OSB Blender Optimization

5/24/2018 OSB Blender Optimization

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DEVELOPMENT AND USE OF A DISCRETE ELEMENT MODEL FOR

SIMULATING THE BULK STRAND FLOW IN A ROTARY DRUM BLENDER

by

Graeme Dick

B.Sc., University of British Columbia, 2006

A THESIS SUBMITTTED IN PARTIAL FULFILMENT OF

THE REQUIREMENTS FOR THE DEGREE OF

MASTER OF SCIENCE

in

The Faculty of Graduate Studies

(Forestry)

UNIVERSITY OF BRITISH COLUMBIA(Vancouver)

August 2008

Graeme Dick, 2008


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ABSTRACT

In 2006 resin accounted for approximately 17% of the direct manufacturing costs for oriented

strand board (OSB). Because of their increased dependency on pMDI-resins, this percentage

is likely greater for oriented strand lumber (OSL) and laminated strand lumber (LSL). Thecost of PF- and pMDI-resins is expected to face upward pressure as the cost of their primary

constituents, natural gas and crude oil, continue to reach new highs. Therefore, there is

strong economic incentive to optimize the use of resin in the production of these three

products. This can be accomplished by addressing two key issues: reducing resin wastage

and optimizing resin distribution on the strands. Both issues will be overcome by focusing

on the blending process, where resin is applied to the strands.

This work focused on development and use of a discrete element model (DEM) for

simulating strand flow in a rotary drum blender using the EDEM software package. EDEM

required the input of three material and three interaction properties. Development of the

model involved creating the simulated environment (i.e. physical dimensions) and assigning

appropriate material and interaction properties given this environment and the assumptions

that were made. This was accomplished in two steps, completing baseline bench-top

experiments and a literature review to determine appropriate parameters and initial value

ranges for these properties, and then fine-tuning these values based on a validation process.

Using the validated model, an exploratory study was conducted to determine the effect of

four blender design and operating parameters (flight height, number of flights, blender

rotational speed, and blender fill level) on bulk strand flow. The results were analyzed with

regards to overall trends and by focusing on two perspectives, end users and blender

manufacturers. It was found that there was a strong relationship between these key

parameters and bulk strand flow. These results suggest that operating parameters of ablender, namely rotational speed and tilt angle, should be linked directly to the blender feed

rate to ensure an optimal blending environment is maintained. In addition, manufacturers of

blenders must take into consideration the range in final operating conditions when designing

and positioning flights.


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TABLE OF CONTENTS

Abstract....................................................................................................................................iiTable of Contents....................................................................................................................iiiList of Tables ........................................................................................................................... vList of Figures........................................................................................................................viiList of Abbreviations ............................................................................................................... x

Acknowledgements.................................................................................................................xi

Chapter 1: Introduction ....................................................................................................... 1

1.1 Rationale........................................................................................................................ 31.2 Objectives and structure of thesis.................................................................................. 7

Chapter 2: Literature Review.............................................................................................. 9

2.1 Rotary drum blending.................................................................................................... 92.2 Discrete element method ............................................................................................. 11

2.2.1 Applications for discrete element methods....................................................... 132.3 Friction ........................................................................................................................ 15

Chapter 3: Laboratory Determined Coefficient of Static Friction ................................ 18

3.1 Introduction ................................................................................................................. 183.2 Materials...................................................................................................................... 193.3 Procedure..................................................................................................................... 203.4 Results ......................................................................................................................... 243.5 Conclusions ................................................................................................................. 27

Chapter 4: Determination of Suitable Material and Interaction Properties for use

as Input Parameters in the RDBM ................................................................ 29

4.1 Introduction ................................................................................................................. 29

4.2 Procedure..................................................................................................................... 314.2.1 Screening design ............................................................................................... 314.2.2 Strand representation in EDEM........................................................................ 344.2.3 Characterization of resination potential ............................................................ 34

4.3 Validation process selection of appropriate input parameters.................................. 394.4 Results ......................................................................................................................... 49

4.4.1 Screening design material properties ............................................................. 494.4.2 Screening design interaction properties ......................................................... 504.4.3 Coefficient of rolling friction............................................................................ 514.4.4 Validation - coefficient of static friction........................................................... 52

4.5 Conclusions ................................................................................................................. 60

Chapter 5: Measuring the Effect of Rotary Drum Blender Design and Operating

Parameters on the Bulk Strand Flow using a Response Surface Design .... 62

5.1 Introduction ................................................................................................................. 625.2 Methodology ............................................................................................................... 625.3 Results and discussion................................................................................................. 66

5.3.1 Overall predictive trends................................................................................... 665.3.1.1 Skewness ............................................................................................. 675.3.1.2 Effect of an atomizer boom on the skewness results........................... 73


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5.3.1.3 Kurtosis ............................................................................................... 775.3.1.4 Effect of an atomizer boom on the kurtosis results ............................. 805.3.1.5 Average time a strand spent in the resination region .......................... 835.3.1.6 Effect of an atomizer boom on the average time a strand spent

in the resination region ........................................................................ 855.3.1.7 Discussion ........................................................................................... 85

5.3.2 Research applications........................................................................................ 865.3.2.1 Wood strand-based product manufacturers......................................... 875.3.2.2 Effect of the atomizer boom for wood strand-based product

manufacturers ...................................................................................... 895.3.2.3 Blender manufacturers ........................................................................ 915.3.2.4 Effect of the atomizer boom for blender manufacturers ..................... 945.3.2.5 Discussion ........................................................................................... 97

5.4 Conclusions ................................................................................................................. 98

Chapter 6: Summary and Future Work......................................................................... 100

6.1 Future Work .............................................................................................................. 101

Literature Cited ................................................................................................................. 103

APPENDIX A: Coefficient of Friction SAS Analysis and Results.................................... 108APPENDIX B: Mechanical Properties of UHMW and HDPE.......................................... 113APPENDIX C: Blender Drawing Provided by Coil Manufacturing.................................. 114APPENDIX D: Write-out Every Time Interval Calculation .............................................. 116APPENDIX E: VBA Macro for Sorting, Filtering and Analysing EDEM Data ............... 121APPENDIX F: Macro for Performing Analysis in Image Pro Plus................................... 130APPENDIX G: ANOVA Results for the Mechanical Properties of Aspen Strands .......... 132APPENDIX H: ANOVA Results for the Interaction Properties of Aspen Strands

and Polyethylene....................................................................................... 134

APPENDIX I: Results from the Student t-test for Shoulder and Toe Angles .................. 136APPENDIX J: Results from the Exploratory Study Without and With an

Atomizer Boom......................................................................................... 137


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LIST OF TABLES

Table 1. OSB costs for benchmark north-central US mills from 2000 to 2006....................... 5Table 2. Pricing data for the constituents of PF- and pMDI-resin from 1999 to 2005............ 6Table 3. Strand combinations used for static friction coefficient tests. ................................. 21Table 4. Summary of test results for the coefficient of static friction between two wood

strands at 22C and 55% relative humidity ............................................................. 25

Table 5. Summary of test results for the coefficient of static friction between a woodstrand and HDPE at 22C and 55% relative humidity............................................. 26

Table 6. Regression parameters for determining the coefficient of static friction betweentwo wood strands at 22C and 55% relative humidity............................................. 27

Table 7. Regression parameters for determining the coefficient of static friction betweena wood strand and HDPE at 22C and 55% relative humidity. ............................... 27

Table 8. Required material and interaction properties for the RDBM. ................................. 29Table 9. Materials used and materials that may come in contact in the RDBM.................... 29Table 10. (Left)Aspen wood strand material properties and (right)interaction properties

simulation design.................................................................................................... 31Table 11. Factor levels for Quaking Aspen material properties............................................. 32

Table 12. Factor levels for interaction properties................................................................... 32Table 13. Fixed factor levels for the blender operation and design. ...................................... 33Table 14. Fixed factor levels for the liner and flights ............................................................ 33Table 15. Blender rotational speed and fill level combinations for laboratory video

recordings. .............................................................................................................. 41Table 16. Simulation settings. ................................................................................................ 33Table 17. Frame export settings used in Adobe Premiere Pro CS3. ...................................... 46Table 18. Factors and response variables for simulations investigating the impact of the

material properties.................................................................................................. 50Table 19. Factors and response variables for simulations investigating the impact of the

interaction properties.............................................................................................. 50

Table 20. Rolling and static friction coefficients for the simulations aimed at determininga suitable coefficient of rolling friction.................................................................. 51

Table 21. Pairs of static friction coefficients used to identify a suitable set of values........... 53Table 22. Student t-test for two sample means assuming unequal variance for the shoulder

and toe angles in Run 4 and the laboratory results................................................. 54Table 23. Student t-test for two sample means assuming unequal variance for the shoulder

and toe angles in Run 5 and the laboratory results................................................. 54Table 24. Student t-test for two sample means assuming unequal variance for the shoulder

and toe angles in Run 6 and the laboratory results................................................. 55Table 25. Summary of runs 4 to 6 and the laboratory taken shoulder and toe angle results.

Italicized values indicate those angles that are significantly different (= 0.05)

from the image results. ........................................................................................... 55Table 26. Summary of shoulder and toe angles obtained at 15.5 to 25.5 RPM with the

coefficients of static friction set at 0.14 and 0.07................................................... 56Table 27. Summary of material and interaction properties for use with EDEM.................... 61Table 28. Response surface design matrix. ............................................................................ 64Table 29. Response surface design factor levels .................................................................... 64Table 30. Factor levels used in the skewness and average time spent in resination region

analyses. ................................................................................................................. 67


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Table 31. List of effects for the skewness showing (left)the significant effects and (right)the significant effects as well as those that were included to maintain the modelhierarchy. ................................................................................................................ 68

Table 32. List of effects for the skewness when an atomizer boom is present, showing (left)the significant effects and (right)the significant effects as well as those that wereincluded to maintain the model hierarchy. ............................................................. 74

Table 33. List of effects for the kurtosis showing (left)the significant effects and (right)the significant effects as well as those that were included to maintain the modelhierarchy. ................................................................................................................ 78

Table 34. List of effects for the kurtosis when an atomizer boom is present, showing (left)the significant effects and (right)the significant effects as well as those thatwere included to maintain the model hierarchy. .................................................... 81

Table B1: Mechanical properties of UHMW ...................................................................... 113Table B2: Mechanical properties of HDPE ......................................................................... 113Table G1: ANOVA results for the impact the material properties have on the skewness

of the resulting histogram. .................................................................................. 132Table G2: ANOVA results for the impact the material properties have on the kurtosis of

the resulting histogram........................................................................................ 132Table G3: ANOVA results for the impact the material properties have on the count of

the resulting histogram........................................................................................ 132Table G4: ANOVA results for the impact the material properties have on the processing

time of the respective simulation. ....................................................................... 133Table H1: ANOVA results for the impact the interaction properties have on the skewness

of the resulting histogram. .................................................................................. 134Table H2: ANOVA results for the impact the interaction properties have on the kurtosis

of the resulting histogram. .................................................................................. 134Table H3: ANOVA results for the impact the interaction properties have on the count

of the resulting histogram. .................................................................................. 134Table H4: ANOVA results for the impact the interaction properties have on the

processing time of the respective simulation...................................................... 135Table I1: Student t-test for two sample means assuming unequal variance for the

shoulder and toe angles in Run 4 and the laboratory results ran at 15.5 RPM. .. 136Table I2: Student t-test for two sample means assuming unequal variance for the

shoulder and toe angles in Run 4 and the laboratory results ran at 25.5 RPM. .. 136Table J1: Results from exploratory study without an atomizer boom................................ 137Table J2: Results from exploratory study with an atomizer boom ..................................... 138


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LIST OF FIGURES

Figure 1. Canadian structural panels production from 1982 to 2005 ...................................... 1Figure 2. Size of the total North American framing lumber market and the potential

market size for engineered wood products............................................................... 4Figure 3. Flow chart showing the basic constituents that are used in the production of PF-

and pMDI-resin. The shaded constituents are included in Table 2 ......................... 6

Figure 4. Turners spinning disc blender showing a vertical spray pattern........................... 10Figure 5. Lignex spinning disc blender showing a diagonal spray pattern............................ 11Figure 6. Flow chart for operations performed in a typical DEM algorithm......................... 12Figure 7. Diagram showing the pair of forces that determines the friction torque................ 16Figure 8. Photograph of sliced aspen veneer strands, (a) primary surface and

(b) secondary surface.............................................................................................. 20Figure 9. Inclined plane jig with the various components indicated ..................................... 21Figure 10. Two sleds used for the inclined plane test, (left)sled equipped with dowels for .... larger contact pressures and (right)sled equipped with adhesive surface for

lower contact pressures. ........................................................................................ 22Figure 11. Photograph of the testing procedure for the coefficient of static friction of wood

strands using the inclined plane technique, showing a parallel parallelorientation. ............................................................................................................ 23

Figure 12. Schematic of inclined plane jig showing the measurement locations forEquation 5 ............................................................................................................. 24

Figure 13. Static coefficient of friction between two wood strands for increasing contactpressures and different strand sample orientations............................................... 25

Figure 14. Static coefficient of friction between a wood strand and HDPE for increasingcontact pressures and different strand and HDPE sample orientations. ............... 26

Figure 15. Schematic showing the representation of sticks using a series of six spheres(left)and a schematic showing the placement of a template over top of thesix spheres to aid in the visual analysis process(right). ....................................... 34

Figure 16. Blender schematic showing the resination region outlined in blue. ..................... 35Figure 17. Simulation and photographed examples of increasing skewness caused by

increasing rotational speeds from 15.5 RPM to 25.5 RPM .................................. 37Figure 18. Sample histogram with a respective skewness, kurtosis, and count of -0.3024,

-0.2794, and 22 453. ............................................................................................. 38Figure 19. Schematic showing the placement of the lights and camera/video camera

relative to the laboratory blender, with the axis indicated in blue. ....................... 40Figure 20. Photograph showing the placement of the lights and camera/video camera

relative to the laboratory blender. ......................................................................... 40Figure 21. Example of (left)a screen shot taken of an animated GIF illustrating the

simulation results and (right)a screen shot taken of the video footage taken

in the laboratory. ................................................................................................... 41Figure 22. Schematic showing the shoulder, , and toe, , angles for two points of

detachment. The 0oand 90

oreference angles are shown in blue. ......................... 42

Figure 23. Illustration showing the identification of the shoulder and toe angle from thelaboratory video footage. ...................................................................................... 44

Figure 24. Illustration showing the identification of the shoulder () and toe () anglefrom the simulation results using the streaming effect. ........................................ 45

Figure 25. Schematic of the x, y, z coordinate system relative to the blender....................... 46


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Figure 26. Screen shot taken in Image Pro Plus v6 showing the placement of the thick,line profile and the cooresponding grayscale values ............................................ 48

Figure 27. Example of simulation results overlaid on top of grayscale results...................... 49Figure 28. Skewness as a function of the coefficient of rolling friction ................................ 52Figure 29. Baseline grayscale results for the laboratory blender running empty................... 57Figure 30. Grayscale results for the blender running at 15.5 RPM and 1/8

thfull. ................. 58

Figure 31. Grayscale results for the blender running at 20.5 RPM and 1/8th

full. ................. 59Figure 32. Grayscale results for the blender running at 25.5 RPM and 1/8thfull. ................. 59Figure 33. Schematic of a blender fitted with an atomizer boom, shaded grey..................... 66Figure 34. Prediction profiles generated in SAS showing the relationship between the

skewness and the (top-left)number of flights, (top-right)flight height,(bottom-left)fill level, and (bottom-right)blender rotational speed..................... 68

Figure 35. Schematic showing the angle of repose, , for a pile of wood strands on ahorizontal surface.................................................................................................. 69

Figure 36. Simulation images showing the charge level per flight and the discharge patternwhen a relatively small number of flights are employed. The simulated blenderhas 4-6 inch flights and is rotating at 23.39 RPM and is 1/8thfull ....................... 70

Figure 37. Simulation images showing the charge level per flight and the discharge patternwhen a relatively large number of flights are employed. The simulated blenderhas 16-6 inch flights and is rotating at 23.39 RPM and is 1/8

thfull ..................... 71

Figure 38. Simulation image showing strands rolling in the corner of the drum, where thereare 8-4 inch flights and the blender is rotating at 18.71 RPM and is 1/4 full....... 71

Figure 39. (Left) Simulation image showing the dispersion of strands across relatively fewflights when the blender is rotating at 18.71 RPM and (right) across many flightswhen the blender is rotating at 28.07 RPM. In both cases the blender has 16-4inch flights and is 1/8

thfull. .................................................................................. 72

Figure 40. Prediction profiles generated in SAS showing the relationship between theskewness and the (top-left)number of flights, (top-right)flight height,(bottom-left)fill level, and (bottom-right)blender rotational speed when an

atomizer boom is included in the simulation ........................................................ 75Figure 41. Simulation images showing the dispersion of strands across the blender diameter

when there is (top-left)no atomizer boom and there are 2 inch flights,(top-right)no atomizer boom and there are 6 inch flights, (bottom-left)anatomizer and there are 2 inch flights, and (bottom-right)an atomizer boomand there are 6 inch flights. In all cases there were 16 flights and the blenderrotated at 23.39 RPM. ........................................................................................... 76

Figure 42. Simulation image showing strands as they become wedged between theatomizer boom and blender wall when operating at elevated fill levels,indicated by the dashed oval. In this case the blender is full and is equippedwith 8, 4 inch flights and is rotating at 28.07 RPM.............................................. 77

Figure 43. Prediction profiles generated in SAS showing the impact of the (left) numberof flights on the relationship between (right) the kurtosis and the flight height... 79

Figure 44. Prediction profiles generated in SAS showing the impact of the (right) flightheight on the relationship between (left) the kurtosis and the number of flights.. 79

Figure 45. Prediction profiles generated in SAS showing the impact of the (left) numberof flights on the relationship between (right) the kurtosis and the flight heightwhen an atomizer boom is present........................................................................ 82


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Figure 46. Prediction profiles generated in SAS showing the impact of the (right) flightheight on the relationship between (left) the kurtosis and the number of flightswhen an atomizer boom is present........................................................................ 82

Figure 47. Prediction profiles generated in SAS showing the relationship between theaverage time spent in the resination region and the (left)flight height and(right)blender rotational speed............................................................................. 83

Figure 48. Simulation images showing (top-left)the clustering of strands at relativelylow speeds with 2-inch flights, (top-right)the dispersion of strands at relativelyhigh speeds with 2-inch flights, (bottom-left)the clustering of strands atrelatively low speeds with 6-inch flights, (bottom-right)the dispersion ofstrands at relatively high speeds with 6-inch flights. .......................................... 84

Figure 49. Contour graphs for the skewness based on the fill level and blender rotationalspeed using 3, 4, 5, and 6-inch flights. The number of flights has been fixedat 14....................................................................................................................... 88

Figure 50. Contour graphs for the skewness based on the fill level and blender rotationalspeed using 3, 4, 5, and 6-inch flights when an atomizer boom is present. Thenumber of flights has been fixed at 14.................................................................. 90

Figure 51. Contour graphs based on number of flights and flight height. The rotationalspeed ranged from 23 to 28 RPM and the fill level was fixed at 25%. ................ 93

Figure 52. Contour graphs for the inclusion of an atomizer boom based on number offlights and flight height. The rotational speed ranged from 23 to 28 RPM andthe fill level was fixed at 25%............................................................................... 95

Figure 53. Simulation images showing the streaming of strands off of the atomizer boomat (a)18.71 RPM, (b)23.39 RPM, and (c)28.07 RPM. The simulated blenderswere each equipped with 8-4 inch flights and filled 1/4 full. The angles that thestrands stream off of the boom are approximately 14, 12, and 7 from verticalrespectively. .......................................................................................................... 96

Figure C1: Schematic of blender layout and atomizer spray patter. ................................... 114Figure D1: Schematic of an example where the write-out time interval is set too large..... 117

Figure D2: Schematic of the extreme case scenario where an object falls from the top ofthe blender, A, through the resination region, B to C, and collides with thebottom of the blender, D.................................................................................... 118

Figure D3: Location of the write-out every time intervals within the resinating region,relative to the top of the blender, when the first interval is located marginallyless than one full twfrom the top of the region. .............................................. 119

Figure D4: Location of the write-out every time intervals within the resinating region,relative to the top of the blender, when the last interval is located marginallyless than one full twfrom the bottom of the region......................................... 119

Figure D5: Location of the write-out every time intervals within the resinating region,relative to the top of the blender, when the first interval is located marginally

below the top of the region................................................................................ 120Figure D6: Location of the write-out every time intervals within the resinating region,

relative to the top of the blender, when the last interval is located marginallyabove the bottom of the region.......................................................................... 120


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LIST OF ABBREVIATIONS

ANOVA Analysis of variance

BBF Billion board feet

BSF Billion square feet 3/8 inch basis

COV Coefficient of variation

DEM Discrete element modeling

EWP Engineered wood products

GIF Graphics interchange format

HDPE High density polyethylene

IPP Image pro plus

LSL Laminated strand lumber

OSB Oriented strand board

OSL Oriented strand lumber

PE Polyethylene

PF Phenol formaldehyde

pMDI Polymeric diphenyl methane diisocyanate

RPM Revolutions per minute

RSM Response surface methodology

RDBM Rotary drum blending modelSF Square feet

UHMW Ultra high molecular weight polyethylene

VBA Visual basic for applications


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ACKNOWLEDGEMENTS

I would like to extend my sincere gratitude to those who have helped ensure that this project

was a success. To my committee members: Drs. Gregory Smith, Paul McFarlane, and Erik

Eberhardt, your guidance throughout this process has certainly been appreciated.

To my colleagues: Jo Chau, Emmanuel Sackey, Solace Sam-Brew, Dr. Kate Semple, and

Chao Zhang, your assistance and support have made this experience enjoyable.

To my family and friends, your ongoing support and devotion have helped me reach this

point. And to my wife, Sara, you have kept me grounded throughout this experience by

simply listening to my challenges and always being there.

A special thank you goes to Weyerhaeuser Canada for their financial support and guidance

throughout this project, and to the Natural Sciences and Engineering Research Council of

Canada for their financial support.


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CHAPTER 1

INTRODUCTION

Beginning in the mid-1970s, structural products composed of reconstituted wood strands

have increasingly become a major component of Canadas forest products industry. This

transition began with the advent of waferboard and quickly progressed to oriented strand

board (OSB), a direct substitute for plywood in the construction market (Figure 1). In 2006,

oriented strand board accounted for approximately 63% of all structural panel production in

Canada (Louisiana-Pacific Corporation 2008; Spelter et al. 2006). Laminated strand lumber

(LSL) and oriented strand lumber (OSL) were subsequently developed to compete with solid

sawn lumber in the same market. In todays North American residential construction market,

these wood strand-based products can be found everywhere from the sheathing on exterior

walls, to the headers used to span garage door openings, and to the specialty studs used

behind kitchen cabinets.

Plywood

OSB

0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

16,000

1982

1983

1984

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

Year

Millionsf-3/8"Basis

Figure 1. Canadian structural panels production (million SF - 3/8" basis) from 1982 to 2005(data from: International Wood Markets Group 2006).


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The North American production capacity for OSB is expected to continue increasing into the

future. In fact, if all of the OSB projects tabled in 2005 were to go ahead as planned,

capacity would balloon from 23.7 million m3(27.5 BSF) in 2005 to 31.9 million m3(37 BSF)

by 2010 (International Wood Markets Group 2006). However, because of recent slowdowns

in the US housing market, largely driven by the sub-prime mortgage crisis, and an

accompanying over-supply of OSB, these previous projections have been stifled. According

to a report by Dixon (2008), North American OSB capacity had already reached 26.2 million

m3by the end of 2007; however, any additional capacity that was planned to come on stream

by 2010 had been postponed indefinitely.

The increased production of OSB is also being experienced beyond North American borders.

While most of the additional capacity that was expected to come on stream in the near future

is located in North America, Europes OSB industry has also been expanding, albeit at a

considerably lower volume (International Wood Markets Group 2006). This is largely

because of the relatively slow adoption of OSB into European building codes (World Forest

Institute 2007). This progress has been further hindered by the effect of North American

OSB producers dumping excess supply in Europe, discouraging the development of new

domestic facilities (Higgs 2008). The total European capacity reached 3.9 million m3by the

end of 2007. By 2009 an additional 1.4 million m3is expected to come on stream.

As mentioned, there are three products that fall beneath the umbrella of wood strand-based

products: OSB, LSL, and most recently OSL. During the manufacture of these products a

mat consisting of a large number of strands is consolidated under heat and pressure to form a

single entity, or billet. In order for the consolidation to be effective, resin must be employed

to hold the final product together. The application of resin onto the strands is perhaps the

least studied and understood aspect of the manufacturing process; however, it has one of the

most significant effects on the strength and durability of the final product and in 2006

accounted for nearly 17% of the direct manufacturing costs (Spelter et al. 2006).

The application of resin onto the strands begins when the strands are fed from dry strand bins

and deposited in the blender. Blenders are between 8 and 11 feet in diameter and extend 20

to 35 feet in length. As the drum rotates at typically between 8 and 22 RPM the strands

tumble along its length and become resinated (Smith 2005).


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The strands are resinated using either a series of spinning disc atomizers mounted along the

drums axis of rotation for liquid resin, or using a conveyor metering system for powdered

resin. Spinning disc atomizers are the predominant system employed in blenders

commissioned since the early-1990s. The objective of blending is to achieve a uniform resin

distribution on both sides of all strands. The ability of the process to effectively resinate the

strands is dependent on the overall blender design, such as the drum diameter, atomizer

locations, and flight design; as well as on the operational environment, such as the rotational

speed, fill level, and tilt angle of the blender (Coil 2007b; Coil 2008; Maloney and Huffaker

1984; Smith 2005; Smith 2006).

1.1 Rationale

The respective market share of structural products composed of reconstituted wood strands is

forecast to continue increasing. This is particularly true for OSB where its share of the

structural panel demand in North America has increased from approximately 35% in 1995 to

63% in 2008. OSB market share is forecast to further increase to approximately 72% by

2012 (Louisiana-Pacific Corporation 2008).

In addition to OSBs market share growth however, there is great potential for LSL and OSL

to increase their share of the framing lumber market. LSL and OSL are both members of thefamily of products referred to as engineered wood products (EWP). Currently, EWP only

capture approximately 30% of the potential 12 BBF North American framing lumber market,

with the sub-sector of wood strand-based products only accounting for 5% (Figure 2)

(Louisiana-Pacific Corporation 2008).


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Figure 2. Size of the total North American framing lumber market and the potential marketsize for engineered wood products (adapted from: Louisiana-Pacific Corporation 2008).

Although the five year period between 2001 and 2006 saw the growth of the wood strand-

based product sector outpace the growth of the overall construction market (by 11%

compared to 2% (Louisiana-Pacific Corporation 2008), continued growth will be largely

dependant on the relative manufacturing costs. As an example, in 2004 the average total

manufacturing cost for structural lumber, OSL, and LSL was similar. Lumber was 188

US$/m3,while OSL and LSL were approximately 180 US$/m3(International Wood Markets

Group 2006; Spelter et al. 2006). These figures assume that OSL and LSL have a similar

cost structure as OSB. In reality the cost of OSL and LSL will be marginally greater than

OSB because of the type of resin employed, product density, and wood utilization.

Subsequently, the costs are likely nearer, or even past those of structural lumber.

In recent years the total manufacturing cost of wood strand-based products has faced

increased pressure. In 2006 the average cost reached 201 US$/m3of OSB (Table 1) (Spelter

et al. 2006). Much of this increase was caused by escalating wood costs, affecting solid

lumber and EWP alike. In addition to wood costs however, wood strand-based products have

experienced escalating resin costs, increasing by 61% between 2000 and 2006 (Table 1)

(Spelter et al. 2006). Increasing resin costs have created a need to optimize and subsequently

reduce the amount of resin employed in the manufacture of these products in order to remain

LVL & I-Joists25%

Applicationssuitable for EWP

substitution

50%

Lumber

70%

OSL & LSL5%

Total North AmericaFraming Lumber

(~24 BBF)

Current & PotentialEWP Market(~12 BBF)

Potential MarketGrowth for EWP


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cost competitive in the structural panel and framing markets. Optimization is only possible

however if there exists a thorough understanding of the process in which resin is applied to

the strands in a rotary drum blender.

Table 1. OSB costs for benchmark north-central US mills from 2000 to 2006 (data from:

Spelter et al. 2006).Cost (US$/m

3)

2000 2001 2002 2003 2004 2005 2006

Direct costs

Wood 56 54 53 60 67 85 82Labor 20 20 20 21 21 22 22Resin 18 19 19 26 27 32 29Wax 6 6 6 7 7 8 7Energy 11 13 12 15 17 19 19Supplies 14 14 14 15 15 15 15

Total direct 125 125 124 144 154 181 175

Fixed costsGeneral 6 6 6 6 6 6 6Depreciation 23 21 20 21 20 20 20

Total fixed 29 27 26 27 26 26 26

Total costs 154 153 150 171 180 207 201

There are only a few studies on the operation of a rotary drum blender in the literature with

these dating from the mid-1980s (Beattie 1984; Coil and Kasper 1984; Lin 1984). More

recently, Smith (2005) examined the modes of tumbling in a full-sized rotary drum blender.

All of these studies were focused on OSB. To date there have not been any published studies

on the blending of OSL and LSL. The blending of these products differs from OSB in

several important aspects. First, OSL and LSL strands exceed 6 inches in length, while OSB

strands rarely exceed 5 inches. Second, LSL operations only employ polymeric diphenyl

methane diisocyanate (pMDI) resin, compared with OSB operations that typically use a

combination of phenol formaldehyde (PF) and pMDI resins.

During blending approximately 3.5% resin based on the oven dry weight of furnish is added.The precise amount depends on the operation, resin type, and product grade (Spelter et al.

2006). As indicated, resin costs are a major materials cost in the production of wood strand-

based products. Because PF- and pMDI-resins are derived from crude-oil and natural gas

(Table 2 and Figure 3), it is very likely that resin costs will remain high or even increase over

the next five years. Significant resin savings may be possible through blending optimization.


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Due to the large volume of resin used in the manufacture of strand technology products, even

small decreases in resin consumption will lead to significant savings.

Table 2. Pricing data for the constituents of PF- and pMDI-resin from 1999 to 2005 (datafrom: Winchester 2005).

MethanolUS$/USG

UreaUS$/ton

PhenolUS$/lb

Crude OilUS$/Barrel

Natural GasUS$/MMBTU

1999 0.26 0.40 100 140 0.25 0.36 17 3.752001 0.35 0.80 140 290 0.30 0.40 24 5.242003 0.75 1.00 160 230 0.40 0.45 26 5.812005 0.90 0.95 270 - 300 0.50 0.70 60+ 9.80

Figure 3. Flow chart showing the basic constituents that are used in the production of PF-

and pMDI-resin. The shaded constituents are included in Table 2 (adapted from: Winchester2005).

The design and operation of rotary drum blenders has remained virtually unchanged since the

mid-1980s when the long-retention time, Mainland Manufacturing blenders emerged as the

blender of choice amongst the wood strand-based product industry. Although Mainland

Manufacturing has subsequently been bought out by Coil Manufacturing, very little has been

changed with regards to the operation and design of the blenders. In general, as the capacity

demands have increased over the years with new, sophisticated operations, the blender

dimensions have been scaled up. In the 1950s and 60s, rotary drum blenders were 4 to 5 feet

in diameter and 20 feet long (Coil 2002; Watkins 1981). More recently, blenders can be up

to 11 feet in diameter and 45 feet long (Coil 2007b; Coil and Kasper 1984; Smith 2005).

While this process of scaling the blenders has been widely accepted throughout the industry,

as previously mentioned resin costs have placed increased pressure on the end users of rotary

Phenol

PF

Formaldeh de

Methanol

Natural Gas

MDI

Phenol

Formaldeh de

Methanol

Crude Oil

Benzene


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drum blenders to optimize the process. The primary challenge hindering progress is the lack

of research into the inner dynamics of the blending process and the inability to quantify and

model the bulk strand flow.

In the past, blender design and operating parameters have been selected based on visual

inspection and past experience. Although these empirical approaches have resulted in a

blender design that fits with the spectrum of end-users needs and a reasonable understanding

of the impact design and operating parameters have on the strand flow, a quantitative,

systematic approach is necessary to further enhance the process.

1.2 Objectives and structure of thesis

This study seeks to understand the blending process by developing and evaluating a

quantitative discrete element model (DEM) of the blending process.

Chapter 2: Literature review on discrete element modeling and friction. A review of static,

kinetic, and rolling friction will facilitate the selection of initial coefficients as

input parameters in the DEM. Additionally, this review will help to understand

how these coefficients should change to achieve better correlation between the

simulated and laboratory results.

Chapter 3: Determination of static friction coefficients between Aspen wood strands andbetween an Aspen wood strand and high density polyethylene. The flights and

the inside liner of the blender are constructed of either high density polyethylene

or ultra high molecular weight polyethylene. For the purpose of this project, and

because the specifications of these two materials do not differ considerably, it

will be assumed that the liner and flights are both constructed of high density

polyethylene. The friction values will be used as a starting point within the

EDEM software package and may be adjusted accordingly during a subsequent

study.

Chapter 4: Calibration of the rotary drum blending model (RDBM) with experiments

conducted in the 6 foot laboratory blender. This process will be completed by

adjusting various material and interaction properties in the model.

Chapter 5: Completion of an exploratory study aimed at determining the impact several

blender design and operating parameters have on the bulk strand flow within a


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simulated 6 foot blender. This study will provide a profound understanding of

the impact changes made to the blending environment will have on the strand

flow through a blender.

Chapter 6: Evaluation of potential future work. As the software and computing technology

advances many of the limitations that were present during this project will

become less significant. The most logical progression will be the scaling of the

simulated blender up to a full size industrial blender. This will enable research

to be completed that focuses on the impact blender tilt angle has on the residence

time of strands.


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CHAPTER 2

LITERATURE REVIEW

2.1 Rotary drum blending

Rotary drum blending has been the method used for coating wood strands with resin and wax

for the manufacture of oriented strand board since this products industrial emergence in the

late-1970s (Moeltner 1980). In fact, this blending technique was also employed for OSBs

predecessor, waferboard, since its emergence in 1955 (Gunn 1972). Although the resin was

applied exclusively in a powdered form until the mid- to late-1970s, the fundamental process

was similar to todays blenders that tend to employ liquid resins.

During the blending process strands enter from one end of the blender and are then lifted by a

series of flights, which extend from the inside of the drums circumference. The strands

eventually fall from the flights and migrate along the length of the blender. This process

repeats until the strands are discharged from the opposite end of the blender. The rate of

migration along the blender length is a function of the blender tilt angle. As reported by

Smith (Smith 2005), the strands move forward the most while they are in freefall. As the

strands move along blender length they are coated with wax and resin.

The manner in which powdered and liquid resin adhere to and coat the strands is

considerably different. While powdered resin adheres to the strands via the wax droplets that

are applied at the onset of the blending process, liquid resin adheres directly to the strands in

droplet form (Coil 2002). The transition towards liquid resin was largely driven by the

relative cost advantage of liquid resin, the health concerns caused by the dust from powdered

resin, and the relatively high wax content required to improve the affinity of the powdered

resin to the wood surface (Chiu and Scott 1981; Maloney and Huffaker 1984).

Early rotary drum blenders were 4 to 5 feet in diameter and 20 to 25 feet long (Coil 2002;

Watkins 1981). By the early 1980s it was widely accepted that blender diameters of 8 feet

and greater were required to ensure adequate resin coverage on the strands and/or wafers and

to meet capacity requirements (Beattie 1981). At around this same time liquid resin


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increased in popularity and a system was developed and adopted across the industry for

metering liquid resin into the blenders and distributing it onto the strands. This system

involved using a series of spinning disc atomizers (Beattie 1984; Coil and Kasper 1984; Lin

1984). After several variations in the positioning of these atomizers within the blenders

(Figures 4 and 5), a design was eventually accepted whereby the atomizers were mounted

along a stationary shaft running down the length of the blender. The atomizers were

positioned to disperse resin in a horizontal plane. Except for the plane of the spray pattern

and the stationary shaft or boom, this design was similar to Turners design (Figure 4).

Figure 4. Turners spinning disc blender showing a vertical spray pattern (adapted from:Beattie 1984).

Resin spray

region

Spinningdisc Rotating

shaft


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Figure 5. Lignex spinning disc blender showing a diagonal spray pattern (adapted from:Beattie 1984).

Modern blenders are provided nearly exclusively by Coil Manufacturing Limited of Surrey,

British Columbia. Their blenders range in size from 8 feet in diameter and 20 feet long up to

11 feet in diameter and 45 feet long; however, the 11 foot diameter blenders are most

common in newer operations requiring relatively high capacity (Coil 2007b; Smith 2005).

These blenders operate with strand volumes ranging from 25% to 50% of the blender volume

and with a tilt angle of approximately 3. Blenders revolve at between 8 and 22 RPM

depending on the blender diameter, number of flights, flight height, and resin type (Coil

2008). As a general rule, liquid resins require higher speeds than powdered resins and as the

diameter increases the speed decreases (Coil 2008).

2.2 Discrete element method

Discrete element methods (DEM), or distinct element methods as they are also known

(Cundall 1989), are a family of numerical techniques suitable for modeling the movementand interaction of rigid or deformable bodies, particles, or arbitrary shapes that have been

subjected to external stresses or forces (Bicanic 2004). As reported by Mustoe and Miyata

(2001), most of these methods are based on cylindrical- or spherical-shaped particles because

of the inherent ease in detecting contacts between particles. In recent years there has been an

increased number of DEMs based on noncircular-shaped bodies, such as polygonal bodies,

for specific applications. The vast majority of the commercially available software packages

Resin sprayregion Spinning

disc

Resin

feed


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however, still rely on cylindrical- or spherical-shaped particles for 2D and 3D modeling

respectively. These particles may be clustered and/or overlapped rigidly or elastically to

form different shaped bodies (Collop et al. 2004; Mustoe 2001).

DEMs are based on Newtons Second Law of Motion (Bertrand et al. 2005; Serway 2000):

itotalii Fam ,= [1]

or, itotali

i Fdt

xdm ,2

2

= [2]

where:miis the mass of particle i,aiis the acceleration of particle i,xiis the position of particle i, and

Fiis the total force acting on particle i.

This equation is used to calculate the total force that acts on a particle due to a collision and

is subsequently integrated to find the respective particles new velocity and distance of travel

(Bertrand et al. 2005). During a simulation, the location of all particles is tracked at a

specified time interval. When a collision between particles is detected Newtons Second

Law of Motion is applied to determine each particles resulting position and velocity. Figure

6 shows the steps of a typical DEM algorithm.

Figure 6. Flow chart for operations performed in a typical DEM algorithm (Schafer et al.2001).

Calculate force incrementcaused by each contact

between particles

Calculate velocity andposition increments caused

by forces

Find which particles havecome into contact

t = t + t


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Although the basis of the DEM approach is relatively straight forward, the computational

requirements quickly become overwhelming when more than a few particles are present.

Because DEMs are routinely used for simulating a large number of particles, the

effectiveness of the method is largely dependent on the ability of the model algorithms to

detect particle contacts quickly and efficiently (Bicanic 2004).

There are several DEMs that have been developed for describing how the particles behave

when they come into contact with each other. A typical DEM has the following features

(Cundall 1989; Bertrand et al. 2005; Mustoe 2001):

1. They allow finite displacements and rotations of discrete bodies, including complete

detachment, and

2. They recognize new contacts automatically as the calculation progresses.

Two of the more commonly applied models include the linear spring-dashpot model and the

Hertz Mindlin model. As Bertrand (2005) described, the principal difference between these

two models is that the linear spring-dashpot model considers any particle contact to lead to

inelastic deformation, while models based on Hertz theory considers this contact to lead to

elastic deformation. There is no consensus on what model is best; however, DEM solutions

(2008) report that the linear spring model is simpler because it requires less computationaloverhead. For EDEM, the selected software package for this research project, the Hertz

Mindlin model is the default model because of its accurate and efficient force calculation

(DEM Solutions 2008). This model was also used for the duration of this project.

Ultimately, the choice of model will depend on the environment being simulated and the

ability to validate the results. For additional information concerning the choice of models,

Bertrand (2005) provides a reasonable explanation of several of the more commonly

employed models. Additionally, Cundall and Strack (1979) and DEM Solutions (2008)

provide information on the model algorithms.

2.2.1Applications for discrete element methods

The DEM was first pioneered by Cundall (1971) for problems involving rock mechanics.

Since the early-1970s this method has branched out and adapted for use in a wide range of

engineering applications. Mining has perhaps benefited most from DEMs where they have


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been shown to be particularly effective at analyzing granular material flow, power draw, and

liner wear in semi-autogenous grinding mills (Cleary 1998; Cleary 2006; Djordjevic et al.

2004; McIvor 1983; Mishra and Rajamani 1992; Powell 1991). In addition to mining, other

industries that have benefited include: pharmaceutical, chemical, agricultural, advanced

materials, and food (Bertrand et al. 2005).

An area that has gained recent attention is the modeling of granular material mixing. This

topic covers a variety of industries, but its significance is seen most prominently in the

pharmaceutical manufacturing arena. As Bertrand (2005) reported, even slight changes to

ingredient properties or process operating conditions can have significant implications on the

quality of a drug and/or resulting health effects. Consequently, pharmaceutical companies

are reluctant to make process changes based on DEM results alone and still rely heavily on

process monitoring to ensure quality (Bertrand et al. 2005).

Despite the widespread use of DEMs in various engineering applications, it has never been

used specifically for modeling the rotary drum blending of wood strand-based particles;

however, its successful use in semi-autogenous grinding (SAG) and other rotating drum type

processes suggests that it is possible (Kaneko et al. 2000; Moakher et al. 2000; Stewart et al.

2001). In this process wood strands are deposited inside a rotating drum at the front end.

The strands are then lifted by a series of flights and cascade and tumble along the drumlength. The dynamics of the process are similar to those encountered in a SAG mill;

however, the process objectives more closely resemble those of pill coating in the

pharmaceutical industry (Thibault 2008).

In a rotary drum blender as the strands migrate along the drum length resin is applied in

either liquid or powdered form. The objective is to maximize the resin deposition on the

strands and the distribution of resin amongst the strands, while minimizing strand breakage.

In a SAG mill the aim is to breakdown and grind the rocks. As mentioned, the objectives of

resination are therefore more closely related to those of pill coating. In pill coating the pills

tumble in a drum while a coating is sprayed onto them. Although the primary objective is to

coat the pills, the pills must also remain intact in order to avoid contamination (Thibault

2008). Because DEMs, and in particular the EDEM software package, have been used for


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modeling both processes it is believed that this is a suitable method for modeling the rotary

drum blending process as well.

For this project a DEM will be used for simulating the trajectory and distribution of strands

as the blender revolves. The primary challenges associated with its use are the relatively

high slenderness ratio and thinness of the wood strands and the large quantity of strands in

the process. Simplifications and assumptions will be required to assemble a model that can

simulate the process with reasonable accuracy and within a reasonable time span.

2.3 Friction

Friction forces are a critical phenomenon when performing discrete element modeling. For

objects that slide relative to each other the key friction properties are static and kinetic

friction (Serway 2000). The only difference between the equations used for determining

these two forms of friction is the relevant coefficient of friction, . This equation is known

as Amontons Law (Equation 3).

iNf FF = [3]

where:iis either static kinetic friction, and

FNis the normal force.

If the shape of an object permits rolling to occur, such as a sphere, then the resistance to

rolling manifests as a torque that opposes the direction of rolling, Tf(Equation 4). Rolling

friction is caused by the deformation of either the rolling sphere/cylinder or the plane (Figure

7).

RNf FT = [4]

where:FNis the normal force, andRis the rolling coefficient.


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Figure 7. Diagram showing the pair of forces that determines the friction torque, whereFNisthe reaction force acting on the object by the plane andFgis the normal component of theobjects weight. The coefficient of rolling friction is the arm of the pair of forces (Domenechet al. 1987).

According to Amontons Law, knowledge of the coefficient of friction is vital when

examining the interaction between objects. Because this coefficient depends on the

interaction between surfaces of different objects, it is most accurately considered to be a

system property rather than a material property. This is particularly relevant for this research

as true coefficients of frictions were not known. Instead coefficients were chosen based on

the resemblance of the model system to the actual observed systems.

For modeling the rotary drum blending process for wood strands there are broadly three

systems of objects that must be considered: strands and flights, strands and drum liner, and

the interaction between strands themselves. In light of the limited published information

pertaining to the static coefficient of friction values for the aforementioned systems, a series

of tests aimed at determining the respective values for the particular materials used in the

laboratory was conducted. Because it was anticipated that during the modeling stage the

drum liner and flights would be grouped as one material type, the strand and drum liner

interaction was dropped and instead replaced with the strand and flight interaction properties.

Classic theoretical research related to static and kinetic friction has shown that frictional

coefficients are independent of surface area and contact pressure. However, more recently

Bejo et al (2000) found that these generalizations are not necessarily true if at least one of

the [objects] in the system is wood or a wood-based composite. As a result, the initial study

Lower rolling friction Higher rolling friction

Fg F

F FN


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of this project focused predominately on determining the relationship between the range of

contact pressures that may be encountered during blending and the friction coefficient.


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CHAPTER 3

LABORATORY DETERMINED COEFFICIENT OF STATIC FRICTION

3.1 Introduction

Friction coefficients are perhaps the most important material interaction property to consider

when developing a rotary drum blending model based on the discrete element method

because of the significant impact they have on particle dynamics (DEM Solutions 2008). As

described in section 2.3, assigning the respective friction coefficients is complicated by the

fact that the friction coefficients within systems involving at least one wood substrate are

dependent on the contact pressure. The selected software package for this research, EDEM,

assumes constant coefficients regardless of the contact pressure, as is widely accepted for

most systems of materials. In addition, most of the published friction coefficient values for

systems involving wood are based on clear wood blocks, rather than strands. Wood strands

tend to be less smooth and relatively flexible, generally resulting in higher coefficients. As a

result, a series of experiments were conducted in the laboratory to determine the impact of

contact pressure and wood grain orientation on the coefficient of friction for wood on wood

and wood on polyethylene (PE) systems of materials. Collectively, these experiments will

investigate all of the material interactions that will occur during the simulations: strand strand, strand flight, and strand blender liner.

It was hypothesized that the coefficient of friction would increase with decreasing contact

pressure and that the coefficient of friction would increase from parallel parallel to parallel

perpendicular and to perpendicular perpendicular grain orientation. These results will

be used as a starting point for the initial development of the RDBM. Subsequently, blending

experiments and simulations will be compared to adjust these values until there is close

correspondence in the strand dynamics.

Objectives:

1. To determine the relationship between contact pressure and coefficient of friction for

wood wood and wood PE systems of materials,


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2. To determine the relationship between grain orientation and coefficient of friction for

wood wood and wood PE systems of materials, and

3. To determine the ratio between the coefficients of friction for wood wood and wood

PE systems of materials.

3.2 Materials

A total of 40 sliced veneer, aspen wood strands were randomly selected from a 10.1 kg bag

of strands. The strands had been previously cut to approximately 12 inches long by 1

inches wide and 0.030 inches thick. Aspen strands were used in this case because it

represents the predominant species used in the manufacturing of wood strand-based products

in Canada (Industry Canada 2007). There were two requirements for the selected strands.

First, the strands had to have an area that was at least 9 inches by 1 inches void of any

splits, and second the strands could not exhibit excessive warp. Either of these flaws could

impact the experiment.

The selected strands were divided into two sets of twenty, one to be used as the primary

surface and one to be used as the secondary surface. The primary surface strands were

trimmed to 9 inches by 1 inches using a guillotine paper cutter, removing any splits or

defects. The edges of the strands were then lightly sanded using 220 grit sandpaper to

remove any burrs that might otherwise affect the test results. The secondary surface strandswere prepared in a similar manner; however, they were trimmed to 8 inches by 1 inch so that

they would easily lay flat atop the primary strands without their edges contacting. If the

edges were to come in contact the concern was that any remaining burrs may mechanically

interlock, resulting in a confounded reading of the static friction coefficient. These relatively

large strands were used for this experiment because it provided adequate room for weights to

be added to the surfaces, as described in Section 3.3. Ultimately the surface area does not

impact the friction coefficient so this was assumed to be a reasonable simplification for the

test procedure (Serway 2000).

The strands in each set were labeled 1 through 20 with the appropriate suffix added: a for

the primary surface strands and b for the secondary surface strands (Figure 8). Each set of

strands was then lightly clamped with a protective wood block on either face. Clamping

helped to prevent the strands from warping before testing.


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Figure 8. Photograph of sliced aspen veneer strands, (a) primary surface and (b) secondarysurface.

In addition to the wood samples, two HDPE specimens were also prepared. The samples

were removed from an extra 5 inches T-flight for the 6 foot by 3-foot Coil laboratory

blender. The flight was trimmed into two specimens measuring 9 inches by 4inches and

8 inches by 1 inches. The edges of the samples were also sanded using 220 grit

sandpaper to remove any burrs and then washed in warm water and left to air dry.

3.3 Procedure

The experimental procedure is based on the inclined plane technique (American Standardsfor Testing and Materials 2002a; American Standards for Testing and Materials 2002b; Bejo

et al. 2000). This method was selected because of its relative simplicity and the shape of the

test specimens. A photograph of the testing apparatus is shown in Figure 9.

a b

1-inch


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Figure 9. Inclined plane jig with the various components indicated ((1)scissor lift, (2)measuring guides, (3)platform with stop block, (4) raisedprimary surface platform, (5)bullseye level, (6)base, and (7)leveling glides).

The coefficient of static friction for the system involving two wood strands was determined

using five combinations of primary and secondary surface strands. These combinations were

generated using a random number generator in Microsoft Excel (Table 3).

Table 3. Strand combinations used for static friction coefficient tests.

Pair Primary surfacestrand

Secondary surfacestrand

1 9 182 5 173 14 184 19 95 8 11

Because of the orthotropic nature of wood, each combination was tested for all three

combinations of grain orientation. In addition, seven contact pressures were used to study

the impact contact pressure has on the coefficient of static friction. The included orientations

and target contact pressures are listed below:

1. Strand orientations (secondary on primary): parallel - parallel, perpendicular

perpendicular, and perpendicular - parallel.

2. Target contact pressures: 23 Pa, 47 Pa, 94 Pa, 188 Pa, 375 Pa, 750 Pa, and 1500 Pa.

2

1

3

6

7

7

4

2

5


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For each orientation the primary surface strand was attached to the raised primary platform

using double-sided tape. The platform was then placed on the inclined plane in contact with

the stop block.

The secondary surface strand was attached to one of two sled designs (Figure 10) also using

double-sided tape. For the first two strand orientations the sled equipped with dowel

extensions was used for contact pressures greater than, and including, 188 Pa. Weights were

hung from the extensions to adjust the contact pressure (Figure 11). For contact pressures

less than 188 Pa, the sled equipped with an adhesive surface was used. This was necessary

as the first sled produced a contact pressure that was greater than 94 Pa without the addition

of any weights. Small weights were attached to the adhesive surface of the second sled to

adjust the contact pressure. For the third strand orientation the second sled was used

exclusively. Because the contact surface area was significantly less for the third orientation,

the weights had to be reduced accordingly to achieve the appropriate contact pressure. For

each pair of strands the same surfaces were in contact for the three orientations tested.

Figure 10. Two sleds used for the inclined plane test, (left)sled equipped with dowels forlarger contact pressures and (right)sled equipped with adhesive surface for lower contactpressures.

1-inch


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Figure 11. Photograph of the testing procedure for the coefficient of static friction of woodstrands using the inclined plane technique, showing a parallel parallel orientation.

For the coefficient of static friction tests involving wood strands and HDPE the same test

format was followed, including three orientations and seven contact pressures. Instead of

using a variety of HDPE samples however, only two were used. The larger HDPE sample

was used for the first two orientations and the smaller sample was used for the third

orientation. In all three cases the HDPE was the primary surface and the wood strand was

the secondary surface. The HDPE sample was placed directly on the inclining plane in

contact with the stop block for the first two orientations. For the third orientation the HDPE

was attached to the raised platform, which was then placed on the inclined plane. The raised

platform added stability to the HDPE sample, preventing it from shifting during the tests.

The same secondary surface strands from the first set of tests were used for this second set of

tests. The secondary surface was prepared identically as before using the two sleds.

After the strands were mounted to the respective surfaces the testing apparatus was leveled

using the adjustable glides. The appropriate weight was then added to the sled. The angle of

inclination was slowly increased until the secondary surface began to slip along the primary

surface. The heights from the base of the apparatus to two predetermined points along the

inclined plane as well as the distance between those two points along the inclined plane, the


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hypotenuse, were recorded when the sled began to slip. These measurement points remained

constant across all of the tests. The coefficient of static friction was then determined

according to Equation 5. This procedure was repeated for each contact pressure, strand

orientation, and system of materials. The complete set of results were then analyzed using

SAS version 9.1 to develop a model that predicted the coefficient of static friction based on

the material combination, orientation, and contact pressure.

=

l

hh 12arcsintan [5]

Figure 12. Schematic of inclined plane jig showing measurement locations for Equation 5.

3.4 Results

The test results aimed at determining the coefficients of static friction for strand to strand and

strand to flight interactions clearly showed a decreasing coefficient of static friction value for

increasing contact pressures (Table 4, Figure 13, Table 5, and Figure 14). This is particularly

apparent when increasing from 23 Pa to 94 Pa. The coefficient of static friction appears to be

constant for pressures greater than and including 94 Pa. These results are consistent with

hypotenuse, l

height 2, h2 height 1, h1


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those obtained by Bejo et al. (2000). Bejo found that as the contact pressure decreased below

approximately 5 kPa the coefficient of static friction increased substantially. Above 5 kPa the

static friction coefficient began to stabilize. It was also found that the grain orientation had a

considerable impact

Table 4. Summary of test results for the coefficient of static friction between two woodstrands at 22C and 55% relative humidity

1.

Approximate contact pressure (Pa)Primarystrand

orientation

Secondarystrand

orientation23 47 94 188 375 750 1500

Parallel Parallel Mean 0.63 0.55 0.53 0.49 0.41 0.44 0.41COV % 24 21 19 17 26 20 25

Perpendicular Perpendicular Mean 0.88 0.81 0.77 0.66 0.64 0.63 0.70COV % 13 12 19 12 12 18 6

Parallel Perpendicular Mean 0.89 0.60 0.50 0.51 0.41 0.36 0.35COV % 28 11 20 15 10 12 11

1Average ambient strand moisture content was 9.2%.

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 100 200 300 400 500 600 700 800 900 1000 1100 1200 1300 1400 1500 1600

Contact Pressure (Pa)

StaticC

OF

PERP/PERP

PAR/PERP

PAR/PAR

Figure 13. Static coefficient of friction between two wood strands for increasing contactpressures and different strand sample orientations.

Perpendicular - Perpendicular

Parallel - Parallel

Parallel - Perpendicular


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Table 5. Summary of test results for the coefficient of static friction between a wood strandand HDPE at 22C and 55% relative humidity

2.

Approximate contact pressure (Pa)Primarystrand

orientation

Secondarystrand

orientation23 47 94 188 375 750 1500

Parallel Parallel Mean 0.31 0.30 0.26 0.22 0.24 0.25 0.24

COV % 16 23 12 12 8 6 16Perpendicular Perpendicular Mean 0.38 0.31 0.29 0.29 0.29 0.28 0.28COV % 18 10 9 6 1 8 5

Parallel Perpendicular Mean 0.39 0.40 0.32 0.29 0.26 0.26 0.27COV % 20 20 12 20 27 12 17

2Average ambient strand moisture content was 9.2%.

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

0.45

0 100 200 300 400 500 600 700 800 900 1000 1100 1200 1300 1400 1500 1600

Contact Pressure (Pa)

StaticCOF

PERP/PER

PAR/PERP

PAR/PAR

Figure 14. Static coefficient of friction between a wood strand and HDPE for increasingcontact pressures and different strand and HDPE sample orientations.

The test results were analyzed using multiple regression analysis in SAS version 9.1. A

logarithm transformation was necessary to normalize the data so that a model could be fit

that accurately predicted the static friction coefficient (Equation 6), while meeting the

assumptions of multiple linear regression analysis at a = 0.05 (Kleinbaum 1988).

Perpendicular - Perpendicular

Parallel - Parallel

Parallel - Perpendicular


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)()( 2101010 xbxLogbbs++=

[6]

where:sis the predicted static friction coefficient,xis the contact pressure in pascals, andbiis the predicted parameters for i= 0 to 2.

The appropriate coefficients for the respective systems of materials and orientation are shown

in Tables 6 and 7. In general, these results show an approximate 2:1 ratio between the static

coefficient of friction for wood to wood and wood to HDPE. The complete SAS analysis is

included in Appendix A. It is important to note that this relationship has only been verified

for the range of data presented above and at the ambient temperature and relative humidity

encountered during the test, i.e. approximately 21oC and 50% respectively.

Table 6. Regression parameters for determining the coefficient of static friction between twowood strands at 22C and 55% relative humidity.

Primary strandorientation

Secondary strandorientation

b0 b1 b2

Parallel Parallel 4.242 x 10-2 -1.748 x 10-1 8.835 x 10-5

Perpendicular Perpendicular 1.112 x 10-1 -1.290 x 10-1 8.835 x 10-5Parallel Perpendicular 2.454 x 10-1 -2.634 x 10-1 8.835 x 10-5

Table 7. Regression parameters for determining the coefficient of static friction between awood strand and HDPE at 22C and 55% relative humidity.

HDPE orientation Strand orientation b0 b1 b2

Parallel Parallel -3.530 x 10-1 -1.222 x 10-1 8.835 x 10-5

Perpendicular Perpendicular -2.842 x 10-1 -1.216 x 10-1 8.835 x 10-5Parallel Perpendicular -1.500 x 10-1 -1.179 x 10-1 8.835 x 10-5

3.5 Conclusions

The coefficient of static friction tests confirmed previous work by Bejo et al. (2000) where it

was found that, contrary to classical theoretical research pertaining to friction, the coefficient

of static friction is in fact dependent on the contact pressure between surfaces when at least

one surface is wood. In general, the contact pressure and coefficient of friction were

inversely related, as the contact pressure decreased the friction coefficient increased and

vice-versa. It was also found that the coefficient of friction was highest when the wood

grains were aligned perpendicular - perpendicular and least when they were aligned parallel -

parallel to the sloped plane. Finally, the relationship between the coefficient of friction

between two aspen strands versus that between an aspen strand and HDPE was found to be

approximately 2:1.


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These findings suggest that knowledge of the contact pressures encountered during the

blending operation is necessary to fully describe the slipping of strands in the RDBM.

Because the currently available discrete element modeling software packages are unable to

accommodate for a variable coefficient of static friction, a value must be selected that results

in the most accurate resemblance between the RDBM simulations and the actual observed

dynamics. As an initial starting point for the model validation process, the average of the

two extreme strand orientations will be used together with the average of the higher, more

rapidly changing friction coefficients (contact pressure 188 pa) and the lower, more stable

friction coefficients (contact pressure >188 pa).


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CHAPTER 4

DETERMINATION OF SUITABLE MATERIAL AND INTERACTION

PROPERTIES FOR USE AS INPUT PARAMETERS IN THE RDBM

4.1 Introduction

The RDBM required the input of three mechanical properties for each material used in a

simulation and three interaction properties for each pair of materials that may come in

contact during a simulation (Tables 8 and 9). While all of these properties must be included

for a simulation to be initiated, not all of them have a significant effect on the bulk strand

dynamics. Instead, some of these properties are only significant outside of the tested ranges

and/or are used for measuring incidents of little consequence to this study, such ascompressive forces acting on the particles. Because this research is focused predominantly

on measuring the bulk strand flow within a rotary drum blender, it is only necessary to select

representative values for those factors that are vital to the accurate representation of said

strand flow.

Table 8. Required material and interaction properties for the RDBM.

Material properties Interaction properties

Modulus of rigidity (G) Coefficient of restitutionPoissons ratio () Coefficient of rolling frictionDensity () Coefficient of static friction

Table 9. Materials used and materials that may come in contact in the RDBM.

Materials Interactions

Aspen wood strands Strand - StrandPolyethylene

1 Strand - Polyethylene

1Polyethylene (PE) has been used here to describe both the high density polyethyleneflights (HDPE) and the ultra high molecular weight polyethylene drum liner(UHMW). This will be discussed further in section 4.2.1.

Focusing on the behavior of the overall system introduces several inherent challenges. First,

published values for these properties are typically based on measurements taken of individual

pieces of clear wood samples. These values may or may not be accurate for characterizing

the behavior of large collections of strands. They do however provide a useful foundation for

beginning such research. Second, these properties must also incorporate other events that are

occurring in an actual rotary drum blender but are not able to be incorporated into the model


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because of constraints, such as the representation of strands using a series of spheres or the

lack of any air flow dynamics in the model.

In addition to potentially affecting the strand dynamics, preliminary simulations and

literature (DEM Solutions 2008) have also shown that the material properties have a

significant impact on the processing time for a simulation. The processing time increases

with increasing shear modulus of rigidity and decreases with increasing density and

Poissons ratio. If it can be found that any of the above listed material properties do not

significantly impact the bulk material dynamics then values may be chosen that minimize the

time required for processing a simulation. The relationship between the density, ; modulus

of rigidity, G; Poissons ratio, ; particle radius,R; and processing time as represented by the

idealized time step, TRis (DEM Solutions 2008):

+=

G

RTR

8766.01631.0. [7]

In this case where strands are being modeled, the particle radius refers to the individual

particles, or spheres, that are joined to form a strand. As a result, the particle radius is inch

as will be discussed further in Section 4.2.2.

This study was therefore divided into two distinct phases. The first phase consisted of two 2-

level, full factorial experimental designs aimed at determining which material properties and

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