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By B Newcombe

From many years practical industrial experience the author has drawn together a list of the key points to consider when embarking on a large-scale geometallurgical project. Fundamental aspects which must be considered include (but are not limited to):understanding the variability of the orebody both laterally and with depth;comminution properties and variability;flotation properties, requirements and variability – differing ore zones may have substantially different requirements for optimal performance;the role of texture – the consequences of both grain size and distribution and how the circuit will handle these;realistic expectation in terms of the financial requirements and time frame results are required within; andintegration of, and communication between key personnel within the various departments of the site (mining, geology and processing) is a fundamental aspect of any geometallurgically based processing system. An example of a highly complex nickel sulfide ore will be used as the basis of discussion.

Sep 5, 2011

By G Harbort

The use of geological data for optimisation of operating plants has become a significant part of the modern process mindset. The underlying principle is to convert spatial geological data into metallurgical information to drive production and mine planning, blast design, blending strategies and plant set-up. Increasingly the process designer is required to interface with geological and mining staff to propose the best investment strategy for a project’s benefit.The geometallurgical model development for the Zafranal copper-gold project commenced in March, 2010, several months after the start of a major drilling campaign by AQM Copper Inc. The initial evaluation stage required review of historical Teck RC data to determine areas of metallurgical interest within the project. As drilling transitioned from RC to diamond drilling, core that was considered representative of the resource was selected and subjected to MLA mineralogy analysis. When coupled with sequential copper analyses this allowed calibration of an assay derived mineralogy model, allowing process concept options and a preliminary test program to be developed. This Phase 1 test program provided a preliminary process design, operating costs and anticipated concentrate grades and recoveries. Both this data and resource estimation were used to generate a preliminary Whittle pit optimisation based on maximising net smelter return, which in turn fed into a preliminary mine plan. The preliminary mine plan, in conjunction with geological logs and core results was used to develop a phase 2 metallurgical test program, where variability tests were conducted throughout the resource and composites formed to investigate specific years of the mine plan in locked cycle flotation. In addition comminution tests were modelled against geometallurgical domains allowing comminution components values to be calculated for 36?000 m of core. A flotation geometallurgical model was developed and incorporated into the resource model allowing concentrate grades and recoveries to be estimated on a block-by-block basis.

Sep 5, 2011

By V Liebezeit

Data management is a significant, but perhaps unrecognised, consideration for any organisation embarking on a geometallurgy program. While resource geologists are accustomed to dealing with thousands or millions of samples and their associated assays, metallurgists and mineralogists have traditionally carried out only a few expensive and complicated tests and consequently tend to manage the resultant data in spreadsheets. The emerging discipline of geometallurgy arrives with significant challenges as a result of the sheer volume of data generated. As an example, the Olympic Dam geometallurgy program has generated more than:60 000 physical subsamples analysed for up to 30 analytes; 25 000 spreadsheet-based laboratory test results; 30 000 sized fractions analysed via QXRD, MLA and/or QEMSCAN;multiple external providers of data, and more than seven terabytes of data. Considerations for any geometallurgy data management system include storage space, data integrity, accessibility, traceability (sample provenance), maintenance and ease of use, integration with data providers and the ability to modify the system to adapt to changing requirements. Further, aspects of a geometallurgy program, such as conducting a range of tests on one sample, producing subsamples such as concentrates and tailings, compositing and routine QA/QC may not be well handled by existing software and providers geared to standard geochemistry, mineralogy or metallurgical test programs. The development of a geometallurgy database using existing software (AcQuire), and the associated file management system will be described for a large geometallurgy program at Olympic Dam. As with many aspects of geometallurgy there is no approach that meets the needs of every site or program. Recognition of data management as an issue, a structured approach in the program planning phase and commitment to executing the strategy will increase the chances of successfully mining the geometallurgical data.

Sep 5, 2011

By J N. Farrell

Metallurgists frequently use a small number of large samples to characterise expected mill performance, with the a priori assumption that these few samples are representative of the entire deposit. Geometallurgical sampling is becoming more prevalent as a way to use many smaller samples to indicate the expected recovery as part of the Mineral Resource or Ore Reserve estimation.The evaluation of magnetite deposits involves specialised approaches to sample preparation, sample analysis and resource estimation. The sampling protocol for magnetite deposits will typically include the use of Davis Tube analysis. The Davis Tube is a mini-pilot plant that is used for determining the mass magnetic recovery from a small amount of sample ground to a specific size. The magnetic concentrate may then be analysed and together with the mass magnetic recovery, may be used to simulate the expected mill recovery. The magnetite sampling protocol is specific to each deposit, and is typically designed around a specific metallurgical flow sheet.During resource modelling, the mass magnetic recoveries and concentrate grades may be estimated directly or assigned to a block model using regressions with other chemical constituents. For estimation, the concentrate grades are weighted to account for the different mass magnetic recoveries of each sample and the impact of these differences on the concentrate estimates.

Sep 5, 2011

By R Hartner

Digital optical microscopy (DOM) and automated SEM-based (ASEM) mineralogy systems (MLA, QEMSCAN) have experienced significant developments within the last decade. However these developments have been independent from each other and the two mineralogical techniques have so far not yet been integrated to combine the strengths and technical benefits of both analytical platforms. This detailed comprehensive mineralogical information is critical support for geometallurgy.Major hardware and software advances in DOM in the last few years have provided important new capabilities with potential applications to automated mineralogy. These technological advances have been largely driven by sectors outside mining (eg medical pathology) and have not yet been widely adopted within the minerals industry. The advent of DOM offers significantly more automated mineralogy capabilities than traditional expert-mineralogist driven optical microscopy. This is based on advances in automated image acquisition, high resolution cameras for digital imaging, imaging of large areas through mosaic options, integration of multiple layers and application of advanced image analysis techniques.Ongoing research involves combining the outputs of DOM and ASEM-based microscopy to create new capabilities for integrated microscopy based on development of advanced cross-platform image fusion and data integration between DOM and ASEM (exploiting the benefits of both analytical platforms). This requires non-linear image registration and transfer of mineralogical identification from ASEM to DOM systems using sophisticated image manipulation and data analysis software.Examples will be given of image fusion and data registration for a range of different ore types. Image fusion techniques are demonstrated using a porphyry copper deposit sample where sulfides and precious metals are classified using the MLA and gangue mineralogy obtained from DOM images. Data integration enables creation of a library containing optical property variability information for minerals identified by the MLA; thus reducing the reliance on skilled mineral identification by supplementing human interpretation

Sep 5, 2011

By L L. Kuhar

The AMIRA International P843A Geometallurgical Mapping and Mine Modelling (GeMIII) project is an interdisciplinary research collaboration between 17 global mineral companies and four research organisations aimed at providing support for the emerging industry trend of geometallurgy. The main aim of the project is to develop predictive geometallurgical indices which can be incorporated into a resource block model to maximise the commercial value of an orebody and to minimise the social and environmental impact of mining operations.Researchers at the Parker Centre/CSIRO have been focusing on the development of predictive hydrometallurgical indices for the GeMIII project via rapid, small-scale, cost-effective tests and protocols for comparative ranking of attributes relevant to leach performance. The focus of the small-scale leach tests has been to determine the leach index (relative indication of leach performance), recovery, impurity deportment, reagent consumption, effect of size/degree of liberation and mineralogy of samples for their relative ranking. Predictive tests for heap leach hydrology (related to agglomerate characteristics) and slurry rheological behaviour are also being developed.

Sep 5, 2011

By K Ehrig

Two irrefutable facts in geology and metallurgy are: ore deposits are comprised of variable mixtures of minerals which are systematically distributed throughout the deposit, and mineralogy exerts the primary control on metallurgical performance. If the distribution of process critical minerals can be quantified and mapped, then a logical assumption is that metallurgical performance can be predicted across the deposit.Olympic Dam is a +9 billion tonne Fe-oxide Cu-U-Au-Ag deposit which is hosted entirely within a magmatic-hydrothermal breccia complex consisting of over seventy ore and gangue minerals. Each has a unique set of metallurgical performance properties. Fortunately, 15 of the 70 minerals accounts for >98 per cent of the total rock mass.The mineral mapping journey commenced in the early drilling days shortly after the discovery of Olympic Dam in 1975. Geologists quickly recognised that the copper-iron sulphides had a predictable zoned distribution. However, geological models reflected the presence or relative abundances, not quantitative concentrations, of the minerals. This type of mineral mapping remained relatively unchanged for ~20 years and was of limited use outside of geology.Several things changed during the mid-1990s: assaying costs decreased significantly, instruments capable of automatically measuring mineralogy became readily available, the recognition of two quantifiable relationships at OD (ie mineralogy is a function of chemical composition (ie assays), and metallurgical performance is a function of assays, mineralogy, texture and process conditions. The linkage of metallurgical performance to mineralogy and mineralogy back to assays allows for every assayed sample in the resource model to be assigned mineral concentrations and metallurgical performance predictors. At Olympic Dam, block models (ie 3D maps) are routinely produced for 27 elements, density, 15 process critical minerals and greater than 50 metallurgical performance variables. These geometallurgy-enabled resource models are used for mine planning, process plant design and process optimisation studies. *This is an abstract only. No paper was prepared for this abstract.*

Sep 5, 2011

By C Lower

An unprecedented, quantitative, deposit-scale understanding of major clay mineral distribution throughout the Yeelirrie uranium deposit has been achieved by adapting the existing HyLogger™/HyChips™ analytical technique to measure resource assay pulps. Spectral analysis via HyLogger™ and HyChips™ systems has previously been used as a small-scale geometallurgical research tool, but never before has it been quantitatively applied to an entire resource sample population for the purpose of developing stand alone block models for major clay minerals. Economic uranium mineralisation within the thin but extensive Yeelirrie?palaeochannel?primarily exists within the transition zone between a groundwater-derived calcrete and the underlying clay-quartz regolith. Early geometallurgical investigations identified that certain clay minerals could be deleterious to the efficiency of the uranium recovery process. Accurate calculation of clay mineralogy from resource assays proved too difficult due to chemical similarities between co-existing kaolinite, smectite and magnesium carbonate minerals. A relatively low-cost, efficient and accurate method of quantitative clay mineral analysis was required to measure over 70 000 resource assay pulps. After initial testing and comparison with QXRD, the HyChips™ 3-1 spectral logging system was implemented as a primary method of clay mineral analysis for the Yeelirrie uranium deposit. The resultant spatial data set allowed the interpretation and geostatistical analysis of geometallurgical domains across the deposit. Clay block models were generated and used in geometallurgical resource calculations to benefit the mine planning process.

Sep 5, 2011

By J Vann

Geometallurgy is inherently multidisciplinary and it gains its power to deliver improved outcomes for the minerals industry by bridging silos between disciplines. This cross-disciplinary approach unlocks value, identifies (and avoids) value destruction and provides a pathway to better implementation of an energy efficient and sustainable minerals industry. Despite this promise, we argue that the cross disciplinary approach also represents the greatest threat to the success of geometallurgy. Some key cross disciplinary issues identified are:Geometallurgy has so far evolved in the technical sphere, mainly by collaborations of geologists and metallurgists. Consequently, in many cases the business dimensions and value have not been incorporated. Financial and risk evaluation viewpoints need to be assimilated into the geometallurgical paradigm. In fact, until such measures are integrated into practices, the value cannot be fully demonstrated.The historical and current efforts on geometallurgy have been highly focused on improved sampling and measurement of geometallurgical properties or appropriate proxies. This work has been driven by professional and academic geologists and metallurgical engineers. It is notable that mining engineers (who are critical in the value delivery of geometallurgy through changed planning approaches) have so far been at the margins, or absent. The construction of valid three dimensional (3D) geometallurgical models raises some important organisational, mathematical and geostatistical considerations. The enhancement of resource models to account for geometallurgical aspects must be managed carefully if the full value of geometallurgical thinking is to be realised.The use of 3D geometallurgical models to change business decision making and practices is the real nub of the problem. We believe sophisticated scenario based evaluation methods are required if the potential impacts of spatial rock properties on processes further down the mining value chain are to be realistically evaluated. Such changed practices require ‘whole of value chain thinking’ to embed this change into the business in an ongoing way.Because profitable geometallurgy demands changed approaches to business decision-making and also organisation of the firm (radically changed in some cases), the senior executive viewpoint must be incorporated into the language and thinking of geometallurgy. This is unavoidable if the establishment of a geometallurgical approach to project evaluation, development and operation is to be successful and sustained.A powerful analogy for the problem faced by geometallurgy is the transformation of safety culture in the mining industry in the past 20 years. We argue that, as in the safety revolution, explicit models, cultural change and management of the ‘soft’ dimensions of the problem are pivotal to success of geometallurgy. In light of this, we propose a model (‘the Geomet Curve’) that gives a framework and pathway for geometallurgy in seven steps. The implications of this model for technical and organisational steps required at different stages are discussed.

Sep 5, 2011

By E Macmillan

For any processing plant there will be deleterious elements or minerals which affect production or quality, and a strategy to predict their occurrence and mitigate their impact is required. An early objective of the Olympic Dam geometallurgy program was prediction of the acid consumption and gelling behaviour of the ore, which affects leach recovery, throughput and circuit availability. The geometallurgical test program measured the response of a wide range of ore types, and then relationships between mineralogy, metallurgy and chemistry were developed. Thus the drivers of acid consumption and triggers for gelling events could be described in ways that were meaningful to plant operations.To enable optimisation of the mine planning and ore blending processes, the parameters driving metallurgical response must be translated to the block model. Data support is critical in populating a block model. Assay information is easily obtained, but mineralogical and metallurgical information is more expensive and time consuming to collect. Hence population of a block model with mineralogical and metallurgical values using a conventional geostatistical approach is not always possible.An alternative approach employed at Olympic Dam was the development of a suite of assay-to-mineral algorithms. These algorithms were calibrated using all available mineralogical information, and this enabled the calculation of mineral abundances for more than 1.5 million assayed samples. Estimation of the minerals in the block model, and the generation of acid consumption values for each block could subsequently occur.The use of back-calculated mineralogy from assays as part of process model generation is an innovative tool which can be used for mine planning and optimisation and to ensure the plant does not receive any unwelcome surprises. Ultimately, one purpose of geometallurgy is to predict potential processing issues and their drivers so that these can be mitigated. A strategy to achieve this is outlined hereafter.

Sep 5, 2011

By R Knight

The spatial variability of geometallurgical characteristics in a mineral deposit can have significant impact on financial and mine planning, mineral processing, concentrate quality and tailings composition throughout the life of a project. These characteristics that may determine the success of a project are not readily apparent to geologists unless they are integrated with deposit geology and assay data, modelled in 3D and incorporated into resource block models.One of the primary objectives of geometallurgy is to integrate and incorporate various geologic, geotechnical and geometallurgical parameters into resource block models, which typically are limited to in situ tonnes and grade of economic metals. Critical attributes, such as concentration of deleterious elements that affect the project viability need to be identified and integrated into the resource block model early in the exploration stage to predict project profitability and address potential environmental impacts.Using data from a volcanogenic massive sulfide deposit we present an example of the integration of ore geochemistry (cadmium, arsenic and antimony) with the results of preliminary, bench-scale metallurgical test work in a 3D geometallurgical model that predicts subdomains of poor-quality concentrate and tailings containing environmentally-sensitive elements. This critical information was incorporated into the existing resource block model in order to inform both financial modelling and mine planning.This example demonstrates that geometallurgical data can be effectively integrated with geological and assay data using Leapfrog MiningTM software (Leapfrog Mining) early in the exploration stage of a project. Three-dimensional representations of geometallurgical data can be created directly using primary additive data, as well as from proxies of non-additive parameters. Resource block models can be updated continually with new information as the project passes from exploration through production. In addition, the software allows for 3D visualisation of the characteristics of the materials that will eventually constitute the mill concentrate, tailings, and waste. This study emphasises the critical importance of establishing a systematic geometallurgical approach early in the exploration stage that can be continuously updated with new information throughout the life of the project. In the example that we present, sulfide ore that had otherwise been identified as high-value (in the absence of geochemical and metallurgical data) turns out to be low-value ore due to concentrate penalties and increased environmental impact.

Sep 5, 2011

By L Lorenzen

The selection of samples for metallurgical test work is generally focused on providing geologically representative samples. One of the major problems experienced with metallurgical test work using drill hole data, is the small sample sets selected for metallurgical testing. Major process and financial decisions are being made from results obtained using snapshot samples. Snapshot samples means that samples are basically selected only by grade and spatial location with the assumption that the rest of the orebody or portion of orebody is similar and will respond metallurgically the same way – ie the assumption is that the mineralogy and lithology are basically the same as the test sample. From the test work, algorithms were usually developed and used to describe throughput and recovery using some explanatory variables including grade (mineral chemistry) and ore hardness. These algorithms were then used in the recovery models to determine tonnes produced and plant throughput. However, the metallurgical parameters are a function of many geological factors including grade, lithology, alteration, mineralogy, spatial relationships and SG. In previous years, the limited mineralogical information available was deemed enough for the metallurgist to make decisions as to how many samples are needed to evaluate the metallurgical performance of the ore during processing, and subsequently the basis for the plant design. If the orebody is consistent and not complex or refractory (thus, relative simple lithology), then only spatial and grade samples will be needed to evaluate the throughput and recovery, so limited mineralogical information may be acceptable. There is now, however, an increasing realisation that the mineralogy and geology change, as do the metallurgical characteristics of the orebody, so limited samples may not be sufficient for effective metallurgical test work.It is the purpose of this paper to discuss the effect correct mineralogical information has on the subsequent metallurgical test work program and process selection using a case study. Factors such as type of rock, rainfall and topography are thought to influence the minerals that ultimately form in various orebodies, with the result that different orebodies often react differently during metallurgical test work programs. It is therefore important to obtain accurate mineralogical and elemental deportment data, which will also indicate where the valuable metal containing minerals are located within the orebody. The better you plan and execute your metallurgical test work program during the early phases, the more accurate the decision-making process will be during the selection phase. Improved integration between geology and metallurgy data gathering (geometallurgy) during early phases will result in more accurate predictions with regard to throughput and recovery during modelling and process. Equipment selection will be considerably simpler in later phases with early planning, with improved mine and process scheduling and subsequent net present value (NPV).

Sep 5, 2011

By M J. Newton

As part of the AMIRA P843 project Keeney and Walters (2008) describe how the principal components analysis (PCA) method can be used to identify classes of samples with similar metallurgical characteristics. However PCA does not take account of the location of each sample in 3D space. This paper describes a study to analyse the spatial distributions of the classes and determine appropriate modelling methods to control the estimation of the comminution parameters and mill performance indices into a 3D block model. This includes but is not restricted to geostatistical techniques that are used for grade modelling. The starting point for the study is the class definitions and the regression models for estimating the comminution parameters and performance indices. Each sample is assigned a class and the spatial distribution of each class has been analysed using different techniques including the calculation of variograms. This establishes whether the classes can be used as domain control for estimation into the block model – a procedure similar to using geological rock type domains for controlling the estimation of grades for a resource model. However, standard geostatistical estimation methods such as ordinary kriging assume that the variable being estimated is additive or linear. This assumption is valid for variables such as grade but this is not necessarily the case for the comminution parameters or the performance indices. Therefore the estimation procedure must either use variables that are definitely additive or the effect of potential non-additivity must be tested and accounted for where possible.The results of the study show how performance indices such as recovery, throughput and specific power can be estimated into the block model. This model is then combined with the geological model to provide a multi-parametric model that is used to optimise the mine planning and scheduling activities which can lead to significant improvements in NPV.

Sep 5, 2011

By A Stradling

For a geometallurgical program to be successful, linkage must be made between the geologists and geostaticians who understand and model the orebody, the metallurgists who determine the value that can be extracted from the different geometallurgical units, and the mine planners who develop and optimise the plan to extract maximum economic value from the resource. One of the primary purposes of geometallurgical models is in the determination of the economic cut-off between ore and waste. Recognition of the level of confidence required in the estimation of grade, throughput and recovery is critical to the planning of a successful geometallurgical program. It also helps clarify the scope of work needed to mitigate risk at each stage in the development of a new mine projectThis presentation provides some of the author’s experience with development and application of geometallurgical models for existing base metal operations. It discusses how geological units defined by lithology, structure, alteration etc, can be translated into geometallurgical ore types defined by mineralogy, ore hardness, and flotation behaviour. Examples of how these characteristics have been used to estimate key economic parameters such as mill throughput and flotation recovery at two brown-field operations are provided. The first example relates to the modelling of metallurgical performance at Teck’s Red Dog zinc-lead mine in Alaska. Prior to 2000, zinc, lead and silver recoveries from a block of ore at Red Dog were either assumed constant, or were varied according to simplistic models. Throughput was not thought to vary substantially between blocks, so an average value was used for each blended stockpile. It is quite possible that some ore-value blocks were classified as waste, and some waste-value blocks treated as ore, as a result of poor understanding of the true economic cut off across the wide range of ore types observed at Red Dog. After an extensive flotation test program, the first assay-based zinc recovery model was developed for the Main pit reserve in 2003.**This is an abstract only. No paper was prepared for this abstract**

Sep 5, 2011

By R C. A Minnitt

The Witwatersrand Supergroup is host to the world’s largest gold deposits. The location, quantity, grade, continuity and other geological characteristics of a Mineral Resource are either known, estimated from specific geological evidence and knowledge, or interpreted from a well constrained and portrayed geological model. Establishing such a model is an integral part of the evaluation process in the Anglogold Ashanti (AGA) group, and is referred to as a Geoscience Business Model for the mining operations. The geological model established over the years has relied on detailed descriptions of the mineralisation, and understanding of the regional and local geology of the Witwatersrand sediments, in the West Wits and Free State goldfields. The processes of data collection in regard to underground stope and developing mapping, structural and sedimentological mapping, and the geological inputs to mine planning systems are described, together with aspects of drilling, sampling, grade control, and data management. Anglogold Ashanti’s Geoscience Department employs a complex and unique process to generate the Mineral Resource, including a 3D geological structure model of the orebodies which are usually highly faulted, and a robust mineralisation/facies model on which the decisions about domain boundaries are established. A Mineral Resource Inventory System (MRIS) has a dynamic graphic interface to the resource database that is used for compiling the resource statement. It is also used to determine the gold-called-for, as well as short and long-term planning. A unique estimation process using the extensive closely spaced stope face sampling database and the more widely spaced surface drilling information is used in combination in the Macro Co-Kriging estimation. The use of different support sizes in the C0-Kriging process is preceded by the equally unique ‘clusterisation’ and ‘acceptorisation’ procedures by which the data are quality controlled and assured prior to their use in the kriging process. The Mineral Resource and Ore Reserve estimates are classified according to the SAMREC code through a method that ensures that they are compliant, auditable, transparent and reconcilable with previous Mineral Resource and Ore Reserve statements.

Aug 22, 2011

By M J. Nugus

The structural models developed by exploration and mining geologists are most commonly concerned with the ‘main structures’ or key constituents that pertain to the associated mineralisation. To this end, it is recognised that the geological and exploration models, which are developed, are essentially ‘fit for task’ and the scale-related variances are not always realised at an early stage. In fact, in many cases anomalous intercepts along different trends and of variable apparent continuity have often been neglected as ‘back scatter’ and not important. Recognition of the importance of subordinate structures as mineralising hosts suggests that additional opportunities exist outside the primary and main structures. History shows that opportunities are often not realised where gold is hosted in orientations not parallel to the predominant trend of the schistosity, main fault or shear zone.Another reason for the importance of subordinate structures can be understood from their roles in a percolation network. Subordinate structures can access the large fluid fluxes provided by backbone elements, but, as dangling elements, they provide greater opportunities for gold precipitation mechanisms and hence may constitute essential parts of the resource. Therefore, the role, understanding and consideration of the smaller-scale and orthogonal structures should be considered during all facets of exploration, resource development, delineation and mining.

Aug 22, 2011

By O Rojas

The Telégrafo copper – gold deposit is located in the Centinela district of the Antofagasta region in northern Chile. Telégrafo is a porphyry copper deposit of Eocene – Oligocene age which has a total mineral resource in measured, indicated and inferred categories equal to 2.6 billion tonnes with an average grade of 0.38 per cent copper.Currently the project is in the prefeasibility study phase, including an infill drilling campaign to improve the knowledge of the deposit that should result in an improvement of the mineral resource classification. In this context was decided to review the classification criteria in order to optimise the drill hole density necessary to support the project.This paper presents an application of conditional simulation to evaluate the risk related to drill hole spacing, using a geostatistical approach that mimics the process of evaluation of resources from the perspective of geological modelling and grade estimation. The first step of the approach is the generation of conditional simulations using the available samples to generate several plausible scenarios of the deposit in terms of copper grades and geological units. These scenarios are considered as the unknown reality of the deposit and sampled by synthetic drill holes in order to replicate the geological model process and copper resource estimation. In this way it is possible to establish differences between the possible scenarios and the generated resource estimation models for several simulated drill hole spacing alternatives. These differences or errors can then be quantified in terms of estimated mean, metal content and tonnage over different production volumes.The resulting errors were useful in assisting the decision making process related to the design of drill hole spacing for each category of mineral resource in the Telégrafo project, and to redistribute the budget associated to the drill campaigns for the next stage of the project.

Aug 22, 2011

By G Chitombo

Mass mining methods have become the mining industry’s preferred way of extracting most of the world’s most important metal and mineral commodities. Given the rapidly increasing global demand for commodities, the requirement to increase the contribution of some of these (eg copper and gold) from underground sources also continues to grow. In the case of copper this contribution is needed by a number of major producers to supplement production from their ageing deep open pits. Satisfying this increased demand will require the design, construction and operation of more effective, higher capacity and larger scale mines which, in turn, will require enhanced mass underground mining techniques such as block caving (BC), panel caving (PC) and sublevel caving (SLC).It is now widely accepted that future discoveries of orebodies, which will be developed using these methods, are almost certainly to be much deeper than the deposits presently being mined in this way and made more challenging because of the likelihood that the associated grades may be much lower than currently mined. The design and mining of future large-scale cave mines (supercaves) will become crucial in order to extract full value from such deposits. An essential prerequisite of this will be a much improved knowledge of the deposit, ie Total Deposit Knowledge. In this context, Total Deposit Knowledge is the characterisation of the ore deposit and its surrounds at all scales and includes; rock type (lithology), the spatial orientation of a range of geological features or discontinuities, rock mass fabric knowledge, metal grade and distribution data, geometallurgical data (including alteration), geo-hydrology, permeability and porosity. This is the information that will be used, as now, to delineate geological and geotechnical domains and construct models, but with outcomes superior to those presently available. From this information better-informed feasibility study design decisions and performance predictions must be made. In summary more geological information and analysis is required and this needs to be aligned to potential mining and processing requirements much earlier in the project than commonly occurs.

Aug 22, 2011

By D A. Sketchley

The Merlin Zone, a 2008 high-grade Mo-Re discovery, is a part of the Mount Dore Deposit in the Eastern Succession of the Mount Isa Inlier in Queensland, Australia. Hosted by carbonaceous metapelites and phyllites, the Merlin mineralisation comprises molybdenite veins, breccia infillings, stylolitic fracture fillings, and disseminations. Because rhenium is geochemically similar to molybdenum and commonly associated with it, global production of rhenium has been mostly a by-product of molybdenum refining. The high-grade rhenium in the Merlin Zone is an exciting discovery that is expected to become a direct source of rhenium. At the time of discovery, quality assurance (QA) procedures for rhenium were not in place at the Merlin Zone; therefore, they needed to be developed.The Merlin Zone was discovered using reverse circulation drilling, which led to concerns about molybdenite loss due to poor sample recovery. The twinning of the discovery hole with diamond core demonstrated a two-fold increase in molybdenum grade over several tens of metres. Consequently, the resources of the Merlin Zone were defined using assays from diamond drill core.Early analytical work, which was initially done only for molybdenum, comprised geochemical analysis with an aqua regia digestion, which have poor precision and can be biased low. Although initial testing for rhenium used similar procedures to those for molybdenum, a scarcity of commercial rhenium reference materials led to a validation process using neutron activation analyses and different digestions. This resulted in introduction of four-acid digestion assays, development of matrix-matched reference materials, and improvements to the quality of molybdenum and rhenium assays for reporting resources. Coarse and pulp duplicates were used to check for sampling bias and to ensure that levels of precision were adequate for the style of mineralisation and analytical methods. In addition, the direct and consistent geochemical correlation between molybdenum and rhenium provided another method for checking analyses.

Aug 22, 2011

By D Cooke

In 2010, Kalgoorlie Consolidated Gold Mines (KCGM) was Australia’s largest gold producer, with total annual production of 788 000 ounces. Of the gold produced by KCGM in 2010, 95 per cent was extracted from the Fimiston open pit, also known as the Superpit. The Fimiston operation encompasses the historic Golden Mile, from which more than 55 million ounces of gold have been extracted from the world-class Fimiston lode style mineralisation.The operational performance of the KCGM Fimiston open pit was reviewed for the period between 2005 and 2010. Key performance metrics reviewed included mined tonnes and grade, cost per ounce of gold produced and the mine to mill reconciliation trends. The years 2007 and 2008 were characterised by a decline in performance against key performance metrics. The ore zones were diluted, with the excess tonnes mined resulting in lower grade and an increase in the cost per ounce. Poor reconciliation between ore mined, the ore reserve model and the grade control polygons was also observed.A series of improvement initiatives were implemented in late 2008. As a result of changes made to the grade control and mining practices, the site performance recovered. Unplanned dilution was reduced, which had a positive impact on mined grade. The lower tonnes combined with higher grade reduced the cost per ounce to below budgeted levels in 2009 and 2010, enhancing the profitability of the operation. The operational performance between 2005 and 2010 was compared to the turnover statistics for the KCGM geology department over the same period. Turnover accelerated between 2005 and 2007, peaking at an annualised rate of 43 per cent. With the onset of the Global Financial Crisis and the implementation of retention initiatives, turnover reduced significantly between 2008 and 2010. A strong consistent correlation was observed between the turnover trends in geology and the performance of the grade control function and resultant site metrics.The cost of turnover is known intuitively, however, rarely quantified. To evaluate the benefit of the improved performance, a detailed financial model was used to quantify the impact of the grade control function. In 2010, the headgrade exceeding budget by just four per cent contributed an additional A$24 M of value to KCGM. In contrast, if the 2008 performance had been replicated in 2010 (-9 per cent on headgrade), the result would have removed A$61 M in value from the business. The KCGM experience between 2005 and 2010 demonstrates the value of human capital in a mining operation and the importance of a high performing geology grade control team to the profitability of a mining operation.

Aug 22, 2011