constantino suazo
TRANSCRIPT
-
7/27/2019 Constantino Suazo
1/27
Geometallurgical Modelling
of the Collahuasi Grinding Circuit
for Mining Planning
Constantino Suazo
Alejandro Hofmann
Marcelo Aguilar
Yuan Tay
Gustavo Bastidas
-
7/27/2019 Constantino Suazo
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.
-
7/27/2019 Constantino Suazo
3/27
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
-
7/27/2019 Constantino Suazo
4/27
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
-
7/27/2019 Constantino Suazo
5/27
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
-
7/27/2019 Constantino Suazo
6/27
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
-
7/27/2019 Constantino Suazo
7/27
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
-
7/27/2019 Constantino Suazo
8/27
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
-
7/27/2019 Constantino Suazo
9/27
DEFINITION OF GEOMETALLURGICAL UNITS
DRILL HOLE CAMPAIGN
PQ HQ Drill Cores
ROSARIO DEPOSIT : 2,000 Mtonnes 0,85%Cu 250 ppm Mo
-
7/27/2019 Constantino Suazo
10/27
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
-
7/27/2019 Constantino Suazo
11/27
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
.
-
7/27/2019 Constantino Suazo
12/27
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)
-
7/27/2019 Constantino Suazo
13/27
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
-
7/27/2019 Constantino Suazo
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
-
7/27/2019 Constantino Suazo
15/27
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
-
7/27/2019 Constantino Suazo
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
-
7/27/2019 Constantino Suazo
17/27
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
-
7/27/2019 Constantino Suazo
18/27
Transfer Size Sampler (T80)
TRANSFER SIZE VALIDATION
-
7/27/2019 Constantino Suazo
19/27
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
-
7/27/2019 Constantino Suazo
20/27
COLLAHUASI MODEL
TRANSFER SIZE FORECASTING
TRANSFER SIZE VALIDATION
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
-
7/27/2019 Constantino Suazo
21/27
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
-
7/27/2019 Constantino Suazo
22/27
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
-
7/27/2019 Constantino Suazo
23/27
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
-
7/27/2019 Constantino Suazo
24/27
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
-
7/27/2019 Constantino Suazo
25/27
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)
-
7/27/2019 Constantino Suazo
26/27
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.
-
7/27/2019 Constantino Suazo
27/27
THE END