mineral mapping techniques to optimize downstream

Mineral mapping techniques to optimize

downstream processing routes

Eric PIRARD

ChemysteryWhat chemistry won’t tell you

Chemystery

• What is this ?

o O 65 %

o C 18 %

o H 10 %

o N 3 %

o Ca 1,4 %

o P 1,1 %

• The molecular base (nucleotides) of our DNA

o Adenine C5H5N5

o Thymine C5H6N2O2

o Cytosine C4H5N3O

o Guamine C5H5N5O

MOLECULAR BIOLOGY

...not so much a technique as an approach

… with the leading idea of searching below the large-

scale manifestations of classical biology…

© Wikipedia

Our body

Our body

• The Human Genomeo 22 000 genes

o 3,4 billion pairs of nucleotides

Total cost of sequencing a human genome over

time as calculated by the NHGRI

DNA sequencing is the process of

determining the precise order

of nucleotides within a DNA molecule.

OrebodiesChoosing the right perspective

The Geologist’s Orebody

• Exploring the pasto Geology & Tectonics

• Regional,…

• Local,…

o Fluids

• Compositions,…

• P,T,…

o Petrography

• Host rock,…

• Orebody,…

o Timing

• Mineral parageneses,…

• Alteration,…Burnotte, E, Pirard, E, & Michel, G. (1989). Genesis of gray monazites : evidences from the Paleozoïc of Belgium. Economic Geology, 84, 1417-1429

Massif de Stavelot (E.Belgium)

Cambrian-Ordovician

© B

RG

M

The Economist’s Orebody

• Evaluating the presento Reserves

• Mt

• Grade

o Mine Planning

• Pit optimisation,…

• Infrastructure,…

• Blending, …

o Simulations

• Grade / tonnage curve

• Financial simulation

© Detour Gold

The Metallurgist’s Orebody

• Anticipating the futureo Understand downstream processing / end use

o Identify useful features

• Mineralogy, …

• Porosity, fractures, …

o Quantify variability

• From deposit…

• … to grain scale

o Perform small scale tests

• Comminution, …

• Concentration,…

Jaimes Contreras, R. A, Pilawski, D, Califice, A, & Pirard, E. (2010). Quantitative MicrotextureAnalysis of Carbonate Rocks Using Bireflectance Imaging. Proceedings IAMG 2010

Bridging the cross-discipline divide

The prime focus of

geometallurgy is bridging the

cross-discipline divide between

geology and mineral processing

There are many disciplines

involved in major mining

operations with potential

for significant disjoints….

GeometallurgyEmbracing the view from mine to mill

Geometallurgy : what we need

• Molecular Geologyo Fast and Accurate Mineral Identification

• Valuable Minerals

• Gangue Minerals

• Elemental Deportment

• Sequencing Oreso Fully Automated Quantitative Analysis

• Modal Analysis (% mass)

• Porosity and fractures

• Grain/Crystal size

• Grain shape

• Microtextures

• Predicting functionalityo Process Oriented Modelling - Indices

✓ Breakability, Floatability,…

✓ Leachability, Thermal expansion,…

Minerals Ores

Function

Towards Molecular GeologyA need for mapping minerals

A need for mapping minerals

• Do you perceive any difference?

O 35 %

Si 21 %

Al 11 %

Fe 6 %

Ni 1,5 %

Ga 27 ppm

Pb 15 ppm


• 2,5Mt Ottoman slags in Küre (TK)

• Is it economical to recover Co ?o Under which form is Co ?

o How could we separate it from the rest ?

• Flotation, Gravity, Pyro/Hydrometallurgy,…

o Fe, Si and Al are shown as oxides (is this correct ?)

FeO 59.7 %

SiO2 23.5 %

Al203 11.6 %

S 1.8 %

Cu 0.76 %

Co 0.38 % !


• ELEMENTAL DEPORTMENT

A low flotation recovery (92.6% cobalt remained in the tailings) of cobalt was

probably due to the formation of non-floatable cobalt spinels and silicates in the slag

… Cobalt was leached with an 86.5% dissolution efficiency at a roasting temperature

of 500°C, a roasting time of one hour and a pyrite:slag ratio of 3:1.

(Bulut et al., 2006)

Pirard, E. (1991), Quantitative mineralogical analysis of Cobalt and Copper distribution in historical slags from Küre (Turkey), CIM Bulletin, v84, 87-91

Quantitative Image Analysis

+ Microprobe

Fayalite Fe2SiO4

Wüstite FeO

« Pyrrhotite » FeS

Cu Sulphides

« Glass »

Hercynite Fe Al2O4

Reflected Light Optical Microscopy

of a polished block of slag

Cobalt Deportment in the slag


• Geometallurgical modelling of orebodieso Towards a Mineralogical Information System (MIS)

© GOCAD

HyperspectralCore-Scanning

Microscopical Imaging

Towards Molecular GeologyPrinciples of Mineralogical Imaging

Principles of Mineralogical Imaging

• 3S : Source-Sample-Sensor

Source

Emission of a given

electromagnetic

spectrum

Sensor

Material with a specific

capability to convert photons

into electrons (Quantum

Efficiency)

Target object with

specific surface properties

Sample

Target specific response in terms of light

reflection, diffusion, absorption, fluorescence,

back-scattering, etc.


• Punctual Scanning Modeo 2D Scanning beam

• Beam spot size (µm)

• Scanning speed (µm/s -> m/s)

• Sensitivity of sensor (integration time) (ms)

Necessary compromise between

spectral/spatial resolution

© Z

eis

s


• Backscattered Electrons Imaging (BSE)

• Energy Dispersive X-Ray Imaging (EDX)

© Z

eis

s

Hercynitefrom FeO.Al2O3 to 5FeO.3Al2O3

LeuciteKAlSi2O6

Al

Fe

Si


• From elements to minerals

© B Tordoff – Zeiss (2016)


• Line Scanning Modeo Linear sensor

• typ. 2000 – 8000 pixels for video

o Sample translation (conveyor, tray,…)

• horizontal resolution : optical

• vertical resolution : mechanical


• Core-Scanning Prototypeo Visible to Near Infrared: 400 - 1000 nm

o Short Wave Infrared: 1000 - 2500 nm

o Resolution VNIR: 30 pixels/cm (300 µm)

o Resolution SWIR: 10 pixels/cm (100 µm)

(Barnabé et al. 2015)

DMT, leader

ULiege – GeMMe

Fraunhofer EZRT; BGR; GTK

LTB; Bachmann; ; U Paris Sud; Eramet; MATSA


• Area scano Staring array

• typ. 1 – 14Mpixels for (still) video

o No mechanical movement

o Resolution fixed by sensor


• MultiSpectral Imaging in Reflected Light Microscopy

Criddle & Stanley QDFIII, 1993

Califice A. (2008)

AMCO

Automated Microscopic Characterization of Ores

UPM Politecnica de Madrid

Uliege - GeMMe

TSL Labs

First Quantum (CLC) - KGHM

Towards Sequencing OresProcess Oriented Characterization

Modal Analysis : PP=AA=VV

• Stopping criterion for Modal Analysis from random sections of particleso Based on Gy’s theory (Califice et al., 2012)

EF S

dC 2

952 '.=

C’ : « ore constant »d95 : max particle sizeSE : total surface to analyze


• Stopping criterion for Modal Analysis from random sections of particleso Based on Gy’s theory (Califice et al., 2012)

Average pyrite grade after analysing 150 images

Confidence Interval for pyrite

grade after analysing 150 images

Py %

Nb

Histogram of pyrite area fractionsin 250 images.

Estimation of Pyrite area fraction from a well-liberated 9% Pyrite mix


• Modal Analysis of Concentrate vs Tailing

o Operator 1 : 1h-2h analysis, short dwell time

o Operator 2 : 5h-6h analysis, double dwell time

ICP AUTOMINERALOGY% Fe (ICP) % Cu (ICP) Nb particles Fe (Mineralogy) Error Cu (Mineralogy) Error

CONC +75 34,06 26,02 601 32,7 4% 26,6 2%

CONC +38 32,73 24,76 2624 30,4 7% 27,9 13%

TAIL +75 2,52 0,11 98 47,7 1792% 4,7 4206%

TAIL +38 2,43 0,09 369 50,1 1963% 2,2 2359%

ICP AUTOMINERALOGY% Fe (ICP) % Cu (ICP) Nb particles Fe (Mineralogy) Error Cu (Mineralogy) Error

CONC +75 34,06 26,02 2755 31,83 7% 27,31 5%

CONC +38 32,73 24,76 2173 30,13 8% 28,64 15%

TAIL +75 2,52 0,11 2497 3,71 47% 0,17 53%

TAIL +38 2,43 0,09 2406 3,80 57% 0,06 30%


• Using Chemical Assays for Cross-Validationo Element to Mineral Conversion

o Modal Analysis adjusted to Chemical Analysis

Particle Size Analysis

• 3D from 2D sections is tricky (Wicksell problem)o Equivalent square diagonal (Petruk) or Equivalent disk diameter are often used as “size”

• Touching particles (!)

𝐷3𝐷 = 2. 𝐴 𝑋

Image of random sections from a narrow-sized galena mix

Particle Size Analysis

• Prepare sized fractionso Measure “maximal inscribed disk” of only the largest particles (P75) particles

• Indicator correlated to thickness (dissolution time),…

DIN

Liberation Analysis

• 3D liberation analysis from 2D sections is « crazy » (King; Schneider; Barbery; Gay ;…)

Liberation Analysis

o Work on sized fractions

o Limit liberation analysis to only the « largest » particles

Beyond liberation… texture

• Advanced Textural Indiceso Intercept based indices instead of areal liberation

12.4%

66.4%

11.9% 1.9%5.1%

2.3%

Liberated Chalcopyrite

Chalcopyrite in Simple Intergrowth

Matrix crossed by Stockwork

Inclusion coated by a rim or semi-rim

Not Classified

Coated and crossed by Stockwork

Perez Barnuevo L., Pirard E. and Castroviejo R. (2013) ‘Automated characterisation of intergrowth textures in mineral particles: a case study’, Minerals Engineering, 52, pp. 136–142

Beyond liberation… texture

• Advanced Textural Indiceso Intercept based indices instead of areal liberation

𝐿𝛼 =𝐴(𝛼)

𝐴(𝑋)= 54%

=

i

i

i

i

VL

N

S

)(

2)(

Breakability

=

)0,(2

)0,(i

iNd

B

Floatability

Towards predicting functionalityProcess Oriented Modelling

Process Oriented Modelling

• Quantitative Analysiso Modal mineralogy

o Porosity and fractures

o Crystal / Grain size

o Grain shape

o Mineral connexity

BreakabilityMagnetic

susceptibility

Floatability

Sinterability

MINERALOGICAL MAPPING

PROGNOSTIC MINERALOGY

Leachability

Chalcopyrite

Stannite

Sphalerite

Pyrite

Quartz

Al-Si


• Mineral Intelligenceo Need for databases with physical properties of minerals/phases

• Density

• Hardness

• Magnetic Susceptibility

• Resistivity

• Dielectric Constant

• Hydrophobicity

• Thermal resistance

• Mechanical resistance

• Etc…

HSC GEO Mineral Database © Outotec


• Particle Tracking in process simulationo Lamberg & Vianna, 2007

ConclusionsStill a long way to go…

Conclusion

• 3 steps roadmap towards a More Automated Mineralogy

MINERALS

INTELLIGENCE

II

RoxelsParticulate Systems

AnalysisInteractive Graphics (EDA)

ROCKePEDIA

REPOSITORY

III

Cytomine PlatformRemote Image

AnnotationSmart Mineral Databases

ADVANCED

MINERAL

MAPPING

Correlative MicroscopyMultimodal SegmentationOptimal Sampling Strategy

I

Imaging Platforms

AnalyticalServices

Customers

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mineral mapping techniques to optimize downstream

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