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By L E. Howard

Micro-computed tomography (Micro-CT) is an X-ray imaging technique that produces high-resolution 3D computerised ‘virtual’ models of geological specimens based on the X-ray attenuation of different materials in the specimen. The ‘virtual’ specimens are made up of voxels, the 3D equivalent of pixels, each assigned a gray value based on X-ray absorption. Micro-CT has enormous potential for allowing non-destructive, digital analysis of structure and composition when the mineralogical phases can be digitally separated. However, variation in X-ray flux between scans results in highly variable grayscale values for identical materials. Consequently, materials usually have to be segmented manually, which is subjective, inaccurate, labour-intensive and time consuming. Here we present a protocol and imaging software package that can be used to accurately and precisely extract quantifiable information in a repeatable and objective fashion. The software will allow the separation, visualisation, measurement and analysis of spatial and volumetric distribution of different mineral phases without damaging the specimen. This paper explains the potential use of this protocol and software package to ultimately allow automatic segmentation and quantification of gold in drill core samples, removing subjectivity and inaccuracies from data processing.

Sep 5, 2011

By S C. Dominy

Gravity amenable gold ores are those that after comminution produce liberated particles, composites and/or carriers that can be recovered by gravity separation means. Recovery depends upon mineralisation type and the comminution and concentration method used. Any in situ ore with more than 20 per cent of gold greater than 100 µm in size is likely to have a strong gravity recoverable component. A less traditional approach is where the gold is sulfide-locked and the gold carrier plus any liberated gold is recovered via continuous gravity recovery following fine crushing. Specific tests are undertaken to determine the level of gravity amenable gold within a deposit. The principal methodology is the gravity recoverable gold (GRG) test, which consists of three sequential liberation and recovery stages. For continuous gravity recovery (CGR), the sample is fed over a laboratory Wilfley table to simulate continuous recovery of the gold. Tails from the feed can be progressively re-ground and re-tabled to develop a grade-recovery curve. Primary geology and gold particle size-distribution have a strong effect on the mass of sample required to provide representative results for both tests. Where low-grade ore contains very coarse gold, substantially larger sample masses are required. Experience has shown that GRG and CGR values can vary throughout an orebody, particularly within different geological-grade domains. A testing regime must consider sample frequency and support.

Sep 5, 2011

By S Dunham

A cross-discipline approach combining geology, metallurgy and mine planning, geometallurgy is seen as the next logical advance in improving the design and operation of mining businesses. This rapidly evolving field is focused on realising the value that has often been lost in the chasms between the aforementioned technical silos. When successful, the geometallurgical approach promises the ability to tailor mining and mineral processing development and operational options more closely to the characteristics of the resource with resulting lower costs (both capital and operating), improved recoveries and fewer project failures.As with any technological (or managerial) change, there will be side effects from the success of the geometallurgical approach that will impact areas beyond obvious linkages between geology and processing. Some of these effects have the potential to be beneficial to the resource industry and society in general. Beyond the basic geometallurgical promise of closer alignment between processing design/strategy and the multivariate 3D spatial characteristics of the mineralisation, this paper discusses the broader implications and ramifications of the emergent geometallurgical approach: enhancing exploration and discovery; improving project development success; achieving sustainability in an industry working with non-renewable resources; in a world of increasingly hard won license to operate, improving the management of potentially harmful by-products and waste materials (with knock on effects for proactive management of closure issues); smarter approaches to energy management and thus optimisation of projects with respect to carbon footprint; and potential to re-value assets and thus realise a strategic advantage in the area of acquisitions, divestments, mergers and take overs. The authors strongly believe that companies that actively engage with the above issues (and ensure that technical efforts in the area of geometallurgy are not isolated from the larger value chain activities of the firm) will be able to exploit the opportunities that consequentially arise. These early adopters will gain first mover advantages. Firms taking the broad view of geometallurgy will have an opportunity to maximise stakeholder value and win greater success than their less insightful competitors.

Sep 5, 2011

By P A. Montoya

La Colosa is a world-class porphyry Au project located in Colombia, currently undergoing prefeasibility analysis. As part of collaboration between AngloGold Ashanti and the AMIRA P843A GeMIII Project, the application of emerging geometallurgical characterisation testing and modelling tools has been incorporated in early stages of project development. The aim of the geometallurgical study is to map inherent comminution variability across the La Colosa deposit providing information critical for mine/mill design and optimisation. In this study, site-based data incorporating multi-element assays, quantitative analyse X-ray difraction QXRD bulk mineralogy measurements, EQUOtip rebound hardness data, and routine geological logging information from 59 diamond drill holes has been integrated with a range of geometallurgically specific comminution test (ie GeMCi, A × b, BMWi). The work in this paper demonstrates the integration techniques used at La Colosa to link routine data acquisition methods with comminution test results through the development of proxy support models. The integration incorporates a range of statistical techniques including principal component analysis and regression modelling. By creating proxy models, comminution index estimates can be propagated into the geological database enabling comminution processing domains to be defined. These domains provide the first spatial representation of comminution performance and variability at La Colosa and can be compared against traditional geological domains. This study provides the foundation for ongoing comminution characterisation as the La Colosa project evolves through project cycles into an operational mine.

Sep 5, 2011

By B Armstrong

The Proterozoic poly-metallic Trilogy deposit was discovered in 1997, and until recently, mining evaluation was stalled due to poor metallurgical performance. Unlocking the substantial economic value of the deposit required a coming together of geologists, mineralogists, metallurgists and mining engineers, who by working collaboratively, uncovered the detail necessary to allow the required metallurgical breakthroughs to occur. The deposit itself is described as a sedimentary exhalative (SEDEX), with an enriched lead-zinc-silver core surrounded by a broader envelope of copper-gold mineralisation. The geological domains were defined in broad terms based on the style of mineralisation, assay values and lithology. These domains, while adequate for the resource estimation process, failed to translate into representative metallurgical domains. Additional work with new techniques defined regions in the deposit with altered processing properties which could not be recognised by conventional geological logging. Such additional complexities could not be appreciated at the outset of the metallurgical test work program.These regions were defined by applying the EDTA analysis method for recognition of where the valuable sulfides, particularly galena, had been partially modified by oxidising conditions. Hence, the flotation response of the valuable sulfide minerals was diminished and the increased amount of heavy metal cations available in the system significantly reduced selectivity in the flotation process.Understanding the detail of this complexity was essential in determining representative metallurgical domains, which was the first step in the long road of development.

Sep 5, 2011

By M A. Wells

A new approach to more reliably predict the Ni (and Fe, Mg) composition of Ni laterites was tested for RC samples from the (siliceous) oxide–style, Siberia North deposit, Kalgoorlie Nickel Project (KNP), in the north-eastern Yilgarn (WA) using partial least squares regression (PLSR) analysis of diffuse reflectance spectra over the 380 - 2500 nm wavelength range with a multi–element chemistry data set. PLSR cross-validation models were developed to predict FeO, MgO and Ni contents in composite samples with bulk composition limits of >0.08 wt per cent Ni and <6 wt per cent MgO. Calibration and validation models showed a strong, linear trend for predicting FeO and, to a lesser extent, Ni content, whereas PLSR models for predicting Mg content were less reliable. This reflected the prevailing mineralogy and associated element deportment within the oxide laterite profile. For example, Fe and Ni distribution is controlled by relatively few minerals, predominantly goethite and, to a lesser extent, the Fe3+-bearing smectite, nontronite. Magnesium is associated with a range of Mg-bearing minerals, some of which are compositionally complex (eg serpentine, talc, chlorite, smectite and carbonates). Magnesium chemistry alone does not reflect the varied mineralogy of samples with relatively high (5 - 6 wt per cent) MgO contents, which can be spectrally distinct. Hence, samples with similar Mg contents, though comprising Mg-bearing minerals with distinct reflectance spectra, were difficult to model (eg smectite versus Mg-carbonates). The findings of this study demonstrate that characterisation of Ni laterite composition may be achieved as part of routine logging of either drill chips or diamond drill core using PLSR analysis, but that a detailed understanding of the mineral-element association is essential for reliable predictive analysis. This has particular relevance to characterising the geometallurgy of Ni laterites, especially where deposits may be classed as the same type, but have distinct compositional and mineralogical differences.

Sep 5, 2011

By S Leichliter

La Colosa, Colombia is a large gold-porphyry deposit currently undergoing feasibility studies by AngloGold Ashanti. This period of development allows the opportunity to apply innovative and emerging testing and modelling methods to provide a predictive geometallurgical recovery model for the deposit. By partnering with the AMIRA P843A Geometallurgical Mapping and Mine Modelling (GeMIII) research project, the geological and mineralogical data are analysed with respect to liberation and recovery methods. This will allow the variability in the geology and mineralogy of the deposit and key relationships recognised in the data to be included in the development of a model to help predict recoveries of the gold. Many aspects of the gold mineralisation such as gold paragenesis, associations, grain size and texture are determined and analysed using optical microscopy, Mineral Liberation Analysis (MLA), and laser ablation (LA-ICP-MS). At La Colosa the gold has been located in sulfides (eg pyrite, pyrrhotite, and chalcopyrite), silicates, along silicate phase boundaries, and among the pyrite-rich intermediate argillic alteration. Further testing and analysis will also determine if ‘invisible’ gold is present in the pyrite, pyrrhotite, and chalcopyrite. Recovery methods can also be determined and tested after combining the results of mineralogy tests with other data (eg results of comminution tests). Indices for the mineralisation and recovery are developed using image analysis and analytical testing. These indices are correlated with recovery data from the company to develop a predictive block model for recovery. The goal is to develop cost-effective, efficient methods to analyse the mineralogy and geology of the deposit; understand the variability in the mineralogy, geology, and recovery; and construct a predictive geometallurgical recovery model.

Sep 5, 2011

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