Near Infrared Spectroscopy for biomass studies

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Near Infrared Spectroscopy for biomass studies
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OVERVIEW

• 1. About the Center NIRCE
• 2. NIR spectroscopy on biomass
• 3. MSPC an example
• 4. Offline mixtures

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OVERVIEW

• 1. About the Center NIRCE
• 2. NIR spectroscopy on biomass
• 3. MSPC an example
• 4. Offline mixtures

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NIRCE 2002-2003

• Biofuels Umeå
• Biofuels Vasa
• Forest seeds Umeå
• Calibration Umeå
• Medical and Optical Vasa
• Short courses

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NIRCE 2004-2006

• NIRCE ONLINE
• NIRCE IMAGE
• NIRCE CLINICAL

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What do we offer?

• Graduate courses and short courses
• Research projects
• Advice and consulting
• Method development
• Instrument pool
• Workshops and symposia
• NIR2007

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OVERVIEW

• 1. About the Center NIRCE
• 2. NIR spectroscopy on biomass
• 3. MSPC an example
• 4. Offline mixtures

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Bioenergy
Pulp and paper
Forestry
Non-food
Building materials
Textiles
Biomass
Consumer products
Food feed
Feed and safety
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Where is biomass found?

• Biotechnology
• Natural products
• Bioenergy

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What is special about biomass?

• O-H
• C-H
• N-H
• CO
• different atom sizes good
• IRNIR energy movements of bonds

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?
?
?
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Near Infrared Spectra (NIR)
Cosmic Gamma Xray Ultraviolet Visible NIR
Infrared Microwaves

• 780-2500nm
• Suitable for all organic and bio materials
• Robust for industrial use
• Good penetration depth
• Many modes of measuring
• Powerful multivariate results

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Near Infrared Spectra

• Fast
• Simple sample preparation
• Nondestructive
• Online for process applications
• Need for calibration
• Opportunity for data analysis

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OVERVIEW

• 1. About the Center NIRCE
• 2. NIR spectroscopy on biomass
• 3. MSPC an example
• 4. Offline mixtures

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NIR for Process Monitoring in Energy Production
by Biofuels

• Tom Lillhonga
• Swedish Polytechnic
• Vasa, Finland
• tom.lillhonga_at_syh.fi

• Paul Geladi
• Head of Research
• NIR Center of Excellence
• Umeå, Sweden
• paul.geladi_at_btk.slu.se

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Alholmens Kraft

• Worlds largest biomass-fuelled power plant
• Fuels biofuels, peat and coal
• Almost 1 km2 of storage
• Furnace is 15 ton sand fluidized-bed
• One 20 ton truck every 5 min.
• www.alholmenskraft.com

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A reminder
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Problem definition

• Biofuel consumption 750-1000 m3/h
• Large variations in moisture content
• Moisture determination off-line is very slow and
  not valuable for process monitoring
• Unwanted variations in steam and
• electricity production
• Reduced competitive strength

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Controls
z1
zJ
Industrial process
x1
y1
Inputs
Output(s)
xK
yM
y(t) Fx(t),z(t)
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y(t) Fx(t),z(t)

• F should be known
• x(t) should be known
• z(t) set by operators

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Inside

• Ambient temperature -25 to 25
• Dust
• Humid
• Steam and compressed air
• Heavy equipment

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Sampling and measurements

• Samples were collected manually from a conveyor
  belt (at line)
• A digital photo was taken of every sample
• NIR-spectra at-line
• Reference samples analysed off-line by industrial
  standard 17h_at_105

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Sampling and measurements

• Measurements were done during summer of 2003
• Samples were collected manually from a conveyor
  belt (at line)
• Sample temperature was measured
• A digital photo was taken of every sample
• Grinding was tried (Retsch Mill SM2000)
• NIR-spectra at-line
• Reference samples analysed off-line by industrial
  standard

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Foss NIRSystems 6500 grating instrument (Direct
Light)
2 Si 4 PbS
?0
71 W
13 cm
monochromator grating
5 cm ø
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Det
Det
Integrating sphere
Det
Mirror
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Process NIR spectrometer based on moving grating
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Dataset

• NIR-spectra, 400-2500 nm, every 2 nm
• All spectra averages of 32 scans
• Calibration set 160 samples
• Test set 61 samples

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Spectra of calibration set (3 outliers)
Milled samples
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PCA-model

• All calculations are done with MATLAB 6.5 and
  PLS_Toolbox v. 2.1 and v. 3.0
• Identification and removal of outliers
• Clustering observed

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Score plot of PCA-components 1 and 2
Series start
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Sample moisture (replicates with red)
Moisture,
Sample number
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Moisture histogram
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PLS-model

• Pre-treatment of spectra
• - noisy wavelengths removed
• (2300-2500 nm)
• - smoothing and second derivative
• calculated with Savitzky-Golay method
• Mean-centred spectra
• NIPALS- algorithm and cross validation (venetian
  blinds) used
• RMSECV 2.6 for 7 components

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Percent Variance Captured by PLS-Model
-----X-Block-----
-----Y-Block----- LV This
LV Total This LV Total
1 18.09 18.09 45.48 45.48
2 19.52 37.61 17.75 63.23
3 41.02 78.63 3.91 67.14
4 1.728 0.35 10.07 77.21
5 2.118 2.46 4.76 81.97
6 1.138 3.59 4.06 86.02
7 0.788 4.38 3.96 89.98
8 1.008 5.38 1.90 91.88
9 0.688 6.06 1.75 93.63
10 0.498 6.55 1.54 95.17
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Loading-plot for PLS-component 1
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Diagnostics for PLS-model
Moisture,
RMSECV 2.6 for 7 components
RMSEC
PLS Comp.
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Predicted vs. measured moisture of calibration set
r2 0.85
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PLS-predictions on test set
Moisture,
lab o NIR pred.
Sample number
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Acknowledgements
Stig Nickull Bo Johnsson Johanna
Backman Sari Ahava Morgan Grothage
Sten Engblom
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Standard deviation for replicates
       
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Future experiments

• Off-line measurements on fuel mixtures (H2O, ash,
  energy)
• Improved sampling probe
• Seasonal effects?
• Temperature
• Time series analyses
• On-line measurements
• Model included in process monitoring

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OVERVIEW

• 1. About the Center NIRCE
• 2. NIR spectroscopy on biomass
• 3. MSPC an example
• 4. Offline mixtures

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Off-line work

• At SYH
• CD 128l InGaAs 900-1700nm
• Integrating sphere with lamp
• Large glass plate
• Mixtures
• Linda Reuter of Wismar Polytechnic

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Simplex mixture design
Coal
1/0/0
0.5/0/0.5
0.5/0.5/0
0.33/0.33/0.33
0/1/0
0/0/1
0/0.5/0.5
Biofuel
Peat
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Coal
Peat
Biofuel
H2O
H2O x 3
Mixing (remixing)
Ash x 3
Energy x 3
10x
NIR spectrum 32 scans
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Average reference values moisture, energy, ash,
spectra all 10 replicates
Average spectra and average reference values
Individual references values and average spectra
33x128
Figure 10
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Conclusions

• Max bias / variance
• -moisture 1.8/ 3
• -energy 0.5 / 0.75 MJ/Kg
• -ash -5 / 7
• Reference replicates important
• Spectral replicates important

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Works well

• Design repeated in score plot
• Classification possible
• Within run error smaller than between-run error
• PLS prediction H2O, ash, energy