Constantino Suazo

7/27/2019 Constantino Suazo

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Geometallurgical Modelling

of the Collahuasi Grinding Circuit

for Mining Planning

Constantino Suazo

Alejandro Hofmann

Marcelo Aguilar

Yuan Tay

Gustavo Bastidas


2/27

Collahuasis value optimization is introduced in a

simplified manner using the following graphics.

The main idea behind the approach is to develop robust

INTRODUCTION

estimation of the point at which copper production per

unit of time reaches a maximum.


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4000

6000

8000

ughputtph

THROUGHPUT AS A FUNCTION OF P80 TO FLOTATION

In general throughput increases as

P80 increases.

0

2000

0 50 100 150 200 250 300 350

P80, microns

Th

ro

A robust grinding model should

include variables such as:

Geological Units Blend

P80Grinding Circuit Features

Maintenance Plan


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60

80

100

very(%)

RECOVERY AS A FUNCTION OF P80

In general recovery increases as P80decreases.

A robust flotation model should

0

20

40

P80, microns

Rec

include variables such as:Headgrade

Geological Units Blend

P80Flotation Circuit Features


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MAXIMIZING THE ECONOMIC VALUE OF THE COMPANY

6000

8000

10000

hput

tph

80

100

very(%)

Flotation P80 Grinding P80

0

2000

4000

P80, microns

Throu

40

60 Rec

o

Flotation P80Grinding P80


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60000

80000

100000

Copper Production per unit of time

MAXIMIZING COPPER PRODUCTION PER UNIT OF TIME

Tonnes Copper per time: [Treatment (P80) x Headgrade (%) x Recovery(P80)]

0

20000

40000

P80, microns Business P80Flotation P80 Grinding P80

80

between high throughputand high recovery; however,

it is neither flotation P80 nor

grinding P80. It is the

Business P80


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How is the TPH vs P80 curve built?

6000

8000

10000

ghput

tph 80

100

overy(%)

0

2000

4000

P80, microns

Thro

u

40

60 Re

c


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DEFINITION CRITERIA:

Representative geological units.

Grouping based on similar geologicalfeatures (mineralization, lithology, alteration).

Intersection of these geological features.

Mineralization Alteration

G

M

U

GEOMETALLURGICAL UNITS (GMU) DEFINITION

Lithology

GMU

1

2

3

45

6

%

18

26

19

257

5

Total 100


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DEFINITION OF GEOMETALLURGICAL UNITS

DRILL HOLE CAMPAIGN

PQ HQ Drill Cores

ROSARIO DEPOSIT : 2,000 Mtonnes 0,85%Cu 250 ppm Mo


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Pitshell LOM 2009-2033

DEFINITION OF GEOMETALLURGICAL UNITS

DRILL HOLE CAMPAIGN

SAMPLES SELECTION

Spatial representivity

within the Deposit

Every 40 mt, a 8m length drill coresample was selected as a variability

sample


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SAMPLE SELECTION PROTOCOL

PQ Sample Selection every 2 m length

20 cm length sample for generating one

composite per GMU to JK Drop Weight Test

To Assay

To laboratorytest program

Duplicate

Duplicate

1/2 To Assay

1/4 To laboratory test program

HQ Sample preparation

.


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GMU

Pit Shell

CURRENT TESTED VARIABILITY SAMPLES

PERIODVARIABILITY SAMPLES / GMU

TOTAL

1 2 3 4 5 62008-2011 37 30 33 35 12 17 164

2012-2016 21 21 39 35 9 16 141

2017-2021 16 10 14 15 6 10 71

2022-2026 11 17 4 13 10 11 66

2027-2031 9 10 11 7 4 7 48

2032-2036 1 1 1 2 5

2037-2041 2 5 2 7 16

TOTAL 97 93 104 112 42 63 511

1

2

3

4

5

6

The following laboratory tests were performed on

each variability sample:

SMC (DWI, A, b , Axb) Ball mill Wi SPI Specific gravity

AbrasionCrush index Full JK DWT (on composite samples)


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SAMPLE TYPES

1. COMMINUTION TEST ON EACH GMU: COMPOSITESComminution test

Mass Required(Kg)

Drill holediameter

JK Drop Weight Test (SAG)

(20cm lengthsamples every two

meters of drillcore)

PQ

Composite Sample (Bond Work Index, SPI,Abrasion, Flotation test)

----- PQ y HQ

SMC (DWI, A, b, Axb)

SPI

Ball mill Bond work index

AbrasionSpecific gravity

Flotation test

511 currently

tested

HQ

2. VARIABILITY TEST SAMPLES


14/27

GRINDING TESTS RESULTS ON GMU COMPOSITE SAMPLES

Sample A b A*b Ta Resistance to Impact

Breakage

Abrasion Range

GMU 1 59.1 0.9 52.6 0.80 Medium Soft

GMU 2 61.7 0.6 37.0 0.73 Hard Soft

GMU 3 63.6 0.8 52.8 0.64 Medium Soft

GMU 4 49.5 1.2 59.4 0.78 Moderately Soft Soft

GMU 5 58.9 0.8 49.5 0.56 Medium Moderately Soft

GMU 6 61.6 1.0 58.5 0.95 Moderately Soft Soft

, ,

GMU 1 12.4 59.2 13 0.1953

GMU 2 13.7 97.6 9 0.1957

GMU 3 11.5 48.0 12 0.2666

GMU 4 12.6 58.8 15 0.4297

GMU 5 11.8 42.2 12 0.2351

GMU 6 10.9 36.5 7 0.1985


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GRINDING TESTS RESULTS ON GMU VARIABILITY SAMPLES

Bond Work Index, Wi,

kWh/t

Average on

composite sample

Variability Samples

Number of

Samples

Sample

average

Standard

Deviation

UGM1 11.5 28 11.9 1.9

UGM2 13.7 28 12.9 2.4

UGM3 11.5 51 11.3 1.9

UGM4 12.6 46 12.4 2.3

UGM5 11.8 17 10.9 1.9

UGM6 10.9 25 11.5 1.5

SMC, DWi

Variability Samples

Number of

Samples

Simple

average

Standard

Deviation

UGM1 87 5.1 1.5

UGM2 50 6.6 2.1

UGM3 133 5.6 1.7

UGM4 138 6.8 2.0

UGM5 37 4.0 1.8

UGM6 56 4.6 1.7


16/27

How is the TPH vs P80 curve built?

SAG

KW

Ball Mill

P (KW)

Pebble

Crusher

P80

JKSimMet Simulation

Bond Equation

tph

tphT80

SAG

KW

Ball Mill

P (KW)

Pebble

Crusher

P80

JKSimMet Simulation

Bond Equation

tph

tphT80

1.- The instantaneous throughput was increased for each iteration.

2.- SAG Mill was simulated using JKSimMet. For each iteration, the SAG

mill power draw, total load and transfer size were recorded as shown inthe table below.

3.- The transfer size and the instantaneous throughput were fed to the

Bond equation to predict the P80.

4.- The iterative process continued until one of the following restrictions

were met:

1) Maximum Power Draw = Installed Power

2) Maximum SAG Mill Total Load = 30%

TPH versus

Line 1-2: 1* 32ft *15ft SAG Mill (8000 KW) + 1* 22ft*36ft Ball Mill (9700 KW)

Line 3: 1* 40ft *22ft SAG Mill (21000 KW) + 2* 26ft*38ft Ball Mill (15500 KW)

JKSimMet Simulations Bond Equation

Iteration N tphPower

Draw KWTransfer Size

(T80 um)Ball Mill

Power Draw (KW)P80 estimated from

Bond Equation, microns

1 2800 17605 3778 14812 100

2 4300 18600 4737 14812 200

3 4800 18900 4944 14812 241

80 curve


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COLLAHUASI MODEL

TRANSFER SIZE FORECASTING

TRANSFER SIZE VALIDATION

TRANSFER T80 SIZE LINE 3

3,000

4,000

5,000

6,000

ferSize(microns)

0

1,000

2,000

600 1,600 2,600 3,600 4,600 5,600 6,600 7,600

Instantaneous tph

T80

Tran

GMU1GMU2

GMU3

GMU4

GMU5

GMU6


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Transfer Size Sampler (T80)



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TRANSFER SIZE SAMPLES

Transfer Size

60

80

100

120

vePassing(%)

C1

C2

C3

Blend of GMU fed to the plant during survey

GMU %

GMU 1 34

GMU 2 10

GMU 3 32

GMU 4 3

GMU 5 18

GMU 6 3

0

20

40

1 10 100 1000 10000 100000

microns (um)

Cumulati

C5 SAMPLE T80 (microns)

C 1 4386

C 2 4159

C 3 3736

C 4 3385

C 5 4576

AVERAGE 4048


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COLLAHUASI MODEL

TRANSFER SIZE FORECASTING


TRANSFER T80 SIZE LINE 3

1,000

2,000

3,000

4,000

5,000

6,000

T80TransferSize

(microns)

GMU1

GMU2

GMU3

Blend of GMU fed to the plant during survey

Measured Modelled

(Weighted average)

T80 (mm) 4.05 4.02

0

600 1,600 2,600 3,600 4,600 5,600 6,600 7,600Instantaneous tph

GMU4

GMU5UGMU

GMU %

GMU 1 34

GMU 2 10

GMU 3 32

GMU 4 3

GMU 5 18

GMU 6 3


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GENERAL TREATMENT CAPACITY MODEL FOR TOTAL GRINDING PLANT

6,000

8,000

10,000

12,000

TPH

0

2,000

4,000

90 110 130 150 170 190 210 230 250 270 290 310 330 350 370 390

P80 (microns)

GMU 3 GMU 4

GMU 5 GMU 6


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TREATMENT CAPACITY MODEL FOR MINING PLANNING

Ton (P80) : Total processed tonnes per period.

H: Total hours in the period

m l i : rogramme ma n enance ours n gr n ng ne

Hf l i : Un-programmed maintenance hours in grinding line iN l i : Number of shut-downs within the period

H t : Transient time to achieve stable operation after shut downs

PT tchp: Treatment losses due to Crusher Pebbles shut downs

Hmchp: Crusher Pebbles Programmed maintenance hours

Hfchp: Crusher Pebbles Un-programmed maintenance hours


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GRINDING MODEL FORECASTING CAPACITY

800,000

1,000,000

1,200,000

1,400,000

1,600,000

1,800,000

tonnes

TREATMENT FORECASTING 2007-2011

Observed (tons) Modelled

%Error = 4.5%

-

200,000

400,000

600,000

1 611

16

21

26

31

36

41

46

51

56

61

66

71

76

81

86

91

96

101

106

111

116

121

126

131

136

141

146

15

1

15

6

161

166

weeks 2007-2011


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GRINDING MODEL FORECASTING CAPACITY

800,000

1,000,000

1,200,000

1,400,000

1,600,000

1,800,000

40%

50%

60%

70%

80%

90%

100%

UGMP

roportion

s

TREATMENT FORECASTING 2007-2011

GMU 6 GMU 5 GMU 4 GMU 3 GMU 2 GMU 1 Observed (tons) Modelled

%Error = 4.5%

-

200,000

400,000

600,000

0%

10%

20%

30%

1 611

16

21

26

31

36

41

46

51

56

61

66

71

76

81

86

91

96

101

106

111

116

121

126

131

136

141

146

151

156

161

166

weeks 2007-2011

Tonnes


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R2 = 0.94

800.000

1.000.000

1.200.000

1.400.000

1.600.000

1.800.000

Modelled(tons)

SCATTER PLOT : MODELLED v/s OBSERVED

-

200.000

400.000

600.000

-

200.

000

400.

000

600.

000

800.

000

1.

000.

000

1.

200.

000

1.

400.

000

1.

600.

000

1.

800.

000

Observed(Ton)


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CONCLUSIONS

The grinding modelling currently used by Collahuasi Mining Company has beenpresented showing an updated validation of the predictive capacity of the total treatedore per week from September 2007-May 2011.

The aim of developing a robust and accurate forecasting model has been satisfactorily

achieved through the use of a combination of simulation and power-based modelling.

The model has shown an average relative error of 4.6% as inferred from the statisticalanalyses using production data from the period September 2007 to June 2011

The Collahuasi grinding modelling allows planning engineers to maximise grindingcircuit treatment capacities on the basis of appropriate blending of GMU and also on the

basis of the concentrator's maintenance program.


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