jah video sampling

7/23/2019 Jah Video Sampling

http://slidepdf.com/reader/full/jah-video-sampling 1/17

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.



2

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.



3

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



4

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



5

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

20

40

60

80

100

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.



6

0

20

40

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.

0

20

40

60

80

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



7

conveyor size distributions were determined by averaging 80 images rather than the five

used for trucks.

Crusher

0

20

40

60

80

100

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.

0

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.



8

0

20

40

60

80

100

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.

0

20

40

60

80

100

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.



9

0

10

20

30

40

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.

0

20

40

60

80

100

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.



10

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.

0

20

40

60

80

100

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.



11

0

20

40

60

80

100

1 10 100 1000

Size, mm

P e r c e n

t P a s s i n

g

Truck 4292

Model, EI = 9.5 kWh/mt

0

20

40

60

80

100

1 10 100 1000

Size, mm

P e r c e n

t P a s s i n

g

Truck 4296


0

20

40

60

80

100

1 10 100 1000

Size, mm

P e r c e n

t P a s s

i n g

Truck 4298


0

20

40

60

80

100

1 10 100 1000

Size, mm

P e r c e n

t P a s s

i n g

Truck 4293


Figure 13. Best fit explosive breakage model size distributions for 4 ore types.

0

20

40

60

80

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



12

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.

0

20

40

60

80

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.



13

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



14

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.

0

20

40

60

80

100

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



15

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.



16

4200

4400

4600

4800

5000

5200

5400

0 60 120 180 240

Time, min.

P o w e r ,

k W

700

720

740

760

780

800

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

5

10

15

20

25

0 60 120 180 240

Time, min.

F i l l i n g ,

%

30

32

34

36

38

A n g

l e o

f R e p o s e , d

e g

Estimated Ore Filling, %

Estimated Ball Filling, %

Estimated Angle, %

50

150

250

350

450

0 60 120 180 240

Time, min.

F e e

d r a

t e ,

l t p h

0.03

0.06

0.09

0.12

0.15

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.



17

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.

jah video sampling

Documents