Search Documents

By J Berg, M Hahn, R Winkel

Imaging sensor systems designed for the mining and minerals industry incorporate different physical measurement technologies that can be used to detect the machine environment, such as laser, ultrasonic/sonar, video and RF-sensors. The underlying physical measurement principles cannot be employed in most applications near the face without regular maintenance due to the harsh environmental conditions like dust, dirt or moisture. However, that is the area, where imaging sensors are most valuable to the machine operator. In the domain of 2D radar sensors these have been either restricted by the angular coverage area or by limited adaptability of signal processing algorithms. The unique full digital solid state design will establish a generation step in mining applications.In order to fill this gap of terrain imaging sensors, the Institute for Mining and Metallurgy Machinery (IMR) of the RWTH Aachen University, in cooperation with the radar company indurad, developed an imaging sensor based on radar technology employing the digital beam forming (DBF) concept. Through multiple transmitting and receiving antennas the sensor delivers high accuracy range and angular information. Using adaptive digital signal processing with multiple imaging algorithms and target detection routines, data of the surrounding environment can be used to enable automation functions in mining.A comprehensive range of radar scans in different environments have been evaluated and benchmarked by using laser scanners. Accuracy measurements in range and angular direction have shown minimal differences. Experimental measurements have been performed at slopes, on conveyor belts and hot cement clinker silos. The experimental system uses a frequency modulated continuous wave (FMCW) front-end combined with a processing unit for signal analysis and target detection.

Sep 26, 2011

By M Oyarzún

Direct relationships between rock texture and BWi (grindability index) have been established based on geological and metallurgical information supported by optical microscopy observations, gathered from 731 samples.Each recognised rock texture is observed to be related to a certain BWi range and, this texture is also characteristic of distinct lithological subunits (recognisable at mapping scale). Therefore, this relationship may provide an important tool for early milling in situ estimation.Since each textural type is a response to rock formation conditions, the causality of the relationship between BWi and texture is based on such conditions and, particularly, on the interfacial free energy among its components.According to the abovementioned textural features, the greater the free interfacial energy between mineral components, the greater the necessary energy required to break its grain boundaries and, consequently, the greater the necessary energy required for milling.If the textural evidence indicates that the rock was able to adjust the shape and/or size of its components – in a natural tendency of all systems to minimise energy– then the necessary energy required to break its grain boundaries will be lower.

Sep 5, 2011

By D Lawie

Prior to resource modelling, sample scale data is normally grouped into spatial domains that reflect zones of homogenous properties. These domains are usually based on a combination of grade and other variables that reflect, for example, geology and alteration, or variables known to correlate with properties of the samples that affect downstream processing. Domaining and wire-framing sample scale data is a time intensive task and it is difficult to track more than a few variables simultaneously while establishing domain boundaries. This is especially the case in operations with complex data, for example, a combination of multi-element assaying of total element concentrations, and/or phase specific assaying such as sulfide sulfur, carbonate carbon, cyanide available gold, and/or downhole geophysical measurements. To minimise unintentionally grouping samples of contrasting properties within the same domain, unsupervised statistical methods are available that can automatically identify samples atypical of the domain, both as univariate tests, and as tests that take correlation within the domain into account. Unsupervised methods in this context means methods of identifying atypical samples that require just the selection of the domain and input variables. Thresholds are derived robustly from the data itself, and the results are therefore repeatable and operator independent. Atypical samples may be thought of as outliers, ie, samples that are separated from the ‘majority’ of the data. If these atypical samples are themselves spatially associated, they may be allocated to an alternative existing domain or allocated to a new domain. Care should always be taken to ensure that identified outliers are due to meaningful causes and not due to data coding and storage errors, although the identification of the latter is still valuable. *This is an abstract only. No paper was prepared for this abstract.*

Sep 5, 2011

By L Keeney

Geometallurgy is not a mechanism designed to replace current geological and metallurgical deposit characterisation but an added component required to understand orebody variability. Current geological characterisation approaches focus on understanding deposit genesis aimed at identifying new mineralised regions and metallurgical characterisation determines parameters required for engineering design of processing plants. Geometallurgical integration has the task of drawing on aspects of each stream to enable spatial mapping of performance indices across a deposit. It is a complimentary activity to current practice and adds a new dimension to orebody knowledge. Although the notion of conducting an integrated geological and metallurgical analysis is not a new concept, limited publications exist in the field proposing integrated methods, with several technical issues existing in current geometallurgical integration approaches. An iterative method drawing on existing geological and metallurgical characterisation and new measurement tools and inputs is proposed. The integrated method comprises of discrete components dynamically linked together through iterative feedback loops with each component having a specific purpose achieved by applying a range of data acquisition technologies and statistical tests. The key outcomes of the method are:overcoming the lack of optimised geometallurgical domains relevant to processing performance,development of a rigorous approach to identifying parameters controlling processability,improved protocols for relevant metallurgical test data and sample selection, andability to generate greater data support to enable rigorous geostatistical modelling of geometallurgical attributes. This paper outlines the integrated mapping and modelling method developed and draws on multiple data acquisition technologies applied within the AMIRA P843 GeMIII Project.

Sep 5, 2011

By G Turner-Saad

An improvement to cut-off grade optimisation theory based on geometallurgy has been completed. The improvement fundamentally consisted of taking into account mineralogical and textural characteristics instead of grades. These characteristics are related to the spatial variability of mineral abundance, association, particle size and liberation properties. Additionally, the improve- ment considered the spatial variability of mineral processing liberation and selectivity properties associated with mineralogy and texture. The enhanced optimisation enables estimation of the economic value of mining operations based on an optimum cut-off policy attributed to liberated and selected ore minerals.The enhanced optimisation was developed as an essential component of a joint cut-and-fill mining and mineral processing methodology based on mixed integer mathematical programming. The formulation considers static and dynamic modifying factors that vary over the projected life of mining. The objective function of the mathematical formulation consists of maximising the realistic expected economic value of concentrates or products of liberated and selected ore minerals, whilst minimises liberated and selected gangue minerals. The optimal solution is obtained when the objective function is subject to geological, mining, processing, marketing, smelting, refining, environmental and financial constraints. The methodology accesses geometallurgical multivariate resource models, which integrate the spatial variability of mineralogical and textural characteristics and mineral processing liberation and selectivity properties.The geometallurgical process concurrently optimises stope geometries, ore reserves, mining sequences, and mining and mineral processing production based on the enhanced cut-off of liberated and selected ore minerals. In addition, the process considers additive and non-additive transfer functions associated to mutually exclusive geometallurgical spatial domains. The transfer functions also take into consideration the blending of mineralogical and textural characteristics with mineral processing liberation and selectivity properties.This contribution presents an example of cut-off grade optimisation via mineralogical and textural characteristic, and liberation and selectivity processing parameter optimisation for a spatially variable orebody.

Sep 5, 2011

By P Kittler

In the spectrum of mining-related disciplines, geometallurgy sits between resource geology and metallurgical engineering. Unlike its neighbours, it is a relatively young discipline and its concepts, procedures and techniques are still evolving. Relevant geometallurgical issues differ from one deposit to the next so there is no single template that can be used in all situations, meaning that geometallurgy programs are nearly always customised and tailored to the deposit and the situation.Over time, a number of key mistakes are seen to emerge regularly. The eventual impact of these mistakes can vary from suboptimal performance of mine and plant to complete failure of the operation. For instance, poor characterisation of ore lithologies and their spatial distribution can have negative impacts on processing plant throughput. However, when combined with failure to identify critical blending constraints it can result in a totally inoperable plant.A number of common mistakes committed during geometallurgical programs are discussed and some preferred alternative approaches are suggested

Sep 5, 2011

By J E. Everett

An essential requirement of product quality control in the mining industry is to be able to reliably predict key quality properties of finished product from the data available before the extraction of the ore. From a production viewpoint, the unit of data collection is generally the input and output data set for each shift of crusher production, but could be any period where mine precrusher data can be reliably matched with product data.This paper demonstrates that linear regression models can be used to predict crush grades from blast grades, even where the crush material is blended from multiple sources or pits for each of which differing regression models might apply. The advantage of using increasingly complex regressions models to match the available data to give increasingly more powerful models is examined. The best model for any application will be a balance between required predictability, available data and the business’ tolerance for complex models.The regression modelling approach has several advantages over the more classic method of run-of-mine (ROM) crusher trials, which will be discussed.The models can use any predictor variable such as grade, geotype and in situ density provided the preextraction data set can be reliably matched with post-crusher data and is significant as a predictor. The models have been used extensively in the generation of the daily crusher plan with the aim of maintaining finished product grade. This approach has also been used associated with exploration drilling and long-term planning.It is acknowledged that there are inherent problems in fitting lump and fines grade to a linear model. However, these problems are minor when they are used for interpolation within the window spanned by the shift blend records used to produce the model. This paper discusses some of the issues limiting linear regression models in this application, and suggests methods enabling consistent models to be formulated

Sep 5, 2011

By S Walters

The emerging discipline of ‘geometallurgy’ is not new but is becoming increasingly recognised as a discrete and high-value activity. Geometallurgy involves a quantified and spatially constrained approach to ore characterisation in terms of relationships to critical processing performance behaviours. A key outcome is enhanced economic optimisation of mining and improved risk management tools.Industry recognition of the need for large-scale, integrated research to support this emerging area resulted in commencement of a major ongoing initiative in mid-2005. The AMIRA International P843 ‘GeMIII’ project (Geometallurgical Mapping and Mine Modelling) is a cross-discipline collaboration involving a number of world-class research organisations supported by over 20 industry sponsors.The project is delivering integrated tools and methods which link intrinsic rock characteristics to prediction of mineral processing performance. These indices must be capable of spatial distribution in 3D resource models and incorporation into mine planning and optimisation. A key aspect is provision of new cost-effective tools and methods which can provide the large numbers of measured and estimated data points required to undertake effective modelling and spatial distribution. The integrated methodology and enabling technologies being developed through the ongoing AMIRA GeMIII project are outlined, together with implications for supporting industry best practice.

Sep 5, 2011

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 R Berry

This paper looks at two ways to estimate the bulk mineralogy of the rocks for assay intervals. The aim is to find an efficient indicator of the most common minerals in the rock. Phase (modal) analysis has traditionally been done using visual methods such as point counting and image analysis. A modern version of this process is the X-ray point counting routine using the SEM-EDS based software. These methods are too slow and expensive for routine analysis of bulk sample mineralogy at the normal assay spacing.Two sources of data were considered that provide information that can be used to determine the mineral abundance in assay samples. The most widely applied method is (semi-) quantitative X-ray diffraction (QXRD). The QXRD method is most applicable to major minerals and has limited application to minerals at low abundance. The nominal detection limit is 0.5 per cent. Values below five per cent have large errors. A second, less common, method is calculation of mineralogy from chemical assay data. Conversion of chemical analyses to mineralogical analyses depends on the unique chemical composition of each mineral. Elements only found in one mineral are easily accounted for, but many compositions are ambiguous. Deciding on the actual mineralogy is not simple. Recalculation of mineral mode from chemical analyses is more accurate than QXRD when the correct minerals, and mineral compositions, are known.Where only a few QXRD analyses are available they can be used to setup a protocol for calculation of mineralogy from assay data. Linear programming works well in this environment. The best results are obtained when both H2O and CO2 are directly measured. Loss-on-ignition (LOI) should be included if these are not available.Where both QXRD and chemical analysis are available for all samples, the best results are obtained using the least squared method to merge the data sets assuming QXRD has much higher analytical errors than chemical assays. The combined method provides more robust results because the high abundance minerals are controlled by the QXRD measurements while the chemical assays improve the precision for low abundance minerals

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