mine to mill optimization

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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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A. Jankovic and W. Valery50

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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Mine to mill optimisation for conventional... 51

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 78 78 60 60

P80 (mm) 1.36 1.1 0.292 0.294

Primary Cyclone Feed Secondary Cyclone U/F


% 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.

J. Min. Met. 38 (1 4) A (2002)


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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.

References

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Conference, AusIMM, Brisbane

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7. S.S.Kanchibotla, W.Valery and S.Morrell, (1998), Modelling fines in blast

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J. Min. Met. 38 (1 4) A (2002)

mine to mill optimization

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