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UTILIZING BASIC STATISTICAL ANALYSIS AND FRAGMENTATION ANALYSIS TO IMPROVE FRAGMENTATION

Dave Lilly, P.E., MBA

Senior Engineering Consultant Dyno Consult, a Division of Dyno Nobel Inc.

Pawleys Island, South Carolina dave.lilly@am.dynonobel.com

Ran Tamir

Mine Superintendent Lafarge North America

Ravena, New York ran.tamir@lafarge-na.com

Larry Mirabelli

Senior Project Manager Dyno Consult, a Division of Dyno Nobel Inc.

Roswell, Georgia larry.mirabelli@am.dynonobel.com

INTRODUCTION While it seems deceptively simple, the process to produce rock is actually very complex and involved. This paper will demonstrate some simple methods to maximize fragmentation and productivity results. In addition, several methods to further refine the analysis will be introduced. The Lafarge Ravena plant is using basic statistical analysis and digital fragmentation analysis to optimize fragmentation size and productivity. Digital fragmentation analysis from blast photographs and video is becoming an essential tool to analyze blast results and determine the success of changes to blast design. This paper details ways to statistically relate the results of blast fragmentation analysis to changes in blasting parameters such as powder factor. Methods to integrate data into a single data pool using Excel spreadsheets to relate blasting parameters and fragmentation analysis to crusher productivity are described. THE PROBLEM Because of the unique nature of the blasting process, equally unique mining processes, and time inconsistencies between the results of each, the processes have not always been fully integrated. Both processes are statistical in nature and results are site-specific making integration of the separate modeling

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processes difficult at best. In addition, the blasting and mining variables are interrelated and affect each other significantly with complex interactions. The expansion and use of extensive blasting and production databases has been the first step in the integration. A statistical description and evaluation of significant blasting variables effecting the mining process is furthering the integration of the two processes while first principle modeling and development is proceeding. Below are some of the variables that enter into final productivity and product sizing. Every mine is different and data is kept differently to describe the process and results. Product from a specific blast is not normally segregated in the operating data. What we are attempting to do in this paper is to describe a simple method to relate blasting parameters to product size and productivity in ways that can be related to all mines. In addition we will introduce some of the more advanced methods of analysis.

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THE DATA Every mine must keep blast records for regulatory purposes. A typical Lafarge blast report is shown below: Dyno Nobel catalogs the blast reports in a database for retrieval in many forms of reports-a portion of one is shown below. The pertinent data can be taken from the blast reports if a database is not available.

Quarry Blast Date QBench Blast # Tons Hole Diameter BXS holes

Ravena Aug 25, 2009Ravena: CM Main Pit

33-CM-10-2009 72505.26 6.5 18X20 34

Ravena Aug 04, 2009Ravena: CM Main Pit

31-CM-09-2009 65869.32 6.5 16X19 37

Ravena Jul 21, 2009Ravena: CM Main Pit

29-CM-08-2000 103098.9 6.5 16X19 58

Ravena Jun 25, 2009Ravena: CM Main Pit

26-CM-07-2009 68026.3 6.5 17X19 34

Ravena May 20, 2009Ravena: CM Main Pit

19-CM-06-2009 88834.09 6.5 17X19 45

Ravena Apr 09, 2009Ravena : CM West

15CM 06-2009 97318 6.5 17X19 48

Ravena Apr 02, 2009Ravena: CM Main Pit 13-09 99345.98 6.5 17X19 49

Ravena Mar 25, 2009Ravena: CM Main Pit 11-09 70001.28 6.5 17X19 41

Ravena Feb 26, 2009Ravena: CM Main Pit 07-09 62427.36 6.5 17X19 38

Ravena Feb 11, 2009Ravena: CM Main Pit 04-09 64265.34 6.5 17X19 33

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Lafarge retains productivity data in a database also. A portion of a monthly database is shown below. Most mines have a similar database but not always in this form. Many mines are keeping a variety of KPIs that can be helpful to the analysis. The trick to combining the blasting and productivity data is to relate the blast to specific productivity data. In this example, the few Cement blasts in 2009 can be related to the day’s production after the blast. When there are more blasts in a month, it is possible to estimate an average monthly powder factor and relate that to monthly productivity. An alternative method of establishing the relationship is to time specific crushing loads related to a blast. The procedure is illustrated in the referenced article The Powder Factor: More is not Necessarily Better. (1992). Additional information on data can be found in “Tools to help Improve Operational Performance and Control Operating Costs-or Reducing the Guesstimation Factor (2007)”.

DAY CM hours Becraft hours AGG hours total crushed total crushing hours

1 - - - - - - 0 02 - - - - - - 0 03 6,339 6 1,339 2 8,036 8 15,714 164 8,393 8 - - 7,946 8 16,339 155 6,964 8 - - 7,857 8 14,821 156 - - - - 15,804 15 15,804 157 - - - - - - 0 08 - - - - - - 0 09 - - - - - - 0 0

10 6,339 8 - - 15,000 15 21,339 2311 6,339 8 - - 8,036 8 14,375 1512 6,607 8 - - 7,500 8 14,107 1513 2,679 3 - - 12,411 13 15,089 1614 - - - - 6,964 8 6,964 815 - - - - - - 0 016 - - - - - - 0 017 - - - - 16,250 16 16,250 1618 8,929 8 - - 7,589 8 16,518 1619 3,393 6 - - 5,536 7 8,929 1320 10,893 11 - - 3,214 4 14,107 1521 - - - - 5,089 7 5,089 722 - - - - - - 0 023 - - - - - - 0 024 - - - - 7,321 8 7,321 825 - - - - - - 0 026 6,696 7 - - 7,411 8 14,107 1427 - - - - 13,661 14 13,661 1428 3,214 4 - - 3,929 5 7,143 929 5,982 7 - - - - 5,982 730 - - - - - - 0 031 1,518 2 1,786 - - - 3,304 2

Total 84,286 91 3,125 2 159,554 163 246,964 256

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Blast Muckpile Fragmentation data can be acquired using digital photos and commercially available fragmentation analysis programs or with a very general manual method. An output of one of the commercially available fragmentation analysis programs is shown below. The data illustrated here is a merged analysis was from photos taken of areas in the blast muckpile. An alternate and more representative method is to take photos as the loads are dumped into the crusher. A more general and manual alternative to commercial fragmentation analysis packages to analyze muck pile photos described in the paper A Blaster’s Tool to Measure Fragmentation (2008). Make no mistake the results of commercial photoanalysis are more accurate. Regardless the procedures and possibly the person taking photos are most important to both methods and should be constant for best results. It must also be noted that determining the percentage of fines is difficult with either of the photoanalysis methods. In addition, the crusher manufacturers data can be utilized for additional guidance.

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For the two examples (Cement and Aggregate) in this paper the data flow is as follows.

Ravena, NY Cement Rock

Blast Muck

Loader Haul

Crusher

Stockpile

Belt

Date

Operating Hrs

Loads

Tons

Tons / Load

Swell Factor

Date

Operating Hrs

Loads

Tons

Tons / Load

Swell Factor

Date

Operating Hrs

Tons/hr

Date

Operating Hrs

Tons/hr

Date

Operating Hrs

Loads

Tons

Tons / Load

Date of Blast

Explosive

Tons

Distribution Factor

Powder Factor

Fragmentation

Start Load

End Load

Ravena, NY Aggregate Rock

Blast Muck

Loader Haul

Crusher

Stockpile

BeltDate

Operating Hrs

Tons/hr

Date

Operating Hrs

Tons/hr

Date

Operating Hrs

Loads

Date of Blast

Explosive

Tons

Distribution Factor

Powder Factor

Fragmentation

Start Load

End Load

Date

Operating Hrs

Loads

Tons

Tons / Load

Swell Factor

Date

Operating Hrs

Loads

Tons

Tons / Load

Swell Factor

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A SIMPLE METHOD TO RELATE PRODUCT SIZE AND PRODUCTIVITY TO BLASTING PARAMETERS Below is data taken from the above reports to illustrate a simple analysis of the method to relate the different parameters. All data is from the Ravena Plant in 2009. Normally, the mean size is used and not the minimum but the Ravena crusher does not scalp fines and fines are a significant percentage of the final product. Fines can slow a crusher as easily as oversize. Video analysis of blasts do not discriminate fines though it is possible in other configurations. A sieve analysis of a Ravena blast prior to the study indicated a high fines percentage. In the case of Cement fines are an essential part of the product.

Cement From the above data, simple graphs of the relationships can be made as shown below. Below is the relationship between crusher productivity (tons per operating hour) and powder factor (tons/lb explosive). While the product in this example is cement feed and productivity should be maximized, when sized aggregate is produced, productivity should be analyzed based upon the size preferred. See previous 2007study “Aggregate Size Optimisation Program at the Lafarge Marblehead Plant”.

BLAST POWDER FACTOR TONS HOURS PRODUCTIVITY WHIPFRAG

DATE TONS/LB CRUSHED CRUSHED TONS/HOUR MINIMUM SIZE

11-Feb 1.71 50357 56 899.226-Feb 1.87 63304 63 1004.825-Mar 1.73 45625 53 860.8 0.30502-Apr 2.36 33929 38 892.99-Apr 2.02 91349 88 1038.1 0.4440

20-May 1.67 82768 105 788.3 0.261025-Jun 2.15 82232 86 956.2 0.277021-Jul 1.74 77857 80 973.2 0.32004-Aug 1.47 52143 58 899.0 0.1180

CRUSHER PRODUCTIVITY

R2 = 0.2717700.0

750.0

800.0

850.0

900.0

950.0

1000.0

1050.0

1100.0

1.3 1.5 1.7 1.9 2.1 2.3 2.5

POWDER FACTOR TONS/LB

TO

NS

/OP

ER

AT

ING

HO

UR

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The relationship indicates that fines are slowing the crusher at the lower powder factors (more lbs of explosive per ton of rock) and also at the higher powder factors with oversize. The optimum powder factor for crusher productivity appears to be at 2.0 to 2.1 tons/lb Basically the same relationship was found in a previous study done in 2007 “Factors Driving Continuous Blasting Improvement at the Lafarge Ravena Plant” using monthly data. Charts from that study are shown below The relationship between powder factor and minimum block size is shown below. It is basically a linear relationship. It can be refined by using the techniques described in a previous 2007study “Aggregate Size Optimisation Program at the Lafarge Marblehead Plant” to separate the relationship by pattern area or burden and spacing. Because of the limited data and closeness of the pattern areas, this was not attempted in this example.

R2 = 0.4474

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

0.5

1.3 1.5 1.7 1.9 2.1 2.3 2.5

POWDER FACTOR TONS/LB

MIN

IMU

M B

LO

CK

SIZ

E -

IN

MONTHLY CRUSHER PRODUCTIVITY CEMENT FEED

R2 = 0.1567

950

1000

1050

1100

1150

1200

1250

1300

1350

1.5 1.7 1.9 2.1 2.3 2.5

MEDIAN MONTHLY POWDER FACTOR - TONS/LB

TO

NS

PE

R A

VA

ILA

BL

E H

OU

R

DAILY CRUSHER PRODUCTIVITY CEMENT FEED

400.00

600.00

800.00

1000.00

1200.00

1400.00

1600.00

1800.00

2000.00

1.2 1.7 2.2 2.7 3.2

POWDER FACTOR - TONS/LB

TO

NS

PE

R A

VA

ILA

BL

E

HO

UR

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The relationship between minimum and mean block size and crusher productivity is indicated below – again indicating that both fines and oversize affect crusher productivity. Below is a similar analysis for the aggregate production at the Ravena Plant. Note this time the maximum and minimum sizes are used for correlation. Mean size is shown also as a reference.

AGGREGATE

CRUSHER PRODUCTIVITY

R2 = 0.3061

700.0

750.0

800.0

850.0

900.0

950.0

1000.0

1050.0

1100.0

0.1 0.2 0.3 0.4 0.5

MINIMUM BLOCK SIZE - IN

TO

NS

/OP

ER

AT

ING

HO

UR

AGGREGATE

R2 = 0.444420

25

30

35

40

45

50

1.6 1.7 1.8 1.9 2 2.1 2.2

POWDER FACTOR - TONS/LB

MA

X B

LO

CK

SIZ

E IN

AGGREGATE

R2 = 0.3704

4

5

6

7

8

9

10

1.6 1.7 1.8 1.9 2 2.1 2.2

POWDER FACTOR - TONS/LB

ME

AN

BL

OC

K S

IZE

IN

CRUSHER PRODUCTIVITY

R2 = 0.8247

750.0

800.0

850.0

900.0

950.0

1000.0

1050.0

1100.0

5 6 7 8 9 10

MEAN BLOCK SIZE - INT

ON

S/O

PE

RA

TIN

G H

OU

R

BLAST POWDER FACTOR TONS HOURS PRODUCTIVITY WHIPFRAG

DATE TONS/LB CRUSHED CRUSHED TONS/HOUR MAXIMUM SIZE

23-Apr 1.81 94196 112 841.04 27.80002-Jun 1.69 56518 64 883.09 26.5800

16-Jun 2.17 44286 49 903.80 45.304024-Jun 1.94 22929 28 818.89 22.89806-Jul 1.92 130268 146 892.25 29.53505-Aug 2.08 119911 126 951.67 29.5890

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Again the powder factors for aggregate should be adjusted to take into account the productivity and costs to produce the aggregate size required. See the referenced paper “The Enterprise Solution to Integrating Blasting Into the Mining Process (2008)”. Below illustrates that powder factors most likely can be increased for aggregate production – more tons per lb of explosive. While not possible presently at Ravena, the monthly-scaled production of sized aggregate can be related to powder factors for a better estimate. Again, this is a reality that most mines find in trying to relate blasting to mining production but the efforts to quantify can be very rewarding. Efforts may also indicate other data that is more important. In addition, a comparison of the blast fragmentation distribution with the crusher belt fragmentation distribution is helpful to determine blast efficiency.

A more complete analysis of many of the factors affecting Ravena productivity can be found in the paper “Factors Driving Continuous Blasting Improvement at the Lafarge Ravena Plant (2007)”. , a productivity improvement study that utilized “lean thinking” principles is described in the referenced paper “Missouri Quarry Productivity Improvement – Casework (2009)”

AGGREGATE

R2 = 0.5932800

820

840

860

880

900

920

940

960

20.0000 25.0000 30.0000 35.0000 40.0000 45.0000 50.0000

MAXIMUM BLOCK SIZE - IN

TO

NS

/OP

ER

AT

ING

HR

AGGREGATE CRUSHER PRODUCTIVITY

R2 = 0.2277

800

820

840

860

880

900

920

940

960

1.6 1.7 1.8 1.9 2 2.1 2.2

POWDER FACTOR - TONS/LB

TO

NS

/OP

ER

AT

ING

HR

Muckpile & Primary Belt ComparisonBlast 40-AGG-10-2009Lafarge Ravena, NY

0

10

20

30

40

50

60

70

80

90

100

0.10 1.00 10.00 100.00

Fragment Size (inches)

Cu

mm

ula

tive

Pas

sin

g%

40-AGG-10-2009 Belt Coarse 40-AGG-10-2009 Belt Fine40-AGG-10-2009 Belt Merge 40-AGG-10-2009 Blast Coarse40-AGG-10-2009 Blast Fine 40-AGG-10-2009-Blast Merge

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ADVANCED METHODS OF ANALYSIS Several enhancements to the simple methods described above can refine the analysis for further gain and are summarized below. MULTIVARIABLE NON-LINEAR REGRESSION ANALYSIS Crusher productivity is a function not only of blasting parameters but also mine operating parameters such as number of trucks, volume, seasonality, and other factors. Two studies are referenced which illustrate the method – “A Statistical Approach to Integrating Blasting Into the Mining Process (2007)” and “Factors Driving Continuous Blasting Improvement at the Lafarge Ravena Plant (2007)”. The use of these techniques has proven to substantially improve operating results. “LEAN THINKING” ANALYSIS Before base lining is started, a value map is built and “Lean Thinking” principles are applied to identify all areas of improvement. Lean Thinking is quite simple: you define value in terms of what your customer wants; you map how that value flows to the customer; and then you ensure the process is as efficient as possible by making it as cost effective and quick as possible. It examines subjects such as productivity, operational effectiveness, operational efficiency, waste elimination; and profitability - words and phrases that many people understand but which can mean very different things in different contexts. Operational effectiveness is about adapting, achieving and extending best practices and lean thinking -- it's running the same race faster. You make the process more cost efficient by eliminating waste. ‘Waste’ is defined as any part of your process that does not add value. Wastes in “lean thinking” are categorized into 7 different types: Overproducing (producing too much or producing it before it is needed), Waiting (idle time that causes workflow to stop), Over processing (processing things that the customer does not want), Inventory (Excess stock of anything), Motion (typically more walking, reaching or bending than is necessary), Defects or Correction (this is what most people thing of as waste (i.e. scrap) but the knock on costs of disruption caused by rework need to be taken into account as well), Transport (Many things, including people, are moved too many times or too far). When you can work out where these wastes are and eliminate them then the impacts can be invariably huge. A full description of “lean thinking” is in the reference “Missouri Quarry Productivity Improvement – Casework (2009)”.

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CUMULATIVE SUM ANALYSIS Cumulative sum (CUSUM) statistical process control methods have been used in many industries to monitor quality control results. CUSUM charts are a method to determine when small changes in data indicate a major change in results. While CUSUM charts do not directly give causal information about productivity changes, they can be used with other techniques to determine why change is occurring. In particular, CUSUM charts of the main blast variables affecting productivity can be married with the results to help explain changes. The ability of CUSUM charts to highlight the timing of changes is important when changes are made. CUSUM charts are easy to implement and the visual results are helpful to interpret changes. See the paper “CUSUM for the Control of Vibration and Air Blast (2009)” for details of this method. An example using productivity is included in the paper. PRINCIPAL COMPONENT ANALYSIS In blasting many variables are highly correlated (powder factor, pattern area, explosive weight and density, hole size, etc.) and principal component analysis can combine these variables into a more concise form to solve problems relating to specific goals such as mining productivity, environmental concerns and highlight the effects of major changes. Principal component analysis has been successfully adapted to many industries to create summary information. The summary information concisely expresses the status of a process from the diverse data that best expresses the result or a specific outcome. The principal component analysis method creates an overall measure, which is a refined dashboard type of performance measurement using statistical data compression techniques. See The Use of Principal Component Analysis to Integrate Blasting Into the Mining Process (2009)

Biplot (axes D1 and D2: 90.77 %)

after Varimax rotation

Tonnes/M anhour

KG/M 3

Op Hours

-3

-2

-1

0

1

2

3

4

-4 -3 -2 -1 0 1 2 3 4 5

D1 (51.78 %)

D2

(38.

98 %

)

TONNES PER MANHOUR35+ TPMH

30 TO 35 TPMHLESS THAN 30 TPMH

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ACKNOWLEGEMENTS The authors would like to thank the following for their assistance and help with this project: John Truszkowski, Dave Beckwith, Dave Warpula, Baron Fidler, Dyno Nobel; and Chris Luchkiw, Lafarge. REFERENCES (Email one of the authors from the title page to receive copies of any of the references) Budin, M., Fidler, B., and Lilly, D. (2009). CUSUM for the Control of Vibration and Air Blast (2009), International Society of Explosives Engineers –Annual Conference on Blasting Technique. Bremer, D., Ethier, R., and Lilly, D. (2007), Factors Driving Continuous Blasting Improvement at the Lafarge Ravena Plant. International Society of Explosives Engineers –Annual Conference on Blasting Technique. Cory, J., Hissem, B., and Lilly, David P. (2009), The Use of Principal Component Analysis to Integrate Blasting Into the Mining Process. Oxford Business and Economic Conference. Dann, M., Smith, C., and Lilly, D (2007), Aggregate Size Optimisation Program at the Lafarge Marblehead Plant. 7th Large Open Pit Mining Conference, Perth, Australia. Lilly, David P. (2007), A Statistical Approach to Integrating Blasting Into the Mining Process, Oxford Business and Economic Conference. Lilly, David P. (2008), The Enterprise Solution to Integrating Blasting Into the Mining Process, Oxford Business and Economic Conference. Lilly, D. (1988). Blasting and Crusher Productivity. Pit & Quarry Magazine. Lilly, D. (1989). Blasting Related Crusher Productivity. Pit & Quarry Magazine. Lilly, D. (1992). The Powder Factor: More is not Necessarily Better. Pit & Quarry Magazine. Lilly, D. (1993), Neural Network Simulation: Charting the Future of Blast Process Control. Pit & Quarry Magazine. Mirabelli, L. (2000). Mining Automation Program

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Mirabelli, L., Lilly,D. (2008) A Blaster’s Tool to Measure Fragmentation. International Society of Explosives Engineers –Annual Conference on Blasting Technique. Mirabelli,L.; Hissem, B.; and Veltrop, G. (2009). Missouri Quarry Productivity Improvement – Casework. International Society of Explosives Engineers –Annual Conference on Blasting Technique. Tamir, R. (2007).Tools to help Improve Operational Performance and Control Operating Costs-or Reducing the Quesstimation Factor