Geomet for process design 8 8 2012 kv

Geometallurgical capability at Ausenco and
how it might support Process Design
Presented by Simon Michaux | 8 August 2012

Geometallurgical capability at Ausenco and how it might support Process Design | 8 August | 2
Geometallurgy is about cross-
discipline integration…..
…a team-based approach to support a rock-based manufacturing process
Everyone has to change their perception and their language describing
the mining process to create a new discipline

Economic justification of geomet
• More intelligent full scale test (DWT) sampling to produce
a result that works well as opposed to works OK
• Fit for purpose quantification of variability in a processing
context
• Used as a design tool to determine the most efficient style
of circuit
• Risk management of process performance of designed
plant
• More sophisticated modeling of economic net position

Geomet can be used to turn a Type 4 into a Type1 operation
NPV makes the first few years critical to successful operation
Plant design
capacity
Fast run up to
full capacity
Slow run up to
partial capacity

The efficiency in which capital, labour, materials,
services, and energy are utilised to generate a
unit of product
Multifactor productivity
Australian Bureau of Statistics 2011, Experimental Estimates of Industry Multifactor Productivity, 2010-11, ABS, Cat no:
5625.0.55.002, Canberra.
50
60
70
80
90
100
110
Indexed2000-01=100
Topp et al. (2008) ABS (2011)
It now takes 40% more inputs
to generate a single unit of
mineral product
Australian Bureau of Statistics 2011, Experimental Estimates of Industry Multifactor Productivity, 2010-11, ABS, Cat no: 5625.0.55.002,
Canberra.

About 2-3% grade was the operational threshold
Bingham Kennecott
(Grade=0.2-0.57%)

Economic goal posts are shifting for future deposits
(compared to 30 years ago)
• Huge low grade deposits
• Operating on an economy of scale never been seen
before (4MT blasted rock a day, 60% of which is
ore!)
• Energy & water shortages
• Penalty minerals present in deposit that prevent
efficient processing
• Ever decreasing grind sizes
• Economic penalties for ‘carbon footprint’
• Challenging site locations with social issues are
influencing decision making of final outcome
Geomet in the short term is about risk mitigation, reducing costs and extracting more profit.
In the long term it could be used for mine sites to start at all and to stay in business.

The pickle and the rub for our clients
NPV
Capital Cost
If this ratio is too low, then the project doesn’t start
Projects are paid for by net profit from high grade parts of
the deposit processed in the short term. There seems to
be no Plan B if there are no high grade parts!
So what is the
real NPV?
What is the real
needed plant
size?

Geometallurgy is about cross-
discipline integration…..
The rock is the Rosetta Stone for the mining process. All stages of
mining measure it and design from those measurements.
Rename Geometallurgy: Leveraging Rock Properties
(LRP)

The geologists view of rocks
Geologists have a predisposition to describe rocks in terms of their genesis.
The language is interpretative, often influenced by exploration and at times
poetic…..

The mineral processing view of rocks
A bulk feed material for a manufacturing process that should be described in
terms of its processing characteristics and variability relative to specification.
Tell me what my feed is relative to size not what the rock used to be……

Challenge of scale-dependent views
Whatthegeologistsseeatcorescale
Whatthegrindingandflotationcircuitssee

Geometallurgy is process defined data relationships
The Holy Grail:
the multi-shell geomet
block model as
a stand alone complex
Everyone has a definition
Geomet is ore response behaviour
data interrelationships
Rename Geometallurgy: Leveraging Rock Properties
(LRP)

Trends Rankings Calibrations Behaviors
Assays
Rock type
Alteration…
Hardness
Mineralogy
Texture…..
Ci Index
A*b values
Grind Index
Recovery…
Texture-based
finite element
modelling…
Turning data into processing attributes
Geometallurgical dataset building
Indirect
proxies
Direct
Proxies
Direct
Measures
Linking
Models

For a given job, where are you in the mining cycle?
Very different approaches
& methodologies
Resource Model
• Geomet passed into block models
• Issues of additivity and intrinsic vs.
derived attributes
Process Mill Design
• Key output built on long term models
• Crucial decisions usually driven by
engineering
Deposit Valuation
• Understanding economic implications
of geomet changes to resource
model and operational outcomes
• Linking geomet variability with
conditional simulation
• Risk mitigation
Life of Mine Simulation
• Total site footprint
• Mine closure and environmental
rehabilitation
Geomet for long term variability
• Conceptual Study
• Pre-feasibility
• Feasibility
• Process Design
• Life of Mine Footprint Simulation
Geomet for scheduling
• Scheduling & Life of Mine Planning
Geomet for operations
• Detailed Study Process Design
• Commissioning start up
• Operation and production
• Expansion
Life of Mine Simulation
• Total site footprint
• Mine closure and environmental
rehabilitation
Process Operation
• Show stoppers & penalties in feed
stream (clay, Cl, Fl, As, etc)
• Predicted variability in ore hardness
• Metal reconciliation
• Maintenance schedule
• Environmental site impact & rehabilitation
• Dust generation
• Acid Mine Drainage
• Tailings management
Geomet for Enviro Impact

Support Mine
Design
SupportMine
Scheduling
Economic Scenario
Assessment
BlockModel
Generation
Process Design
Environmental
Impact
The geomet plan…..
Conceptual Study
Pre-Feasibility Study
Feasibility Study
Detailed Study
Geomet Study
Economic Scenario Study

Geomet test scope 1500-2000m of half drill core
siteDiscontinuous destructive tests
Develop process definedefficiencyranking
• 1000’s of data points for a single study
• Each sample has multiple
tests/signatures from the same spatial
point (2m interval of core)
• Data collected by client and assay lab
(ALS Chemex)
• Each comminution Ci test is of the
order of $50
• Data analysis is a couple of weeks
once data collected

0
20
40
60
80
100
120
140
160
180
6.00 8.00 10.00 12.00 14.00
Axb
BMWi @212 mm cls
ErnestHenry
18
A*b as a function of BMWi – Ernest Henry
KSpar dominant
324
Carbonate-magnetite
236
246
377
428
211
481
290

Batch grind product size distribution
Aqqaluk Phase 1 Batch Grind Product
0
5
10
15
20
25
30
0.01 0.10 1.00 10.00
Size Fraction (mm)
(%)
Exactly the same
procedure has been run on
all of these samples
Not only are the deposits
very different, but variation
within each deposit is
highly visible
Feed to grind test is 700cc of crushed sample 99% passing 3.35mm
(same as Bond Ball Mill test feed)
Boddington Batch Grind Product
0
5
10
15
20
25
30
0.01 0.10 1.00 10.00
Size Fraction (mm)
(%).
QXRD at the 100mm size
fraction would define the
mineralogy reason for the
variability in grinding
behaviour
Ernest Henry Phase 1 Batch Grind Product
0
5
10
15
20
25
30
0.01 0.10 1.00 10.00
Size Fraction (mm)
(%)

Core Imaging
Core imaging
Part of GEOTEK Core
Logging Suite
Continuous imaging
composited into 1 m intervals
Unclassified RGBimageClassified image (Definiens)
FEM Modelling
Process focused
Similarity Groupings
Co-occurrence matrix
Simplicity software
classified
unclassified
Similarity Groupings
Co-occurrence matrix
Simplicity software
Modal
Mineralogy

EQUOtip hardness tester
500
550
600
650
700
750
800
850
500
550
600
650
700
750
800
850
150 200 250 300 350 400 450
Depth (m)
500
550
600
650
700
750
800
850
1100 1150 1200 1250 1300 1350 1400 1450 1500
Direct proxies?

Global EQUOtip comparison
Boddington high impact hardness clearly stands out
– Bingham is very soft in comparison, Aqqaluk has the widest range

Comminution index Ci
0.1
1.0
10.0
100.0
0.01 0.1 1 10 100
Size Fraction (mm)
PercentRetainedonSieve(%)
EH512_236 CI=1.61
EH512_290 CI=3.17
CE107_364 CI=4.29
CE107_176 CI=5.88
Crush core sample
@ 2.5:1 reduction ratio
Then measure Size Distribution
Crushing behaviour
Grinding behaviour
Comminutionfootprint
domainingatsimilar scale
to assays
Domaining of Ball mill behaviour
Domianing of AG/SAG mill behaviour
Selected core intervals e.g.2m
Comparative comminution testing of individual
rock type textures.
Can provide estimates of key ore parameters
A*b and BMWi used to forecast plant throughput.
Key inputs into defining comminution domains.
Ci CRU
Ci GRD
0
5
10
15
20
25
0 5 10 15 20 25
Measured BMWI (kWh/t)
PredictedBMWI(kWh/t)
Measured BMWi (kWh/t)
Proxy(Ci,modifiedbond)
A $50 test that can be done on
2m of half drill core by an assay lab in
numbers of 1000’s

Gemetallurgical modelling
Intrinsic rock-based multivariate attributes
Bulk Mineralogy
Aggregated Mineralogy
aka ‘texture’
Direct measuresIndirect proxies
Direct proxies Texture terms
Deterministic
multivariate
inputs
Physical processing performance attributes
Breakage energies Throughput Grinding energy Flotation recovery Sizefraction data
Trainingsets
for processing
performance
Principal Component
Analysis (PCA)
Multiple
regression
Neural
networks
Data
visualization
Engineering
equations
Software-based
class modelling
v v v v
Class-based
predictive
models and
control
diagrams

Analyse data on continuous drill core data (1-15km of drill core)
Geological Assays Petrophysical Equotip Comminution
• Geophysics, IR logging, core RQD, Lithology, Geotechincal, Ci, assays
• Discontinuous sampling of SMC, Batch Flotation, Selective Leach, ARD
• PCA class based analysis

What minerals control process behaviour?
HARD
SOFT
Plagioclase
Feldespar
Chalcopyrite
Pyrite
Albite
Magnetite
EQUOTIP
(n = 278)
How do we divide a ore deposit into
domains?

Geometallurgical Mapping (Cadia East)
PCA identifies the factors controlling the natural variability within a deposit in relation to the
processing performance indicator of interest.
Cadia East Mineralogical Discriminant Diagram
PrincipalComponent1
n=27814

Geometallurgical mapping (Cadia East)
We can interactively query the diagram to
understand what mineralogy occurs in different
regions of the diagram.
Principal Component 2
PrincipalComponent1
n=27814

Geometallurgical mapping (Cadia East)
Diagram illustrates fundamental mineralogical rock compositions controlling deposit variability, which is a
critical factor in being able to predict comminution response.
Principal Component 2
PrincipalComponent1
Silica/Albite
Magnetite
Carbonate
Chlorite
Magnetite
K-
Feldspar
Transitional
Grade
Mineralogy

Discrete class-based models (Cadia East)
Each Class has a Discrete A*b
and BMWI Predictive Model
Domain 10:Fitted A*b Regression Model
20
30
40
50
60
20 30 40 50 60
Measured A*b
PredictedA*b
A*b=-53.19-82.2Au+0.189e-1CUCN+0.258EQstd+234.5Poisson
S.E.=1.2913
20
25
30
35
40
45
20 25 30 35 40 45
Measured A*b
PredictedA*b
A*b=24.6-2.962Fe-0.2952EQ-0.2679EQstd+0.2871EQ90+0.2513YMod
S.E.=1.006
20
30
40
50
60
70
20 30 40 50 60 70
Measured A*b
PredictedA*b
A*b=18.06+2.2Au-0.1712e-2CUCN+0.5227e-3S+0.1136EQstd
S.E.=0.98111

Pilot Scale Trial - Cadia East ore Cadia Hill Mill
• A cut down flow sheet of a mill circuit
• All parameters are constrained except the rock
parameters
• Throughput model is based on engineering
power based design equations to estimate the
specific power and transfer size (developed from
28 operating mills*)
• Circuit capacity is dependent on both the SAG
and Ball Mill performance as they interact in
practice
• Engineering equations are combined with the
Bond ball mill design equation to complete the
circuit throughput model, providing a link
between the SAG and ball milling stages
• Circuit used to estimate throughput for Cadia
East
• Tph= f(rock parameters, machine parameters,
circuit configuration)
• Rock parameters: f(Density, A*b, BMWI, F80,
P80)
• Machine parameters: f(Mill Diameter, Mill Length,
Speed, Ball Load, Mill Load)
19MW
installed power
17MW
installed power
2000tph
Final overflow at
P80 of 150mm
ROM
200tph
F80 of 150mm
500 tph
2000 tph
Sample A*b BMWi
(kWh/t) ¹
UG Ore
Plant TPH
GeM Plant
TPH
Newcrest U/G Ore Samples
(ave)
28.9 20.8 1400 1417
GeM Hole 1 – ave 29.7 20.2 - 1458
GeM Hole 2 – ave 32.0 20.3 - 1460
GeM Hole 3 – ave 30.8 20.4 - 1451

Class throughput variability
Different classes have different
processing response and some classes
have the same response.
Can we identify what the fundamental
controls are so we can then refine our
class definition process?

Ausenco can and will do all that better!

Linkages to spatial modeling
Class
Group
Copper
Domain
Throughput
Domain
Recovery
Domain
Group C Group B
Group B Group C
Group A Group D
Group A Group D
Group C Group A
Group D Group A

Case Study: - Batu Hijau (10 years +)
Demonstrated ability to predict long term plant
performance to within 2%

How does Geomet talk to process design?
Hypothesis driven test work
Process Attribute Ore Domain 1 Ore Domain 2 Ore Domain 3 Ore Domain 4
Ore Value
Valuable metal 1 (Au) grade 0.6g/t - - 1.1g/t
Valuable metal 2 (Cu) grade 1.20% - - 0.50%
Valuable metal 3 (Ag) grade - 2.2g/t 1.3g/t -
Valuable metal 3 (Mo) grade - 0.99% 0.47% -
Valuable metal x (?) grade
Penalty elements Yes (High As content) No No Yes (Low As content )
Ore Charatersiation
Mineral liberation size 75 micron 30 mciron 160 micron 212 micron
Property deportment siganture Yes (Upgrade factor 2.1) No No Yes (Upgrade factor 1.6)
Ore sorting feasible Yes No No No
Energetic conditioning feasible No No No No
Impact breakage energy consumption High High Very High medium
Low energy abrasion energy consumption High High High Low
Bed breakage energy consumption Medium Medium Medium High
Grinding Energy consumption High Medium High Low
Fine grinding energy consumption - Medium High -
Separation Process
Flotation recovery - High Medium -
Flotation kinetics - Medium Medium -
Leaching recovery Medium - - Low
Leaching kinetics Medium - - Low
Gravity Au recovery Yes - - No
Pressure oxidisation required for Au recovery No - - Yes
Engineering Design Request
Ore Charatersiation
Separation Process
Floation circuit? - Yes Yes -
Concetrate regrind circuit? - Yes No -
Leach dump? No - - Yes
Leach heap? Yes - - No
Leach tank? No No No No
CiL carbon and leach circuit? Yes - - No
Ore sorting technology? Yes - - No
Energetic conditioning technology? No No No No
Ore Value
Ore Charatersiation
Separation Process
Separation Process
Crushing? Yes Yes Yes Yes
Grinding? No Yes Yes No
HPGR? No No Yes No
Fine grinding? No Yes No No
Ore Value
Ore Charatersiation
Separation Process
Crushing? Yes Yes Yes Yes
Grinding? No Yes Yes No
HPGR? No No Yes No
Fine grinding? No Yes No No
Ore Value
Ore Charatersiation
Separation Process
Ore Domain 2 Ore Domain 4 Ore Domain 7

Different parts of the deposit could be more efficiently processed by different circuits
Not all ore types are mined at the same time
Ore Domain 2 Ore Domain 4 Ore Domain 7
Planned process plant expansion
?
SAG
Closing screen
300 & 106 µm
M.W.
-31.5 mm
Feed
(t/hr)
Product
(t/hr)
Feed
(t/hr)
Produ
ct
(t/hr)
Bond Ball
Mill
Jaw
crusher
CSS 3.35
mm
Jaw
crusher
CSS 9.5
mm
Jaw
crusher
CSS 9.5
mm
Bond Ball
Mill
Feed
(t/hr)
Produ
ct
(t/hr)
Bond Ball
Mill
Jaw
crusher
CSS 3.35
mm
Jaw
crusher
CSS 9.5
mm
Feed
(t/hr)
Product
(t/hr)
Jaw
crusher
CSS 9.5
mm
Bond Ball
Mill
Feed
(t/hr)
Product
(t/hr)
Jaw
crusher
CSS 9.5
mm
Bond Ball
Mill
M.W. M.W.
M.W.
Closing screen
300 & 106 µm
Closing screen
300 & 106 µm
Closing screen
300 & 106 µm
Closing screen
300 & 106 µm
-31.5 mm
-31.5 mm -31.5 mm -31.5 mm
Micro wave treatment at
Nottingham University, UK
MLA analysis at Nottingham
University, UK
Flowsheet 1 Flowsheet 2 Flowsheet 3 Flowsheet 4 Flowsheet 5
Pressure
Set A
Pressure
Set B
Flotation test @ JKMRC
HPGR
So what is the most
cost effective option?

GeMModel
Economic Models
LS
LC
ROM
Leach
Crushers ProcessMine
LS
LC
ROM
Leach
Family of Solutions
Data Collection
Mining Sequences
Mine Plan
Feedback to improve model
Constraints Constraints
LS
LC
ROM
Leach
LS
LC
ROM
Leach
LS
LC
ROM
Leach
LS
LC
ROM
Leach
SlopeModels
1 1.5 2 2.5 3 3.5 4
x 10
8
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
X
Probability Distribution of Expected Cash Flow @ PP-1
2.6 2.7 2.8 2.9 3 3.1 3.2 3.3 3.4 3.5 3.6
x 10
9
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
x
F(x)
Empirical CDF
Cash flow stream
Capital
Investment
CurrentMine
ProjectValue
Production
Period
Millionof$
V
I
0t 1t 2t 3t 4t
()711tiiitWACCCFR==+
7t5t 6t
1 1.5 2 2.5
x 10
8
0
0.02
0.04
0.06
0.08
0.1
0.12
X
Probability Distribution of Expected Cash Flow @ Last PP
Simulation
Confidence Model
0 5 10 15 20 25 30
-1
0
1
2
3
4
5
6
7
x 10
8
Stochastic Expected Cash Flow
Production Period
CasfFlow
1 1.5 2 2.5 3 3.5 4
x 10
8
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
X
1.5 2 2.5 3 3.5 4 4.5
x 10
8
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
X
1 1.5 2 2.5
x 10
8
0
0.02
0.04
0.06
0.08
0.1
0.12
X
Probability Distribution of Expected Cash Flow @ Last PP
Probability of cash flow can be assessed for each year.
Geomet/LRP
Study
Whittle
Pit to Port
Detail
Design
Study
Pre-feasibility
Class study
Economic scenario mapping
efficiency window
Feasibility
Class study

To design one of these, disciplined and
sophisticated ore characterisation is required

Who is going to use this?
Ausenco perspective
• Process design provides
outcome of Type 1
commissioning and run up time
• A different type & scale of design
job would now be possible
• A more sophisticated design
outcome would now be possible
Client Perspective
• Economic risk mitigation
• More sophisticated prediction &
scheduling
• Potential to integrate design
steps into a coherent outcome
• Ability to justify and manage
projects on a much bigger scale
with a high capital risk
• Ability for more accurate decision
making in a challenging business
environment

Very important to get this right
(scope and deployment)
Appropriate selection
of feed samples
Correct QA/QC
on experimental test work
Appropriate analysis
In context of expt objective
Appropriate scoping of
and design of type of study
Outcomes of analysis
are correctly understood
by analyst and client
$$$ agreed upon up front
Client happily pays the bill
Client site cooperation
Competent laboratory
Competent analyst

Conclusions
• Mining is becoming more challenging and much larger in scale
• Economic risk is much greater than 40 years ago
• More sophistication in ore characterisation and deposit knowledge is
required on a greater scale
• A systems approach in design across the mining process is now
considered the objective
• This requires large quantities of sophisticated data at the beginning of
the design process, not the end
• Geometallurgy is an approach and methodology to do this
• Leverage of Rock Properties could be a good new name for this

One process stream, flexible operation
• Engineered ability to more easily adapt to variable feed
• Engineered to a series of dynamic conditions, not a steady state
• More sophisticated process control capabilities to manage dynamic non
steady state conditions
• Operational protocol needs to be developed accordingly

Multiple process streams in same operation, each
with its own stockpile
Geomet
Block Model
Blast
Sorting
Dump
Leach
Pad
Tank
Leach
Flash
Flotation
Flotation
Flotation
Each stream with its own closing size and cutoff grade

Flexible operation to process different size fractions
in different streams
pebble Mill
Cone
crush
RoM
20 mm
1mm
5mm
Pump
Sump
HPGR
AG
50 mm
pebbles
Variable
splitter
2500 tph
700 tph
600 tph
400 tph 1100 tph
fresh
300 tph
1800 tph
Waste
sorting
Dry fluidised
bed separator
LG circuit / heap
leach / waste
Dry Clay
clumps200 tph
Coarse
flotation
Tailings
concentrate
300 tph
Reject
Middlings
3-product cyclone
Wet
screen

Flexible operation that uses sorting to remove
waste rock throughout the whole mining system
Geomet
Block Model
Blast
Sorting
Dump
Leach
Pad
Flotation
Flotation
Sorting Sorting
Future Ore
Working Ore
Waste dump
Problem ore with
‘show stoppers’
Only a fraction of the ore volume goes to ball mil for same recovery

Flexible operation to meet challenging external
conditions
• Engineered ability to more easily adapt to changes to
external supply
• Power shortages and outages
• Potable water shortages
• Fluctuating price of steel consumables
• Operational protocol needs to be developed accordingly
Mining
Industry

Geomet for process design 8 8 2012 kv

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