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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)

    mailto:[email protected]:[email protected]
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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

    http://upload.wikimedia.org/wikipedia/commons/c/cf/EM_Spectrum_Properties_edit.svg
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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!