1. Estimation of Moisture Content in Paper Pulp Containing Calcium Carbonate Using Fringing Field Impedance Spectroscopy Kishore Sundara-Rajan, Leslie Byrd II, and Prof. Alexander Mamishev Sensors, Energy, and Automation Laboratory Department of Electrical Engineering University of Washington Seattle, WA, USA kishore@ee.washington.edu http://www.ee.washington.edu/research/seal

2. 4/21/20042 Outline n Introduction n Experimental Results n Data Analysis n Validation Tests n Conclusion

3. 4/21/20043 Motivation n Annual worldwide paper production is nearly 312 million tons  Huge application market. n Machine controlled using feedback systems  Stable, but slow. n 10 sec delay on a 2000 m/min machine leads to over 0.2 miles of bad quality paper !! Solution: Incorporate Feed Forward Control Wet End Dewatering Section Finishing Section Existing Sensors FEF Sensors

4. 4/21/20044 Fringing Field Interdigital Sensor n For a semi-infinite homogeneous medium placed on the surface of the sensor, the periodic variation of the electric potential along the X-axis creates an exponentially decaying electric field along the Z-axis, which penetrates the medium.

5. 4/21/20045 Experimental Setup n Pulp is blended using a blender to a consistency of a suspension. n Sensor is attached to the outer side of the base of an acrylic tray. n A guard plane is placed underneath the sensor electrodes to provide shielding from external electric fields.

6. 4/21/20046 Experimental Setup n Sensor Used: ä Spatial Wavelength : 40 mm ä Finger Length : 160 mm ä Penetration Depth : 8 mm n Wall thickness of the tray : 5 mm n RCL Meter : ( Fluke Manufactured, Model PM6304 ) ä Single Channel Measurements ä One Volt RMS Sinusoidal AC Voltage ä 50 Hz to 100 kHz Frequency Range

7. 4/21/20047 Experimental Results

8. 4/21/20048 Data Analysis n 3 unknown variables, of which 2 are independent. ä X, Y, and Z are measured electrical parameters. ä m 11 to m 33, and C 1 – C 3 are constants. ä p, t, and w respectively are the estimated fiber, additive and moisture concentrations.

9. 4/21/20049 Parameter Selection Algorithm n Automatic selection of parameters and constants based on training data set. n The accuracy of the estimation is dependent on the quality of the training data set. n Two interlinked algorithms operating in parallel ä Learning Algorithm ä Estimation Algorithm

10. 4/21/200410 Learning Algorithm Parameter Formulation Parameter and Model Evaluation Best Fit Extraction

11. 4/21/200411 Learning Algorithm Start Load Training Data Set Calculate Basic Electrical Parameters Level 1 parameters Calculate Level 2 Parameters 1 Obtained by combination of level 1 parameters. P 21 = f(P 11, P 12 ) Calculate Level 3 Parameters Obtained by combination of level 1 and level 2 parameters in frequency domain. P 31 = g(P 11, f 1, f 2 ) A Parameter Formulation Parameter and Model Evaluation Best Fit Extraction

12. 4/21/200412 Learning Algorithm A Load Fitting Models Fit Training Data Set To Given Model Use all of the available parameters: Basic, level 1 and level 2. Mostly Linear Models. Y = aX + b Y = (aX 1 + bX 2 + c)/X 3 Rank the Fit Ranked on the basis of Error-Sensitivity product. Last Model ? Load Next Model No B Yes Parameter Formulation Parameter and Model Evaluation Best Fit Extraction

13. 4/21/200413 Learning Algorithm B Last Data Set? 1 No Determine the Best Fit Yes Determines the best fitting Model, Parameters and Loading based on the average rank of the fit. Write the Best Fit Information to a File. Stop This information would be used by the estimation algorithm. Parameter Formulation Parameter and Model Evaluation Best Fit Extraction

14. 4/21/200414 Estimation Algorithm Start Load the Best Fit Information from File. Loads the information on best fitting Model, Parameters and Loadings as determined by learning algorithm. Make Measurements Using IFEF Sensor. Real-time online measurements. Estimate Physical Parameters of the Pulp. A 1

15. 4/21/200415 Estimation Algorithm Last Measurement? No A 1 Add Measured Data Set to Training Set and Retrain System. Stop Yes Retraining can be done during the re-calibration breaks.

16. 4/21/200416 Estimated Values ä Mean of residuals = 0.032166667 % ä Standard deviation of residuals = 0.007359801 %

17. 4/21/200417 Estimated Values ä Mean of residuals = 0.235766667 % ä Standard deviation of residuals = 0.124537764 %

18. 4/21/200418 Estimated Values ä Mean of residuals = 0.238071667 % ä Standard deviation of residuals = 0.124058865 %

19. 4/21/200419 Validation Tests n Measurement Validation ä Repeatability Test –Ability to repeat the measurements for the same sample ä Reproducibility Test –Ability to reproduce the measurement for similar samples n Estimation Validation ä Blind Test –Ability to estimate for untrained data points

20. 4/21/200420 Repeatability Test Results ä Pulp composition: 90 % moisture,7.5 % fiber, and 2.5% CaCO 3 ä Standard deviation is 4 orders of magnitude lesser than the mean.

21. 4/21/200421 Reproducibility Test Results ä Pulp composition: 90 % moisture,7.5 % fiber, and 2.5% CaCO 3 ä Standard deviation is 3 orders of magnitude lesser than the mean.

22. 4/21/200422 Blind Test Results

23. 4/21/200423 Summary n Advantages ä Non contact measurement ä Static sensor array ä Very high measurement speeds ä Simultaneous estimation of multiple components ä Accuracy better than state-of-art ä Inexpensive n Disadvantage ä The accuracy is highly dependent on the training data set

24. 4/21/200424 Acknowledgements n A special thanks goes out to: ä Sponsors –Center for Process Analytical Chemistry, UW –National Science Foundation –Electric Energy Industrial Consortium, UW ä Undergraduate Research Assistants –Abhinav Mathur –Nick Semenyuk –Cheuk Wai-Mak –Alexei Zyuzin

25. Questions?