near infrared spectroscopy for biomass studies
DESCRIPTION
Near Infrared Spectroscopy for biomass studies. OVERVIEW. 1. About the Center NIRCE 2. NIR spectroscopy on biomass 3. MSPC + an example 4. Offline mixtures. OVERVIEW. 1. About the Center NIRCE 2. NIR spectroscopy on biomass 3. MSPC + an example 4. Offline mixtures. NIRCE 2002-2003. - PowerPoint PPT PresentationTRANSCRIPT
Near Infrared Spectroscopy for biomass studies
OVERVIEW
• 1. About the Center NIRCE
• 2. NIR spectroscopy on biomass
• 3. MSPC + an example
• 4. Offline mixtures
OVERVIEW
• 1. About the Center NIRCE
• 2. NIR spectroscopy on biomass
• 3. MSPC + an example
• 4. Offline mixtures
NIRCE 2002-2003
Biofuels Umeå
Biofuels Vasa
Forest seeds Umeå
Calibration Umeå
Medical and Optical Vasa
Short courses
NIRCE 2004-2006
NIRCE ONLINE
NIRCE IMAGE
NIRCE CLINICAL
What do we offer?
Graduate courses and short courses
Research projects
Advice and consulting
Method development
Instrument pool
Workshops and symposia
NIR2007
OVERVIEW
• 1. About the Center NIRCE
• 2. NIR spectroscopy on biomass
• 3. MSPC + an example
• 4. Offline mixtures
Biomass
Non-food
Food & feed
Bioenergy
Pulp and paper
ForestryBuilding materialsTextiles
Consumer products
Feed and safety
Where is biomass found?
• Biotechnology
• Natural products
• Bioenergy
What is special about biomass?
• O-H• C-H• N-H• C=O• different atom sizes = good• IR+NIR energy = movements of
bonds
O
H H
O
H H
O
H H
O
H H
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
Near Infrared Spectra• Fast
• Simple sample preparation
• Nondestructive
• Online for process applications
• Need for calibration
• Opportunity for data analysis
OVERVIEW
• 1. About the Center NIRCE
• 2. NIR spectroscopy on biomass
• 3. MSPC + an example
• 4. Offline mixtures
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
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
A reminder
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
Industrialprocess
Inputs Output(s)
Controls
y1
yM
x1
xK
z1 zJ
y(t) = F[x(t),z(t)]
• F should be known
• x(t) should be known
• z(t) set by operators
y(t) = F[x(t),z(t)]
Inside
Ambient temperature -25 to +25
Dust
Humid
Steam and compressed air
Heavy equipment
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°
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
Foss NIRSystems 6500 grating instrument (Direct Light)
5 cm ø
13 cm
71 W
monochromator grating
λ0
2 Si4 PbS
DetIntegratingsphere
Det Det
Fiberoptic Fiberoptic Mirror
Process NIR spectrometer based on moving grating
Dataset
• NIR-spectra, 400-2500 nm, every 2 nm
• All spectra averages of 32 scans
• Calibration set: 160 samples
• Test set: 61 samples
Spectra of calibration set (+3 outliers)
Milled samples
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
Score plot of PCA-components 1 and 2
Series start
Sample moisture (replicates with red)
Sample number
Moi
stur
e, %
Moisture histogram
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
-----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
Loading-plot for PLS-component 1
water peaks
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
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
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
Acknowledgements
Stig Nickull Bo Johnsson Johanna BackmanSari Ahava Morgan Grothage
Sten Engblom
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
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
OVERVIEW
• 1. About the Center NIRCE
• 2. NIR spectroscopy on biomass
• 3. MSPC + an example
• 4. Offline mixtures
Off-line work
• At SYH
• CD 128 InGaAs 900-1700nm
• Integrating sphere with lamp
• Large glass plate
• Mixtures
• Linda Reuter of Wismar Polytechnic
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
Coal Peat Biofuel
Mixing
(remixing)
NIR spectrum32 scans
10x
H2O x 3
Ash x 3
Energy x 3
+H2O
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
110x128
11x128
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)
Conclusions
• Max bias / variance
-moisture 1.8%/ 3%
-energy 0.5 / 0.75 MJ/Kg
-ash -5 / 7 %
• Reference replicates important
• Spectral replicates important
Works well
• Design repeated in score plot
• Classification possible
• Within run error smaller than between-run error
• PLS prediction H2O, ash, energy