near infrared spectroscopy for biomass studies. overview 1. about the center nirce 2. nir...

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

tom.lillhonga@syh.fi

Paul Geladi

Head of Research

NIR Center of Excellence

Umeå, Sweden

paul.geladi@btk.slu.se

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