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Geometallurgy Geometallurgy - beyond conception Conference Paper · Paper  · June 2016

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Geometallurgy - Beyond Conception S C Dominy1,2,3 and L O’Connor 4 1.

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FAusIMM(CP), Adjunct Professor, Department of Mining and Metallurgical Engineering, Western Australian School of Mines, Curtin University, GPO Box U1987, WA 6845, Australia. Email [email protected] Visiting Associate Professor, Camborne School of Mines, University of Exeter, Penryn, Cornwall TR10 9FE, UK. Group Executive  –  Projects and Development, Exchange Minerals Ltd, 44 Esplanade, St Helier, Jersey JE4 9WG. MAusIMM, Senior Lecturer, Department of Mining and Metallurgical Engineering, Western Australian School of Mines, Curtin University, GPO Box U1987, WA 6845, Australia. Email [email protected]

OVERVIEW Geometallurgy is an important addition to any evaluation project or mining operation.  As a discipline, its seeks to maximise the Net Present Value (NPV) of an orebody, while minimising technical and operational risk. It also aims to promote sustainable development and initiatives by ensuring that all stages of extraction are performed in an optimal manner from a technical, environmental and social perspective. To achieve these goals, development of innovative technologies and approaches along the entire commodity value chain are being established (Glass, 2016). Geometallurgy has been shown to increase operational stakeholder collaboration, creating an environment for knowledge sharing and improved data acquisition and interrogation, with the end result being the integration of such data into mine planning and scheduling. All of these aspects create better business optimisation, utilisation of staff and targeted and realistic key performance indicators. The mining industry faces numerous challenges including, but not limited to:        

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declining ore grades geometrically and/or internally more complex deposits deep seated deposits, with potentially high in-situ stress regimes increasing quantities of mine waste that needs to be managed appropriately higher energy, water, and chemical costs processing of more challenging and refractory ores – textural complexities stricter environmental/permitting conditions – so-called “licence to mine”. increasing demand for specialist metals (e.g. rare earth elements, In, Ge, Te, Rh, Se, Ga and Li) commodity market volatility difficult funding environment

Given these challenges, geometallurgy provides the opportunity to control or at least manage some of them. METALLURGICAL VS GEOMETALLURGICAL APPROACH Metallurgical approach The traditional ‘metallurgical’ approach to plant design involves the testing of a number composite samples that are reported to be representative of the ore body. Testwork is carried out to determine factors such as grindability, floatability, leach recovery and/or other parameters. The testwork is often assumed to be appropriate and precise. Subsequently, a process plant is constructed and commissioned and at some point, often within the first year of operation, found to be not performing to

design (Williams, 2013). The common reason for this relates to insufficient and unrepresentative samples and potentially inappropriate testwork. The traditional approach generally fails to represent the orebody and likely variability within (Guresin et al ., 2012; Kittler et al ., 2011). Classical geometallurgical approach ‘Classical’  geometallurgy is the collaboration between geology (mineralogy) and metallurgy, with one discipline supplying the other with information and vice versa for a better understanding of the deposit and ore character (Williams, 2013). In essence pushing more mineralogical knowledge into the plant design and/or process operation. However, little or any quantitative spatial models result from this work and plant design is based on so-called ‘average’ ore – which in reality does not exist. Modern geometallurgical approach Modern geometallurgy seeks to integrate geoscientific disciplines with minerals and mining engineering. It aims to understand grade, metallurgical and mining (rock mass) variability based on information such as geochemistry, mineralogy, grade and lithology obtained from spatially distributed samples or sample points (Ashley and Callow, 2000). Multiple spatially distributed small-scale tests are used as proxies for grade, mineralogy, process parameter and rock mass variability (Keeney and Nguyen, 2014; Kojovic, Michaux and Walters, 2010; Mwanga, Rosenkranz and Lamberg, 2015; Vos, Stange and Bradshaw, 2014; Walters, 2009).  A key output of geometallurgy is the definition of geometallurgical domains. Mineral deposits are typically multivariate systems with many factors contributing to their overall complexity. A deposit can cut across several lithological units and have differing mineralisation and alteration styles, which will affect metallurgical response. Using lithological boundaries to control the distribution of metallurgical indices and testwork without effective evaluation may prove erroneous. Early stage geometallurgical domains are more likely to correlate with lithology and/or mineralisation/alteration style (Figure 1). Comminution and recovery domains may well be different. Geometallurgical domains are qualitative attributes amenable to spatial block modelling. Powell (2013) emphasises the need for high-resolution models for process optimisation based on geometallurgical properties. Smoothly estimated kriged models are insufficient for optimising processing operations for heterogeneous mineralisation. A model with too low variability will understate the local variation in properties, which should be considered in the design and operation of the mine and plant. In addition to estimates with t oo low variability, kriging may introduce a bias f or variables that do not average linearly, such as metallurgical properties (Carrasco, Chilès and Seguret, 2008; Coward, et al ., 2009). Evaluation of geometallurgical domains is via estimation or simulation using classical statistical and geostatistical algorithms such as multivariate regression and Gaussian simulation (Deutsch, 2013; Deutsch et al ., 2016).

FIG 1 – Canahuire deposit (Peru) geometallurgical domains based on lithology and alteration type. Blue: domain 400 [subeconomic silver mineralisation], orange: domain 300 [copper-gold breccia mineralisation], yellow: domains 200 [gold replacement mineralisation in limestone] and light blue: domain 100 [structurallycontrolled gold in calcareous sandstone]. For more information see Baumgartner et al . (2011 and 2013). Figure from Baumgartner et al . (2013). The integration of core logging (e.g. lithology, alteration, EQUOtip, RQD, etc.), mineralogical (e.g. QXRD/pXRD), geochemical (e.g. pXRF and ICP-AES/MS) and physical testing (e.g. SPI, BWi, A*b, etc.) data commonly leads to the prediction of comminution parameters through correlation and 3D modelling (Alruiz et al ., 2009; Harbort, Lam and Dola, 2013; Hunt, Kojovic and Berry, 2013; King and Macdonald, 2016; Figure 2).

FIG 2 - Block model coloured by Bond work index (BWi) values for the Productora and Alice Cu-Au-Mo pits (Chile). BWi for the main Productora deposit was estimated using aluminium and potassium values as proxies. The Productora pit is

approximately 2.4 km long in design. For further details see King and Macdonald (2016). Geometallurgy drives the need for multiple samples (taken from a well-defined sampling programme) across a deposit, though these and their subsequent testing should be carefully designed to fit the mineralisation type in question (Dominy, 2016; Dominy, Xie and O’Connor, 2016; Figure 3). A major concern is ensuring fit-forpurpose data through proper procedures and quality assurance/quality control programmes during data collection, sampling, testwork and analytical programmes.

FIG 3 – Data (e.g. EQUOtip and pXRF) and samples collected from drill core are critical geometallurgical inputs. High quality drill core, data readings, samples, subsamples and subsequent testwork (e.g. flotation) are required to support estimates or studies to be reported within the framework of any international code. Data management is a significant consideration for any organisation embarking on a geometallurgical programme (Liebezeit et al ., 2011). While geologists are experienced at dealing with thousands of samples and their associated assays, metallurgists have traditionally carried out relatively few tests and consequently often manage data in spreadsheets. Geometallurgy possesses with significant challenges as a result of the large volume of data generated. Considerations for any geometallurgical data management system include storage space, data integrity, accessibility, sample provenance, maintenance and ease of use, integration with data providers and the ability to modify the system to adapt to changing requirements (Liebezeit et al ., 2011). In addition to the traditional grade model, block models display the distribution of key metallurgical and mining parameters throughout the orebody to support financial analysis and mine planning (Coward and Dowd, 2015; Dowd, Xu and Coward, 2016; Dunham and Vann, 2007; Figures 1 and 2). Coward and Dowd (2014) summarise the current general approach to geometallurgical modelling as:   

identify the variables required to understand critical process responses sample and measure these variables develop techniques to estimate and simulate these characteristics spatially at the correct scale and incorporate the values into block models.

Block models then form the basis for project economic evaluation. Approaches such as scenario thinking to project evaluation encourages the project team to re- perceive

the systems aspects of the project and allows empirical testing of different strategies (Vann et al ., 2012). This will be achieved by realistically modelling and propagating the spatial variability and uncertainty of the deposit throughout the value chain. Modelling is a pathway to seeking high NPV options that are robust in the face of plausible future scenarios. A scenario model makes it harder for an optimistic project manager to bias project outcomes unreasonably. It is critical to ensure that the entire geometallurgical process, inclusive of data collection and testwork, data storage and interpretation, modelling and analyses are of the highest quality to ensure that any Mineral Resource and Ore Reserve estimates and subsequent economic studies can be reported in accordance with the JORC Code 2012 (Keeney, 2013; David, 2014). Stages and strategies for geometallurgy The geometallurgical approach emphasises early stage intervention and progression across the mine value chain (Baumgartner et al ., 2011, 2013; Bye, 2011; Dunham and Vann, 2007; Ehrig, 2013; King and Macdonald, 2016; Leichliter and Larson, 2013; Leichliter, Jahoda and Montoya, 2013; Table 1). It can be broadly split into two key approaches: project and operational (or strategic versus tactical geometallurgy: McKay et al ., 2016). The project approach focuses on the whole orebody and long-term life-of-mine view, whereas operational geometallurgy relates to the short- to medium-term view during mining. Operational geometallurgy adds to, and draws from the project database (David, 2010; Liebezeit et al ., 2016; McKay et al ., 2016). Operational geometallurgy is primarily focused on defining feed variability for forecasting and steady state blending purposes. Table 1  –  Generalised mine value chain showing broad geometallurgical activities, inputs and outputs Stage

Study

Project geometallurgy Exploration-early evaluation Scoping

Resources/ reserves

Inferred Mineral Resources

Key geomet. activity

Develop orebody knowledge

Geomet. inputs

Core logging Develop proxy tests Mineralogy Geochemistry Met. Testing Physical testing

Geomet. outputs

Establish database Geological model Prelim. geomet. domains

Definition drilling Pre-Feasibility Inferred and Indicated Mineral Resources Develop orebody knowledge Data analysis and modelling Core logging Proxy tests Mineralogy Geochemistry Met. testing Physical testing Expanded database Geomet. domains Block model Prelim. mine plan Geomet. models Prelim. flow sheet

Detailed drilling

Feasibili ty

Feeds into Feasibility See Feasibility

Feasibility

Develop orebody knowledge Data analysis and modelling Core logging Proxy tests Mineralogy Geochemistry Met. Testing, incl. pilot testing Physical testing Expanded database

Develop orebody knowledge Data analysis and modelling Core logging Proxy tests Mineralogy Geochemistry Met. Testing, incl. pilot testing Physical testing Expanded database Geomet. domains Block model Mine plan Geomet. models (incl. waste model) Flow sheet Scenario analysis Economic analysis

Mineral Resources and Ore Reserves

Operational geometallurgy Mining [Grade/ore control] [Expansion studies] Mineral Resources and Ore Reserves Develop orebody knowledge Data analysis and modelling Core logging Proxy tests Mineralogy Geochemistry Met. Testing Physical testing Expanded database Geomet. domains Block model Mine plan Geomet. models Forecasts Reconciliation

Collaboration is key  A key aspect of any effective geometallurgical programme are multi-disciplinary teams, thus bringing together exploration and mining geologists, mining and geotechnical engineers, metallurgists and mineral process engineers, and financial modellers, economists and risk specialists (Williams, 2012). By drawing on different disciplines and on specialists from other areas (for example, data mining and computing specialists, mathematical modellers and risk specialists) geometallurgy also sees industry practitioners, consultants, service providers and academics working closely together. GEOMETALLURGICAL EDUCATION Incorporating a highly applied discipline and subject such as geometallurgy into a university’s curriculum is extremely challenging. There are trains of thought in so far as students should be made aware of geometallurgy as soon as reasonably possible. On the other side, they need to understand fundamentals and grasp concepts well in order to apply those key learnings in to the world of geometallurgy. New initiatives in mining and extractive metallurgy courses must be sought and the inclusion of core and elective geometallurgy units is required. These should include the re-introduction of ore mineralogy and microscopy, which forms such a core base to many geometallurgical considerations. Integrating geometallurgy theory and practice would probably best suit a final year undergraduate and post graduate student; at this point in education, their ability to approach problems through systemic thinking, logic and to handle large data sets would be at an appropriate level. There are more industry roles asking for geometallurgical knowledge and practical experience now more than ever. However, academics trained in geometallurgical practices are few, so academia should be looking for assistance from industry to put back into the system, through sessional training, workshops and guest lecturing. University courses can be inflexible and hard to change. It is via direct industry feedback and the Alumni groups that proposed changes can be voiced influencing the development of new units delivered. Included in this, educating site personnel who do not have the ability to leave site for further education and development. In parallel with voicing feedback of the requirement for geometallurgical programmes/units, universities need to understand the delivery expectations i.e. online or blended learning. Experience would suggest that any online learning in the geometallurgical discipline should be reserved for post graduate level alone, though this is certainly open to suggestion. GEOMETALLURGICAL CHALLENGES Given the aim of geometallurgy to support mine value chain optimisation, it has great potential to increase the effectiveness of diverse mining projects globally. Many projects are geologically and metallurgically complex and in turn have lower grades. To make these projects viable in uncertain times, it is critical that there are no ‘surprises’ across the life-of-mine. Geometallurgy aims to define and manage these surprises. The major challenges and areas of development within geometallurgy are:   

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gaining corporate support – projects may require separate financing geometallurgical teaching – integration across disciplines implementation of studies across a wide range of technical disciplines and ensuring effective communication application across feasibility studies and mine operations

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representative sampling programmes and effective ore/waste characterisation ensuring fit-for-purpose data through proper procedures and quality assurance/quality control programmes integration and interpretation of very large databases modelling/simulation of numerous different data types process and mine optimisation tools

GEOMETALLURGY AND NEW HORIZONS Tailings storage facility ‘resources’  An emerging area for geometallurgy is the modelling of some of our most obvious and dormant resources  –  tailings storage facilities (Hunt et al ., 2016; Mudd and Jowitt, 2016). The success of using geometallurgy to model tailings facilities has been well noted. There is huge potential to apply geometallurgical theory and methodologies to better understand the location and character of a wealth of saleable metals and minerals. Characterising tailings storage facilities will also introduce other operational stakeholders into the project, such as geophysicists and geochemists. As tailings are a near-future resource for many mining operations, the area should be investigated, funded and researched now. Mineral processing technologies and deficiencies have changed so much since many of the tailings facilities were constructed that there is significant potential for mineable reserves. Acid rock drainage prediction Tailings are not the only interesting and obvious fit for geometallurgical practice. Undertaking effective environmental ore characterisation at the pre-feasibility and feasibility stages is essential for both efficient mine operations and reducing environmental impacts post-closure. Environmental parameters requiring characterisation include the propensity of a rock unit to generate acid, mapping deleterious element deportment, and characterising the release of toxic dusts as a result of blasting (Parbhakar-Fox and Lottermoser, 2015; Fox, Parbhakar-Fox and Lottermoser, 2016). Advanced 3D mineralogical characterisation  A wealth of information and data came into existence with the development of automated mineralogy, which is a very successful and respected analytical tool used today. There are specific occasions when ‘standard’ analytical tools do not provide the next level of information critical to a programme. It is known that optical microscopy, automated SEM-based microscopy (e.g. MLA, QEMSCAN and TIMA), microprobes, laser ablation (LA-ICPMS) used in conjunction with assay and geological logging data is a typical suite of tools, but recently the use of high-resolution X-ray computed tomography (HRXCT) is being used in research to investigate a range of ore types (Becker et al ., 2016; Dominy et al ., 2016). HRXCT is useful when characterising textures for liberation studies (Becker et al ., 2016), but also extremely useful for characterising rock mass for leaching purposes, whether that be heap leaching or in-situ potential (Miller and Lin, 2009). The latter requires information about pore connectivity providing a relationship between that and flow of lixiviants for metal recovery. The 3D analysis provided by the HRXCT software allows a determination of particle shape or morphology and could provide key information about the best comminution approach and recovery practices thereafter (Dominy et al ., 2016; Evans, Wightman and Yuan, 2015; McGrath, O’Connor and Eksteen, 2015).

Continuous HRXCT scanning of drill core is the ultimate goal and may be possible given that such technology (though low energy) is already used in the forestry sector to image defects in logs (Giudiceandrea, Ursella and Vicario, 2012). The routine and continuous scanning of drill core would provide many advantages to a project across early stage commencement, automation and speed. Automated core scanning Much non-grade information derived from drill core is a function of visual inspection as part of geological and geotechnical logging. Visual logging is generally highly subjective, experience-based and often conducted by relatively junior personnel.  Automated core logging, across short wave length infrared reflectance, thermal infrared reflectance and petrophysical technologies are playing an increased role in characterisation across geological and engineering parameters (Harraden, Berry and Lett, 2016; Walters, 2012; Vantandoost and Fullagar, 2009). For example, the HyLogger TM  automated spectral analysis and imaging system collects systematic short wave length infrared reflectance spectra (Huntington et al ., 2006). These can be classified into the dominant responsive mineral species and their compositional variations (chlorites, white micas, clays, etc.). Distribution of ‘soft’ responsive phases show relationships to comminution behaviour related to decrease of bulk strength (Walters, 2012). CONCLUSIONS Geometallurgy has reached a maturity beyond its early simplistic “geology + metallurgy” conception. It is recognised as an approach that can both maximise value and predict the risks associated with resource development. It is however not a ‘quick fix’, but a long-term commitment to adding value (Williams, 2012). Geometallurgy complements, but does not replace existing approaches to design and optimisation of mining and processing operations. Key drivers for the geometallurgical approach come from the following: 

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deposits are becoming lower grade, and geologically and/or metallurgically more complex ore bodies are variable both in terms of grade and metallurgical response, where this variability is a source of uncertainly that affects plant design, mine design and capital investment decisions rightly or wrongly, throughputs are increasing and profit margins decreasing  – thus financial risk is also increasing mining project risk needs to be carefully managed for projects to attract funding

The process of ore variability testing for metallurgical response and the use of quick, inexpensive metallurgical proxies have been developed in the past decade. Technology continues to advance, and techniques such as hand-held analytical tools and automated core scanning allow for faster, less expensive in-situ t esting. In the current mining industry downturn and with more complex deposits, geometallurgical application for short-term operational modelling and mine planning is vital. The data-rich nature of geometallurgy allows orebody variability to be incorporated into an optimised mine plan. The geometallurgical approach has tended to be used on large multi-million tonne type deposits (Baumgartner et al ., 2011, 2013; Beniscelli, 2011; Leichliter and Larson, 2013), though is now becoming applied more to smaller deposits (Dominy, Xie and O’Connor, 2016) and across different commodities (Glass, 2016).

ACKNOWLEDGEMENTS The authors thank Dr Regina Baumgartner (Gold Fields, Peru) and Dr Belinda Van Lente (CSA Global, UK) for for their constructive comments on the manuscript. Figures 1 and 2 are reproduced courtesy of Dr Regina Baumgartner and Grant King (AMEC, Australia) respectively. The opinions expressed in this paper are those of the authors and not necessarily those of their affiliations. REFERENCES  Alruiz, O M, Morell, S, Suazo, C J and Naranjo, A, 2009. A novel approach to the geometallurgical modelling of the Collahuasi grinding circuit, Mins Eng , 22: 10601067.  Ashley, K J and Callow, M I, 2000. Ore variability: exercises in geometallurgy, Eng Min Journ, 201: 24-28. Baumgartner, R, Dusci, M, Gressier, J, Trueman, A, Poos, S, Brittan, M and Mayata, P, 2011. Building a geometallurgical model for early-stage project development  – a case study from the Canahuire epithermal Au-Cu-Ag deposit, Peru, in   Proceedings International Geometallurgy Conference, pp 53-59 (The Australasian Institute of Mining and Metallurgy, Melbourne). Baumgartner, R, Dusci, M, Trueman, A, Poos, S and Brittan, M, 2013. Building a geometallurgical model for the Canahuire epithermal Au-Cu-Ag deposit, Peru  – past, present and future, in Proceedings International Geometallurgy Conference, pp 5157 (The Australasian Institute of Mining and Metallurgy, Melbourne). Becker, M, Jardine, M A, Miller, J A and Harris, M, 2016. X-ray computed tomography: a geometallurgical tool for 3D texural analysis of drill core, in Proceedings International Geometallurgy Conference, pp 000-000 (The Australasian Institute of Mining and Metallurgy: Melbourne). Beniscelli, J, 2011. Geometallurgy – fifteen years of developments in Codelco: Pedro Carrasco contributions, in Proceedings International Geometallurgy Conference, pp 3-7 (The Australasian Institute of Mining and Metallurgy: Melbourne). Bye, A R, 2011. Case studies demonstrating value from geometallurgy initiatives, in Proceedings International Geometallurgy Conference, pp 9-30 (The Australasian Institute of Mining and Metallurgy, Melbourne). Carrasco, P, Chilès, J-P and Seguret, S, 2008. Additivity, metallurgical recovery and grade, in Proceedings International Geostatistics Congress, pp 237-246 (Gecamin: Santiago). Coward, S, Vann, J, Dunham, S and Stewart, M, 2009. The primary-response framework for geometallurgical variables, in Proceedings International Mining Geology Conference, pp 109-113 (The Australasian Institute of Mining and Metallurgy: Melbourne). Coward, S and Dowd, P A, 2015. Geometallurgical models for the quantification of uncertainty in mining project value chains, in Proceedings of the 37 th  APCOM Conference, pp 360-369 (SME: Littleton). David, D, 2010. Operational geometallurgy, in Flotation Plant Optimisation  – A Metallurgical Guide to Identifying and Solving Problems in Flotation Plants , Spectrum

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