Pulp Uniformity Measurement of Single Fiber Properties

1. Pulp UniformityMeasurement of Single Fiber Properties

2. A HANDFULL OF PULP IS A LOT OF FIBERS FIVE MILLION PULP FIBERS

3. AN HYPOTHESIS • The distribution of single fiber properties has a significant affect on the properties and performance of pulp, paper, and absorbent products. • This is difficult to prove or to take advantage of without single fiber measurements.

4. Single Fiber Properties Kappa Length Kink Curl Surface Charge Cell Wall Thickness Fiber Performance / Pulp Behavior

5. Objectives • Develop an optical method for measuring single fiber chemical properties such as kappa number and charge. • Build an instrument capable of performing the analysis quickly on many fibers. • Apply instrument to assess pulping uniformity and the relationship between pulp uniformity and pulp performance.

6. Fluorescent Probes • Fluorescent probes may be used to investigate chemistry of fibers, mammalian cells, or other small particles • High signal-to-noise • Flow cytometry application • Fluorescence response to substrate chemical environment • Emission Spectral Shift – Kappa measurement • Emission Intensity Shift – Charge measurement

7. Single Fiber Kappa Measurement

8. Why pulp kappa uniformity is important ? • Brownstock pulp strength • Bleaching cost • Target kappa limitations Mean

9. Acridine Orange Stained Cellulose Fibers Green = 14 kappa Orange = 32 kappa Red = 83 kappa

10. AO Fluorescence Spectra for Fibers of Different Kappas

11. 3.00 2.50 S. Pine Eucalyptus 2.00 Red/Green Ratio 1.50 1.00 D. Fir 0.50 0.00 0 20 40 60 80 100 kappa Change in Acridine Orange Fluorescence Ratio with Kappa for Three Wood Species

12. Epi-Illumination Flow Cytometer

13. Epi-Illumination Flow Cytometer Green CCD Bandpass Filters Fibers out Dichroic Mirrors Red CCD Flow Cell Fibers in Light Source Condensing lens and Bandpass Filter Green Intensity = ### Red Intensity =###

14. Epi-Illumination Flow Cytometer

15. Instrument Operation • Sample preparation ~10 minutes • Instrument collects images, applies image processing algorithms ~ 10 min. • Statistics on 1000 - 2000 fibers

16. Instrument Performance • Evaluation of instrument noise • Reproduce fluorescence microscope results • Comparison with independent method • Kappa distribution measured at IPST with a density gradient column

17. Measurement Noise Standard fluorescent beads: 6.5% CV Propagates to +-1 kappa for kappa 30 fiber

18. Red/Green Fluorescence vs. Kappa for IPST Samples

20. Uniformity of Laboratory and Commercial Pulps

21. Statistical Representation of Pulp Uniformity • Coefficient of variation (COV) • gamma one Gamma one= 1.89 Gamma one ~ 0

22. Softwood and Hardwood Pulps • Hardwood • COV ~ 0.3-0.5 • gamma one ~ 0-1.0 • Softwood • COV ~ 0.3-0.7 • gamma one ~ 1.0-3.0 COV=0.39 Gamma one =0.19 COV=0.41 Gamma one =1.89

23. Commercial vs. Laboratory Pulps COV=0.34 Gamma one =1.59 COV=0.54 Gamma one= 1.6

24. Effect of Chip Thickness on Hardwood Pulps

25. Effect of Enzyme pretreatment on Hardwood Pulps

26. Effect of pre-steaming and pressure COV= 0.27 Gamma one =1.42 COV= 0.31 Gamma one =1.29 COV= 0.41 Gamma one =1.89

27. Effect of Pulping Temperature • SuperBatch softwood pulps • Cooking temperature: 168ºC vs. 176 ºC • Varied temperature and time at temperature to reach target Kappa (~20) • ‘time to temperature’, and chip thickness distribution also varied, but not controlled. • 68 samples were analyzed

28. Results • Linear regression analysis to investigate correlation between COV and cooking variables; temperature,time to temperature,thick chip percentage,thin chip percentage Regression model for all 68 trials

29. Linear regression analysis for all 68 trials • Temperature has a major effect on pulp uniformity • Temperature and time to temperature are correlated. Hence, analysis should split into low (~168ºC)and high temperature (~176ºC) groups Relative effect of individual variables

30. Linear regression model, low temperature (~168ºC) trials Effect of variables at low temperature Linear regression model, high temperature (~176ºC) trials Effect of variables at high temperature

31. COV=0.45 COV=0.48 COV=0.55 COV=0.54 Effect of temperature and ‘time to temperature’

32. Pulp uniformity from different digesters

33. Single Fiber Charge Measurement

34. Why is Fiber Charge Important? • Charge facilitates retaining papermaking additives and fines • Better economics • Lower environmental impact • Charge has a profound effect on paper formation • Poor formation leads to poor appearance • Uneven distribution of papermaking materials affects function (printing, absorbency, etc.) • Drainage on papermachine

35. Monitoring and Controlling Charge • Bulk Solution Measurements • Titration techniques with cationic chemicals • Assume uniform charge between particles • Electrokinetic Methods • Differences in charge between single particles • Many assumptions: • Electrophoresis: fines only; spherical particles; etc. • Electro Kinetic Analyzer (EKA): bulk solution • Poor correlation with bulk titration results

37. Stain Selection • Charge-Sensitive Cationic Stain

38. Charge-Sensitive Stains

39. Stain Selection • Charge-Sensitive Cationic Stain • MQAE (Blue 460nm Emission) • Charge-Insensitive Reference Stain • Acridine Orange (Red 630nm Emission)

40. Charge IN-sensitive Stain

41. Charge-Sensitive Blue Stain - MQAE NOT Charge-Sensitive Red Stain - AO LOW CHARGE FIBERS 0.03meq/g HIGH CHARGE FIBERS 0.29meq/g

42. CalibrationBlue/Red Emission vs. Mean Fiber Charge

43. Epi-Illumination Flow Cytometer Blue CCD Bandpass Filters Fibers out Dichroic Mirrors Red CCD Flow Cell Fibers in Light Source Condensing lens and Bandpass Filter Green Intensity = ### Red Intensity =###

44. 0.0 0.10 0.20 0.30 0.40 0.50 0.60 Charge, meq/g Charge Distribution

45. Commercial Fiber Analyzer