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Near Infrared Spectroscopy for biomass studies

Near Infrared Spectroscopy for biomass studies

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Near Infrared Spectroscopy for biomass studies

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  1. Near Infrared Spectroscopy for biomass studies

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

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

  4. NIRCE 2002-2003 Biofuels Umeå Biofuels Vasa Forest seeds Umeå Calibration Umeå Medical and Optical Vasa Short courses

  5. NIRCE 2004-2006 NIRCE ONLINE NIRCE IMAGE NIRCE CLINICAL

  6. What do we offer? Graduate courses and short courses Research projects Advice and consulting Method development Instrument pool Workshops and symposia NIR2007

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

  8. Bioenergy Pulp and paper Forestry Non-food Building materials Textiles Biomass Consumer products Food & feed Feed and safety

  9. Where is biomass found? • Biotechnology • Natural products • Bioenergy

  10. What is special about biomass? • O-H • C-H • N-H • C=O • different atom sizes = good • IR+NIR energy = movements of bonds

  11. O O O O H H H H H H H H   

  12. 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

  13. Near Infrared Spectra Fast Simple sample preparation Nondestructive Online for process applications Need for calibration Opportunity for data analysis

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

  15. 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 NIR for Process Monitoring in Energy Production by Biofuels

  16. 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

  17. A reminder

  18. 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

  19. Controls z1 zJ Industrial process x1 y1 Inputs Output(s) xK yM y(t) = F[x(t),z(t)]

  20. y(t) = F[x(t),z(t)] F should be known x(t) should be known z(t) set by operators

  21. Inside Ambient temperature -25 to +25 Dust Humid Steam and compressed air Heavy equipment

  22. 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°

  23. 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

  24. Foss NIRSystems 6500 grating instrument (Direct Light) 2 Si 4 PbS λ0 71 W 13 cm monochromator grating 5 cm ø

  25. Fiberoptic Fiberoptic Det Det Integrating sphere Det Mirror

  26. Process NIR spectrometer based on moving grating

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

  28. Spectra of calibration set (+3 outliers) Milled samples

  29. 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

  30. Score plot of PCA-components 1 and 2 Series start

  31. Sample moisture (replicates with red) Moisture, % Sample number

  32. Moisture histogram

  33. 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

  34. 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

  35. water peaks Loading-plot for PLS-component 1

  36. Diagnostics for PLS-model Moisture, % RMSECV = 2.6 % for 7 components RMSEC PLS Comp.

  37. Predicted vs. measured moisture of calibration set r2 = 0.85

  38. PLS-predictions on test set Moisture, % * = lab o = NIR pred. Sample number

  39. Acknowledgements Stig Nickull Bo Johnsson Johanna Backman Sari Ahava Morgan Grothage Sten Engblom

  40. Standard deviation for replicates 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

  41. 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

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

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