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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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Biomass
Non-food
Food & feed
Bioenergy
Pulp and paper
ForestryBuilding materialsTextiles
Consumer products
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• C=O• different atom sizes = good• IR+NIR energy = movements of
bonds
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O
H H
O
H H
O
H H
O
H H
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Near Infrared Spectra (NIR)
• 780-2500nm
• Suitable for all organic and bio materials
• Robust for industrial use
• Good penetration depth
• Many modes of measuring
• Powerful multivariate results
Cosmic Gamma Xray Ultraviolet Visible NIR Infrared Microwaves
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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
Paul Geladi
Head of Research
NIR Center of Excellence
Umeå, Sweden
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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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Industrialprocess
Inputs Output(s)
Controls
y1
yM
x1
xK
z1 zJ
y(t) = F[x(t),z(t)]
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• F should be known
• x(t) should be known
• z(t) set by operators
y(t) = F[x(t),z(t)]
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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@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)
5 cm ø
13 cm
71 W
monochromator grating
λ0
2 Si4 PbS
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DetIntegratingsphere
Det Det
Fiberoptic Fiberoptic 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)
Sample number
Moi
stur
e, %
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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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-----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
Percent Variance Captured by PLS-Model
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Loading-plot for PLS-component 1
water peaks
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1 2 3 4 5 6 7 8 9 10 110.5
1
1.5
2
2.5
3
3.5
4
4.5
5
PLS Comp.
RMSEC
RMSECV = 2.6 % for 7 components
Moisture, %
Diagnostics for PLS-model
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Predicted vs. measured moisture of calibration set
35 40 45 50 55 60 6535
40
45
50
55
60
65
Y Measured (moisture-%)
Y Predicted (moisture-%)
r2 = 0.85
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0 10 20 30 40 50 6025
30
35
40
45
50
55
60
65
70
75
Sample number
Moisture, %
* = labo = NIR pred.
PLS-predictions on test set
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Acknowledgements
Stig Nickull Bo Johnsson Johanna BackmanSari Ahava Morgan Grothage
Sten Engblom
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Replicate sample
numbers
Standard deviation for
five replicates, %
Standard deviation for PLS predicted values
of replicates, %
1 0.86 0.95
2 0.99 3.52
3 1.07 3.17
4 1.14 not calculated
5 1.84 not calculated
6 2.25 not calculated
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 128 InGaAs 900-1700nm
• Integrating sphere with lamp
• Large glass plate
• Mixtures
• Linda Reuter of Wismar Polytechnic
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1/0/0
0/1/00/0/1
0.5/0.5/0
0/0.5/0.5
0.5/0/0.5
0.33/0.33/0.33
Coal
Peat Biofuel
Simplex mixture design
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Coal Peat Biofuel
Mixing
(remixing)
NIR spectrum32 scans
10x
H2O x 3
Ash x 3
Energy x 3
+H2O
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110x128Average reference valuesmoisture, energy, ash, spectra all 10 replicates
11x128
33x128
Average spectra and average reference values
Individual references values and average spectra
Figure 10
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110x128
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11x128
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Table 3: RMSECV results (in parentheses number of components used)
Data set Moisture % Energy MJ/kg Ash %
110S 0.94 (14) 0.39 (8) 2.1 (12)
11S 2.3 (5) 0.63 (4) 5.6 (5)
33S 1.8 (7) 0.83 (6) 2.6 (8)
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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