mine to mill optimization
TRANSCRIPT
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Journal of Mining and Metallurgy, 38 (1-4) A (2002) 49-66.
MINE TO MILL OPTIMISATION FOR CONVENTIONAL
GRINDING CIRCUITS A SCOPING STUDY
A. Jankovic and W. Valery,
JKMRC, University of Queensland, Brisbane, Australia
(Received 18 June 2002; accepted 6 October 2002)
Abstract
The scoping study a for a Mine to Mill optimisation program was carried out in
a gold mine in Australia. The specific objective of this scoping study was to identify
the problems and potential benefits for a Mine to Mill optimisation project.
Available mining and milling data were collected during the visit and
preliminary analysis was conducted to identify the potential benefits and best
course of action during a Mine to Mill optimisation program. The main
conclusions from the scoping study were:
Preliminary blast fragmentation modelling confirms that finer ROM size
distributions could be generated with significant reduction in the amount of
oversize material.
Assessment of crushing and milling operating strategies and preliminary
simulations using JKMRC and Bond methodologies indicated possibility of 4-
5% increase in milling throughput and better energy utilisation.
Key words: Crushing, Milling, Mass balance, simulation, modeling.
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1. Background
As a general practice, blasting engineers design production blasts to
achieve optimal shape and swell of the muckpiles and size of fragmentation
primarily for increased shovel and truck productivity. This is in addition to
ensuring that the same blasts produce minimum negative impact on dilution
and on the integrity of adjacent pit walls and floors. It is however now
recognized that blast results can be made to satisfy not only the digging,
handling and grade control requirements but also crushing and milling
requirements. This has been demonstrated to have a significant and positive
impact on the overall economics of mining operations. However, to achievethis requires a disciplined implementation of the Mine to Mill concept.
With respect to milling, the capacity and efficiency of comminution
processes are strongly influenced by the ROM fragmentation distribution
which in turn is influenced by the blasting [3, 6, 7, 9, 12, 14, 16].
Throughput gains of 5-15% have been recorded and confirmed at
operations with SAG mills such as Highland Valley Copper (Canada),
Minera Alumbrera (Argentina) and Cadia (Newcrest Mining in Australia)
through implementation of the Mine to Mill concepts. Current "Mine to
Mill" trials being conducted at Escondida (Chile), Porgera (Placer Dome in
Papua New Guinea), OK Tedi (BHP in Papua New Guinea), are indicating
similar gains.
Throughput gains and an increase in crusher availability should be
achievable with the crushing and ball milling circuit (without SAG mills) if
a disciplined Mine to Mill approach is introduced and properly managed.
This involves modifications to current mining, crushing and milling
practices without necessarily compromising some of the mining
requirements such as productivity, good grade control and integrity of the
intermediate and final walls.
2. Crushing circuit survey
In order to estimate performance and to model the crushing circuit, a
detailed survey was carried out. Tonnages and power draw from the
crushers were monitored and samples from different streams were collected
for sizing. Measurement of the size distributions of the streams around the
crusher circuit was also carried using the SPLIT image analysis system.
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Images of the crusher circuit products were obtained using a video camera.
Still camera pictures were taken of the grizzly oversize stockpile The video
and still images were used for size distribution analysis using the SPLIT
system. The oversize and primary crusher feed size distributions obtained
from the SPLIT system were combined to estimate the ROM size
distribution.
3. Size analyses from the SPLIT system
Using conventional methods to size coarse material such as ROM ore,
muckpiles, primary crusher products, etc., is extremely difficult and costly.
At the same time, accurate information about the ore fragmentation is
essential for the Mine to Mill optimisation process. Image analysis
techniques such as those used in the SPLIT system enable fragmentation to
be estimated with reasonable accuracy and with relative ease [1, 16]. The
size distribution of the grizzly oversize was estimated based on still
photographs taken from the oversize stockpile. Video images were used to
analyse the products from the crushing circuit.
Fig. 1: An example of photograph of the oversize stockpile used for SPLIT
system analysis
Figure 1 shows an example of the photograph taken from the oversize
stockpile. The SPLIT system sizing results obtained from several
photographs are presented in Figure 2. It can be seen that the most of the
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oversize is in the 0.5 - 2 m size range. A significant proportion of the ore
(up to 20%) is larger than 1.5 m.
0
20
40
60
80
100
0 500 1000 1500 2000 2500
Size (mm)
C
um%Pass
image 1 image2 image 3 image 4
image 5 image 6 image 7 average Fig. 2: SPLIT system analysis of the oversize stockpile
0
20
40
60
80
100
1 10 100 1000
Size (mm)
Cum%
Pass
Fig. 3: SPLIT on-line results primary crusher product
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Figure 3 presents the size distributions from the on-line SPLIT system
analyses of the primary crusher product. A large envelope of the size
distributions can be observed. This variability comes from the ROM
material feed to the grizzly as well as the material flow pattern trough the
grizzly feed chute.
4. Crushing circuit mass balance
In order to determine the throughputs of the secondary and tertiary
crushers and to check the quality of the crushing circuit survey data, a massbalancing procedure was carried out. The mass balancing flowsheet
constructed in JKSimMet is shown in Figure 4. A summary of the mass
balancing results is presented in Table 1.
Fig. 4: Mass balance flowsheet
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The total circuit SSQ (the sum of differences squared, between the
experimental and mass balance data) was 275.3 i.e. 34.4 per stream which is
considered as satisfactory for this survey considering the method of sample
collection and the variability of the primary crusher feed.
Table 1: Mass balance summary
Throughput (t/h) P80 size (mm)Stream
EXP MB EXP MB
Primary crusher feed / 489.6 223.6 223.6
Primary crusher product / 489.6 128.9 121.5
Secondary crusher product / 644.6 40.1 44.8
Tertiary crusher product / 445.7 13.7 13.2
Screen O/S 1 / 644.6 99.4 105.6
Screen O/S 2 / 445.7 21.5 21.1
Screen feed 1550 1580 48.3 52.2
Final crushing product 490 489.6 6.8 6.8
Note: EXP = experimental; MB = mass balance
5. Model fitting of the crushing circuit
The grinding software simulator JKSimMet [11] was used for the circuit
modelling and simulation. The throughputs obtained from the mass balance
procedure and the experimental size distributions were used to fit the model
parameters of the crushers and the screens. The primary crusher model was
fitted separately using the feed size distribution obtained from the SPLIT
system and the product size distribution obtained from the belt cut sample.
The secondary and the tertiary crusher and screens model constants were
fitted simultaneously. A summary of the model constants obtained from the
fitting procedures is presented in Table 2.
The predictions of the tertiary and secondary crusher power were in
agreement with the survey information. This is important for the simulations
of the crushing circuit under the different operating conditions.
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Table 2: Summary of model fitting results
Model constants K1 K2 K3 t10Primary crusher,
CSS 120 mm
129.9 249.4 2.3 7.6
Secondary crusher,
CSS 3538 mm
31.8 76.6 2.3 16.0
Tertiary crusher,
CSS 12-15 mm
12.0 17.2 2.3 30.8
Screen d50cDeck 1, 38 mm aperture 7.0 28.7
Deck 2, 11 mm aperture 6.5 9.6
6. Blasting simulations
During the crushing circuit survey the ROM ore was not monitored. An
estimate of the ROM size distribution was obtained from the primary
crusher feed and the grizzly oversize sizing (from the SPLIT system). Based
on the primary crusher down time it was estimated that a maximum of 10%
0
10
20
30
40
50
60
70
80
90
100
1 10 100 1000 10000
size (mm)
cum%pass
current BIF, frag sim
current volcanoclastic,
frag sim10 m BIF, frag sim
5m BIF, frag sim
ROM JK, estimated
Fig. 5: ROM size distribution: estimated from the survey data and
simulated using blasting simulation software
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of the ROM is coarser than the grizzly size (600 mm). The calculated ROM
size distribution is shown in Figure 6. The ROM ore size distribution was
also simulated by using the JKMRC blast fragmentation model [4, 5, 6, 7,
8]. It can be seen from Figure 5 that a coarser product was predicted by the
blasting model simulation (labelled as current, frag sim) compared to the
estimated one (labelled as ROM JK, estimated). It is important that
agreement is good for particle sizes below 20 mm. The predicted ROM size
distributions for the modified blast design (5 and 10 m B/F, frag sim) show
significant increases in 20 mm material and only around 2% material
coarser than 600 mm. With this type of material as ROM much lower
crusher down time could be expected, as well as improved crushing circuitthroughput.
7. Crushing simulations
Simulations were carried out using the JKSimMet software to assess the
effect of different ROM size distributions on the crushing circuit. The
summary of the results is presented in Table 3.
The response of the crushing circuit to the estimated ROM based on the
SPLIT system measurements (see previous section) and the simulated ROM
(see Figure 5, current BIF) was simulated initially. It can be seen from Table3 that simulations with the estimated ROM (ROM est) gave similar results
to the mass balance results (see Table 1). This was expected as the model
fitting was carried out using the mass balance data.
As the results in Table 3 show, considerably more grizzly oversize
material was obtained with the simulated ROM size distribution (ROM
sim). The primary crusher feed and the final product throughput were
therefore reduced. The secondary and tertiary crusher feed throughput was
reduced by approximately 14 %, proportional to the reduction in primary
crusher feed. The simulated final product feed size distributions were
similar. The above simulation results suggest that the crushing circuit is
mainly affected by the amount of coarse (+ 600 mm) and fine (-20 mm)
material in ROM. It is relatively insensitive to the size distribution
difference in the 600 + 20 mm fraction.
Simulations were also carried out with the finer simulated ROM material
(5m B/F, frag sim in Figure 5). The current circuit configuration (crusher
CSS and screen sizes) was compared to a modified circuit designed to
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produce a finer feed to the ball mill circuit. In the modified circuit the
secondary crusher CSS was reduced to around 28 mm, the tertiary crusher
CSS to around 10 mm, the top screen size to 30 mm and the bottom screen
to 9 mm. These simulation results are presented in Table 4.
Table 3: Crushing circuit simulation results with the estimated and
simulated ROM ore
Throughput (t/h) P80 size (mm)S t r e a m
ROM est ROM sim ROM est ROM sim
ROM 540 540 284.0 532
Grizzly O/S 49.0 114 1627 905Primary crusher feed 489.6 426 223.6 352
Primary crusher product 489.6 426 127 128
Secondary crusher product 644.6 556 36.6 36.6
Tertiary crusher product 445.7 379 10.7 10.6
Screen O/S 1 644.6 556 114 116
Screen O/S 2 445.7 379 24.3 24.6
Screen feed 1580 1361 49.7 50.8Final crushing product 490 426 7.33 7.24
Note: est = estimated; sim = simulated
Table 4: Crushing circuit simulation with the finer ROM and modifiedcrusher gaps and screen sizes
Throughput (t/h) P80 size (mm)
S t r e a m Currentcircuit
Modifiedcircuit
Currentcircuit
Modifiedcircuit
ROM 540 450 307 307
Grizzly O/S 18.1 15.1 753 753
Primary crusher feed 522 435 292 292
Primary crusher product 522 435 128 128
Secondary crusher product 627 644 35.7 30.6
Tertiary crusher product 482 434 10.6 9.57
Screen O/S 1 627 644 120 110
Screen O/S 2 482 434 24.9 18.9Screen feed 1631 1512 50 37.9
Final crushing product 522 435 7.6 5.7
Note: current current circuit; modified modified circuit
It can be observed from Table 4 that with the finer ROM and current
circuit configuration the circuit throughput could be increased to 522 t/h
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compared to the current 490 t/h. The amount of +600 mm material would be
reduced significantly which would in turn significantly reduce the primary
crusher down time. The secondary and tertiary crusher loads would remain
similar to the current situation. The final crushing product would be slightly
coarser. The overall positive effect of the finer ROM obtained by the
changed blasting practices would be limited to the reduced primary crusher
down time. Increase in the crushing circuit throughput (t/h) may not be
relevant due to ball milling circuit constraints.
On the other hand, with the modified crushing circuit (finer crushing), a
significantly finer circuit product size could be obtained (P80=5.7 mm
compared to current 7.2 mm). In this case the crusher circuit throughputwould be reduced to 435 t/h compared to current 490 t/h. The secondary and
tertiary crusher loads would remain similar to the current situation. The
reduced crushing circuit capacity may not have a detrimental effect on the
whole process as it may be balanced by the increase in crushing circuit
availability due to the reduction in primary crusher down time. With the
finer feed, the milling circuit throughput could be increased.
8. Milling simulations
The milling circuit consist of the primary and the secondary ball mill as
shown in Figure 6. Primary ball mill operates with large 71 mm ball size
and the secondary ball mill uses 45 mm balls. The final milling circuit
product 80% passing size is around 90 m. The model parameters for the
ball mills and hydrocyclones were determined in a previous study and used
for the Mine to Mill simulations in this study.
Simulations of the milling circuit were carried out with the feed obtained
from the finer ROM/finer crushing simulations in order to estimate the
increase in throughput which might be obtained when milling this material.
The results are compared with the current milling practice in Table 5.
The simulation results presented in Table 5 suggest that an 4.8%increase in ball mill circuit throughput would be obtained with the finer
milling circuit feed. It can therefore be concluded that the potential benefits
from the finer blasting could be increased capacity of the milling circuit as
well as increased availability of the crushing circuit.
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Fig. 6: Milling circuit simulation flowsheet
Table 5: Ball mill circuit simulations
Feed Current Finer Current FinerNew Feed Secondary ball mill product
Solids mass flow (t/h) 353 370 602 628
% Solids 99 99 71 71
P80 (mm) 7.5 5.7 0.246 0.250
Primary ball mill product Secondary Cyclone Feed
Solids mass flow (t/h) 639 646 955 998
% Solids 78 78 60 60
P80 (mm) 1.36 1.1 0.292 0.294
Primary Cyclone Feed Secondary Cyclone U/F
Solids mass flow (t/h) 639 646 602 629
% Solids 68 68 73 74
P80 (mm) 1.36 1.1 0.396 0.40
Primary Cyclone U/F Secondary Cyclone O/F
Slurry volume flow (m3/h) 286 276 353 370
% Solids 80 80 45 45
P80 (mm) 4.9 3.2 0.093 0.093
Primary Cyclone O/F
Solids mass flow (t/h) 353
% Solids 61
P80 (mm) 0.42
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9. Evaluation of possible mine to mill benefits using Bond
methodology
The efficiency of the crushing and grinding can be estimated using the
Bond based methodology [2,13]. It was found in the crushing area that there
are significant differences between the real plant data and the Bond
calculations and therefore empirical corrections were introduced. The
following modified Bond equation was proposed for crushing [10]:
Wc =
P
AWi (
10
P
-10
F
) (1)
where: Wc is energy consumed in crushing (kWh/t),
Wi is Bond rod mill work index (kWh/t),
P is sieve size passing 80% of the ore after crushing (m),
F is sieve size passing 80% of the ore before crushing (m),
A is a empirical coefficient, dependant on the ore and the crusher
properties
For the tumbling mills the correction factors[2,13] were introduced to
calculate specific grinding energy:
Wr = K7 K4 K3 Wi (10
P1-
P
10) (2)
where: Wr is energy consumed in milling (kWh/t),
P1 is sieve size passing 80% of the mill product (m),
P is sieve size passing 80% of the mill feed (m),
K4 is correction factor if the ore feed size is coarser then the
optimum size
K7 is correction factor for the size reduction ratio in the ball mill
K3 is correction factor for the mill diameter
The correction factor K4 for the ore feed size is calculated as follows:
K4 = [Rr+ (Wi - 7) (P P
P
0
0
)] / Rr (3)
P0 = 16000 (Wi
13)0.5 for rod mills (4)
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P0 = 4000 (Wi
13)0.5 for ball mills (5)
Rr= P/P1 (6)
where: Rr size reduction ratio
P0 optimum mill feed size (m),
P actual mill feed size (m),
P1 mill product size (m),
From equation (5) an optimum primary ball mill feed size (Wi=18.5kWh/t) is calculated, P0 =3.15 mm. The correction factor is therefore
applied for the feed sizes P > 3.15 mm.
The correction factor K7 for the size reduction ratio in the ball mill is
calculated as follows:
K7 =)35.12(R
26.0)35.12(R
r
r
+(7)
where: Rr size reduction ratio in the ball mill
The need to use size reduction factor would not occur often as this only
applies when the size reduction ratio is less than 6.
The correction factor K3 for the mill diameter is calculated as follow:
K3 = ( ).
20 2
.44
D= 2.0)
5.0
.442( = 0.87 (8)
where: D mill diameter inside liners (m)
10. Modelling methodology
To create a Bond based energy-size reduction model of the crushing
operation, results from the survey were used. The relevant informationextracted from the survey is presented in Table 6.
The energy consumption for each crushing stage is calculated using two
methods:
1. the Bond formula assuming Wi=21 kWh/t and the product sizes
presented in Table 6.
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2. using the actual crusher power draws (screening energy not included)
and the plant throughput.
Table 6: Crushing data
crushing
stage
crusher
type
CSS Crusher
power
Throughput F80 P80
(mm) (kW) (t/h) (mm) (mm)
primary Jaw C140 110-120 70 500 450 125
secondary HP500 35-38 350 500 125 25
tertiary HP500 12-15 700 500 25 7
Note: CSS - crusher close site setting
The calculated energy consumption is presented is Table 7. It can be
seen that differences exist between two methods. The Bond Formula
overestimates the energy consumption for primary crushing two times,
while the Bond calculations for the secondary and tertiary crushing were
similar to the actual plant data.
Table 7: Energy consumption for crushing (screening energy not included)
Product
80%passing
size
crushing
energymethod 2
cumulative
crushingenergy
method 2
crushing
energymethod 1
cumulative
crushingenergy
method 1
Crusher (mm) kWh/t kWh/t kWh/t kWh/t
primary 125 0.14 0.14 0.28 0.28
secondary 25 0.7 0.84 0.73 1.01
tertiary 7 1.4 2.24 1.18 2.20
The cumulative crushing energy calculated using the actual crusher
power draws and the plant throughput (method 2) represents the real Sunrise
dam ore crushing energy and it was used to estimate the coefficient A in
equation (1). The estimated value was A = 100.
The energy consumption for the Sunrise dam ore crushing + primary ball
milling can be estimated using the following Bond based model:
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Wcr = Wc + Wr (9)
Wcr =0.5P
100Wi (
10
P-
10
F) + 0.87 {[Rr+ (Wi -7) (
P P
P
0
0
)] / Rr}
()35.12(R
26.0)35.12(R
r
r
+) Wi (
10
P1-
P
10)
It should be emphasized that correction factors are applied in the model
only if the criteria given by Bond are met [2, 13]. The model predictions of
the energy consumption for the crushing and primary ball milling for the
different crushing product and primary ball mill product sizes are presented
in Figure 7.
Figure 7 shows that the energy consumption decreases with decreasing
the crushing product size, i.e. ball mill feed size. The model predicts that the
decrease in ball milling energy consumption would outweigh the increase in
energy consumption from finer crushing. This trend is dominated by the
correction factor for ball mill feed size (K4). The optimum feed size for the
primary ball mill is 3.15 mm and (K4) increases significantly for the coarser
feed. The model predicts that the energy consumption could be reduced by4% if the crusher product size is reduced from the current 7 mm to 6 mm.
10
11
12
13
14
15
16
17
18
4 6 8 10 12 14
crushing product size P (mm)
s
pecificenergy(kWh/t)
P1 =0.8mm
P1=0.6mm
P1=0.4 mm
P1=0.3mmactual kWh/t
actual crushing product
predicted kWh/t
Fig. 7: Total energy consumption for crushing and primary ball milling
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It can be seen from Figure 7, that the model predicts higher energy
consumption (14.6 kwh/t) than the actual plant (13.7 kWh/t) for the product
size P1 = 0.4 mm. This is mainly due to over-prediction of the crushing
power. It suggest that a better model for crushing is required.
The predicted primary ball mill kWh/t is close to the actual. The primary
ball mill operating work index was calculated based on the survey data
Wi0 = 29.35 kWh/t. The corrected Bond index was calculated based on the
laboratory Bond test index Wi = 18.5 kWh/t and appropriate correction
factors (K4 , K3), Wic = 27.8 kWh/t. Comparing the operating work index
and the corrected Bond index, 94.7% efficiency of the primary ball mill
circuit can be calculated. It suggest that the primary ball performs close toits Bond best for the given conditions.
In summary, using the developed model based on the Bond calculations,
analysis of the crushing primary ball milling circuit was carried out to
determine the potential benefits of a mine to mill optimisation and optimum
crushing product size. It was found that the comminution circuit power
consumption decreases as crushing product size decreases. By reducing the
crusher product 80% passing size (P) from 7 mm to 6 mm, the power
consumption may be reduced by around 4%. This gains are in line with
those indicated by JKMRC methodology and simulations presented in the
previous section of this report. Note that the above calculations do not
include screening power and capacity. Separate calculations are required to
estimate the cost benefits from finer crushing which would include
increased operational and maintenance costs.
11. Conclusions
The scoping study simulations indicated that the predicted finer ROM
size distribution could result in primary crusher throughput increase to 522
t/h compared to the current 490 t/h. The amount of +600 mm material would
be reduced significantly which would in turn significantly reduce theprimary crusher down time. The secondary and tertiary crusher loads would
remain similar to the current situation. The final crushing product would be
slightly coarser. The overall positive effect of the finer ROM obtained by
the changed blasting practices would be limited to the reduced primary
crusher down time. Increase in the crushing circuit throughput (t/h) is not
relevant due to ball milling circuit constraints.
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On the other hand, with a modified crushing circuit (finer crushing), a
significantly finer circuit product size could be obtained (P80=5.7 mm
compared to current 7.2 mm). Simulations indicate that in this case the
crusher circuit throughput would be reduced to 435 t/h compared to current
490 t/h. The secondary and tertiary crusher loads would remain similar to
the current situation. The reduced crushing circuit capacity would not have a
detrimental effect on the whole process as it would be balanced by the
increase in crushing circuit availability due to the reduction in primary
crusher down time.
The simulation results using JKMRC methodology suggest that an 4.8%
increase in ball mill circuit throughput (370 t/h vs. current 353 t/h) would beobtained with the finer milling circuit feed from the modified crushing
circuit. Therefore, the potential benefits from the finer blasting are increased
capacity of the milling circuit and increased availability of the crushing
circuit.
A similar increase in throughput and better energy utilisation was
predicted using Bond based calculations.
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