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Cost-Effective Means for Identifying Acid RockDrainage Risks Integration of the

Geochemistry- Mineralogy-Texture Approach

and Geometallurgical Techniques

Conference Paper October 2013

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1 author:

Anita Parbhakar-Fox

University of Tasmania

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143THE SECOND AUSIMM INTERNATIONAL GEOMETALLURGY CONFERENCE / BRISBANE, QLD, 30 SEPTEMBER 2 OC TOBER 2013

INTRODUCTION

Undertaking effective environmental ore characterisation at

prefeasibility/feasibility stages is essential for both efcient

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; this study

focuses on the prior.

International practice of predicting acid rock drainage

(ARD) formation has broadly evolved into the wheel

approach (Morin and Hutt, 1998) whereby laboratory-based

geochemical assessments are predominately recommended

to predict acid forming potential. The most widely-used

predictive geochemical tests can be grouped as either static

or kinetic. Static tests are the most utilised, as they have the

advantage of being rapid and less costly when compared to

Cost-Effective Means for IdentifyingAcid Rock Drainage Risks

Integration of the Geochemistry-Mineralogy-Texture Approach andGeometallurgical TechniquesA Parbhakar-Fox1, B Lottermoser2and D J Bradshaw3

ABSTRACTBest practice for acid rock drainage (ARD) risk assessment still relies solely on the geochemicalproperties of suldic rocks and mineral processing products, despite the fact that a rocks tendencyto produce acid also depends on mineralogy and texture. Consequently, there are a plethora ofgeochemical tests routinely utilised by the mining industry to predict ARD formation. Due tolimitations associated with these tests and their relatively high costs, analysis of recommended bestpractice sample numbers is rarely achieved, reducing the accuracy of waste management plans. Ourresearch addressed this through examining the application of geometallurgical data for predictingacid formation, and led to the identication of potential environmental geometallurgy indicators.

Samples obtained from an iron-oxide copper gold deposit were subjected to the geochemistry-mineralogy-texture (GMT) approach, an improved methodology for classifying acid formingpotential. GMT results were compared against geometallurgical and assay data sets to evaluate:

relative carbonate content measurements (measured by HyLogger) and identify how thesecould be used to calculate effective acid neutralising capacity

mineral hardness (measured by EQUOtip) to determine application of this data for calculating

lag-time to acid formation opportunities to automate the acid rock drainage index (ARDI) using classied imagesproduced by a GEOTEK logger and automated microscopy.

Links between the GMT approach and geometallurgical data sets were identied. Classiedmineralogy data has application at stage one, HyLogger and EQUOtip at stage two and computer-based ARDI evaluations of classied images at stage three. Through such integration, ARDcharacterisation costs can be reduced, with value added to geometallurgical data sets. Furthermore,deposit-wide ARD domaining is possible, and acquisition of total-orebody knowledge morelikely. Through adoption of this environmental geometallurgy approach, a better informed wastemanagement plan can be formulated, allowing for best practice ARD sampling in a more cost-effective manner.

1. Research Fellow, Cooperative Research Centre for Optimising Resource Extraction (CRC ORE) Ltd, School of Eart h Sciences, University of Tasmania, Private Bag 79, Hobart Tas 7001.

Email: [email protected]

2. Professor, Environment and Sustainability Institute, Camborne School of Mines, University of Exeter, Cornwall Campus, Penryn, Cornwall TR10 9EZ, United Kingdom; School of Earth Sciences,

University of Tasmania, Private Bag 79, Hobart Tas 7001. Email: [email protected]

3. MAusIMM, Professor, Julius Kruttschnitt Mineral Research Centre, Sustainable Minerals Institute, University of Queensland, Indooroopilly Qld 4068. Email: [email protected]


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THE SECOND AUSIMM INTERNATIONAL GEOMETALLURGY CONFERENCE / BRISBANE, QLD, 30 SEPTEMBER 2 OCTOBER 2013

A PARBHAKARFOX, B LOTTERMOSER AND D J BRADSHAW

144

kinetic testing ($50 versus >$2000 per sample; Lengke, Davisand Bucknam, 2009). There are two main static test procedures;acid base accounting (ABA) and net-acid generation (NAG)testing (White, Lapakko and Cox, 1999). If the best practicenumber of samples required to characterise an ore deposit interms of ARD forming potential are utilised (Table 1), costs ofenvironmental testing signicantly escalates. Consequently,analysis of best practice sample numbers can become too

nancially challenging. Furthermore, data produced by statictests can have limited use if test are inappropriately conducted,thus jeopardising the accuracy of waste management plansformulated based on this data. Limitations with individualmethods are in part recognised by the wheel approach(Morin and Hutt, 1998) with cross-checks between methodsrecommended to overcome these. However, this serves tofurther increase environmental testing costs.

The geochemistry-mineralogy-texture (GMT) approachproposed by Parbhakar-Fox et al (2011) addresses thelimitations associated with existing ARD predictive protocols.The GMT approach provides detailed guidelines for how touse existing ARD predictive tests, resulting in the production

of high-quality, reliable data to base waste managementplans upon. Essentially the approach requires geochemical,mineralogical and textural assessments to be undertaken inparallel over three stages. Results at the end of each stage arecross-checked to provide an accurate sample classication interms of acid forming potential. Prior to undertaking the GMTapproach, samples are assigned a mesotextural group basedon mineralogical, textural and chemical similarities. Next, allsamples are subjected to simple prescreening tests at stage one(eg measurement of total-sulfur, paste pH), with the modalmineralogy and total element contents quantied for at leastone representative sample per mesotextural group. A simpletextural evaluation scheme termed the ARD index (ARDI) was

developed as part of this approach as a stage one test (Parbhakar-Foxet al, 2011).Iron-sulde minerals are individually assessedby ve categories (A to E), specically chosen based on thedirect inuence on acid formation. Parameters A, B and Cexamine contents, degree of alteration and morphology ofsuldes respectively; whilst parameters D and E evaluatethe neutralising mineral content and the spatial relationshipbetween acid-forming and neutralising minerals. Based onthese data, a general ARD-forming potential classication isgiven. Only samples classied as acid forming, or as havingneutralising capacity are required for stage two testing.

Stage-two involves the use of routine static geochemicaltests (ie net acid producing potential: NAPP; and net acid

generation: NAG) in order to cross-check stage one results,and also quantify the acid forming/neutralising potential.Again, samples classied as potentially acid forming are

recommended for stage three tests. Stage-three utilisesadvanced geochemical tests (eg advanced NAG tests, acid-buffering characterisation curves; ABCC) and microanalyticaltools (eg laser ablation inductively coupled mass spectrometry:LA-ICPMS, mineral liberation analysis: MLA) to cross-checkany ambiguous results from the previous stages. Detailedmineralogical and textural characterisation of acid-formingsulde phases is also undertaken to identify the controls on

sulde oxidation, as a means of predicting future behaviour.Based on the nal GMT classication assigned at the end ofstage three, samples can be more appropriately selected forkinetic trials.

The ability to undertake low-cost GMT stage one analyseson best practice sample numbers allows for deposit-wideARD domaining, and is therefore a signicant advantageof the GMT approach. However, it may be considered thatundertaking such specialised geochemical testing (eg thoseused at stages-two and -three of the GMT approach) is actuallya limitation in the way in which ARD is currently predicted,with resulting data only of use for environmental ARDcharacterisation. Instead, to increase efciency in deposit-

wide analyses and add value to existing data sets, proxies forARD data should be identied. Geometallurgical tests anddata are the most appropriate for use. Despite the collectionof a vast range of data for geometallurgical modelling, nopublished examples exist of it being utilised for predictiveenvironmental characterisation. However, the samplingstrategies utilised as part of geometallurgical campaigns,ie 2 m sampling (eg Alruiz et al, 2009; Leichliter et al, 2011)represent an appropriate sampling interval for deposit-scaleARD domaining.

This study aimed to identify links between the GMTapproach and existing geometallurgical data at an operationalmine site, in order to deduce:

how the GMT approach can be integrated at theprefeasibility stage of operations

where geometallurgical data best ts within the existingGMT framework.

A sample set from the Ernest Henry iron oxide coppergold (IOCG) deposit was used in this study, as this sitewas geometallurgically characterised in detail as part of theAMIRA P843 GeM project. Environmental geometallurgyindicators examined in detail were:

application of hyperspectral infrared data for assessingthe accuracy of ANC data

methods for determining weathering rate based onmineral hardness

utilisation of mesotextural and microtextural images fortextural acid rock drainage index assessments.

Phase Description

Exploration: prospect testing At least three to five representative samples should be tested for each key lithology/alteration type.

Exploration: resource definition At least five to ten representative samples should be tested for each key lithology/alteration type.

Prefeasibility Several hundred representative samples of high- and low-grade ore, waste rock and tailings should be collected for geochemical work.

Sufficient samples to populate a block model with reliable distribution of static test data on ore, waste and wall rock. Kinetic tests should be

established for at least one to two representative samples for each key lithology/alteration type.Feasibil ity Continue to refine block model if necessary and cond uct sufficient mineralogical test work to cross-check data for key l ithologies. If there are

insufficient data to assess drainage chemistry and provide a convincing management plan for approval, additional sampling, test work and

refinement of block models will be required.

TABLE 1

Suggested initial numbers of samples and test work (adapted from Australian Government Department of Industry, Tourism and Resources, 2007, in Price, 2009).
https://www.researchgate.net/publication/222566791_A_novel_approach_to_the_geometallurgical_modelling_of_the_Collahuasi_grinding_circuit?el=1_x_8&enrichId=rgreq-0daf6ac1-23cb-4124-9d6a-532d802b0ee5&enrichSource=Y292ZXJQYWdlOzI1ODM3Mzc0ODtBUzoxMDMxNzEwNzAzNjU2OTZAMTQwMTYwOTMwODA2MQ==https://www.researchgate.net/publication/222566791_A_novel_approach_to_the_geometallurgical_modelling_of_the_Collahuasi_grinding_circuit?el=1_x_8&enrichId=rgreq-0daf6ac1-23cb-4124-9d6a-532d802b0ee5&enrichSource=Y292ZXJQYWdlOzI1ODM3Mzc0ODtBUzoxMDMxNzEwNzAzNjU2OTZAMTQwMTYwOTMwODA2MQ==https://www.researchgate.net/publication/222566791_A_novel_approach_to_the_geometallurgical_modelling_of_the_Collahuasi_grinding_circuit?el=1_x_8&enrichId=rgreq-0daf6ac1-23cb-4124-9d6a-532d802b0ee5&enrichSource=Y292ZXJQYWdlOzI1ODM3Mzc0ODtBUzoxMDMxNzEwNzAzNjU2OTZAMTQwMTYwOTMwODA2MQ==https://www.researchgate.net/publication/222566791_A_novel_approach_to_the_geometallurgical_modelling_of_the_Collahuasi_grinding_circuit?el=1_x_8&enrichId=rgreq-0daf6ac1-23cb-4124-9d6a-532d802b0ee5&enrichSource=Y292ZXJQYWdlOzI1ODM3Mzc0ODtBUzoxMDMxNzEwNzAzNjU2OTZAMTQwMTYwOTMwODA2MQ==https://www.researchgate.net/publication/222566791_A_novel_approach_to_the_geometallurgical_modelling_of_the_Collahuasi_grinding_circuit?el=1_x_8&enrichId=rgreq-0daf6ac1-23cb-4124-9d6a-532d802b0ee5&enrichSource=Y292ZXJQYWdlOzI1ODM3Mzc0ODtBUzoxMDMxNzEwNzAzNjU2OTZAMTQwMTYwOTMwODA2MQ==https://www.researchgate.net/publication/222566791_A_novel_approach_to_the_geometallurgical_modelling_of_the_Collahuasi_grinding_circuit?el=1_x_8&enrichId=rgreq-0daf6ac1-23cb-4124-9d6a-532d802b0ee5&enrichSource=Y292ZXJQYWdlOzI1ODM3Mzc0ODtBUzoxMDMxNzEwNzAzNjU2OTZAMTQwMTYwOTMwODA2MQ==


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COSTEFFECTIVE MEANS FOR IDENTIFYING ACID ROCK DRAINAGE RISKS

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MATERIALS AND METHODS

Sample selection and geochemistry-mineralogy-texture analysesA limited number of offcuts (


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mesoscale ARDI evaluations more efciently through using

classied modal mineralogical maps of drill core. At Ernest

Henry, petrophysical measurements for the six drill holes

were recorded on a total of circa 1700 m of NQ half core, with

measurements taken at 9 cm intervals (Vatandoost, Fullagar

and Roach, 2008). After data reduction and processing,

averages of the petrophysical parameters were computed

over 2 m assay intervals.

Automated microscopyQuantifying textural and mineralogical relationships in

rocks that affect processing performance is a critical aspect of

geometallurgy. Berry and McMahon (2008) summarised that

automated mineral recognition had largely been applied to

opaque minerals (eg suldes, magnetite), with little attempt

made to recognise individual gangue phases (cfLane, Martin

and Pirard, 2008). For the purpose of ARD domaining,

it is essential for gangue mineralogy to be well dened.

Considering this, the AMIRA P843 GeM project sought to

improve automated optical microscopy by using a Leica

DM6000 microscope.

The Leica DM6000 microscope (University of Tasmania) has

a high precision stage (


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Stage threeMulti-addition NAG (mNAG) testing results did not change

the geochemical classication assigned at the end of stage two,

with acidity produced through the accelerated oxidation of

pyrite and chalcopyrite directly buffered by calcite. However,

lower mNAG pH values (


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Predicting weathering rateKeeney (2008) dened seven hardness categories for thetested Ernest Henry drill holes as shown in Table 3. EQUOtipdata have been considered here as an indicator for likelyweathering behaviour. For example, samples classied byEQUOtip as very soft are considered to weather at a fastrate, whereas samples classied as very hard are predicted toweather slowly. Interpretation of data in this manner is purelyqualitative. Additionally, no consideration is given to grainsize and the presence of fractures, with the latter inuencingweathering rate by acting as conduits for oxygen and water(Plumlee, 1999). Despite these limitations, EQUOtip valueswere considered against lithology and sulfur assay values in

order to assess the potential for, and lag time to acid formation

(Figures 3 and 4). These values were compared against GMTstage two NAG pH versus paste pH classications.

The predicted lag time to ARD for EH 633 based onEQUOtip and assay data varied between NAF (ie hardzone with relatively low S

Total) and AF (rapid rate of ARD

formation, medium risk) as shown in Figure 3. More variationwas observed for EH 635, particularly from 860 m to 1020 mwith NAF, PAF and AF zones identied (Figure 4). Ingeneral, resulting classications were more conservativethan those assigned by NAG pH versus paste pH, with themost common conict the classication of a NAF zone asPAF (eg EH 633, circa 970 m; Figure 3). However, in EH 635a NAF zone was identied from circa 1030 m to circa 1075 mby this classication, but by the NAG pH versus paste pHclassication, circa 1030 m to circa 1060 m was identiedas PAF (Figure 4). Based on these results, hardness/assayclassication is best suited to provide a general indication ofweathering rate prior to NAG pH, paste pH or kinetic NAGgeochemical data being reported.

Automated acid rock drainage index loggingThe ARDI may be regarded as subjective and limited by the factthat only a small number of grains (n = 20) are recommended

for evaluation. However, in its current form the ARDIsatises the industry-wide requirement for a simple texturalmethod of evaluating acid forming potential, as discussed in

Parbhakar-Foxet al(2011). Automation is the next logical step.This will increase the number of acid forming sulde grainsanalysed,making calculated ARDI values statistically valid,

and eliminating subjectivity.

FIG 2 - Domaining of net acid producing potential in Ernest Henry drill hole EH 635 based on STotal

values (obtained from assay) and relative carbonate abundance(measured using HyLogger by Quigley, 2008). Dark indicates the potential for acid formation, light indicates an acid neutralising capacity, and grey indicates non-acid

forming characteristics. Abbreviations: ANC acid neutralising capacity; NAF non-acid forming, NAF* non-acid forming, but with a likely neutralising capacity;ND no data; PAF potentially acid forming.

EQUOtip hardnessclassification

Mean (Ls) Predictedweathering rate

Very hard 793 Very slow

Hard 763

.

Medium hard 737

Medium 716

Medium soft 695

Soft 648

Very soft 596 Very fast

TABLE 3

Table of EQUOtip hardness categories with average values shown (Ls leebs)with a relative predicted weathering rate assigned (data from Keeney, 2008).


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FIG 3 - Domaining of lag-time to acid formation in Ernest Henry drill hole EH 633 based on STotal

values (obtained from assay) andEQUOtip hardness (measured by Keeney, 2008). Abbreviations: AF acid forming; PAF potentially acid forming; Med medium.

FIG 4 - Domaining of lag-time to acid formation in Ernest Henry drill hole EH 635 based on STotal

values (obtained from assay) andEQUOtip hardness (measured by Keeney, 2008). Abbreviations: AF acid forming; PAF potentially acid forming; Med medium.


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As stated, core photographs collected by a GEOTEKlogger can be utilised for mesoscale ARDI evaluation.Whilst unprocessed core images could be used at a site withrelatively uncomplicated geology and mineralogy, it wouldnot be possible at operations such as Ernest Henry. Insteadclassied images are required, with an example is presentedin Figure 5. Pyrite is located in a ne-grained magnetite-potassium feldspar-carbonate-quartz matrix (Figure 5a). If the

unclassied image alone was used, difculties in assigningvalues for ARDI parameters D and E would be experiencedas discriminating between felsic minerals and quantifyingmineral associations may prove challenging. Using theclassied image overcomes this, as the mineralogy is betterdiscriminated as each mineral is assigned a distinctive colour(Figure 5b). Additionally, Bonnici (2012) demonstrated thatmodal mineralogy information, as well as specic texturaldata from chalcopyrite (ie area, length and width, mineralsassociation, distribution) could be extracted from GEOTEKimages using computer software (ie Deniens). Thus, ifmesoscale ARDI evaluations are going to be performedmanually on-site, then classied images are given preference

for use providing they exist.Again, as stated earlier microscale ARDI evaluations

can be performed using data collected from either optical

microscopy (ie Leica DM6000) or the MLA SPL-Lite function.Berry and McMahon (2008) collected and classied >100images using the Leica DM6000 optical microscope from EH633 and EH 635. However, this was on particulate samples(ie not intact) and therefore not directly usable in an ARDIassessment. Bonnici (2012) undertook an SPL-Lite MLA studyon chalcopyrite grains from these drill holes. This data couldbe reinterpreted to extract ARDI relevant data as indicated

in Table 4. Of the two possible microscale data sets, SPL-Litedata are likely to be of the most use, with the opportunityremaining to undertake this analysis.

DISCUSSIONThe GMT evaluation at this site demonstrated the importancefor precise denition of mesotextural groups at the startof the investigation. Whilst sample grouping systemsestablished on site may state that consideration is given toalteration and texture, they are fundamentally based on justlithology. Developing new mesotextural groups or adheringto geometallurgical groups (ie as dened at Ernest Henry byBonnici, 2012) will lead to effective GMT characterisation.Otherwise, groups such as EH-4 and EH-7, (in which bothdisseminated sulde and clotted suldes textures were

observed) will return a spread of acid forming classications.Consequently, uncertainty will arise with regards to whichsamples to take forward for stage two and stage three testing.If geometallurgical studies are undertaken at a deposit, thenARD studies should aim to use an identical sampling protocolfor stage one of the GMT approach.

Stage one integrationDening modal mineralogy is the key to understanding acidforming potential, and the GMT approach recommends thatone sample per mesotextural group is analysed by QXRD toprovide a quantied indication of this. However, in a depositsuch as Ernest Henry where there is considerable mineralogicalvariability, an alternative is required. In Bonnicis (2012)study, modal mineralogy estimates were obtained from boththe GEOTEK logger and the MLA XMOD technique. Thus,quantied mineralogical data for a large number of sampleswere available. Additionally, assay data were routinely

FIG 5 - (A)GEOTEK Multisensor core logger images of drill core tiles (3 cm 6 cm) in Bonniciet al(2009), taken from EH 633, group EH-7. (B)Classifiedmineral map of the drill core image showing pyrite present as both clots (inthe centre-right of the image) and disseminated towards the border. Pyrite

clots do not appear directly rimmed by carbonate. (C)Extracted pyrite grainswith a five-pixel rim shown, which was 100 per cent quartz. Parameter E of

the acid rock drainage index thus scored this as 0/10.

ARDIparameter

Description Retievable fromMLA and GEOTEK

data?

Relevant MLAparameter

A Size (maximum

diameter of sulfide)

Yes Equivalent area

B Alteration of sulfide No Development

required

C Sulfide morphology Yes PSSA

D Content of primary

neutralisers

Yes XMOD/modal

mineralogy

E Sulfide mineral

association

Yes Mineral extraction

and association

ARDI acid rock drainage index. MLA mineral liberation analysis.

PSSA phase specific surface area. XMOD X-ray modal analysis.

TABLE 4

Potential links between textural data extractable from classified images(collected by a GEOTEK logger (mesoscale), classified optical microscope images

(microscale) and MLA-SPL-Lite analysis) and acid rock drainage indexparameters (with parameter A modified from sulfide content, to sulfide size).


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collected every 2 m, thus both sulfur and metal/metalloidvalues were available at stage one. The ARDI was undertakenon the mesoscale in this study at no extra nancial cost (ieinvolved examination of drill core off cuts). However, toperform on site, appropriate training of the site geologistsis required.

The only additional stage one GMT test required here insurplus to geometallurgical data is the paste pH test ($9;Australian Laboratory Services, 2010). Noble, Lottermoserand Parbhakar-Fox, 2012) stated that the paste pH test is oflimited use for predicting ARD. However, as rock materialis placed onto waste rock piles soon after they have beenmined, the paste pH test provides an indication of the short-term leachate drainage quality. Furthermore, results fromErnest Henry demonstrate that the paste pH test should notonly be considered in terms of reecting inherent acidity, butalso neutralising potential. Thus, the benet of spending theextra sum on paste pH testing is that the short-term ARD risk(either high or low) can be cost effectively determined using acombination of geochemical, mineralogical and textural dataacross a deposit. This practice is not currently undertaken, as

risk is commonly dened based on chemical results alone (egBroadhurst and Petrie, 2010).

Stage two integrationAt stage two, there is an opportunity to integrate HyLoggerdata to identify if Sobek ANC values are effective, as relativeproportions of carbonates are reported. Whilst Quigley (2012)inferred that there may be methods to quantify HyLoggerdata, no further details or examples were presented. Ourstudy indicated that with a large quantity of paste pH andSobek ANC values (comparison not shown), relationshipswith carbonate intensity as measured by HyLogger canbe mathematically dened. Thus, if no geochemical data

(eg paste pH, Sobek ANC) exists for parts of the deposit,carbonate intensity values could be collected, and estimates ofpaste pH and Sobek ANC calculated (Parbhakar-Fox, 2012).

EQUOtip data may has potential application for indicatingthe relative hardness of PAF or ANC zones, allowing forlag-time to acid formation to be domained. For example, anacid forming rock group identied as hard (eg EH-4) wouldbe predicted to weather slowly in a waste rock environment,indicating a signicant lag-time to acid formation. Similarly,an acid neutralising group (eg EH-5) identied as soft wouldbe anticipated to weather relatively quickly if being used ascapping material to PAF material in a waste rock pile. Thus,there would be an initial stage of net-neutralisation, followed

by acid formation. This study showed that by domaining inthis manner more conservative classications are returnedthan when compared against NAG pH versus paste pH values.

Stage three integrationAt stage three fewer ABCC tests (to dene effective ANC)are required, if HyLogger data have been collected at stagetwo (ie only validation samples required). At this stage,geometallurgical data to determine liberation potentialcollected from acid-forming suldes by optical microscopyor MLA techniques should be re-evaluated in terms of theARDI. Computer software to determine ARDI values shouldbe developed to increase the statistical accuracy of thistextural assessment. These values can then be used iterativelyto improve mesoscale ARDI evaluations performed at stageone of the GMT approach. This was undertaken here withARDI values manually calculated based on interpretations ofclassied MLA images returning slightly higher values only

(


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for domaining or predicting ARD formation. Therefore, the

aim of this study was to identify the links between the GMT

approach and existing geometallurgical data at the operationalErnest Henry IOCG mine. Drill core off cuts (n = 30) from

drill holes EH 633 and EH 635 were subjected to the GMT

approach. The sample grouping system (based primarily on

lithology) developed on site was used, and seven groups (EH-1

to EH-7) were identied.

The GMT approach classied groups EH-1 and EH-5 ashaving acid neutralising capacity (ANC). Groups EH-4,

EH-6 and EH-7 were potentially acid forming, and all other

groups were non-acid forming. Results indicate that the GMT

FIG 6 - Proposed environmental geometallurgy approach. Geometallurgical data is shown in bold and italic. Abbreviations: MLA-XMOD mineral liberation analysis-modal mineralogy analysis; NAPP net acid producing potential; ANC acid neutralising capacity; NAG net acid generation; MPA maximum potential acidity;m-, s- and k- NAG multi-, sequential and kinetic-NAG; LA-ICP-MS laser ablation inductively coupled plasma mass spectrometry; SEM-EDS scanning electron

microscopy- energy dispersing spectrometry; EPMA electron probe microanalysis; EAF extremely acid forming; AF acid forming; PAF potentially acid forming;ANC acid neutralising capacity (* indicates S

Sulfidecan be used instead).


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approach can be effectively applied at an operational mine

site, but only if adequate mesotextural grouping is performedat the start of the investigations, which has not beenperformedat Ernest Henry (cfTreddinick and Tuesley, 2000). As groupsEH-4 and EH-7 were not mesotexturally uniform, someconicting classications from these groups were reported.Instead the mesotextural grouping proposed by Bonnici (2012)for geometallurgical characterisation at Ernest Henry shouldalso be adopted for future ARD domaining and assessment.

Interpretation of HyLogger data (specically the relativecontents of carbonate minerals) allowed for effective ANC tobe determined when compared against Sobek ANC values.This was given preference to undertaking ABCC tests. Mineralhardness data used in combination with sulfur assay values

provided highly conservative estimates with regards to thelag-time to acid formation, when compared against NAGpH versus paste pH classications. Automation of the ARDImay be possible on both the mesoscale and microscale usingGEOTEK core images and SPL_Lite images, with suldegrains extracted and evaluated against the ARDI parametersusing Deniens and Texture viewer software. However, thisremains to be undertaken.

Through identifying these environmental geometallurgyindicators, with a second version of the GMT approach isproposed which includes these (Figure 6). By undertakingthis approach, there are signicant nancial advantagesand assessment of best practice samples numbers for ARD

domaining is permitted, consequently improving ARD riskassessment. Therefore, our approach has superiority overexisting industry practice wheel approach type protocolswhich utlise costly geochemical tests as a rst step, rather thanusing an effective prescreening stage. By introducing such aprescreening step at stage one to recognise only acid formingor neutralising samples (selected for further testing), fundsare not inappropriately spent on characterising non-acidforming materials, as is often the case. GEOTEK logger and(MLA) XMOD values are of use at stage one, HyLogger andEQUOtip at stage two and computer-based ARDI evaluationsof classied images at stage three. Further research effortsshould focus on using rapid automated techniques (ie GEOTEKlogger, HyLogger) to gather mineralogical and textural datawhich can be used to compute the ARDI automatically. Thispresents the opportunity to collect a statistically signicantrepository of textural (and mineralogical) data of direct use indomaining and predicting ARD formation.

ACKNOWLEDGEMENTS

This research was conducted as part of the rst authors PhDresearch. The authors would like to acknowledge the support

of CRC ORE (CRC for Optimising Resource Extraction),

established and supported by the Australian GovernmentsCooperative Research Centres program. The CooperativeResearch Centres program is an Australian Government

Initiative. Additional funding for this research was provided

by the ARC Centre of Excellence in Ore Deposits (CODES), the

AMIRA P843 project and the Society of Economic Geologists(SEG). Additional thanks are extended to Professor SteveWalters.

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Deposit type, location Estimatedresource

ARD samplescollected

(and year)

Best practicesample number

Current industrywheel approach

costings (A$)

GMT approachstage one

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GMT approach stageone costings without

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Porphyry, South America 850 96 (2010) 350 91 000 3150 70 000

Porphyry Au-Cu, Australia >1000 188 (2009) 400 104 000 3600 80 000

Epithermal porphyry, Asia Pacific 200 155 (2010) 500 140 000 4500 100 000

TABLE 5

Predicted geochemistry-mineralogy-texture(GMT) approach acid rock drainage (ARD) testing costs (with and without geometallurgical data available) compared tocurrent industry wheel approach using best practice sample numbers (calculated from the hypothetical sample curve shown in Downing, 1999) for select deposits.Figures for actual sample numbers collected for ARD testing also shown to illustrate that best practice sampling was not achieved, most likely due to the high costs

associated with the current industry approach to ARD testing.


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