and financial decisions are made, and it is therefore imperative that appropriate validations and checks against 'reality' are carried out prior to implementation. The process and validation techniques employed for UC will be discussed using a case study from the Nyankanga orebody at Geita gold mine in Tanzania. The deeper portions of the orebody constitute potential underground mining areas, and by using appropriate reconciliations it can be shown that the UC model, in spite of the inherent lack of local spatial accuracy, can be used to estimate the potential underground stopes with a lower error of estimation than other techniques. <![CDATA[<b>Construction of an expert-opinion-based virtual orebody for a diamondiferous linear beach deposit</b>]]> During early-stage diamond exploration projects, hard data underpinning spatial continuity is often very limited. An extreme example of this is a submerged diamondiferous marine placer target area alongside a current onshore mining area in southwestern Namibia. Although an abundance of geological and grade data exists for the adjacent onshore mining area, the target area itself contains no such information. Despite this apparent abundance of data, it is extremely difficult to obtain a variogram (Prins and Jacob, 2014) for use in this study area. The use of traditional simulation techniques is further hindered by the fact that diamond entrapment within the highly gullied footwall is non-stationary. An alternative approach for creating a simulated virtual orebody (VOB) is thus required in order to enable the assessment of sampling strategies. This paper demonstrates how expert opinion is used to generate a composite probability map for diamond concentration using a greyscale hand-sketching technique. The probability map is subsequently calibrated and populated using the diamond distribution for different raised beaches obtained from analog data based on sample results adjacent to the target area. The resultant grade simulation is used to test different sample scenarios and is a first step towards determining an appropriate sampling strategy for the target area. The VOB is used to analyse and rank the efficiency of different sampling strategies for grade determination of submerged diamondiferous linear beach exploration targets. <![CDATA[<b>The basic tenets of evaluating the Mineral Resource assets of mining companies, as observed through Professor Danie Krige's pioneering work over half a century</b>]]> This paper constitutes a write-up of the first Professor Danie Krige memorial lecture in 2014, which was organized by the University of the Witwatersrand in collaboration with the Southern African Institute of Mining and Metallurgy (SAIMM) and the Geostatistical Association of Southern Africa, at which his wife, Mrs Ansie Krige, the SAIMM, and Professor RCA Minnitt also spoke. The memorial lecture was presented by his previous PhD graduate student, Dr Winfred Assibey-Bonsu. During that inaugural memorial lecture, the SAIMM highlighted three activities that the Institute would undertake going forward, so as to remember this great South African mining pioneer: ►The publication of a Danie Krige Commemorative Volume of the SAIMM Journal ►An annual Danie Krige Memorial Lecture to be facilitated by the School of Mining Engineering at the University of the Witwatersrand ►The annual award of a Danie Krige Medal. What follows is both a tribute to his work and a testimony to the great man's deep personal integrity, belief in family, humility, and faith in Christ: all of which led him to become a giant not only in the South African mining industry, but indeed worldwide <![CDATA[<b><b>When should uniform conditioning be applied?</b></b>]]> Blindly applying any methodology to estimate the recoverable resources of a mineral deposit without considering the suitability of the approach to the deposit being evaluated can render misleading results. While 'running the software' provides an answer, one should, amongst numerous other considerations, understand the impact the underlying distributions and assumptions have on the validity of the result. Uniform conditioning (UC) is a nonlinear estimation method that models the conditional distribution of smallest mining unit (SMU) block grades within panels, and localized uniform conditioning (LUC) places these SMU at plausible locations within a panel. The localization process does not improve the accuracy of the UC result, but rather presents the result in a more practical format; particularly for use in mine planning. A case study was carried out to compare the suitability of UC and LUC on two hypothetical data-sets. The data-sets are simulated realizations of a normal grade distribution and a highly skewed lognormal grade distribution which are akin to grade distributions found in mineral deposits. The estimation methods were applied to both data-sets, and the results compared with the actual grades of the simulated realizations. This paper presents an overview of UC and LUC, with discussions around the case study results. <![CDATA[<b>Optimizing open-pit block scheduling with exposed ore reserve</b>]]> A crucial problem in the open pit mining industry is to determine the optimal block scheduling, defining how the orebody will be sequenced for exploitation. An orebody is often comprised of several thousand or million blocks and the scheduling models for this structure are very complex, giving rise to very large combinatorial linear problems. Operational mine plans are usually produced on a yearly basis and further scheduling is attempted to provide monthly, weekly, and daily schedules. A portion of the ore reserve is said to be exposed if it is readily available for extraction at the start of the period. In this paper, an integer programming (IP) model is presented to generate pit designs under exposed ore reserve requirements, as an extension of the classical optimization models for mine planning. For this purpose, we introduce a set of new binary variables, representing which blocks can be declared as exposed ore reserve, in addition to the extraction and processing decisions. The model has been coded and tested in a set of standard instances, showing very encouraging results in the generation of operational block schedules. <![CDATA[<b>Increasing the value and feasibility of open pit plans by integrating the mining system into the planning process</b>]]> We present a model that allows us to consider mine production scheduling coupled with the mining system at different levels of detail: from the standard origin-destination approach to a network considering different processing paths. Each of these is characterized by variable costs, capacities, and geometallurgical constraints. We then apply this model to a real mine, comparing the results with those obtained by traditional methodology: the destination of materials defined a priori, before computing the schedules, using standard criteria like cut-off grades. As expected, using optimization to schedule and define dynamically the best processing alternatives shows a big opportunity for potential value improvement. However, the main result is that using only origin-destination and fixed cut-off grades may produce schedules that are not feasible when the actual constraints of the mining system are taken into account. Therefore, it is essential to include the considerations proposed in the planning process. <![CDATA[<b>An improved meta-heuristic approach to extraction sequencing and block routing</b>]]> Mine production scheduling can be solved through many different techniques that have the drawbacks of either producing sub-optimal solutions or taking a long time. In this paper, a new approach based on a meta-heuristic is proposed. Meta-heuristic approaches use processing, inference, and memory at the same time in order to learn how to improve the solution. Different meta-heuristic techniques and their applications to mine production scheduling are discussed. A meta-heuristic approach, a combination of heuristic memory and simulated annealing, as demonstrated by means of a case study, takes a sub-optimal solution and improves it over time; thus it provides the best solution that it finds in the given time. <![CDATA[<b>Multiple cut-off grade optimization by genetic algorithms and comparison with grid search method and dynamic programming</b>]]> Optimization of cut-off grades is a fundamental issue for mineral deposits. Determination of optimum cut-off grades, instead of application of a static cut-off grade for the life of a mine, maximizes the net present value. The authors describe the general problem of cut-off grade optimization for multi-mineral deposits and outline the use of genetic algorithms, the grid search method, and dynamic programming for optimal cut-off grade schedules for deposits with up to three constituent minerals. The methods are compared by assessing the results of the implications involved in using them. <![CDATA[<i><b>In situ </b></i><b>mining through leaching: Experimental methodology for evaluating its implementation and economic considerations</b>]]> Rising costs in the mining industry have necessitated a search for alternative methods for the recovery of metals from deposits that are no longer economically or environmentally exploitable by conventional mining. These alternative methods include in situ mining. A laboratory model was developed and an experimental programme undertaken to determine the effect of temperature, aeration, material compression, and material extraction on copper recovery by in situ leaching using H2SO4 and FeĀ³+. Recovery was estimated using the shrinking core model. Based on the experimental results and recovery estimations, an economic evaluation was completed comparing in situ mining with conventional mining methods. <![CDATA[<b>Coal quality management model for dome storage (DS-CQMM)</b>]]> Coal quality (ash, sulphur, moisture, and heating value) is one of the fundamental concerns for both coal mines and power plants. In order to deliver uniform coal quality to the power plant, there is a need for realtime monitoring of coal quality from the mine to the coal stockpiles. The specific problem represents the process of stacking the coal inside an enclosed facility such as a dome. The objective of this research was to develop a custom-made and integrated coal quality management model for dome storage (DS-CQMM). The DS-CQMM merges existing technology in surface mines, such as coal analysers, together with automation technologies, information technologies (IT), and mathematical models. The DS-CQMM is organized into four major sections: Delay Time application, Stacker application, Reclaimer application, and Live Stockpile application. A sub-process called Volume Calculation is embedded in Stacker application, while an additional feature called Forecast tool is included in the Reclaimer application. The DS-CQMM model was developed for a surface coal mine in the southern USA.