Title: Near Infrared Spectroscopy for biomass studies
1Near Infrared Spectroscopy for biomass studies
2OVERVIEW
- 1. About the Center NIRCE
- 2. NIR spectroscopy on biomass
- 3. MSPC an example
- 4. Offline mixtures
3OVERVIEW
- 1. About the Center NIRCE
- 2. NIR spectroscopy on biomass
- 3. MSPC an example
- 4. Offline mixtures
4NIRCE 2002-2003
- Biofuels Umeå
- Biofuels Vasa
- Forest seeds Umeå
- Calibration Umeå
- Medical and Optical Vasa
- Short courses
5NIRCE 2004-2006
- NIRCE ONLINE
- NIRCE IMAGE
- NIRCE CLINICAL
6What do we offer?
- Graduate courses and short courses
- Research projects
- Advice and consulting
- Method development
- Instrument pool
- Workshops and symposia
- NIR2007
7OVERVIEW
- 1. About the Center NIRCE
- 2. NIR spectroscopy on biomass
- 3. MSPC an example
- 4. Offline mixtures
8Bioenergy
Pulp and paper
Forestry
Non-food
Building materials
Textiles
Biomass
Consumer products
Food feed
Feed and safety
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10Where is biomass found?
- Biotechnology
- Natural products
- Bioenergy
11What is special about biomass?
- O-H
- C-H
- N-H
- CO
- different atom sizes good
- IRNIR energy movements of bonds
12?
?
?
13Near 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
14Near Infrared Spectra
- Fast
- Simple sample preparation
- Nondestructive
- Online for process applications
- Need for calibration
- Opportunity for data analysis
15OVERVIEW
- 1. About the Center NIRCE
- 2. NIR spectroscopy on biomass
- 3. MSPC an example
- 4. Offline mixtures
16NIR 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
17Alholmens 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
18A reminder
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22Problem 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
23Controls
z1
zJ
Industrial process
x1
y1
Inputs
Output(s)
xK
yM
y(t) Fx(t),z(t)
24y(t) Fx(t),z(t)
- F should be known
- x(t) should be known
- z(t) set by operators
25Inside
- Ambient temperature -25 to 25
- Dust
- Humid
- Steam and compressed air
- Heavy equipment
26Sampling 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
27Sampling 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
28Foss NIRSystems 6500 grating instrument (Direct
Light)
2 Si 4 PbS
?0
71 W
13 cm
monochromator grating
5 cm ø
29Det
Det
Integrating sphere
Det
Mirror
30Process NIR spectrometer based on moving grating
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34Dataset
- NIR-spectra, 400-2500 nm, every 2 nm
- All spectra averages of 32 scans
- Calibration set 160 samples
- Test set 61 samples
35Spectra of calibration set (3 outliers)
Milled samples
36PCA-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
37Score plot of PCA-components 1 and 2
Series start
38Sample moisture (replicates with red)
Moisture,
Sample number
39Moisture histogram
40PLS-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
41Percent 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
42Loading-plot for PLS-component 1
43Diagnostics for PLS-model
Moisture,
RMSECV 2.6 for 7 components
RMSEC
PLS Comp.
44Predicted vs. measured moisture of calibration set
r2 0.85
45PLS-predictions on test set
Moisture,
lab o NIR pred.
Sample number
46Acknowledgements
Stig Nickull Bo Johnsson Johanna
Backman Sari Ahava Morgan Grothage
Sten Engblom
47Standard deviation for replicates
48Future 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
49OVERVIEW
- 1. About the Center NIRCE
- 2. NIR spectroscopy on biomass
- 3. MSPC an example
- 4. Offline mixtures
50Off-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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52Simplex 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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54Coal
Peat
Biofuel
H2O
H2O x 3
Mixing (remixing)
Ash x 3
Energy x 3
10x
NIR spectrum 32 scans
55Average 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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66Conclusions
- Max bias / variance
- -moisture 1.8/ 3
- -energy 0.5 / 0.75 MJ/Kg
- -ash -5 / 7
- Reference replicates important
- Spectral replicates important
67Works well
- Design repeated in score plot
- Classification possible
- Within run error smaller than between-run error
- PLS prediction H2O, ash, energy