Download - 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

Ind

exe

d 2

00

0-0

1 =

10

0

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 W

ha

t th

e g

eo

log

ists

se

e a

t co

re s

ca

le

Wha

t the

grin

din

g a

nd

flota

tion

circ

uits

se

e


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

Sup

po

rt M

ine

Sch

ed

ulin

g

Economic Scenario Assessment

Blo

ck M

od

el

Ge

ne

rati

on

Process Design

Envi

ron

men

tal

Imp

act

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 defined efficiency ranking

• 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

Ernest Henry

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 10.00Size 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 10.00Size Fraction (mm)

(%)


Core Imaging

Core imaging

Part of GEOTEK Core

Logging SuiteContinuous imagingcomposited into 1 m intervals

Unclassified RGB imageClassified image (Definiens)

FEM ModellingProcess focused

Similarity GroupingsCo-occurrence matrix

Simplicity software

classified

unclassified

Similarity GroupingsCo-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)

Perc

en

t R

eta

ined

on

Sie

ve (

%)

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 ratioThen measure Size Distribution

Crushing behaviour

Grinding behaviour

Comminution footprint

domaining at similar scale to assays

Domaining of Ball mill behaviour

Domianing of AG/SAG mill behaviourSelected 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)

Pre

dic

ted

BM

WI (k

Wh

/t)

CE

EH

Measured BMWi (kWh/t)

Pro

xy (

Ci,

mo

dif

ied

bo

nd

)

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 MineralogyAggregated Mineralogy

aka ‘texture’

Direct measuresIndirect proxies

Direct proxies Texture terms

Deterministic

multivariate

inputs

Physical processing performance attributes

Breakage energies Throughput Grinding energy Flotation recovery Size fraction data

Training sets

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

PyriteAlbite

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

Prin

cip

al C

om

po

ne

nt 1

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

Princip

al C

om

ponent 1

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

Princip

al C

om

ponent 1

Silica/Albite

Magnetite

Carbonate

Chlorite

Magnetite

K-

Feldspar

Transitional

Grade

Min

era

log

y


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

Pre

dic

ted

A*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

Pre

dic

ted

A*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

Pre

dic

ted

A*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)

19MWinstalled power

17MWinstalled power

2000tphFinal overflow at

P80 of 150mm

ROM200tph

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

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

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


















HPGR? No No Yes No



Ore Value







Ore Charatersiation










Separation Process


















HPGR? No No Yes No



Ore Value







Ore Charatersiation










Separation Process


















HPGR? No No Yes No



Ore Value







Ore Charatersiation










Separation Process


















HPGR? No No Yes No



Ore Value







Ore Charatersiation










Separation Process


















HPGR? No No Yes No



Ore Value







Ore Charatersiation










Separation Process


















HPGR? No No Yes No


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

Estimate of Blasted product size

distribution

Measured A*b (RBT)

Estimated Bond (Ci GRD)

THROUGHPUT IN GEOMET CONTEXT

?

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?


GeM Model

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


Slope Models

1 1.5 2 2.5 3 3.5 4

x 108

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 109

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

Cap

ital

Inves

tme n

tC

urren

tMin

ePro

j ect

Val

ue

Production Period

Millio

nof

$

V

I

0t 1t 2t 3t 4t

()711tiiitWACCCFR==+

7t5t 6t

1 1.5 2 2.5

x 108

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

7x 10

8 Stochastic Expected Cash Flow

Production Period

Casf

Flo

w

1 1.5 2 2.5 3 3.5 4

x 108

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 108

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

X


1 1.5 2 2.5

x 108

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

PumpSump

HPGR

AG

50 mm

pebbles

Variable splitter

2500 tph

700 tph

600 tph

400 tph 1100 tphfresh

300 tph

1800 tph

Waste sorting

Dry fluidisedbed separator

LG circuit / heap leach / waste

Dry Clay clumps200 tph

Coarse flotation

Tailings

concentrate

300 tph

Reject

Middlings

3-product cycloneWet

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


Download - Geomet for process design 8 8 2012 kv

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