Applications of geometallurgy for waste characterisation across the mining value chain Dr Anita Parbhakar-Fox Senior Research Fellow WH Bryan Mining and Geology Research Centre, SMI, University of Queensland, Brisbane, Australia
What is ‘geometallurgy’? GEOLOGY MINING METALLURGY GEOMETALLURGY Purp rpose: In Increase profit fits
Geometallurgy Matrix concept Keeney (2008): Aimed to propagate measured processing attributes (i.e., hardness, grindabillity) down in the matrix to Level 2 and Level 1 Defined linkages are essential
Geometallurgy Matrix concept For mine waste characterisation a Full-scale, complex, geometallu lurgical matrix ix ap approach high cost could be readily adopted to de- risk projects and improve long- term financial outcomes Representative sampling to capture heterogeneity is a key issue- this helps overcome it Requires the embedding of geoenvir ironmental l proxy tests at the earliest LOM stages (i.e., exploration/prefeasibility) Small-scale, simple, low-cost Defined linkages are essential
The (enviro)geometallurgy tool kit Handheld tools Hyperspectral mineralogy Simple chemical tests Automated ‘Next - gen’ mineralogy technologies Data mining
Hyperspectral mineralogy • Challenges encountered when collecting ‘representative’ geoenvironmental samples at early life-of-mine stages • Increasing ore deposit knowledge will assist with static and kinetic testing sample selection • Hyperspectral data measuring VNIR and SWIR active minerals (e.g., Corescan) and TIR (e.g., HyLogger) • Corescan: ~2,000 m can be collected per day • Value-add opportunity by perform geoenvironmental domaining to support waste forecasting • Ide Identify ify pot potentia ially ly ac acid id form ormin ing, g, non non-acid id form ormin ing and and ne neutralis ising dom domains to o en enable was aste man anagement thr through ea early ly for orecastin ing of of geo eoenvironmental l ch characteris istic ics
Hyperspectral mineralogy Silicate SWIR Type Mineral Group Example VNIR Response TIR Response Structure Response Inosilicates Amphibole Actinolite Non-diagnostic Good Good Pyroxene Diopside Good Moderate Good Cyclosilicates Tourmaline Dravite Non-diagnostic Good Moderate Neosilicates Garnet Grossular Moderate Non-diagnostic Good Olivine Foresterite Good Non-diagnostic Good Silicates Sorosilicates Epidote Clinozoisite Non-diagnostic Good Good Phyllosilicates Mica Muscovite Non-diagnostic Good Moderate Chlorite Chlinochlore Non-diagnostic Good Moderate Clay minerals Illite Non-diagnostic Good Moderate Kaolinite Non-diagnostic Good Moderate Tectosilicates Feldspar Orthoclase Non-diagnostic Non-diagnostic Good Albite Non-diagnostic Non-diagnostic Good Silica Quartz Non-diagnostic Non-diagnostic Good Carbonates Calcite Calcite Non-diagnostic Good Good Dolomite Dolomite Non-diagnostic Good Good Hydroxides Gibbsite Non-diagnostic Good Moderate Non-silicates Sulfates Alunite Alunite Moderate Good Moderate Gypsum Non-diagnostic Good Good Borates Borax Non-diagnostic Good Uncertain Halides Chlorides Halite Non-diagnostic Moderate Uncertain Phosphates Apatite Apatite Moderate Moderate Good Oxides Hematite Hematite Good Non-diagnostic Non-diagnostic Linton et al. Spinel Chromite Non-diagnostic Non-diagnostic Non-diagnostic (2018) Sulfides Pyrite Non-diagnostic Non-diagnostic Non-diagnostic
Hyperspectral mineralogy Ch Chlor lorite match Geotechnical Geo Chlor Ch lorite wavelength Cor Core Min ineral Cl Class s map intensit ity par parameters posit pos ition photography ph
Hyperspectral mineralogy Mixed pixels are classified based on the most abundant spectra Class map colour index Aspectral Quartz/silica Quartz-carbonate Carbonate Core photography Mineral map Carbonate Carbonate match Sericite match Sericite + chlorite Low High Chlorite match match Clinochlore
Hyperspectral mineralogy Core pho Co photogr graphy Min Mineral class ma map Su Sulfid fide di distrib ibutio ion Log Log Su Sulfid ide di distrib ibutio ion
Hyperspectral mineralogy Hyperspectral data Geoenvironmental Domaining Index (GDI) Core images Mineral maps Scaled Neutralising Calculated Relative Potential/ Mineral * * reactivity Acid Potential abundance values values (Sverdrup, 1990) (Jambor et al., 2007; Parbhakar-Fox and Lottermoser, 2014) Example Chlorite: 60 % * 0.006 * 0.02 = 0.00012 Carbonate: 30 % * 1 * 1 = 30 Quartz: 10 % = 0 * 0 * 0.004 Pixel GDI = ~ 30 Jackson et al. (2018)
Hyperspectral mineralogy Fir irst pass GDI (V (V2) valu lue risk risk as assessment with ith su sulf lfid ides ide identif ifie ied defi fines 5 ris risk grade clas lassif ific icatio ion fie field lds GD GDI valu alue GD GDI risk risk grade De Description of of geoenvir ironmental ch characteris istics - 35,0 ,000 to o -900 900 Do Dominance of of acid cid formin ing min inerals ls. Sulfi lfides id identif tified as fir first t Extreme risk risk min ineral l > > 75 %. . No o pri rimary ry neu eutrali lisers (A (AP >> >>NP). nd and 3 rd rd min -900 900 to o 0 Sulfi lfides com ommon. Sulfi lfides es id iden enti tifie ied as 2 nd ineral Hig High risk risk < 75 %. < . No pri rimary neu eutralis isers (A (AP >N >NP). Dominated Do ed by silic ilica/quartz, seri ericit ite, ch chlo lorit ite. 0 to o 10,0 ,000 Pot oten ential l risk risk Few sulfid lfides es present, min inor r pri rimary ry neu eutrali lisers (AP≠NP). Som ome gy gypsum pres esen ent. 10,0 ,000 to o 40,0 ,000 Low risk risk Carbonate abundance < Ca < 50 % (A (AP<NP). Carbonate dom Ca ominates as fir first Co Cores escan min ineral > > 50 %. . 40,0 ,000 to o 100,0 ,000 Very ery lo low risk risk Lon Long ter erm acid cid neu eutrali lising capacity lik likely ely (A (AP<<NP). Jackson et al. (2018)
Hyperspectral mineralogy Sample A: : Skarn Cor ore ph photography Sul Sulfi fide rec ecognition Clas lassi sifi fied min ineral map ap Car arbonate ide identifi fication GD GDI V2: 2: 34,3 34 ,370 Lo Low risk risk Static testin St ing= NA NAF (H (Hig igh ANC) Jackson et al. (2018)
Hyperspectral mineralogy Sample B: : Skarn Cor ore ph photography Sulfi Sul fide rec ecognition Clas lassi sifi fied min ineral map ap Car arbonate ide identifi fication GDI V2: GD 2: 1910 1910 Chlorite dominated Potential l risk risk St Static testin ing= NAF (3% NA (3% sulf su lfid ide-sulf lfur; ; 23% cal 23 alcit ite) Jackson et al. (2018)
Hyperspectral mineralogy Sample C: : Porphyry ry Au-Cu (Potassic Alt lteration Zon one) Cor ore ph photography Sulfi Sul fide rec ecognition Clas lassi sifi fied min ineral map ap Car arbonate ide identifi fication GD GDI V2: 2: -14 140= Hig igh risk risk Sericite-dominated Static testin St ing= PAF/AF Jackson et al. (2018)
Hyperspectral mineralogy Additional applications when scanning column feed materials prior to kinetic testing – results to be published later in 2019
Handheld tools and chemical tests Not all are new, but not routinely applied for geoenvironmental characterisation Environmental En Chemical Ch Har ardness Log Loggin ging Stain St ainin ing measurements me Integration of results provides the best quality information to feed into the geometallurgical Fi Field eld che hemic ical pXRF pXR tes ests matrix
Handheld tools and chemical tests Acid cid Rock ock Dr Drain inage In Index (A (ARDI) Parbhakar-Fox et al. (2011; 2018); Opitz et al. (2016); Cornelius et al. (2017)
Handheld tools and chemical tests EQUOtip Min ineral hardness to o deter ermine rate of of wea eath thering g and predic ict elu eluti tion of of acid cid/ neu eutrali lisation Parbhakar-Fox et al. (2015)
Handheld tools and chemical tests Parbhakar-Fox et al. (2015)
Automated mineralogy Min ineral l Lib Liberation Analyser Buckwalt Buc lter-Davis is (20 (2013) Target sulphide phases Hours Ho Six tailings samples New & characterise grain Caloumet mine, Canada properties SPL Lite Aranta (20 Ar (2010): 4 waste rock samples, Characterise grain XBSE Antamina Mine, Peru properties for mineral of Ho Hours Par arbhakar-Fox (2012): ): interest and examine 10 waste rock samples, associations Lode-Au mine, 9 IOCG Curr rrent practice: samples, Australia Edr draki i et t al al. (20 (2014): Cu-Au Application in in porphyry tailings GXMAP predictiv ive ARD ch characteris isation Commonly Co ly use sed techniq iques do not t allo allow for r lo low-cost testwork an and tail ailin ings ch characteris isation hig igh volu lume analy lysis is- can XMOD be use sed?
Automated mineralogy- tailings fingerprinting 30 mins XM XMOD FEI Quanta 600 Parbhakar-Fox et al. (2017)
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