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Video Sampling for Mine to Mill Performance Evaluation,
Model Calibration and Simulation*
By
J.A. Herbst+ and S.L. Blust ++
+J.A. Herbst & Associates, LLC, Kealakekua, HI
++ National Steel Pellet Company, Keewatin, MN
* This paper is to be published by SME in the proceedings of Control 2000 Symposium to held in
conjunction with the 2000 SME Annual Meeting and Exhibit, February 28-March 1, Salt Lake City, Utah.
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ABSTRACT
Optimizing blasting, crushing and grinding operations is filled with challenges.One of the more difficult tasks is accurately sampling and determining the sizedistributions of blasted and crushed materials at a reasonable cost. The task is difficult
because of the large size of fragments and the tonnage involved. However, the size
distribution measurements are necessary for models that predict the performance of minethrough mill operations. This paper is concerned with the use of video sampling for this
task at National Steel Pellet Company's operation in Keewatin, Minnesota. Datagathering, data analysis, model building and mine-to-mill simulation are all described.
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Video Sampling for Mine to Mill Performance Evaluation,
Model Calibration and Simulation
J.A. Herbst
+
and S.L. Blust
++
INTRODUCTION
Mining companies around the world are seeking ways to optimize performance.
In recent days a great deal of attention is being paid to optimizing the mine/mill interface(Morrell, 1998). A principal challenge in carrying out such an optimization is to measure
the performance of blasting operations, crushing operations, and primary grindingoperations reliably and inexpensively. Measuring fragment size distributions at eachstage of the size reduction process is critical in order to establish a baseline for predictive
simulators to use in calibration, and for evaluation of process improvements.Unfortunately, fragment sizes in muck piles, trucks, crusher dump stations, and on
primary mill feed conveyors are large and highly variable making conventional samplingand screening at least expensive, and in some instances impossible.
National Steel Pellet Company (NSPC) is continuously seeking to optimize its
mine/mill performance through ore blending at the mine and adaptive fine-tuning throughcontrol. The company operations are located in Northern Minnesota in the town of
Keewatin. Annually it processes about 18 M tonnes of taconite ore to produceapproximately 5.35 M tonnes of iron ore pellets. The ore characteristics for differentlocations in the NSPC Pit are quite variable. The flowsheet for mining and grinding
portions of the operation are shown in Figure 1. Blasting is currently accomplished usingammonium nitrate emulsion-based blasting agents. Ore is loaded into trucks (eight 240-
tonne, four 205-tonne) and hauled about one mile to two 1.524 m x 2.59 m (60" x 102") primary gyratory crushers driven by one 600 kW (800 hp) motor and one 675 kW (900hp) motor. The crushed product is conveyed to a 220,000 tonne coarse ore storage barn.
In turn, the ore in storage is conveyed to ten 8.23 m x 5.49 m (27' x 18') SAG mills whichare each driven by two 2625 kW (3500 hp) motors.
+ J.A. Herbst & Associates, LLC, Kealakekua, Hawaii
++ National Steel Pellet Company, Keewatin, Minnesota
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Blasting
Storage
Primary MillingPrimary Milling
LoadingDrilling
Hauling
Crushing
Figure 1. NSPC mine-to-mill operations.
NSPC working in conjunction with J.A. Herbst & Associates has recently beenevaluating the use of video sampling to measure blasting, crushing, and grinding behavior
of different ores. The sampled images are analyzed with transformed video imagesoftware. The resulting fragment size distributions are used to calibrate a mine-to-millflowsheet simulator. This paper describes the video sampling process and the simulator
calibration. Finally, some illustrations of the potential usefulness of the data andsimulator are presented.
VIDEO SAMPLING
Video sampling was accomplished using a JVC Mini Digital Video Camera (GR-DVM5) with a 100X zoom. Truck contents were sampled by collecting video images of
material in the bed of four separate trucks over the entire time each truck was dumpinginto a primary crusher. A reference size for truck images was established based on the
known width of truck tires. Products from the two crushers were sampled by placing thecamera over high-speed conveyors carrying the crushed material to a tripper system for
distribution in the ore storage barn. Finally, SAG feed was measured by placing thecamera over three of the primary mill feed conveyors. Reference sizes on conveyor beltswere established using wooden dowel pieces cut to a length of 25 mm each. A shutter
speed of 1/500th of a second was used for all sampling. Natural light was used for outdoor taping of the trucks at the crusher while auxiliary artificial light was provided for indoor taping.
Raw images were transferred from tape to an IBM 385XD laptop through a VideoPort Pro frame grabber. The raw images were then analyzed using the OPSA Software
developed at University of Utah (Miller, 1999). This software makes a series of enhancements and transformations on each image. The first of these enhancements are
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shown for the case of one image obtained at the beginning of a dump of truck 4292. Here
the raw image captured from the videotape is enhanced by brightening. See Figure 2a and2b. Edge finding is then used to prepare the image for chord length distribution
measurements shown in Figure 2c. The resulting surface chord length distribution isshown plotted in Figure 3. The OPSA software then makes the stereologicaltransformation from the linear chord distribution to the volumetric distribution of
particles in the exposed or surface layer of the truck as shown in Figure 3. Thetransformation from the volumetric distribution of the exposed layer to the desired
volumetric distribution of particles in the bulk of the truck is also shown in Figure 3.
Original Image Brightened Image Separated Image
Figure 2. Processing steps in T-VIS.
0
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1 10 100 1000
Size, mm
W e
i g h t %
F i n e
r
0
20
40
60
80
100
N u m
b e r
%
F i n e
r
Measured chord length
distribution for surface
Transformed volume (weight)
distribution for surfaceTransformed volume
distribution for bulk
Truck 4292 at beginning of dump
Figure 3. Transformation of chord length distribution to volumetric size distribution for the bulk.
Since each image contains only a finite number of fragments, the statistics of counting are important. For this reason, five separate raw images from the beginning of
the dump were analyzed and the resulting size distributions averaged. This procedurewas repeated for five images in the middle of the dump and five more at the end of thedump. The overall average of the beginning, middle and end images is shown in Figure
4. The differences between the average size distribution for the bulk at the beginning,middle and end are relatively small. In contrast, the overall size distributions of the four
trucks from different loading locations varied strongly as shown in Figure 5.
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0
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60
80
100
1 10 100 1000
Size, mm
P e r c e n
t P a s s
i n g
Beginning
Middle
End
Avg
Truck 4292
Figure 4. Size distribution of single truck from average of several images during dump.
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100
1 10 100 1000
Size, mm
P e r c e n t P a s s i n g
Truck 4292
Truck 4296
Truck 4298
Truck 4293
Figure 5. Variation in size distribution from truck to truck.
The image analysis methodology for the conveyor video sampling was identical
to that for the trucks described above. However, the overall analysis of conveyor sizedistributions did differ , because there are fewer fragments per image as is seen bycomparing Figure 6 and Figure 2. The standard deviation of any counting procedure is
inversely proportional to the square root of the number of things counted. Figure 7 shows
a plot of estimated standard deviation versus N 1 for different numbers of images ( N )
on the belt. Due to the unfavorable statistics of counting conveyor images, the overall
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conveyor size distributions were determined by averaging 80 images rather than the five
used for trucks.
Crusher
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1 10 100 1000
Size, mm
P e r c e n
t P a s s
i n g
Figure 6. Crusher product image and transformed size distribution.
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10
20
30
40
0.0 0.1 0.2 0.3 0.4 0.5 0.6
N-0.5
S X
N= 5N=20N=80
Rock
HTG
Fines
Figure 7. Effect of number of images on standard deviation of mean size distribution measurements.
The results of the size distributions determined in this fashion from Crusher 1
(which had a worn mantle) and Crusher 2 (which had a new mantle) are shown in Figure8. Even though the two crushers were operated with "identical" open side settings of 200mm (8") the products are seen to be quite different.
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1 10 100 1000
Size, mm
P e r c e n
t P a s s
i n g
Crusher #1
Crusher #2
New mantle
Worn mantle
Figure 8. Evaluation of performance differences between crushers.
The discharge from either crusher is distributed into 10 piles by two trippers in theore storage barn. Each mill receives feed from its own pile with one or two pan feeders
emptying onto the conveyor belt. Figure 9 shows that after averaging 80 images there aresignificant differences in the average size distribution to each mill. Figure 10 showsmoving average values calculated from images on one line. The data indicates that each
mill experiences significant variations in the feed size distribution over time. Theseobservations are particularly important, since it is known that some media pieces in the
feed (+100 mm) are required to achieve to good SAG throughput, while a large amountof hard-to-grind (50x 100 mm) in the feed limits mill capacity.
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1 10 100 1000
Size, mm
P e r c
e n
t P a s s
i n g
Line 2
Line 4
Line 7
Figure 9. Comparison of size distributions for 3 SAG feed lines.
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0 30 60 90 120 150 180
Time, min
W e i g h t %
10
20
30
40
50
W e i g h t %
Plus100 mm
50 x 100 mm
Minus 25 mm
Figure 10. Time variation of rocks, hard-to-grind and fines in SAG feed to one mill.
With regard to accuracy of the size distributions (i.e. how closely they match
screen size analyses), Figure 11 shows that the screen analysis of a four ton sample of SAG feed is very close to OPSA/T-VIS* volumetric distribution determined for the bulk.
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1 10 100 1000
Particle Size, mm
C u m u l a t i v e % P a s s i n g
Sieve Analysis (Bulk)
OPSA/T-VIS (Bulk)
Figure 11. Confirmation of T-VIS size distribution measurement by sieve analysis.
* T-VIS is the commercial name of the video imaging system containing OPSA software sold
under license by J. A. Herbst & Associates, LLC.
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MODEL CALIBRATION
Important models for the simulation of the NSPC mine-to-mill interface are anexplosive breakage model, a primary crusher model, and a SAG mill model plus auxiliary
transport and storage models. The models used in this investigation were selected fromthose provided in the dynamic flowsheet simulator MinOOcad (Herbst & Pate, 1998).Most of the parameters for these models are the physical variables such as equipment
dimensions and settings that are known. The ore variables are the only ones that must beestimated from performance data. MinOOcad provides a set of reference or default
parameters for a "typical" taconite ore. Using these parameters as starting values, modelcalibration is relatively easy, involving the adjustment of a single calibration constant for each unit operation; e.g. an explosive index, E I ; a crusher index, C I ; a SAG rock
competency index, SR I ; SAG hard-to-grind index, SH I ; and SAG particle index, SP I .
Figure 12 illustrates the calibration procedure for the explosive breakage model.
The adjusted value of E I = 9.5 kWh/MT gives good agreement between the experimentalsize distribution of the fragments from truck and the calibrated explosive model and is,therefore, deemed the best estimate for this ore. Figures 13 and 14 show similar
comparisons of experimental distributions from video sampling and the correspondingMinOOcad model fits for the explosive breakage model and the crusher model.
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1 10 100 1000
Size, mm
P e r c e n t P a s s i n g
PF = 0.177
EI = 10.5
EI = 9.5
EI = 8.6
Blasting for Truck 4296
Measured
Figure 12. Procedure for calibration of explosive breakage model.
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Size, mm
P e r c e n
t P a s s i n
g
Truck 4292
Model, EI = 9.5 kWh/mt
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1 10 100 1000
Size, mm
P e r c e n
t P a s s i n
g
Truck 4296
Model, EI = 9.5 kWh/mt
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Size, mm
P e r c e n
t P a s s
i n g
Truck 4298
Model, EI = 8.1 kWh/mt
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1 10 100 1000
Size, mm
P e r c e n
t P a s s
i n g
Truck 4293
Model, EI = 14.2 kWh/mt
Figure 13. Best fit explosive breakage model size distributions for 4 ore types.
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100
1 10 100 1000
Size, mm
P e r c e n t P a s s i n g Measured
CI=9.4 kWh/mt
Crusher #2
Figure 14. Best fit of crusher model for blended ore feed.
ILLUSTRATION OF SIMULATOR USE
The overall MinOOcad flowsheet used to simulate NSPC mine-to-mill operationsis shown in Figure 15. Before using the simulator , it was necessary to confirm that it
resulted in realistic predictions of flowsheet behavior. Figure 16 shows one such
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confirmation in which predicted and measured feed and products from the crushers are
compared for a mix of the four ore types.
Figure 15. NSPC flowsheet configured in MinOOcad simulator.
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100
1 10 100 1000
Size, mm
P e
r c e n t P a s s i n g
Predicted Product
Measured Product
Predicted Feed
Measured Feed
Figure 16. Test of predictive capability of simulator.
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To illustrate the use of the simulator for mine-to-mill optimization, consider a
case in which it is desired to achieve more capacity in the SAG mills by using acombination of ore blending and control. The following options were evaluated:
1) Process each ore type separately (each of the four trucks represents aseparate ore type.
2) Process a blend of ore types (in this case the average of the four trucks).
3) Change blasting and crushing practice [vary Powder Factor = PF = (kg of explosive/MT of ore) and Primary Crusher Open Side Setting = OSS =
(mm)].
4) Change SAG mill control practice.
In each case, the MinOOcad simulator was used to predict performance variables
from mine-to-mill, including SAG mill throughput and energy consumption. Steady state
simulation results for Options 1-3 above are summarized in Table 1.
Table 1.
Ore FeedrateMTPH
Total EnergyKWh/MT
Option 1: Four ores crushed/ground separately
(PF = 0.177 kg/MT and OSS = 200 mm)
285 18.7
Option 2: Four ores blended at crusher
(PF = 0.177 kg/MT)OSS = 150 mm 287 18.6
OSS = 175 mm 312 17.1OSS = 200 mm 326 16.4
OSS = 225 mm 334 16.0
Option 3: Four ores blended at crusher
(OSS = 200 mm)PF = .200 kg/MT 326 16.5
PF = .177 kg/MT 326 16.4PF = .100 kg/MT 326 16.3
One of the real advantages of the simulation evaluation is that results can beunderstood in fundamental terms. Blending obviously reduces variations in tonnage,making it unnecessary to cap tonnages for soft ores which can overload downstream
processes, or to run equipment very near power limits when hard ore is processed. Thenet result is that at the same crusher setting (OSS = 200 mm), the blend can be processed
at 326 mtph rather than the average of 285 mtph when processed separately. The effectof increasing the crusher OSS may at first seem counter intuitive (more finely crushedfeed requiring more energy in the SAG mills). However, the reason becomes apparent if
one examines Figures 17 and 18. Here we see that coarser crushing provides more rocks
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and associated media pieces (+100 mm) relative to the intermediate, hard to grind (HTG)
fractions (50 x 100 mm) and fines (-50 mm). As the ratio of hard to grind pieces tomedia rocks in the mill becomes more favorable (lower), the grinding rate increases,
yielding a higher feedrate at the same filling (26.7% volume filling of ore and balls). Thesimulations predict that this benefit becomes marginal as the crusher is opened beyond225 mm probably because media rocks begin to take up too much space in the mill.
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1 10 100 1000
Size, mm
P e r c e n
t P a s s i n g
HTGFines Rocks
Crusher Product
OSS
150 mm
175 mm
200 mm
225 mm
Figure 17. Effect of open side setting on crusher product
260
270
280
290
300
310
320
330
340
125 150 175 200 225 250
Open Side Setting, mm
T h r o u g
h p u
t , M T P H
0
1
2
3
4
T o n s
H T G / T o n s
R o c
k s
Primary Mill
Figure 18. Effect of open side setting on throughput and ratio of hard-to-grind to media pieces.
Figure 19 shows that given the current estimated blasting and crushing
efficiencies, one should probably minimize the amount of blasting while keeping in mind
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that blasted material must be small enough to be loaded and fed to the crusher. In
addition, it cannot be so coarse as to exceed the power draw of the crusher. In any case,the reductions in total energy are quite small and therefore other factors may dominate
the decision on blasting practice.
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.00 0.05 0.10 0.15 0.20 0.25
Powder Factor, kg explosive/mt ore
E n e r g y ,
k W h / m t Total Energy
Blasting Energy
Crushing Energy
Figure 19. Tradeoff between explosive breakage and crushing.
It may be possible to decrease total energy even more by programming the
powder factor according to the hardness of the ore being blasted. This possibility is beingexplored.
Finally, as noted earlier the time variations in SAG feed even for blended ores are
quite large (see Figure 10). Dynamic simulation with MinOOcad allows us to ask thequestion "how much additional tonnage might be available through supervisory control of
the SAG mills?" Figure 20 shows actual (unsupervised) feedrate, the associated hardnessestimates from a softsensor (Herbst and Pate, 1998) and the model- based prediction of
the highest feedrate at each time over a two-hour period. The simulation suggests thatmodel based supervisory control could provide an additional 5-7% in SAG capacity byadapting to unavoidable disturbances.
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4200
4400
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4800
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5200
5400
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Time, min.
P o w e r ,
k W
700
720
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760
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820
B e a r i n g
P r e s s u r
e ,
p s
i
M e a s P o w e r E s t P o w e r
M ea s B rn g P r es s E s t B r n g P r e s s
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Time, min.
F i l l i n g ,
%
30
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A n g
l e o
f R e p o s e , d
e g
Estimated Ore Filling, %
Estimated Ball Filling, %
Estimated Angle, %
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Time, min.
F e e
d r a
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l t p h
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G r i n
d a
b i l i t y
, [ k W h / l t ] -
1
Feedrate
Estimated Grindability
Model Based Feedrate
Figure 20. Softsensor estimates of SAG mill variables with predicted model based control performance.
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CONCLUSIONS
This paper has examined video sampling as a tool for mine-to-mill optimization.
It was found that video samples of mine trucks, crusher products and SAG feed materialscollected at National Steel Pellet Company's Keewatin operations provided valuableinsight into the workings of the mine/mill interface. Image analysis of the video samples
provided accurate size analyses for mine-to-mill performance evaluation and also
produced useful input for the calibration of blasting, crushing and SAG milling models.These calibrated models were in turn used in a mine-to-mill simulator to help identify and
evaluate promising alternatives for increasing throughput given current ore conditions.
ACKNOWLEDGEMENTS
The authors wish to thank NSPC management for permission to publish thesefindings. The assistance of Mr. Don Healy and Mr. Phillip Murr during video sampling
and the help of Dr. William T. Pate and Mr. Richard T. Herbst during image analysis arealso gratefully acknowledged.
REFERENCES
Herbst, J.A. and Pate, W.T., Dynamic Simulation of Size Reduction Operations from
Mine to Mill, Mine to Mill 1998 Conference, AusIMM, October 1998, p. 243.
Herbst, J.A. and Pate, W.T., Object Components for Comminution Systems Softsensor Design, 9th European Symposium on Comminution, Prints Volume 2, p.741.
Lin, C.L. and Miller, J.D. Plant-site Evaluations of the OPSA System for Online ParticleSize Measurements from Moving Belts, Preprints Annual SME Meeting, Denver,
Colorado 1999.
Scott, A. and Morrell, S., 1998 Mine to Mill Conference, AusIMM, October 1998.