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, USAkishore@ee.washington.edu

http://www.ee.washington.edu/research/seal

Sensors

, Ene

rgy,

and Automation Laboratory

SE A L

4/21/2004 2

Outline

Introduction

Experimental Results

Data Analysis

Validation Tests

Conclusion

4/21/2004 3

Motivation

Annual worldwide paper production is nearly 312 million tons Huge application market.

Machine controlled using feedback systems Stable, but slow.

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 EndDewatering

Section

Finishing

Section

Existing

Sensors

FEF Sensors

4/21/2004 4

Fringing Field Interdigital Sensor

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.

4/21/2004 5

Experimental Setup

Pulp is blended using a blender to a consistency of a suspension.

Sensor is attached to the outer side of the base of an acrylic tray.

A guard plane is placed underneath the sensor electrodes to provide shielding from external electric fields.

4/21/2004 6

Experimental Setup

Sensor Used: Spatial Wavelength : 40 mm Finger Length : 160 mm Penetration Depth : 8 mm

Wall thickness of the tray : 5 mm RCL Meter : (Fluke Manufactured, Model PM6304)

Single Channel Measurements One Volt RMS Sinusoidal AC Voltage 50 Hz to 100 kHz Frequency Range

Drive

Sense Guard

16 cm

4 cm

4/21/2004 7

Experimental Results

103

104

105

01

2

2468

x 10-6

Frequency (Hz)CaCO3 Content (%)

Ad

mit

tan

ce (

S)

103

104

1050

12

-87

-86

-85

-84

Frequency (Hz)CaCO3 Content (%)

Ph

ase

(deg

)

103

104

1050

12

1.551.6

1.651.7

1.75

x 10-11

Frequency (Hz)CaCO

3 Content (%)

Cap

acit

ance

(F

)

103

104

105

01

2

12345

x 10-7

Frequency (Hz)CaCO3 Content (%)

Co

nd

uct

ance

(S

)

4/21/2004 8

Data Analysis

3 unknown variables, of which 2 are independent.

11 12 13 1

21 22 23 2

31 32 33 3

X m m m p C

Y m m m t C

Z m m m w C

X, Y, and Z are measured electrical parameters. m11 to m33, and C1 – C3 are constants.

p, t, and w respectively are the estimated fiber, additive and moisture concentrations.

4/21/2004 9

Parameter Selection Algorithm

Automatic selection of parameters and constants based on training data set.

The accuracy of the estimation is dependent on the quality of the training data set.

Two interlinked algorithms operating in parallel Learning Algorithm Estimation Algorithm

4/21/2004 10

Learning Algorithm

Parameter Formulation

Parameter and

Model Evaluation

Best Fit Extraction

4/21/2004 11

Learning AlgorithmStart

Load Training

Data Set

Calculate Basic

Electrical

Parameters

Level 1 parameters

Calculate Level 2

Parameters

1

Obtained by combination

of level 1 parameters.

P21 = f(P11, P12)

Calculate Level 3

Parameters

Obtained by combination

of level 1 and level 2 parameters

in frequency domain.

P31 = g(P11, f1 , f2)A

Parameter

Formulation

Parameter and

Model Evaluation

Best Fit Extraction

4/21/2004 12

Learning AlgorithmA

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 = (aX1 + bX2 + c)/X3

Rank the FitRanked on the basis

of Error-Sensitivity product.

Last Model ?Load Next ModelNo

B

Yes

Parameter

Formulation

Parameter and

Model Evaluation

Best Fit Extraction

4/21/2004 13

Learning AlgorithmB

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

4/21/2004 14

Estimation AlgorithmStart

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

4/21/2004 15

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.

4/21/2004 16

Estimated Values

89 89.5 90 90.5 9189.96

89.98

90

90.02

90.04

90.06

Actual Mositure Concentration (%)

Est

imat

ed M

ois

ture

Co

nce

ntr

atio

n (

%)

Mean of residuals = 0.032166667 % Standard deviation of residuals = 0.007359801 %

4/21/2004 17

Estimated Values

7.5 8 8.5 9 9.5 107.5

8

8.5

9

9.5

10

Actual Fiber Concentration (%)

Est

imat

ed F

iber

Co

nce

ntr

atio

n (

%)

Mean of residuals = 0.235766667 % Standard deviation of residuals = 0.124537764 %

4/21/2004 18

Estimated Values

0 0.5 1 1.5 2 2.50

0.5

1

1.5

2

2.5

Actual CaCO3 Concentration (%)

Est

imat

ed C

aCO

3 Co

nce

ntr

atio

n (

%)

Mean of residuals = 0.238071667 % Standard deviation of residuals = 0.124058865 %

4/21/2004 19

Validation Tests

Measurement Validation Repeatability Test

– Ability to repeat the measurements for the same sample

Reproducibility Test– Ability to reproduce the measurement for similar

samples

Estimation Validation Blind Test

– Ability to estimate for untrained data points

4/21/2004 20

Repeatability Test Results

Pulp composition: 90 % moisture,7.5 % fiber, and 2.5% CaCO3

Standard deviation is 4 orders of magnitude lesser than the mean.

5 10 15 20 251.639

1.64

1.641

x 10-11

Trial Number

Cap

acit

ance

(F

)

5 10 15 20 25

-1

0

1

x 10-14

Trial NumberDev

iati

on

fro

m M

ean

(F

)

4/21/2004 21

Reproducibility Test Results

Pulp composition: 90 % moisture,7.5 % fiber, and 2.5% CaCO3

Standard deviation is 3 orders of magnitude lesser than the mean.

5 10 15 20

1.675

1.68

1.685

1.69

x 10-11

Trial Number

Cap

acit

ance

(F

)

5 10 15 20-1

0

1x 10

-13

Trial NumberDev

iati

on

fro

m M

ean

(F

)

4/21/2004 22

Blind Test Results

0 0.5 1 1.5 2 2.50

0.5

1

1.5

22.5

Actual CaCO3 Concentration (%)

Est

imat

ed C

aCO

3 (%

)

89 89.2 89.4 89.6 89.8 90 90.2 90.4 90.6 90.8 9189.96

89.98

90

90.02

90.0490.06

Actual Mositure Concentration (%)Est

imat

ed M

ois

ture

(%

)

7.588.599.5107.5

8

8.5

9

9.510

Actual Fiber Concentration (%)Est

imat

ed F

iber

(%

)

4/21/2004 23

Summary

Advantages Non contact measurement Static sensor array Very high measurement speeds Simultaneous estimation of multiple components Accuracy better than state-of-art Inexpensive

Disadvantage The accuracy is highly dependent on the training data

set

4/21/2004 24

Acknowledgements

A special thanks goes out to:

Sponsors– Center for Process Analytical Chemistry, UW

– National Science Foundation

– Electric Energy Industrial Consortium, UW

Undergraduate Research Assistants– Abhinav Mathur– Nick Semenyuk– Cheuk Wai-Mak– Alexei Zyuzin

Questions?