Near Infrared Spectroscopy for biomass studies

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

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

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

12. O O O O H H H H H H H H   

13. 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

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

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

16. 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

17. 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

18. A reminder

22. 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

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

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

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

26. 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°

27. 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

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

29. Fiberoptic Fiberoptic Det Det Integrating sphere Det Mirror

30. Process NIR spectrometer based on moving grating

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

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

36. 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

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

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

39. Moisture histogram

40. 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

41. 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

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

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

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

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

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

47. 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

48. 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

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

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