SummerSchool Leblon
Transcript of SummerSchool Leblon
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Use of Sensors for theTraceability in the Supply Chain
Brigitte Leblon, Ph.D.Faculty of Forestry and Environmental Management, U
of New Brunswick, Fredericton (NB)[email protected]
With inputs from:
F. Fournier (FORINTEK),
Q. Wei (UNB),
J. Nader (FERIC),K. Tounis and P. Cooper (U of T),
K. Groves (FORINTEK)
M. Defo (FORINTEK)
F. Ding (CRIQ)
T. Trung (PAPRICAN)
Z. Pirouz (FORINTEK)
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Why Using Sensors?
Without sensors, the supplychain is like a blind person
Sensors = of the chain
Sensors = data for the supplychain optimization model
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http://en.wikipedia.org/wiki/Image:EM_Spectrum_Properties_edit.svg
Electromagnetic spectrum
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Relation with wood structures
(adapted from Bucur 2003)
Scale Unit Wood Structure
Submicroscopic Cellulosic crystal
Microscopic nm Fibril
m Cell Wall
mm CellsMacroscopic cm Annual ring
Branches, Leaves, Trunk
Mesoscopic m Tree
Megascopic m-
km
Stand
Gigascopic km Forests
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Relation with wood structures
Spectral domain Wavelength (m)
X-ray angstrm ()
Visible light 0.4-0.7 m
Optical Infrared 0.7-2.5 m
Thermal Infrared 3 100 m
Microwave cm
Ultrasound cm-km
Radio Frequency (NMRI) km
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Types
Need contactEx: Acoustic or pulsated current
No contact, but only on small wood samples
EX: NIR, some X-ray sensors
No contact on full log or board
Point measurements Ex: laser systems
Images: Ex: cameras, other imaging systems
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Forest
(standing tree / outdoor)
Note: only ground-based systems
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Terrestrial Laser Scanning System(LiDAR)
Ex: Riegl (A) 3D Terrestrial Laser Scanning (V) Z-Series based on NIR band(900 nm) + TerraScan software for classifying scan data
Sca
Sitka sprucePlan View
Perspective View
MeasuredDBH
Scanned DBH(Watt and Donoghue 2005)
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Stiffness: Acoustic (Sonic) sensors
Fibr e-Gen (NZ)Hitman PH330
(prototype)
In f luence of the moisture con tent R&D need
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Forest / Merchandizing Yard
(log wood / outdoor)
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Merchandizing yard concept
Block 1Block 1
Mill 1Mill 1
Mill 2Mill 2
Mill 3Mill 3
Block 2Block 2
Block 3Block 3
Bucking and segregation yardBucking and segregation yard
(courtesy of F. Fournier, FP Innovations)
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Laser beam IR optical scanners
Digital optical cameras
Photocells
Prototype:
Comact wi th FP
Innovat ions (CDN):
Mobile Scanner
Rema (S):Log 3D, Log Bark
Mikropuu Oy (Fin)OPMES 604/614
Micr otec (I):Dishape
Metso (Fin):VisiQ
Dimensions / External FeaturesLength, diameter and sweep / External log features or defects
Micr otec (I):ScreenLog
(3D image)
Mic ro tec (I):
iRED, iRAS
Mikrop uu Oy (Fin):
OPMES 211/212
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Stiffness
Acoustic (Sonic) bands
Stiffness = f (frequency of thehammer blow, wood density)
Portable instrument: Fibre-Gen (NZ) Hitman HM200 ,
Fakopp (H) Microsecond timer
In-line instrument:Fibre-Gen (NZ) Hitman L640 Log grader
Influence of the moisture content
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Rot/Moisture Content
Fakopp
Acoustic wave
Rot, holes in standing timber
Limitations:
Sensitive to water along thewhole log
Liquid water log temp > 0C
No simple relationships for somehardwoods when MC > 30%
Variable reg line slope betweenspecies and within the samespecies
Contact measurements
Shigometer
Resistance to a pulsated currentthat decreases with cation cc
Rot in standing timber
Limitations:
Sensitive to water close to thesensor
Liquid water log temp > 0Cand fiber saturation threshold(20-30%)
Not reliable if log surface isdrying
Contact measurements
(from Nader 2007)
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Internal Features X-ray equipments
High energy radiationmaterial penetration internal features
x-ray
Source
X-ray detector
Log
x-ray path
through log
>
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x-ray
Source
x-ray Detector
Density projection in 2D
Good for: determining knottiness locating knot clusters detecting metal (WWII) measuring bark thickness Some species identification
(2) (3)(1)
Not good for pinpointing or separating defects:
Cannot determine the feature (e.g. knot) location along x-ray path (case 1)
Effects of a knot (higher density) and a check (density gap) may cancel out (case 2)
Multiple dense features (knots) may appear as one knot (case 3)
(courtesy of Z.Pirouz, FP Innovations)
One X-ray View Scanner
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Multiple X-ray Views Each view adds more information to the scan
Helps to localize & distinguish defects About 250K ($380K) per additional view
Currently 1-4 views are available (4 European vendors)
Most European mills use 1-2 views
Ex: Rema (S) Log X-Ray, Mikropuu (Fin) OPMES AX1
(Courtesy of Z. Pirouz, FP Innovations)
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Computerized Tomography CT X-Ray images
CT images (or multi X-ray views) for:
Detecting check, resin pocket, etc. Measuring defect size & location Breakdown optimization
Need of good image quality Need of appropriate image processing
algorithms
Bin tec (Fin)Wood X
Microtec(I) Tomolog
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Classified sugar maple CT images
using the MLC classifier
a) raw CT image b) MLC classified image c) MLC classified image
filtered using a
5 5-pixel median filter
(Wei, Leblon et al 2008)
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Black Spruce
a) raw CT image b) MLC classified image
c) MLC classified image
filtered using a
5 5-pixel median filter
(Wei, Leblon et al 2008)
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BP ANN Classifiers topology selection
The number of layers and nodes in each layer define the BP ANN
classifiers topology.
One input layer (9 input nodes), one hidden layer, one output layer (4output nodes).
The hidden node number is empirically selected as function of the
classification accuracy , mean square error (MSE), the number of training iterationsand training time.
(Wei, Leblon et al 2008)
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Classification by the BP ANN classifier
Overall accuracy:
98.5% (training log), 82.0%(validation log) for sugar maple;
97.6% (training log), 67.6%(validation log) for black spruce.
(Wei, Leblon et al 2008)
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3D Reconstruction of Internal Log
Characteristics: sugar maple
(Wei, Leblon et al 2008)
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3D Reconstruction of Internal Log
Characteristics: black spruce
(Wei, Leblon et al 2008)
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Austria: Bentec Wood X
X-ray based log sorting
See details for the UPM Sawmill atSteyrermhl, Austria at the following URL
http://www.suomenlinkki.fi/english/uutinen1.html
http://www.suomenlinkki.fi/english/uutinen1.htmlhttp://www.suomenlinkki.fi/english/uutinen1.html -
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Sweden: Microtec Tomolog Swedish installation of TINA in1980s
One claim of 10-30% productivity increase Sorting based on internal defects (mostly knots) Some mills have more than 80 sorting bins Scaling (under bark diameter) Metal detection (debris from WWII) Customized and calibrated for each mill and their
species / defects
(courtesy of Z. Pirouz, FP Innovations)
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Other imaging systems (experimental)
b) CT imaging
d) Microwave imaging
a) Neutron imaging c) Thermal imaging
e) Ultrasound imaging f) MR or NMR imaging
l
Gamma Ray X-Ray Infra-red
Microwave RadioRadio
(Wei, Leblon et al 2008)
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Long wave penetration Microwave = f(roughness,
dielectric constant e)
Microwave Imaging (Experimental)
Material eWater (20oC) 0.36
Dry material 0.94
Wet material 0.59
Influence of the moisture content
Knot
(N l ) M ti R
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Internal wood structure
Rot detection
Absorption of preservatives
Free water and Bound water (hydrogen)
(Nuclear) Magnetic ResonanceImaging (NMRI) (experimental)
8.9% 8.0% 7.0% 4.0%CW=compression wood
CX= conducting xylem (water)
CZ= cambial zone (water)
Cor= Cortex
LW = latewood
P = pith
Pinus densiflora
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In the Plant
Sawn Wood / Controlled Environment
Sawing, Trimming, Edging Optimization
Grading System
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Video of the Comact Stain detection system
http://www.comact.com/fr-ca/upload/video/52010301042008.wmvhttp://www.comact.com/fr-ca/upload/video/52010301042008.wmv -
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X-ray scanner
Dimension
Defects (knots,rot/decay, stain, wane,
holes, Cracks / Splits,pith, resin)
Density
Fibre cross-sectional
dimensions
Microfibril angle
Coarseness
Examples:
Microtec (I): DenSCAN
Luxscan (L):X-Scan
SilviScan (CSIRO, AUS)
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SilviScan
On wood sample
(Defo, 2008)
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SilviScan: Cell Scanner
(Defo, 2008)
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SilviScan: X-ray Densitometer
(Defo, 2008)
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SilviScan: X-ray Densitometer +Cell Scanner
(Defo, 2008)
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X-Ray Diffractometry
(Defo, 2008)
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X-Ray Diffractometry
Typical diffraction pattern &
main cellulose-I planes
Relationship to MFA
Principle
Stiffness (MOE)
(Defo, 2008)
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SilviScan measurements
Direct Measurements Derived Measurements
(Defo, 2008)
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X-ray + Laser
Dimension, defects (knots, rot/decay, stain,wane, holes, Cracks / Splits, pith, resin)
Ex: Microtec(I) Goldeneye
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Stiffness (MOE)
Acoustic (Sonic) bands
Stiffness = f (frequency of thesound, wood density)
Portable Instrument Hammer blow: Brookhuis Micro-
Electronics BV (NL) Timber Grader MTG
In-line instrument: Vibration: Microtec (I) ViSCAN
Influence of the moisturecontent
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Moisture Content
Microtec (I): M3SCAN
Correction for density
Wavelength??
Brookhuis Micro-Electronics BV
(NL) FMI-L or FMI-X
Correction for density (mass/vol)
Dielectric constant
Dscher & Dscher (D):
Timberscan or Venscan
Microwave
Density independent
SCS Forest Products (USA) (NMI from BC):
MC Pro 1500 Transverse Moisture Density Sorter
(MC PRO TRAC bar code integration system)Measures MC and density (Microtec technology)
Sorts the lumber based on MC and density
Capacitance measurements
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Kiln Drying
SCS Forest Products (USA): MC Pro 2000Kiln Moisture Measurement System
Capacitance measurements
Moisture content of zones
Average moisture content
Schedule data, drying time, date stamp
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NIR spectroscopy (experimental)
Surficialmeasurement
Related to chemical
composition (water,lignin, cellulose)
Density influence
removed Lignin
Cellulose
Water, Cellulose and/or Lignin
Specific absorption bands
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Species Discrimination
-2
-1.5
-1
-0.5
0
0.5
1
1.5
2
-2 -1.8 -1.6 -1.4 -1.2 -1 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2
Spruce
Jack pine
Fir
WSpruce
Lodgepole
Hemlock
(Cooper, Leblon et al. 2008)
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Moisture content
-20
0
20
40
60
-10 0 10 20 30 40 50
pls_CS_EW, ( Y- var, PC): (MC,4) (MC,4)
Slope Offset RMSE R- Square
0.980027 0.285656 2.091686 0.980027
0.955600 0.629399 3.031812 0.960276
Measured Y
Pred i c t ed Y
(Cooper, Leblon et al. 2008)
Southern Pine
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Effect of Surface Orientation
-0.0015
-0.0010
-0.0005
0
0.0005
0.0010
0.0015
-0.003 -0.002 -0.001 0 0.001 0.002 0.003 0.004
RESULT22, X-expl: 79%,3%
1
25
6
78
2
34
5
7
9 1
10
11
12
1315
1618
19
2
20
2223
24
25
26
28
29
3
4
5
6
7
8
9
1
1011
12
1314
15
1617 1819
2
20
2223
24
25
2627
2829
3
31
4
5
6
780
1
2
34
5
6
7
9
0
1
2
3
5
6
7
9
1
P C1
P C 2 S c o re s
Lodgepole Pine
tangential
radial
radial-tangentialcross-section
(Cooper, Leblon et al. 2008)
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Wood Chemistry
(So et al. 2004)
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Wood QualityDensity StiffnessMicrofibril Angle
(Schimleck et al. 2003)
R&D needs:
Sample measurements whole lumber
Single spectra imaging systems
Lodgepole pine
Correctly accepted cants 18%
Incorrectly accepted cants 19%
Correctly rejected cants 53%
Incorrectly rejected cants 10%
Sawmill Trial (Grading based on stiffness)
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Pulp and Paper / BioEnergy
Chips, wood pellets, wood briquettes
Volume
Moisture content
Chemical composition
Color
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Laser Beam
Chip volume:
Metso (Fin) VisIQ
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Color camera + NIR
CRIQ Patents
Chip Management System (CMS):
Digital camera chip brightnesschip freshness
NIR chip moisture content
Bark and Plastic Detection
Chip Weighting System (CWS):
Wet/dry weight
Volume sensor
Mass and volume flow rates
Bulk and basic Density
Chip Sizing System (CSS):
3D Measures (width, length,thickness and area)
Chip size distribution and index
(adapted from F. Ding)
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Long wave penetration Microwave = f(e)
Microwave
Dscher & Dscher (D):
MoistureScan
Microwave
Density independent
Moisture Content
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NIR spectroscopy
Solid organic product chemical analysis
Moisture content
Paper sheet quality monitoring
Ex: FOSS NIRSystems (USA)
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Fourier-transform(FT)-IR
Chemical composition of liquors
Ex: Effective alkali (EA) of the black liquor
FP Innovation patent
FTNIR
Auto-Titrator
(Kestner, Trung et al. 2004)
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Wood Composites
NIR S t
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NIR Spectroscopy Stiffness assessment of Laminated Veneer Lumber
(LVL) panels or plywoods
Density, MOE, MOR and internal bonds for mediumdensity fiber boards (MDF)
Ex: PanelPro ofMetso Panelboard (Fin)
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Oriented Strand Boards Drier wood (Mountain Pine Beetle) fines
NIR camera to estimate the % of fines
FP Innovation Patent
(Groves 2007)
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Oriented Strand Boards
(Groves 2007)
Thermal Imaging System
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Termite activity in woodwalls
Thermal Imaging System
Cracks in lumbers
Monitoring kiln-drying
temperatures
Thermascope SLK
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Conclusions (I)
Variable Forest/Yard(logwood)
Plant(sawn wood)
DimensionsExternal Features
Laser Beam, Digitalcamera, IR sensor
Laser Beam, Digitalcamera
Stiffness Acoustic sensor Acoustic sensor
X-ray scannerDimensionsInternal Features(defects)
X-ray scanner or X-ray CT image
Rot, decay Acoustic sensorPulsated current
Moisture Content Acoustic sensorPulsated current
MicrowaveCapacitance
R&D need
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Conclusions (II)
Pulp and Paper / Biorefinery Chip volume: Laser Beam
Chip freshness: Digital camera
Chip moisture content: NIR spectroscopy, Microwave
Chemical composition: NIR spectroscopy, FT-IR spectroscopy
Paper quality: NIR spectroscopy
Emerging technologies (R&D needs) Moisture content for log wood (NIR spectroscopy, NMRI, microwave)
NIR spectroscopy / hyperspectral imaging for log and sawn wood NMRI for solid wood and chips
Neutron imaging for solid wood and chips
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Acknowledgements
F. Fournier (FORINTEK),
T. Trung (PAPRICAN),
J. Nader (FERIC),
K. Groves (FORINTEK)
M. Defo (FORINTEK)
Q. Wei (UNB),
K. Tounis and P. Cooper (U of T),
F. Ding (CRIQ)
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Thank you!