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By G McCullough, B Mutsaers, J Jessop

"*This is an abstract only* Geometallurgical initiatives have demonstrated considerable value to operations that have trialled them. However, it is extremely difficult for these initiatives to become properly embedded into the operations and for that value to be realised on an ongoing basis.This paper will explore some of the historical reasons why this is the case. Some of these reasons include the limited tools available to manage an integrated operation. These, in turn, have resulted from poor communications infrastructure, inadequate sensor capability and the way technical mining software has evolved over the last 30 years.This paper will then investigate how the evolution of technology in recent years can help overcome these constraints and compare progress with similar industries.CITATION:McCullough, G, Mutsaers, B and Jessop, J, 2013. Obstacles to embedding geometallurgy decision-making into operations and how to increase maturity of this critical capability, in Proceedings The Second AusIMM International Geometallurgy Conference (GeoMet) 2013, pp 139-140 (The Australasian Institute of Mining and Metallurgy: Melbourne)."

Sep 29, 2013

By M S. Powell

In this paper it is argued that we are entering a new mining era, based on considerably more challenging orebodies as we rapidly expand minerals production by exploiting previously uneconomic deposits. Rapidly increasing production costs cannot be sustained by continually increasing commodity prices, as illustrated by the recent downturn in the industry.Driven by reduction in grade, requiring the processing of 50 to 100 per cent more ore to recover the same amount of metal, often accompanied by finer grain structure - necessitating a finer product size and more challenging recovery - the productivity of the mining industry is dropping in real terms. The author suggests that tweaking current technologies and relying on economies of scale will not turn the industry around, but rather a switch to considerably more efficient processing is required.It is imperative to utilise the properties of the orebody, especially in terms of variability, in revolutionising our processing techniques. Our new mantra may be æselective mass mining with flexible processingÆ. This paper explores how this may be implemented using current technology and equipment in order to change the cost profile of the new mining industry. Orebody knowledge, based on the underlying process drivers, rather than proxies, is required. These properties and their variability will need to be propagated into the orebody through utilising the geological data rather than smoothing block averages, which destroys information on variability.Novel flexible circuit designs are presented that can respond to a wide variability in ore properties and throughput requirements in order to slash processing costs and energy utilisation, while enhancing overall recovery. The success of these circuits will be based on an ability to predict at both design stage and in everyday processing the variable ore properties, which is dependent upon reliable orebody knowledge that caries variability and underlying processing properties.CITATION:Powell, M S, 2013. Utilising orebody knowledge to improve comminution circuit design and energy utilisation, in Proceedings The Second AusIMM International Geometallurgy Conference (GeoMet) 2013 , pp 27-36 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Sep 29, 2013

By S Khosrowshahi, A Weeks, W J. Shaw

"To provide an indication of expected performance of the mine and mill, feasibility studies should include an assessment of the expected day-to-day variability in process plant feed. Attempts to do this based on conventional resource models will be flawed as such studies fail to consider and account for the in-built smoothing (averaging) of input data.This can lead to one of two situations: an expensive, over-engineered process plant that can cope with every possible situation, or operational strategies that fail to manage the natural variability within the orebody and the impact of day-to-day operational decision-making.In both cases the value of the project diminishes through excessive capital costs or reduced throughput or recovery during operations.Unlike conventional estimation techniques, the geostatistical method of conditional simulation can reproduce the inherent variability of the deposit. Once a valid model has been developed, it is possible to analyse the model using a transfer function that simulates the proposed mining processes and provides a representation of the likely variation of mill feed characteristics at any time scale.The case study demonstrates how the proposed mining strategy would result in mill feed being considerably more variable than expected, resulting in poor recovery, low throughput and potential breaches of environmental conditions. Mitigation measures included budgeting for additional mining equipment to enable greater flexibility during mining and revised stockpiling and crusher feed strategies.Awareness of the benefits of modelling the variability of geometallurgical parameters will increase the use of this approach in producing more realistic financial models of expected mine performance at the design stage.CITATION:Shaw, W J, Khosrowshahi, S and Weeks, A, 2013. Modelling geometallurgical variability - a case study in managing risk, in Proceedings The Second AusIMM International Geometallurgy Conference (GeoMet) 2013, pp 247-252 (The Australasian Institute of Mining and Metallurgy: Melbourne)."

Sep 29, 2013

By I Swane, R Rait, A Robertson

Geochemical assessment of mine waste materials prior to commencement of a proposed mining operation is a fundamental component of mine feasibility studies and the regulatory approvals process. Static and kinetic geochemical test methodologies are widely used to predict the geochemical nature of mine waste materials and to develop strategies for managing potentially æhostileÆ materials. These data can also be integrated with data from other fundamental studies such as hydrogeology to predict specific environmental outcomes and potential impacts at mine closure and post-closure.A case study is presented for a proposed open pit gold mine in Mongolia, which illustrates how geochemical, hydrogeological and groundwater quality data can be used as part of a geochemical modelling process to predict the final pit lake water quality over time. A series of static and kinetic test geochemical test programs were completed and acquired data integrated with hydrogeological and groundwater quality data provided by the proponent. A widely used geochemical modelling program (PHREEQC) was used to develop the geochemical model and predict water chemistry in the pit void after mine closure. Sensitivity analyses were completed to explore the effects on the model output of groundwater to leachate water ratio, pit wall rock type and climate.Modelling has indicated that pit void water should be pH circum-neutral at mine closure and should remain within Mongolian and World Bank water quality guideline limits under the modelled simulation most likely to occur in the field. Final void water salinity is expected to increase eight fold over 14 years after end of mine life due to a continuous flux of salts (particularly sulfate salts) into the pit lake whilst the pit lake volume will remain relatively constant due to evaporation and a slow recharge rate. Dissolved metal and sulfate salt concentrations in pit water are also predicted to increase over time and could be moderately elevated compared to applied water quality guideline values. Some of the metal concentrations, however, may be lower as these would be expected to precipitate at circum-neutral pH and greater. Recommended additional assessment work on the project when mining commences includes determining the magnitude of adsorption and co-precipitation of metals occurring in the pit void water/sludge system.CITATION:Robertson, A, Rait, R and Swane, I, 2013. Geochemical assessment of mine waste materials and prediction of final pit lake water quality at a proposed gold mine in Mongolia, in Proceedings The Second AusIMM International Geometallurgy Conference (GeoMet) 2013 , pp 165-170 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Sep 29, 2013

By S C. Dominy

The gravity recoverable gold (GRG) parameter reflects liberated gold or gold-dominated composite particles that can be recovered by gravity separation. Recoveries depend upon mineralogy, in situ particle size and the comminution and extraction method used. Any ore bearing >15 per cent of gold greater than 50 ¦m in size is likely to have a GRG component. The principal characterisation methodology applied is the three-stage GRG (3GRG) test, based on a 40 kg to 100 kg sample. An alternative is the single-stage GRG test (SGRG), which is generally based on a 20 kg sample. Experience has shown that GRG values can vary throughout an orebody, particularly within different grade and/or mineralogical domains. Test programs must consider sample frequency and mass. This study demonstrates the variability of GRG results based on different sample masses. Consecutive underground faces on a coarse-gold dominated quartz vein were sampled taking five different masses from each. Each sample was processed using a SGRG approach. Subsequently, each development round was batched through a small gravity production plant. The results show that the small samples understate the plant GRG and show inter-sample variability. Large samples show less variability and a SGRG closer to the plant GRG. A high SGRG broadly correlates to higher gold grades, more coarse gold and larger gold particles. These results are intuitively correct, but indicate the likely challenges of sampling for gold recovery in heterogeneous systems.CITATION:Dominy, S C, 2013. Variability of gravity recoverable gold values in relation to test sample size, in Proceedings The Second AusIMM International Geometallurgy Conference (GeoMet) 2013 , pp 293-302 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Sep 29, 2013

By T Napier-Munn, D J. Bradshaw, M Chauhan, L Keeney

An important aspect of geometallurgy is quantifying mineral processing attributes, which leads to improved orebody understanding. One of these attributes is flotation performance. Several methods have been developed as indicators for flotation performance, including batch testing leading to kinetic information and the standard grade-recovery curve. This paper makes a preliminary statistical comparison between a newly proposed geometallurgical batch flotation procedure and the æstandardÆ batch flotation test commonly used to measure flotation performance in the laboratory. The new test uses much less material than the standard test, and is much faster. It is concluded that the new method follows the final recovery of the standard method well, suggesting that it will be effective in reflecting ore variability for geometallurgical purposes. Comparison of the methods based on the full grade-recovery curve shows less agreement, but this may be due to the fact that the curve is sensitive to the particular experimental set-up and conditions, which make such comparisons quantitatively challenging. Further development work is therefore planned.CITATION:Chauhan, M, Napier-Munn, T, Keeney, L and Bradshaw, D J, 2013. Progress in developing a geometallurgy flotation indicator, in Proceedings The Second AusIMM International Geometallurgy Conference (GeoMet) 2013 , pp 201-206 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Sep 29, 2013

By S R. Williams

There is a sense of geometallurgy being new; a new methodology for mining project development and improved operational strategy. One of the main challenges that any ænewÆ technology faces is a validation of the value and the need to produce case studies. However, producing case studies can often be problematic particularly given the long cycle from mineral deposit project, through feasibility and construction to stabilised operation of a mine.However, the roots of geometallurgy go back quite some time. Successful practices with geometallurgy can be seen from as early as the 1980s. Many times those practices were not called geometallurgy but something else and, in other cases, what can be seen are various components of the geometallurgical ævalue chainÆ.This paper will analyse a selection of published past practices from a geometallurgical perspective to demonstrate the value realised from the application of this methodology. The paper will then make some general comments on where this history of work in geometallurgy has lead us to and what are the challenges today for the geometallurgical effort.CITATION:S R Williams, 2013. A historical perspective of the application and success of geometallurgical methodologies, in Proceedings The Second AusIMM International Geometallurgy Conference (GeoMet) 2013 , pp 37-48 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Sep 29, 2013

By P Vass, C Butler, J Condon, B Hooper

DeGrussa Copper Mine, resource status 10.2 Mt at 5.7 per cent Cu, 2.1 g/t Au (Sandfire Resources, 2013), commenced concentrate production through its 1.5 Mt/a conventional copper flotation processing plant in September 2012. Geometallurgical assessment during the feasibility study identified potential process-critical ore factors that required further investigation to determine potential impact on processing. Ore characterisation and metallurgical processing performance test work of ore types ensued and subsequently, the first quantitative DeGrussa geometallurgy model was born. Now one year into steady state production, the geometallurgical program has evolved significantly from its infancy and provides valuable insight into ore characterisation and behaviour in the processing plant. This paper details geometallurgical investigations undertaken at DeGrussa to date; from feasibility metallurgical test work through to geometallurgical modelling and management practices, as well as reconciliation between observations at feasibility study level and subsequent observations in production.CITATION:Condon, J, Butler, C, Hooper, B and Vass, P, 2013. DeGrussa Copper Mine, Western Australia - feasibility to first production: a case study in developing a working geometallurgy model from first principles, in Proceedings The Second AusIMM International Geometallurgy Conference (GeoMet) 2013 , pp 59-66 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Sep 29, 2013

By J Jackson, D C. Curry, A C. Keith

Australian Bureau of Statistics (ABS) data shows that since 2006 the balance of capital costs associated with mine development has become weighted greater than 75 per cent to enabling infrastructure and less than 25 per cent on process plant, equipment and machinery. The well-developed mine planning techniques including pit optimisation and scheduling and process plant design and optimisation have typically proceeded in a linear fashion from geology, mine planning, through processing, with some iterations back to the mine. Only then is attention paid to planning and costing of enabling infrastructure in any detail.Optimisation has typically involved developing cut-off grades, mine plans and schedules and ore processing that produces maximum economic ore. Infrastructure has then been sized æto suitÆ the mining and processing core business drivers.What the ABS data, together with global trends, indicates is that infrastructure can no longer be an afterthought if miners wish to achieve robust project value estimates, realistic ore to market costing and resource statements based on achievable economic cut-off grades.In addition, with such a large proportion of the costs originating from non-mining or processing activities, a mining operation can only be truly optimal if these aspects are given due consideration in the mine design stages, based on quality geometallurgical inputs so that the size and cost of enabling infrastructure is optimised to maximise project value.This paper outlines an iterative holistic approach to mining project development, which integrates the geometallurgical approach and enables infrastructure planning into project design and evaluation. It presents case studies showing reduced overall costs, increased net present value (NPV) and reduced project delivery timelines.CITATION:Curry, D C, Keith, A C and Jackson, J, 2013. Is a 2.5 per cent success rate good enough? Traditional mine development methods have the tail wagging the dog, in Proceedings The Second AusIMM International Geometallurgy Conference (GeoMet) 2013 , pp 113-128 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Sep 29, 2013

By D David

Geometallurgy is often a misunderstood and poorly defined concept. The first thing the paper will attempt is to provide a definition of geometallurgy. Ultimately this paper has been written to provide guidance to geologists, mining engineers and processing professionals as to what geometallurgy means in the crucial early phases of a potential mining project between the time of discovery and the time of commencing construction, should this occur. The paper covers the options available for conducting a geometallurgical study of a resource or orebody, appropriate to the level of definition required in the projectÆs development cycle. The paper will then describe how the geometallurgical information generated is subsequently used in calculations, planning and preparation of reports designed to inform and reassure stakeholders, such as the market. Ultimately in a successful project, appropriate options adopted in the development phase will form the basis of an ongoing geometallurgical program that links the major site disciplines for the good of the operation.CITATION:David, D, 2013. Geometallurgical guidelines for miners, geologists and process engineers - discovery to design, in Proceedings The Second AusIMM International Geometallurgy Conference (GeoMet) 2013 , pp 129-132 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Sep 29, 2013

By C V. Deutsch

"Geometallurgical variables often cause problems to conventional geostatistical workflows. There are many variables; some are compositional and some are non-additive. They often show: complex multivariate relationships undersampling relative to conventional grade variables unequal sampling; that is, all variables are not available at all data locations.A variety of approaches have been developed over the years, but geostatisticians remain mostly focused on modelling rock types and a few grade variables with an abundance of data. The problems are reviewed and a chained workflow is presented where appropriate techniques are employed in sequence to permit reliable spatial prediction of geometallurgical variables. The spatial models may be used to assess the value of additional data, for prediction of process performance, for improved mine planning and other optimisation tasks. The chained workflow will include data transformations such as ratios and normal scores that make the variables amenable to geostatistical modelling. Data imputation techniques to account for missing values are considered with special consideration to spatial correlation with the available data. Decorrelation techniques such as the principal components, min/max autocorrelation factors and projection pursuit multivariate transformation are employed to manage massively multivariate modelling. Full distribution modelling must be considered to avoid biases in the prediction of non-additive variables. The post-processing of geometallurgical models must be streamlined for robustness and efficiency. The precise workflow varies according to the specific purpose of the study. Guidelines and thoughts on best practice are presented.CITATION:Deutsch, C V, 2013. Geostatistical modelling of geometallurgical variables - problems and solutions, in Proceedings The Second AusIMM International Geometallurgy Conference (GeoMet) 2013, pp 7-16 (The Australasian Institute of Mining and Metallurgy: Melbourne)."

Sep 29, 2013

By R Berry, J Hunt, T Kojovic

Geometallurgy aids the understanding of orebody variability through the integration of geological and processing information, thereby enabling spatial mapping of processing (eg comminution or separation) performance across a deposit. In order to do this, estimates of quantitative geometallurgical attributes are needed so that continuous downhole profiles can be generated for each attribute, providing vital data for the development of processing domains through geostatistical modelling. However, as traditional comminution tests are high cost and/or require large samples, current practice typically results in insufficient data density and distribution of comminution indices making it difficult to model and map deposit scale variability. The aim of this work was to find a method to estimate comminution indices based on data that is widely available at mine sites. This data includes mineralogy and chemistry of drill core samples, plus logging information such as lithology and alteration. Work to date indicates that comminution parameters such as semi-autogenous grinding (SAG) Power Index (SPI), Bond Work Index (BWI) and A*b (Julius Kruttschnitt Mineral Research Centre (JKMRC) drop weight test (DWT) Index) can be modelled using chemical and/or mineralogical data. The estimated hardness indices can then be propagated into the mine site database and used in the definition of processing domains that map inherent variability across a deposit. The estimates can thus provide geometallurgical inputs for spatial modelling, mine planning and optimisation initiatives.CITATION:Hunt, J, Kojovic, T and Berry, R, 2013. Estimating comminution indices from ore mineralogy, chemistry and drill core logging, in Proceedings The Second AusIMM International Geometallurgy Conference (GeoMet) 2013 , pp 173-176 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Sep 29, 2013

By G J. Harbort, K Lam, C Sola

The relationship between the geology of porphyry copper deposits and their processing performance is an area of interest. Being able to predict comminution properties of the ore from physical characteristics and geological descriptions would allow an increase in the precision of capital cost estimates, operating cost estimates and throughput rates. This geometallurgical approach would reduce the requirements for expensive comminution tests.As the comminution circuit is the highest capital cost item in the plant and a possible throughput bottleneck, understanding the variability in hardness within the orebody is critical. A geometallurgical model allows calculation of ore hardness values in mining blocks from ore characterisation data. This model subsequently uses a combination of simulation and power-based approaches to relate ore hardness and flotation feed size to grinding circuit throughput.This paper reviews the geometallurgical models developed for seven porphyry copper deposits and discusses the strengths and shortfalls of the methodology and similarities and differences between the different deposits.CITATION:Harbort, G J, Lam, K and Sola, C, 2013. The use of geometallurgy to estimate comminution parameters within porphyry copper deposits, in Proceedings The Second AusIMM International Geometallurgy Conference (GeoMet) 2013 , pp 217-230 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Sep 29, 2013

By C Czerny, T Elkington, L Lorenzen

The Dutwa Nickel Project is a nickel laterite atmospheric leach project located in Tanzania that has been under development by African Eagle Resources plc (2012). A bankable feasibility study (BFS) has been progressed to evaluate mining of the resource (107 Mt at 0.92 per cent Ni) followed by hydrometallurgical processing on-site to make a mixed nickel-cobalt intermediate product. The unique relatively high silica contents in the Dutwa deposits, as opposed to limonite, which is more commonly occurring in nickel laterites, presented the possibility for beneficiation of the ores to enhance the leach feed nickel grades and provide economic benefits in terms of operating and capital cost efficiencies.Geometallurgical relationships, based on the results of an extensive variability metallurgical test work program for beneficiation and leaching, were developed for the BFS, which resulted in an improved understanding of ore classifications within the deposit and their responses. The geometallurgical information obtained has been instrumental in making key preliminary techno-economic decisions, such as forecasting whether or not mined material should be beneficiated and was ultimately used to drive the mine plan towards the prioritisation of areas within the deposits that delivered the best cost efficiencies and overall project economic outcomes.CITATION:Elkington, T, Lorenzen, L and Czerny, C, 2013. The use of geometallurgical relationships in process route selection and mine plan optimisation for the Dutwa Nickel Project, in Proceedings The Second AusIMM International Geometallurgy Conference (GeoMet) 2013 , pp 207-216 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Sep 29, 2013

By Y Akiti, J Moller, E Esper, R Rugolo, A Pains

"Morro do Ouro Mine is a high tonnage/low-grade mine located in Paracatu, Minas Gerais State, Brazil. The mines move around 55 Mt of ore per year at an average grade of 0.4 g/t. The auriferous mineralisation of Morro do Ouro is related to hydrothermal fluids rich in arsenic (As), gold (Au), lead (Pb) and zinc (Zn). These percolated along favourable structures contained within phyllites of Upper Proterozoic age and defined a mineralised megaboudin which outcropped with 4 km extension and up to 2 km in width. The litologies in the interior of this structure are typically more deformed, sericitised and sulfidised, and exhibit the development of quartz boudins in the centre of the more deformed zones where are found the higher Au grades and the coarser sulfide crystals. Gold is preferentially associated with arsenopyrite, mainly occurring in fractures or aggregations of crystals and is rarely in solid solution or refractory. Over 90 per cent of the gold contained in the sulfide ore is free or extractable by cyanidisation. The current mineral reserve is 17.9 Mt at 0.403 g/t published at the end of 2012.In order to get a good grade estimation that represents the orebody more accurately a solids methodology was used to distinguish the different orebodies. Before 2011 the orebodies were worked with two domains relating to the weathered mantle. The B1 ore, which is oxidised, is located at the top of mineralisation, and is used to feed Plant 1. Underneath B2 ore, which is fresh sulfide bearing, represents over 30 years of production in Plant 2.In order to improve the predictability of gold grade estimation in geological modelling and to get a better predicting behaviour of each domain on the deposit, studies were carried out in which it was possible to show a correlation between elements and the mineralisation. One strong correlation and spatial distribution between Au and As, As and sulfur (S), Au and Pb, Au and Zn were found.Based on geochemistry, mineral correlations, amount of sulfide, structural deformation, texture of sulfides and the daily experience of the mine, it was possible to create solids that individualised bodies with similar mineralogical and chemical characteristics using the parameters listed above.After 2011, a new modelling concept was proposed and built with 11 different domains, compared with the previous, and with two more parameters: recovery acid neutralised capacity (ANC).Each of these bodies have different behaviours in mineral processing, like flotation and metallurgy. For example, bodies with pyrrhotite bring problems in the flotation, bodies with high arsenic do not recover well, bodies with high As with coarse texture have good recovery, bodies with fine texture do not have good adherence in production.In the present state of knowledge, protocols are being developed for bench testing to predict the recovery of each geochemical domain aiming to include these in the geological model for the parameter recovery. It is intended to create a field throughput in the model of ounces per hour, from each block of each domain to better predict the production, using the parameter recovery, Au and Bond Work Index (BWI).CITATION:Esper, E, Rugolo, R, Moller, J, Akiti, Y and Pains, A, 2013. Morro do Ouro geological model with a metallurgical view, in Proceedings The Second AusIMM International Geometallurgy Conference (GeoMet) 2013, pp 67-74 (The Australasian Institute of Mining and Metallurgy: Melbourne)."

Sep 29, 2013

By K S. Smith, J Woodhead, K Olson Hoal

Mineralogy is the link between ore formation and ore extraction. It is the most fundamental component of geomet programs, and the most important aspect of a life-of-project approach to mineral resource projects. Understanding orebodies is achieved by understanding the mineralogy and texture of the materials, throughout the process, because minerals hold the information required to unlock the value they contain. Geomet mineralogy programs absolutely require the appropriate expertise and at least three steps of mineral characterisation prior to using semi-automated or other methods: field examination, thorough core logging, and optical microscopy. Economic geological inputs for orebody characterisation are necessary for orebody understanding, and are exemplified by current research in the Zambian Copperbelt, where revised sequence stratigraphy and understanding of alteration, metasomatism and metamorphism can be used to predict topical issues at mine sites. Environmental inputs for sustainability characterisation are demonstrated by recent work on tailings from the Leadville, Colorado, USA area, including linking mineralogy to water quality issues. Risk assessments need to take into account the technical uncertainties around geological variability and mineral extractability, and mineralogy is the only metric that can be used to make this risk contribution.CITATION:Olson Hoal, K, Woodhead, J and Smith, K S, 2013. The importance of mineralogical input into geometallurgy programs, in Proceedings The Second AusIMM International Geometallurgy Conference (GeoMet) 2013 , pp 17-26 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Sep 29, 2013

By F Minz, P Lamberg, A Mwanga, C Lund, C Wanhainen, M Amiri Parian, J Rosenkranz

A geometallurgical model is currently built in two different ways. The first and the most common way relies on geometallurgical testing, where a large number of samples are analysed for metallurgical response using small-scale laboratory tests, eg Davis tube testing. The second, mineralogical approach focuses on collecting mineralogical information over the orebody and building the metallurgical model based on mineralogy. At Lules University of Technology, Sweden, the latter method has been adopted and taken further in four ongoing PhD studies. The geological model gives modal composition by the help of element-to-mineral conversion and Rietveld X-ray diffraction. Texturally, the orebody is divided into different archetypes, and liberation measurements for each of them are carried out in processing fineness using IncaMineral, a SEM-based technique. The grindability and liberation spectrum of any given geological unit (sample, ore block, domain) are extrapolated from the archetypes. The process model is taken into a liberation level by mass balancing selected metallurgical tests using the particle tracking technique. The approach is general and can be applied to any type of ores. Examples of ongoing studies on iron and massive sulfide ores are given.CITATION:Lamberg, P, Rosenkranz, J, Wanhainen, C, Lund, C, Minz, F, Mwanga, A and Amiri Parian, M, 2013. Building a geometallurgical model in iron ores using a mineralogical approach with liberation data, in Proceedings The Second AusIMM International Geometallurgy Conference (GeoMet) 2013 , pp 317-324 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Sep 29, 2013

By K C. Runge, E Tabosa, A Jankovic

Flotation recovery in geometallurgical modelling is usually predicted based on relationships developed from batch flotation testing of different ore types. These batch flotation tests are usually performed at a constant set of operating conditions and at a fixed feed grind size P80. Usually the laboratory feed grind P80 is that which is expected to be produced in the full-scale flotation circuit for that particular ore type.Flotation recovery will be a strong function of the valuable mineral particle size distribution, with lower recovery of ultra-fines due to poor flotation kinetics, optimum recovery for the intermediate sized particles and lower recovery for the coarser particles due to poorer liberation of this fraction. This valuable mineral particle size distribution is not characterised by the solids P80 parameter alone. The proportion of material in each class will also be a function of the slope of the size distribution and the degree of preferential grinding of the valuable mineral in comparison to the total mass of the ore.This paper will demonstrate (through example) how these other size distribution parameters can change with a change in grinding technology, the size and operating conditions used in a grinding unit and the characteristics of the classification technology used in conjunction with the grinding unit. Case studies will be presented that demonstrate how this can result in these parameters being very different in laboratory testing to that produced in the full-scale comminution circuit. A rule of thumb that laboratory rod milling rather than ball milling produces a size distribution most similar to that of the full-scale grinding circuit will be challenged. These differences can result in a significant change in flotation recovery. It is therefore important to consider these parameters when developing a laboratory geometallurgical program, to enable more accurate prediction of the flotation recoveries that will be achieved in the full-scale process.CITATION:Runge, K C, Tabosa, E and Jankovic, A, 2013. Particle size distribution effects that should be considered when performing flotation geometallurgical testing, in Proceedings The Second AusIMM International Geometallurgy Conference (GeoMet) 2013 , pp 335-344 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Sep 29, 2013

By J E. Everett, K Jupp, T J. Howard

Simulation modelling is a practice commonly used in the mining industry to evaluate alternative process designs. Such modelling is typically undertaken as an optimisation study to increase the efficiencies, productivity and product quality of operating mines. In this situation, real short-term grade variability data of extracted ore are available from production records as input data into the simulation.There is also a need for simulation modelling to be performed before mines are approved for construction to clarify the grade variability characteristics that can be expected from the operating mine and assist in the optimisation of the process design. In this situation no historical short-term grade variability data are available. To achieve meaningful and reliable results from a simulation it is necessary to have input data for the ore to be extracted from the pits, representative of the short-term grade variability that would be expected for the operating mine.This paper describes an example of a path taken to generate realistic input data.A method described as composite cut-off criterion was used to distinguish ore from waste, which gave substantially greater recovered tonnage at target grade compared to the conventional quadrant cut-off grade criteria.The only data available for the project was resource model data in the form of kriged block models, which are known to underestimate true ore grade variability. To achieve realistic results from the simulation it was essential to increase this variability while maintaining the accurate average grades. Areas of certain deposits that were modelled using both kriging and conditional simulation estimation techniques were quantitatively compared to establish the comparative variance and the kriged data was modified to match the conditionally simulated variance.A realistic mining model was generated via a process of discretisation and regularisation of the resource blocks. Quantitative assessment demonstrated that this method adequately compensated for ore dilution and that adjustment for ore loss was not required due to an ore skin surrounding the edge blocks.The conditioned data was then used to generate a schedule of daily mine extraction, considering grade variability, tonnage, equipment constraints and extensive blended-in-blended-out precrusher stockpiles to feed into the process design simulations.For the precrusher stockpiles a number of alternative allocation criteria were examined for ore being extracted from the pits to identify the best method in achieving reduced variability through the daily scheduling system. The study concluded that a single analyte separation criterion produced acceptable variability with minimal complexity.The blending efficiency of manually stacked and reclaimed precrusher stockpiles was studied to determine a realistic blending efficiency within the pile. A recommended method of building and reclaiming was determined to give maximum blending efficiency.Finally, the data was used as input into process design simulation models; simulation from crusher feed to shiploading demonstrating that control of shipment grade variability was achievable and that the conditioning of the data delivered realistic results.CITATION:Jupp, K, Howard, T J and Everett, J E, 2013. Generating synthetic data for simulation modelling in iron ore, in Proceedings The Second AusIMM International Geometallurgy Conference (GeoMet) 2013 , pp 231-238 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Sep 29, 2013

By B J. Beven, A Vatandoost, J Young, S Campbell-Hardwick

The intrinsic value of iron ore depends on its physical and chemical characteristics. For example, silica and alumina are unwanted elements, downgrading iron grade and adversely affecting high temperature processing, attracting penalties in smelter contracts. For this reason, an informed knowledge of processing characteristics and product quality of the ore is valuable and cost effective during mine planning and processing.Ore variability is a significant factor that affects metallurgical behaviour and efficiency of ore processing equipment. Ore of sufficient chemical and physical quality needs to be modelled, mined, processed and usually blended. Feed and product assays are commonly used to assess potential ore upgradability. However, assays alone cannot be all informative of actual processing response given the variability of the physical properties of the ore.This study investigates the potential benefit of implementing a geometallurgical approach during the commissioning stage of the Kings deposit, part of the Solomon Iron Ore project in Western Australia. Kings drill hole database information, ie geological, textural and geochemical data, together with the results of metallurgical test work were used for geometallurgical analysis and modelling. Three main ore types were identified at Kings, namely channel iron deposit (CID), bedded iron deposits (BID) and Detrital Iron Deposit (DID). Additional classifications were utilised to better characterise these units in terms of their physical properties.Bulk sampling for each ore type was carried out across the deposit for metallurgical testing. Multivariate statistical models were developed for prediction of product quality for each geometallurgical unit. A new approach for modelling yield based on ore upgrade factors is introduced. These models were then applied to the Kings regularised mining model to assess product quality across the mining blocks and to plan product stockpiling and blending strategies.CITATION:Vatandoost, A, Beven, B J, Campbell-Hardwick, S and Young, J, 2013. A geometallurgical approach for iron ore product evaluation, in Proceedings The Second AusIMM International Geometallurgy Conference (GeoMet) 2013 , pp 259-266 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Sep 29, 2013