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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 H. Wotruba, L. Gronen, A. Hellmann, S. Sindern, J. Katzmarzyk

"This paper focuses on the beneficiation of the REE-Y-Nb-Zr complex ore from the ‘Khalzan Buregtei’ deposit, Mongolia. Pre-concentration based on mineralogical studies was investigated at the Unit of Mineral Processing (AMR) and the Institute of Applied Mineralogy and Economic Geology of RWTH Aachen University. In order to minimize the energy consumption in the most energy intensive grinding process a pre-concentration process using dry magnetic separation of particle sizes coarser than required for the liberation of the valuable ore minerals was designed. Dry magnetic separation in the particle size range of 63-250 µm is suitable as the pre-concentration method, due to the intergrowth of REE-Y-Nb-Zr bearing minerals with magnetic minerals determined by QEMSCAN© analysis. The results showed that a mass reduction of 45.3 wt.-% at high recoveries of REE, Y, Nb and Zr (90 wt.-% and higher) in the pre-concentration product was achieved at the beginning of the process chain. The improvement of process efficiency as shown in this study depends on the abundance and association of minerals affecting the magnetic properties (e.g. hematite) of the ores. Therefore, these mineralogical parameters are as important as grain size and ore grade for the geometallurgical evaluation of potential mining operations at ‘Khalzan Buregtei’.INTRODUCTION Polymetallic deposits containing other critical elements besides REE have the advantage of being robust to economic development and fluctuations of individual metal prices. (Gronen et al., 2016). These deposits can host economic grades of heavy and light REE, Y, Nb, Ta, Zr in complex mineralogical compositions. About 120 deposits of this type are known worldwide, including ‘Norra Kärr’ in Sweden, ‘Strange Lake’ in Canada and ‘Khalzan Buregtei’ in Mongolia which are not valued economically yet. (Salvi. & Williams-Jones, 2004.)"

Jan 1, 2018

By L Keeney

Cu-Au porphyry deposits are large tonnage low-grade resources dependent on processing large volumes of material. The ability to understand and predict comminution performance is critical, as it is frequently the rate-limiting step during processing. Parameters required from geometallurgical mapping and modelling of comminution performance are: A×b processing domains based on impact breakage variability, and BMWi processing domains showing grind response variability which lead to Mine throughput domains for a specific comminution circuit configuration. Due to the cost and/or large sample requirements of traditional comminution tests, insufficient data density and distribution of comminution indices commonly exist, making it difficult to model and map deposit scale variability. One method routinely applied to three dimensional domaining is to have an a priori zoning based on geological features (lithology, alteration, mineralogy, textures), and then apply to all material within a given domain the metallurgical values measured on a single sample or average of samples from that domain. The problem with this approach is that commonly the geological domains do not reflect comminution response, therefore compromising the quality of the average domain estimate. To resolve this problem an alternate integrated geometallurgical mapping and modelling method is applied to model and map comminution performance at Cadia East. This paper uses a data set of ~32 000 assay measurements and geological logging information, integrated with ~150 comminution measurements to analyse and develop proxy support models for comminution indices (ie A×b, BMWi, and throughput) based on inherent geological variability. Discrete downhole comminution processing domains are created, mapping orebody response while maintaining inherent variability.

Sep 5, 2011

By Jean M. Richardson, Steven R. Williams

"Geometallurgical mapping is a new team-based approach that documents variability within an orebody and quantifies the impact of geology (host rocks, alteration and structure) and mineralogy on grinding, metallurgical response and metal recovery processes. The quantitative, spatially constrained database that results can be readily integrated into 3-D block models and mine planning activities. Thus it is an important tool to reduce the technical risk associated with new mine developments or expansions.Normally undertaken during pre-feasibility or feasibility planning stages, the first step of geometallurgical mapping is to review the geology, mineralogy and other critical parameters and construct a geometallurgical matrix. This matrix provides an objective base to then guide sampling and/or compositing activity for physical property and metallurgical testing. Depending on the project, an array of testing techniques can be used to characterize the ore and feed data into the geometallurgical model.While the range and number of tests needed is project-dependant, the geometallurgical mapping approach allows the development of empirical models or interdependent relationships, grounded by spatially-constrained real data. Extensive data sets collected using less expensive testing methods can be meaningfully correlated with results from complex specialty tests to yield realistic metallurgical recovery data.With geometallurgical mapping completed, the resulting database can be integrated into an overall 3-D block mine model. This means that metallurgical response can be used to realistically forecast the recoveries of specific blocks, perhaps sampled only by drilling. Mine planning can then incorporate the forecasted metallurgical response and generate future project cash-flows; opening the door to economic optimization of the mine exploitation."

Jan 1, 2004

By E. Pizarro, A. Videla, G. Compan

One of the main problems related to mining investment decisions is the use of accurate prediction models. Metallurgical recovery is a major source of variability, and in this regard, the Chuquicamata processing plant recovery was modelled as a function of geomining-metallurgical data and ore characteristics obtained from a historical database. In particular, the dataset gathered contains information related to feed grades, ore hardness, particle size, mineralogy, pH, and flotation reagents. A systemic approach was applied to fit a multivariate regression model representing the copper recovery in the plant. The systemic approach consists of an initial projection of the characteristic grinding product size (P80), based upon energy consumption at the particle size reduction step, followed by a flotation recovery model. The model allows for an improvement in the investment decision process by predicting performance and risk. The final geometallurgical model uses eight operational variables and is a significant improvement over conventional prediction models. A validation was performed using a recent data-set, and this showed a high correlation coefficient with a low mean absolute error, which reveals that the geometallurgical model is able to predict, with acceptable accuracy, the actual copper recovery in the plant.

Jan 1, 2015

By Claudio Bisso, Javier Vargas, Charles Lozano

"This paper summarizes the activities performed to predict SAG milling throughput at Minera Esperanza. Having highly reliable geometallurgical models aided by the new computer tools and specific metallurgical testing to rely on has become a major issue in recent times due to their impact on the business. Minera Esperanza’s SAG throughput model was constructed by characterizing the ore feed and operational parameters of the plant. Producing a robust model required satisfying number of requirements for the parameters used for both the ore and the plant as follow: 1) geologic and geometallurgical characterization parameters; and 2) parameters of a plant running at the rated production under optimum operating conditions. Obtaining these parameters required increasing the quantity of samples and metallurgical testing through drilling campaigns, mapping the drill holes and putting samples through various comminution tests while sampling all operational flows at the plant to obtain the metallurgical information for the operational parameters. The information was entered on the JKSimMet software to enable a throughput model to be developed to output SAG mill throughput predictions that are very close to actual throughput figures, i.e., accurate within a range of 2% on a monthly basis."

Jan 1, 2014

By Dave Bulled, Chris Bennett, Glenn Kosick, Glenn Dobby

"Mining companies around the world are embracing geometallurgical modeling at a rapid rate, as senior executives realize that inputs must be properly measured if the output is to be managed effectively. Geometallurgical modeling is used to scientifically manage risk in project evaluation, for plant design and production forecasting, and optimization of production planning and mineral processing/metallurgical unit operations. The technology also provides a solid foundation for implementing broad corporate strategies with respect to intelligent asset utilization and metal sales. Over the past five years, MinnovEX Technologies Inc. has been actively engaged in developing, promoting and applying geometallurgical modeling technology for major mining companies around the world. This paper describes the methodology for successfully applying the technology for the unit operations of comminution and flotation in the mining industry.INTRODUCTIONGeometallurgical modeling (referred hereon as geomet modeling), in theory, can be applied to its full potential on all unit operations in mineral processing; however, this paper will concentrate on two of the largest unit operations, namely comminution and flotation, where the technology is actually being practiced by many large mining companies. For concept studies and prefeasibility studies, aspects of geometallurgical technology are regularly applied but in a less rigorous manner than for final feasibility studies where justification for funds is sought. This paper will focus on the final feasibility stage of a project and on existing operations or planned expansions, where the full power of geometallurgical technology is brought to bear in a cost effective manner.One of the unique aspects of geomet modeling, when used to its potential, is the ability to design plants, and forecast and plan production with a quantifiable degree of precision, something that was unattainable in the past. This means that when new comminution and flotation plants are designed, the mining companies and financial institutions involved in the project can measure the precision of the design and therefore better understand and manage project risk. It means that for existing and new operations, metal sales personnel can carry out their sales strategies with a much better understanding of the degree of error associated with production forecasts of concentrate/metal and quality of concentrates. For mine and mill managers, geomet modeling allows the effect of the mine plan on the downstream processes to be quantified; thereby allowing the mine plan to be recast in a more optimized manner. Finally, existing concentrator personnel can optimize their unit operations based on a more definitive understanding"

Jan 1, 2004

"The Dumont deposit is a large (1066 Mt probable reserve) low grade (0.27% nickel), nickel sulphide project located in the Abitibi region 25km west of Amos, Quebec. Royal Nickel Corporation acquired the project in 2007 and has since taken the deposit through pre-feasibility. And updated, revised pre-feasibility was released in July 2012.The disseminated nickel sulphide and alloy mineralization is hosted within the serpentinized dunite subzone of the Dumont Sill; a differentiated, pervasively serpentinized, ultramafic sill of komatitic affinity. Pentlandite, heazlewoodite and awaruite are the dominant nickel-bearing, metallic phases and can be recovered by conventional methods (flotation, mag separation). Non-recoverable nickel is also present within the silicate matrix of the serpentinized dunite and is locked in serpentine (Fe and Mg-rich variants) and minor olivine.A detailed mineralogical program including 1097 QEMSCAN® samples and 4300 electron microprobe points were undertaken to increase the understanding of the nickel deportment in the various recoverable and non-recoverable phases in a spatial context across the deposit. Eighty-three (83) metallurgical tests performed under a standard procedure have linked the mineralogy to metallurgical performance. This has allowed the creation and modelling of geometallurgical domains on a block by block basis to understand the recovery variation and metallurgical performance throughout the deposit. Understanding the spatial variability of Ni deportment in recoverable and non-recoverable phases through mineralogy is the key to understanding metallurgical performance thus, unlocking the value of the Dumont deposit and other large tonnage low grade ultramafic nickel deposits."

Jan 1, 2013

By K. Nasrallah

"The Dumont deposit is a large (1,179 Mt proven and probable reserve) low grade (0.27% nickel), nickel sulphide project located in the Abitibi region 25km west of Amos, Quebec. Royal Nickel Corporation acquired the project in 2007 and has since taken the project from a scoping study in 2010 through to completing the feasibility study in July 2013.The Dumont deposit in a nickel deposit with the recoverable nickel contained in three minerals; pentlandite, haezlewoodite and awaruite. The minerals are recovered by a combination of flotation and magnetic separation. Over the course of the studies many flowsheet design decisions and changes were made. This paper outlines an overview of the project, flowsheet design including the comminution circuit, desliming and awaruite recovery. The overall resulting flowsheet that was used for the feasibility study is then presented along with the confirmatory locked cycle testing that showed the feasibility design basis was able to produce the predicted concentrate grade and recovery.IntroductionRoyal Nickel Corporation (RNC) is a mineral resource company headquartered in Toronto, Canada, primarily focused on the exploration, evaluation, development and acquisition of base metal and platinum group metal properties. RNC's principal asset is the Dumont Nickel project (Dumont project) located in the established Abitibi mining camp, 25 km northwest of Amos, Quebec. RNC acquired a 100% interest in the Dumont property in 2007. The mineral claims covering the Dumont deposit are currently held 98% by RNC and 2% by Ressources Québec.The Dumont project is located in the province of Quebec in the municipalities of Launay and Trécesson approximately 25 km by road northwest of the city of Amos, 60 km northeast of the industrial and mining city of Rouyn-Noranda and 70 km northwest of the city of Val d’Or."

Jan 1, 2016

By Andres Salazar, Carlos Delgado Robles, Richard K. Preece

The paper discusses the evolution of the geometallurgical model at the Escondida mine in northern Chile, the world’s largest copper producer. It highlights the transition from early metallurgical forecasting methods to a comprehensive model that includes spatial distribution of metallurgical forecasts. This model helps in planning and adapting to the variability in ore characteristics, such as hardness and mineralogy, which affect processing performance.

Dec 1, 2023

By W J. Shaw

Geometallurgical modelling is now a hot topic in deposit evaluation and in optimising mine performance; however, its history is long and varied. Historically metallurgical sampling has frequently suffered from a lack of data due to the cost of producing representative bulk samples. In the early 1990s geometallurgical modelling similarly suffered from a lack of data due to complex, time consuming and expensive sampling techniques. More recently, simple and relatively cheap tests for geometallurgical variables such as hardness, grindability, throughput, SAG power index, bond work index, crushing index, mineral recovery and concentrate grade have become widespread. The cheaper sampling has resulted in relatively large geometallurgical databases. However, most of the geometallurgical variables sampled are implicitly (and incorrectly) assumed to be additive when modelled by traditional geostatistical techniques, often leading to ill-informed and costly decisions. Geometallurgical variables are mostly non-additive so the current geostatistical challenge is to develop new approaches to dealing with this type of data. This paper briefly describes a historical case study of geometallurgical modelling of the George Fisher Pb-Zn mine completed in the mid 1990s, then outlines some of the current misconceptions of geometallurgical data and modelling techniques regularly applied to it, and finally discusses some recent developments in geometallurgical modelling, with examples from a number of current clients indicating the direction forward.

Jan 1, 2009

By M Wortley, J Korteniemi, D La Rosa, L Rajavuori

Geometallurgical modelling of an orebody provides benefits to a mine by gaining a better understanding of the ore characteristics and how they affect the performance of the concentrator. With this knowledge, plant operating conditions can be adjusted to optimise throughput and recovery in advance of the arrival of particular ore types. Therefore, it is extremely important that the origin of the ore being processed is known as accurately as possible. Depending on the homogeneity of the ore characteristics, reliance on assumptions about stockpile residence times, scheduling and material handling can render the best geometallurgical models useless.The solution adopted at Agnico Eagle’s Kittilä mine was to utilise Metso SmartTags™ and in-house expertise to develop a system that continuously and accurately links geotechnical and lab data from the mine to the performance of the plant. This application presented several unique opportunities and challenges. For example, this was the first installation of a SmartTag™ system for geometallurgical modelling in an underground mine. Challenges included the fact that the system installation is routinely subjected to temperatures below -20°C.The system was installed and commissioned in early 2013 and has been operating continuously since. Kittilä has begun to see the benefits of the system, with an increased understanding of how different ores are processed in the concentrator. Other advantages include the ability to alert operators about the arrival of difficult ores and a better understanding of the ore handling systems.This paper describes the installation and use of the system at Kittilä and details some of the geometallurgical relationships that have been developed using the data collected so far.CITATION:La Rosa, D, Rajavuori, L, Korteniemi, J and Wortley, M, 2014. Geometallurgical modelling and ore tracking at Kittilä Mine, in Proceedings Orebody Modelling and Strategic Mine Planning Symposium 2014 , pp 291–298 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Nov 24, 2014

By L. Voisin, E. Díaz, V. Montenegro, W. Kracht

Grinding media is of great concern in the mining industry mainly because its consumption represents an important part of the costs during mineral processing and consequently, of the total mining business OPEX. In addition, grinding media consumption impacts directly on flotation performance through negative contamination by corrosion, affecting the pulp chemical potential, complicating the understanding and control of the process, as galvanic interactions between mineral species also occur. In addition, during the process there is also a physical consumption of the grinding media through erosion and impact. In both cases, mineralogy has an important role because pulp abrasion power, chemical reactivity, rheology, hardness and other variables depend on the minerals contained in the pulp and their interactions. In the present study, a geometallurgical and phenomenological model, which describes the grinding media consumption and its relationship with mineral hardness expressed by Bond Work Index, was obtained based on advanced mineral characterization techniques such as Hylogger3TM, X-ray diffraction, X-ray fluorescence and experimental grinding tests using a laboratory mill applied on Chilean copper pulps. The results obtained show that pH has an important effect on grinding media consumption, because corrosion varies with hydrogen ions availability. Also, mineral hardness depends on mineralogical composition, but textural analysis must be performed to have a better description of this property. Finally, minerals present in a sample define the grinding media consumption mechanism, because a sample with harder minerals and a greater value of Bond Work index is capable of protecting grinding media from impact wear, while a sample with lower hardness but with a big sulfide minerals content would be more aggressive with grinding media because corrosion is more important in this case. The geometallurgical model obtained is capable to relate the grinding time, mineral hardness, content of sulfide minerals and grinding pH to estimate the grinding media consumption.

Jan 1, 2017

By A. C. de Araujo, M. G. Barbosa, R. Martino, S. Gaydardzhiev

The Iron Quadrangle district is a world-class iron ore mining district in the region of Belo Horizonte, capital of Minas Gerais state, in Brazil. Many mining companies have been mining high-grade hematite ores for decades throughout the region. The mines reserves often include itabirite ores which are available in high volumes, but have lower iron grades than hematite ores and require elaborated mineral processing to meet the requirements of downstream steelmaking. Itabirite ores have acquired importance, with the depletion of high-grade hematite reserves due to their intensive mining. The objective of this paper is to propose a geometallurgical model for the processing of itabirite ores from an Iron Quadrangle deposit, which will allow predicting the behaviour of ores throughout the deposit, according to their local variations. An initial step consists in characterizing representative samples of the different ores to acquire mineralogical and textural data. Then, itabirite ores of known characteristics are processed with different techniques, in order to extract the trends and key parameters upon which the geometallurgical model is developed. At first, the model takes into account the particle size distribution and its shift during mining and screening, as well as the chemistry and mineralogy of the material. Then, using the previously identified key parameters such as iron recovery, the model predicts the composition of the processing products. Finally, this data is used to model the mineralogy and mass recovery of the products. The geometallurgical model is of great importance, as it is able to model the processing of any sampled ore throughout the deposit. The incorporation of the results in a mining block model will assist mine planning and opens new opportunities for an optimized mining schedule.

Jan 1, 2016

By M Sciortino, J Korczak, J Muinonen, A St-Jean

The Dumont deposit is a large (1179 Mt reserve), low-grade (0.27 per cent nickel) nickel sulfide project located in the Abitibi region 25 km west of Amos, Quebec. Royal Nickel Corporation (RNC) acquired the project in 2007 and has since taken the deposit through to feasibility. The disseminated nickel sulfide and alloy mineralisation is hosted within the serpentinised dunite subzone of the Dumont Sill; a differentiated, pervasively serpentinised, ultramafic sill of komatiitic affinity. Pentlandite ((Ni,Fe)9S8), heazlewoodite (Ni3S2) and awaruite (Ni2.5Fe) are the dominant nickel-bearing metallic phases and can be recovered by conventional methods (flotation, magnetic separation). Non-recoverable nickel is also present within serpentine and olivine of the silicate matrix.A detailed mineralogical program including 1420 QEMSCAN« samples and 4400 electron microprobe points was undertaken to increase the understanding of the nickel deportment in the various recoverable and non-recoverable phases in a spatial context across the deposit. A total of 105 metallurgical tests have been performed under a standard procedure which have linked the mineralogy to metallurgical performance. This has allowed the creation and modelling of geometallurgical domains on a block-by-block basis to understand the recovery variation and metallurgical performance throughout the deposit. Understanding the spatial variability of Ni deportment in recoverable and non-recoverable phases through mineralogy is the key to understanding metallurgical performance, thus unlocking the value of the Dumont deposit and other large tonnage low-grade ultramafic nickel deposits.CITATION:Sciortino, M, Muinonen, J, Korczak, J and St-Jean, A, 2013. Geometallurgical modelling of the Dumont Deposit, in Proceedings The Second AusIMM International Geometallurgy Conference (GeoMet) 2013 , pp 93-100 (The Australasian Institute of Mining and Metallurgy: Melbourne).

Sep 29, 2013

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 A Stradling

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

Sep 5, 2011

By 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 Y Xie, L O’Connor, S C. Dominy

Sheeted vein gold deposits are characterised by multiple subparallel veins and often free milling gold. Variability in gold grade and recovery parameters are enhanced by poorly designed sampling and test work protocols. Poor quality samples generally equate to an enhanced nugget effect. A sample can be described as being representative when it results in acceptable levels of bias and precision. Total sampling variability can be quantified by the relative sampling variance, which measures the total empirical sampling variance influenced by the heterogeneity of the lot being sampled under the current sampling / test work protocol. Effort should be made to minimise the relative sampling variance through Theory of Sampling (TOS) and quality assurance / quality control (QA/QC) application. This contribution reports on a case study that exemplifies how high-quality grade and recovery data can be gained from a well-designed and planned drilling, sampling and test work program, followed by bulk sampling. Results across historical resource drilling, geometallurgical drilling, laboratory assay / test work, geometallurgical modelling and a bulk sampling/pilot processing study are reconciled. A whole core, entire-sample test work protocol was used to acquire fit-for-purpose gold grade and recovery data for inclusion into a Pre Feasibility Study reported in accordance with the JORC Code.CITATION:Dominy, S C, O’Connor, L, Xie, Y and Glass, H J, 2017. Geometallurgical sampling protocol validation by bulk sampling in a sheeted vein gold deposit, in Proceedings Eighth World Conference on Sampling and Blending , pp 185–196 (The Australasian Institute of Mining and Metallurgy: Melbourne).

May 9, 2017

By M. A. S. Martins, H. Medeiros, B. Y. B. Dos Santos

This paper contains the geometallurgical studies, performed for Mineração Rio do Norte bauxites, applied to a selected area belonging to Plateau Saracá III, referring to an exploitation period of one year and three months, approximately. It is worth mentioning that Mineração Rio do Norte has the biggest bauxite operation in Brazil producing 8,500,000 tons of concentrate per year. The studies included wide investigations directed to the final objective of establishing a precise mining and production planning, based on the different behavior of the distinct bauxite ores, when submitted to industrial processes. Geometallurgy can be defined as the study of the regionalization (space distribution) of the metallurgical variables of a mineral deposit. This study involves and integrates all process engineering, geology, geostatistics and mining planning, among others. The development and application of the geometallugical studies to Mineração Rio do Norte were motivated by several reasons and necessities, among them: 1. Improving the forecast of the amount of concentrate (metallurgical mass yield) produced; 2. Improving the equipment operational efficiency, providing a more homogeneous feed; 3. Conducting a more precise material balance that permits a better daily, monthly and yearly production control; 4. Improving the forecasts for the final products generated by each ore type; 5. Obtaining better knowledge of the behavior of several ore types, submitted to the different process unit operations; 6. Promoting adequate ore blending, producing an homogeneous feed during mining operation.

Jan 1, 1998