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By J Jackson

The term geometallurgy (or geometallurgical) first appeared in the literature some 40 years ago and has a wide range of definitions and perspectives which increases the barriers of communication between practitioners and across functional groups and management levels within organisations. However the consistent theme running through the diversity of definitions and perspectives is the requirement for close collaboration between the relevant disciplines. This concept of geometallurgy arguably goes back to the earliest days of the exploitation of mineral deposits, through the mid-1500s to the beginning of the 20th century.This holistic and integrated view was diminished during the 20th century due to a number of factors which included the commencement and continuing upward trend of large-scale mining and the expansion in production, the rise of scientific and classical management theories and methodologies, increase in specialisation and the development of traditional project planning. The re-emergence of geometallurgy in the late 20th century required the relevant functional units and disciplines to collaborate as a team. This necessitates a common understanding that the geometallurgical program’s goals must be obtained, which requires clear and consistent communication, particularly with the modern trend of virtual teams.A multi-dimensional construct is proposed to assist in providing a consistent approach in the communication of the context and purpose of both general and specific geometallurgical programs, thus simplifying communication. This geometallurgical construct consists of the dimensions of process span, response knowledge, variability knowledge, spatial knowledge, application and project phase in relation to extraction process(es) applied to geological materials from a mineral deposit. These dimensions are then linked to the phase of mineral project development in which the geometallurgical program is being applied, which ranges from advanced exploration through to operational execution. This paper outlines the multi-dimensional construct with examples of how the context, purpose and scope of geometallurgical programs can be clearly delineated and communicated.

Sep 5, 2011

By A R. Bye

The mining industry is facing some key challenges that provide a compelling case for geometallurgy initiatives. Over the last 30 years, the average grade of Australian orebodies being mined has halved while the waste removed to access the minerals has more than doubled. In the last eight years, the industry’s energy consumption has increased 70 per cent while multi-factored productivity has fallen 24 per cent. The paper documents a broad range of industry case studies which demonstrate strategies for gaining value from geometallurgy.The mining industry constantly faces the key questions of how significant ore variability is on mine valuation and how would knowledge of the variability change the mining schedule, mine to mill design and operation. Using case studies the paper describes the methods used for incorporating mineral extraction attributes into orebody models and their technical integration into daily operation as well as mine planning optimisation. While pit optimisation from a fully attributed geometallurgical model highlights areas of opportunity and risk it is shown that during the scheduling phase of mine planning that significant value can be added. The benefits from these projects included reducing geotechnical and environmental risk, improving mine to mill performance, option analysis and quantified cash flow risk. There is growing acceptance within the minerals industry that ‘realistically assumed modifying factors’ defined by a competent person/s are not sufficient to mitigate the risk of funding mining ventures. Geometallurgy is moving away from factored ore reserves to data-rich block models providing reliable information for mining, metallurgical and environmental considerations.

Sep 5, 2011

By R Baumgartner

The Canahuire deposit is a recently discovered Au-Cu-Ag epithermal deposit in Southern Peru. A geometallurgy program is a key study element of the project Feasibility study. Metallurgical, mineralogical, and comminution studies have been completed over a suite of samples, spatially distributed among the various geological/ore domains. The early stage of Canahuire presents challenges as there is a lack of metallurgical information to guide geometallurgical sampling, in contrast with an established deposit at a more advanced stage of development or mining. Nevertheless, the available information will assist in the development of geometallurgical domains for Canahuire in order to ensure that future metallurgical, mineralogical, and comminution test work is representative of the deposit variability. This supplementary knowledge will be able to predict important metallurgical parameters which will be incorporated into a geometallurgical model to spatial predict material types, metallurgical recovery and through put. The information obtained will be a valuable addition to the future mine plan, influencing process design, engineering design, plant operation, operating and capital costs, and ultimately will reduce project development risk.

Sep 5, 2011

By S C. Dominy

For any gold deposit-based geometallurgical study, the collection and processing of representative samples for gold grade and recovery tests is critical. Sampling protocols should be designed to suit the style of orebody in question. The proportion and size of gold particles has a direct impact on the effectiveness of sampling protocols and metallurgical recovery processes employed. Studies focussing on ore mineralogy and gold particle deportment, size and distribution are required. Key issues are the presence of gold particle clustering (important for sampling optimisation) and proportion of coarse versus fine to refractory gold. X-ray computed tomography (CT) is a radiographic technique that produces high-resolution 3D computerised models of objects based on density distribution. The ‘virtual’ specimens are made up of voxels, the 3D equivalent of pixels each assigned a grey value based on X-ray absorption. This paper presents the results of recent research on the imaging of in situ gold particles in a coarse gold-bearing ore. The technique shows promise as a rapid non-destructive method for preliminary characterisation to facilitate more detailed studies. Early and rapid gold particle determination enables sampling protocol optimisation and metallurgical recovery parameters to be defined prior to detailed feasibility studies

Sep 5, 2011

By J Hunt

A critical aspect of geometallurgy is quantifying mineralogical and textural relationships that affect mineral processing (eg, liberation and recovery). However, to date, this has been an expensive and time consuming venture and only minimal amounts of this type of data are typically available to be included in the mine planning process. Our research is focused on developing methods that will produce the required mineralogical and textural data rapidly and inexpensively. New methods of characterising liberation potential from image analysis of coarse particulate material and direct small-scale flotation measurement are compared with measurement of liberation at final grind size (ie conventional tests on MLA). The new tests are designed to rank the liberation potential of samples in order to classify an ore deposit into domains of low, medium and high liberation potential. This parameter can then be used to adjust the cutoff used to differentiate ore from waste. Results to date show that all tests provide the same ranking for the samples. The measurement based on coarse particulate material compares favourably to the results from MLA analyses on the same samples. Further work is being carried out to determine which of the tests will be the most effective in terms of time, effort and cost

Sep 5, 2011

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 J E. Everett

A block model of the orebody is used to plan a mine. The first requirement is to identify ore to be extracted, discarding waste, so as to meet target grade, generally in multiple analytes. Commonly, cut-off values are set for each analyte, so as to distinguish ore from waste. It will be shown that, if more than one analyte is important, this procedure is wasteful of ore and a composite cut-off function is preferable.A second requirement is to sequence the ore extraction so that the variability in ore grade is controlled: failure to do so will result either in low-quality ore being marketed, or excessive re-handling being required to blend the ore to reduce the grade variability.A third requirement is that the extraction sequence should be such as to control the amount of equipment movement required, enhancing the equipment productivity.The overall objective, to optimise the Net Present Value (NPV) of the mined ore, is simply stated but complex in realisation, since so many factors, such as target grade, grade variability, equipment choice, equipment movement, and downstream blending all have alternatives which can be traded off against each other, and all of which contribute to the costs and benefits making up the total NPV.There exist commercial packages for ore selection and mine planning. However, they are often of a ‘black box’ nature, and users are sometimes not fully aware of the criteria being applied. In particular, the treatment of quality as a multidimensional vector may be problematic. The purpose of this paper is to discuss some of the issues involved and to suggest an alternative set of approaches, which should complement existing commercial treatments.The discussion will be illustrated specifically by reference to the mining of iron ore, but the issues are relevant to a wide variety of mining situations

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