tory Systems 85 (2007) 63–69www.elsevier.com/locate/chemolab

Chemometrics and Intelligent Labora

Modeling of pulp quality parameters from distribution curves extractedfrom process acoustic measurements on a thermo

mechanical pulp (TMP) process☆

Anders Björk a,b,⁎, Lars-Göran Danielsson c

a KTH Chemistry, Analytical Chemistry, Royal Institute of Technology, SE-100 44 Stockholm, Swedenb IVL Swedish Environmental Research Institute Ltd, Box 210 60, SE-100 31 Stockholm, Sweden

c Process Analytical Chemistry, AstraZeneca Tablet Products Supply, SE 151 85 Södertälje, Sweden

Received 18 July 2005; received in revised form 18 April 2006; accepted 25 April 2006Available online 7 July 2006

Abstract

In this paper the feasibility of modeling strength and optical pulp properties from length distribution curves extracted from acoustic data usingcontinuous wavelet transform-fiber length extraction, CWT-FLE (A Björk and L-G Danielsson, ‘Extraction of Distribution Curves from ProcessAcoustic Measurements on a TMP-Process’, Pulp and Paper Canada 105 No. 11 (2004), T260–T264) by use of Partial Least Squares (PLS) have beentested. The curves used have earlier been validated against length distribution curves obtained by analyzing pulp samples with a commercial analyzer(FiberMaster). The curves were extracted from acoustic data without any “calibration” against fiber length analyses.

The acoustic measurements were performed using an accelerometer affixed to the refiner blow-line during a full-scale trial with a Sunds Defibratordouble disc refiner at SCA Ortviken, Sweden. Pulp samples were collected concurrently with the acoustic measurements and extensive physical testinghas been made on these samples. For each trial point three pulp samples were collected. PLS1 and PLS2 models were successfully made linking thedistribution curves obtained using CWT-FLE to pulp tensile strength properties as well as optical properties. The resulting Root Mean Square Error ofPrediction (RMSEP) for all parameters is comparable to what can be obtained by pooling the standard deviations of reference measurements from thedifferent trial points.

The results obtained are compared to FiberMaster datamodeled in the same fashion, yielding lower prediction errors than theCWT-FLEdata.However,this can be partly due to the five-year storage of pulp samples between pulp sampling/acoustic measurement and FiberMaster analyses/sheet testing.

The acoustic method is fast and produces results without dead time and could constitute a new tool for improving process control and optimizing thefiber characteristics in a specific process and for a specific purpose. The technique could be implemented in a PC-environment at a fairly low cost.© 2006 Elsevier B.V. All rights reserved.

Keywords: CWT-FLE; Continuous wavelet transform; Acoustic; Pulp quality; Fiber length; On-line

1. Introduction

In the field of paper pulp production much effort has beeninvested trying to find good descriptors of pulp quality. Suchdescriptors are needed both for evaluation of the final productquality and for measurements during production. Possible de-

☆ This paper is partially based on a poster presentation at 8th ScandinavianSymposium on Chemometrics, Mariehamn, Åland, June 14–18, 2003.⁎ Corresponding author. KTH Chemistry, Analytical Chemistry, Royal

Institute of Technology, SE-100 44 Stockholm, Sweden.E-mail addresses: abjork@kth.se, ab@metabolica.com,

anders.bjork@ivl.se (A. Björk), lars-goran.danielsson@astrazeneca.com,lgd@bredband.net (L.-G. Danielsson).

0169-7439/$ - see front matter © 2006 Elsevier B.V. All rights reserved.doi:10.1016/j.chemolab.2006.04.007

scriptors are e.g. fiber-length fraction curves and results fromphysical pulp sheet tests. Fiber-length measurement techniqueshave been developed to enable faster and more convenientmeasurements. Recent developments have also made measure-ments directly in pulp mills a reality [1]. These instrumentsmeasure on fiber suspensions of rather low concentrations. Thus,prior to analysis samples have to be diluted. These dilutions areperformed manually at the lab but automatically in processcontrol instruments. Physical sheet testing [2] is mainly done inlaboratories but there is at least one instrument available, thePulpExpert, which can be used in mills. All these methods sufferfrom considerable dead times between the time of pulp pro-duction and the availability of the measurement results. These

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delays are caused both by lengthy determinations and by pre-ceding dilutions.

There is a thus a need for faster methods for pulp charac-terization especially when moving to tailor made paper or fiberproducts. Another desired attribute for new methods would be tobe applicable non-invasively to the process stream. This wouldmake it possible to avoid many of the problems associated withsampling and fouling of sensors. The techniques that so far haveshown most promise in this direction are Near Infra Red (NIR)-spectroscopy [3,4] and acoustic or vibration sensors [5,6].

The connection between fiber dimensions and physical prop-erties on laboratory sheets of the pulp has been dealt with by anumber of authors. The first major work was done by Forgacs[7] in the 1960s. In this work he introduced two factors Length,L and Shape, S to describe a certain pulp. Using these factors hemade models predicting strength parameters. However, deter-mination of these factors involves a lot of manual steps and isquite time-consuming. A work following up on the idea ofusing two factors was done by Strand et al. [8]. They used amultivariate statistical method called factor net analysis andutilized simultaneous measurements of pulp strength para-meters and fiber length from an on-line sensor. The methodthey developed has been in use both for process monitoring andfor process development [9,10]. Broderick used Principal Com-ponent Analysis (PCA) to express 11 properties measured on pulpsheets of Chemi Mechanical Pulp (CMP) in four compositevariables [11]. Sundström et al. used PCA to visualize the impactoff varyingwood chip quality on process variables and pulp quality[12]. Kortalainen et al. used PCA and Self-Organizing Maps(SOM) to visualize pulp quality and they also built Partial LeastSquares models that were able to predict tear and tensile indexesfrom length distribution and Canadian Standard Freeness (CSF)measurements from a PulpExpet instrument [13].

In the application presented here, we record acoustic signalsfrom a flow of steam and fibers. The sensor is placed after a qualitysetting process stage for thermomechanical pulp and the results areused for predictions of physical sheet properties of the pulp. In aprevious paper we introduced a method for extraction of fiberlength curves based on continuous wavelet transform that we callCWT-FLE [14]. Here we use curves extracted by CWT-FLE forthe modeling of pulp properties measured by sheet testing.

2. Theory

2.1. Continuous wavelet transform

Wavelet decomposition describes a time-series by dividing itinto scales (frequency regions). These scales are analyzed by useof a scale-dependent wave function traveling along the specificscale “time”-axis. The wavelet transform thereby gives both fre-quency and time resolution while the Fourier Transform only givesfrequency resolution. The Discrete Wavelet Transform (DWT)can be made very fast by using digital filter banks [15]. Thecontinuous wavelet transform, requires a Fourier Transform,FT step in the algorithm and therefore more computing timethanDWT.However, continuouswavelet transform [16] gives theadvantage of higher flexibility than DWT. A more fundamental

difference between DWT and CWT is that in DWT the infor-mation in the signal (f) at a scale is deflated from f before it isanalyzed at the next scale. This step is not taken in CWTmeaningthat redundant information may be present in many scales. For amore thorough treatment of CWT related to this case see [14].

2.2. On the connection between acoustics and pulp quality

What is the link between acoustic and vibration measurementsand pulp properties? Considering each fiber a beam and usingbeam theory [17] and reasonable dimension and material data(density and E-Modula) we can conclude that the Eigen-fre-quency for fiberswould be some kHz to hundreds ofMHz (roughly3 mm to 0.01 mm fiber length). Here we measure vibration andacoustics in the range up to 40 kHz. The resulting measure-ments then contain information directly about the long fiberfractions but over-laid with information about short fibers dueto the physical phenomenon called beating. However, this isnot a complete description of all phenomena that are involvedin the generation of sound in a specific pulp process. There is inthis case a surrounding steam-environment and the fibers aswell as the fiber flow vary, which for example causes a frequencyshift via the Doppler effect. This is a very brief summary of whatis measured and why these measurements should contain in-formation about pulp characteristics. Going into depth with howthe frequency content in the measured signal is created wouldrequire massive laboratory measurements on selected pulps, set-ting up a rather complex simulation model, solving the model andfinally setting up a controlled laboratory setting for testing theoutcome of the simulation model.

Some vibrations from the refiner will of course spread to theblow-line. However, if we make regular power spectra of therecordings used here and compare them with those presented byStrand and Hartler [18] or Loisa et al. [19] they clearly differ. Forinstance Strand's spectra have distinct peaks while our spectra arevery smooth and have a different shape. Loisa's spectra havefewer peaks than Strand's but they still differ clearly in shape fromour spectra.

3. Experimental

3.1. In-mill trials and laboratory testing

Full-scale trials were run in the pulp mill at SCA GraphicSundsvall AB, Ortviken, Sweden in December 1998. The trialswere performed on a Sunds Defibrator double disc refiner of typeRGP 65(60) DD.

The process parameters were varied over a very wide range toprovide extended variation in pulp quality and process disturbance.The experiment design was a full factorial with four variables,production, energy input, dilution water flow and refiner housingpressure. The sampling rate of the acoustic vibration measurementsystem was set to 80 kHz and each sampling lasted for 0.5 s. Foreach point in the design 70 such samples were collected. Whencollecting the acoustic data, a minor change in settling time wasunfortunately made in the software for data collection between theconsecutive days of the trial. For calculation of the pseudo fiber

Table 1Standard methods used for the different properties

Property Testing according to Overall trialaverage value

Pooled within trial pointstandard deviation

Average RMSEP forCWT-FLE based models

Tensile strength SCAN P 67:93 2.02 0.16 0.24Tensile index SCAN P 67:93 and P 6:75 31.37 2.06 2.78Tensile stiffness SCAN P 67:93 213.26 15.46 20.55Tensile stiffness index SCAN P 67:93 and P 6:75 3.32 0.19 0.24Work at break SCAN P 67:93 30.00 3.22 5.03Prolongation at break SCAN P 67:93 2.14 0.09 0.11Elasticity-modula SCAN P 67:93 1.24 0.13 0.17s FMYC ISO 11475 51.45 1.77 3.15k FMYC ISO 11475 1.68 0.08 0.10s R457 ISO 11475 53.55 1.92 3.36k R457 ISO 11475 5.98 0.13 0.25ISO Brightness ISO 2471 62.34 0.42 0.53

Tensile index and tensile stiffness index are a combination of the respective base property and the specific surface weight of the sheet. The trial averages and the pooledwithin trial point standard deviations for each property are also shown.

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length classes via a continuous wavelet transform we used Wave-lab version 802 [20] within MATLAB 6.5. The Unscrambler7.8 fromCAMOPROCESSAS, Oslo, Norway was used for themultivariate modeling.

Fig. 1. Schematic overview of the CWT-FLE method. 1) Splitting the time-series usingweighting window associated with the CW base and wavelet scale. 4) Weighting FFTwdomain. 6) Collecting all scales from the CWT in steps two to five. 7) Averaging the “timin Fig. 1. All blocks mentioned in step one are calculated in the same way and averagedstandard deviation calculated.

In addition, process data were collected from the databasein the mill and pulp samples were collected from the blow-linefor each trial point. The samples were deep-frozen and storeduntil analysis at MoRe Research, Örnsköldsvik, Sweden in

a suitable block-length (4096 points). 2) FFT performed on a block. 3) Building aith weighting window. 5) Inverse FFT to transfer the frequency-series to the time-e” direction and normalizing the obtained length distribution to sum one. Not shown. Also, the results for all time-series belonging to a trial point are averaged and the

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September–October 2002. All physical sheet testing was doneaccording to the Scandinavian Pulp, Paper and Board TestingCommittee SCAN-standards or ISO-standards. The separatestandards used are listed in Table 1, also shown are the trialaverages and pooled within trial point standard deviations for thedifferent properties. These figures are shown to give the reader afeeling of the measurement errors in relation to the level of eachproperty.

The setup of the vibration measurement system and processconfiguration during the trial has been described earlier [9,10].

3.2. Implementation of CWT-FLE

When implementing CWT-FLE, a set of meta parameters haveto be set. For instance, how long blocks of data is suitable in theanalysis, how often should the CWT-FLE be updated, whatmother wavelet should be used and what parameters should beused for the wavelet scales. For more about CWT-FLE imple-mentation please read reference [1]. For a schematic overview ofthe CWT-FLE procedure see Fig. 1.

4. Results and discussion

In Fig. 2we show theCWT-FLE curves for all trial points. Twooutlying samples are highlighted. In addition to these outliersthere was a systematic change between days in the trial. Thiswas corrected for in the comparison between the CWT-FLE andFiberMaster in the previous article [1]. In this work we decidedto build models on both corrected and uncorrected curves sincecorrections might destroy information that could be used in amultivariate calibration. In addition to the CWT-FLE fiber-lengthfractions fiber length and fiber width fractions from FiberMasterwere used as input for modeling of physical pulp properties.

We built separate PLS2 models for the tensile strength andrelated parameters and for the optical parameters obtained fromphysical sheet testing. The quality parameters we try to predict inthe strength model are tensile strength, tensile index, tensile stiff-ness, tensile stiffness index, work at break, prolongation at breakand elasticity module. Note that the reference values are ob-tained from the same test, regular tensile sheet testing. The tensile

Fig. 2. All acoustic curves produced by the CWT-FLE method.

index and tensile stiffness index are obtained by dividing theoriginal properties by the specific surfaceweight of the pulp sheetstested.

The quality parameters wemodel in the optical model are lightscattering coefficients, s, (FMYC and R457), light absorptioncoefficients, k, (FMYC and R457) and ISO Brightness. Thedifference between the R457 and FMYC is that R457 is measuredat 457 nm while FMYC uses filtered light corresponding to theeyes' sensitivity for different wavelengths. After analysis usingPLS2 models we built separate PLS1 models for the differentquality parameters. We also tried to model tear strength and tearindex from a standardized paper tearing test in a PLS2 model.However poor results were obtained. For an overview of pulptesting see [3].

For all models we removed samples 5 and 12which are outliersas can be seen in Fig. 2. Further, we removed two more outlyingsamples on the basis of a PCA (not shown) on the strength andoptical parameters.

All models were validated using leave-one-out cross validationand the optimal number of PLS-componentswas chosen accordingto the criteria implemented in Unscrambler 7.8.

We present the model performance measured as RMSEP ex-pressed as a percentage of the pooled standard deviation of thereference measurements for the specific property. On this scale100 means that our models, CWT-FLE and acoustic measure-ments do not add to the error, all errors come from the referencemethod. By deducting 100 from the value obtained for the model,the additional error introduced by the PLS-model, CWT-FLE andthe acoustic measurements is obtained. For most trial points, threepulp sampleswere tested and an average value calculated. In somecases only one or two pulp samples were taken due to processinstabilities causing shutdowns.

Fig. 3 presents the results for tensile properties. The two CWTbased models give roughly the same prediction errors for thedifferent properties. One would have expected the PLS2 modelsto give slightly lower prediction errors because of the averagingeffect obtained when combining several highly correlated Y-variables. Another pattern is that models based on FiberMasterlength give lower prediction errors than the CWT-FLE. Further,even lower prediction errors are achievedwhen using FiberMasterlength and width data. An exception from this pattern is pro-longation at break which gives almost the same prediction errorfor all models. In addition to the percent of the pooled standarddeviation in Table 1 average RMSEP for all CWT-FLE models,trial average values for the reference values and standard de-viation for the reference values are also presented.

Simultaneousmodels (PLS2) for tensile properties require 2 or3 PCs whether the CWT-FLE data were corrected or not. Thecorresponding separate models for the tensile properties (PLS1)based on CWT-FLE data use consistently 3 PCs while for thecorrected CWT-FLE this shifts to 2–4 PCs. The models based onFiberMaster length and length and width require 1 PC and 2 PCsrespectively for concurrent modeling of the tensile properties.

In what way does the corrected CWT-FLE differ from un-corrected? We can for instance compare the weighted regressioncoefficients for one property when simultaneously modeling thetensile stiffness properties. In Fig. 4 we can see that the

Fig. 3. Relative prediction errors for tensile properties from CWTand FiberMaster based data. For each property the bars are from left to right: Bar 1, CWT-FLE PLS2models using 3 PC. Bar 2, CWT-FLE PLS1 models all using 3 PCs. Bar 3, corrected CWT-FLE PLS2 models using 2 PCs. Bar 4, corrected CWT-FLE PLS1 using2,4,2,4,2,2 respectively 4 PCs. Bar 5 FiberMaster fiber length PLS2 using 1 PC. Bar 6 FiberMaster fiber length and fiber width PLS2 model using 1 PC.

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coefficients for themodel based on the corrected version are lowerthan for that based on the uncorrected CWT-FLE. For the cor-rected CWT-FLE, it seems like a bias is introduced at variable 32and above. In our earlier work [14] we found a negative corre-lation between FiberMaster 0.5–1 mm and the binned CWT-FLEvariables 10–19. Here we exhibit the negative regression coef-ficients for the tensile property. This possibly indicates that CWT-FLE applied on these data have some shortcomings.

Fig. 4. Regression coefficients for models PLS2 of tensile

For the optical models, we observe that all PLS models foroptical properties using CWT-FLE on corrected acoustic datarequire 2 PCs. If correction is not applied one or two extra PCsare needed. Both FiberMaster based PLS2models use 1 PC. Notethat the CWT-FLE data contains 44 variables while FiberMasterlength and FiberMaster length andwidth contain 5 and 10 variablesrespectively. This sets natural limits for the maximum number ofPLS-components to be used. For the CWT-FLE data no more than

stiffness using corrected and uncorrected CWT-FLE.

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5 PLS-components should be used and for the FiberMaster data 1or 2 PLS-components (according to the reasonable physical rank ofdata). The simplest possible model is preferable according to theparsimony principle.

The results from modeling optical properties are shown inFig. 5. The different CWT-based models give closely similarprediction errors whether modeled together with PLS2 or in-dividually with PLS1. Also, correction for the change in set-tling time did not affect prediction errors to any large extent.The prediction errors are in all cases 25–100% higher than theestimated uncertainties in the corresponding reference meth-ods. The models based on FiberMaster data give lower pre-diction errors especially for s FMYC and s R457. The use ofboth length and width fractions improves the predictions onlyslightly. For the corresponding RMSEP-values see Table 1.

Is this the best result we can obtain with these parameters andCWT-FLE? After all, the FiberMaster data give more stablepredictions than the CWT-FLE data for all models. Firstly, sam-pling is difficult in the position used and pulp sheet testing isknown to have rather large errors. Secondly, acoustic recordingswere made concurrently with the primary sampling but the sam-ples were stored for a long time before sub-sampling and analysiswith FiberMaster and preparation of sheets. The pulp sampleswere frozen from December 1998 to October 2002. The lowerpredictability for the CWT-FLE models might be caused by achange in quality during this storage. However, specialists work-ing in the field of mechanical pulp state that freezing should noteffect the pulp quality measurements. But to our knowledge thereare no long-time tests to verify this. Further the fibers in the blow-line are newly beaten in steamwhile fibers in the laboratory testingare suspended in water and relaxed in hot water before forming ofthe sheets. Nevertheless, for keeping the quality at a constant levelin a production-line, this relatively poor prediction may still be

Fig. 5. Relative prediction errors for optical properties from CWTand FiberMaster bamodels using 3 PCs. Bar 2, CWT-FLE using 3,3,3,3 and 4 PCs. Bar 3, corrected CWTBar 5, FiberMaster fiber length PLS2 using 1 PC. Bar 6, FiberMaster fiber length a

sufficient. The faster measurement would greatly reduce the dead-time in a control-loop for quality and more than offset the draw-back of a relatively large prediction error. An implementation ofour technique would likely improve the process stability sinceshort-time (approximately 15–600 s) variations in wood chip dos-age and energy input could be more efficiently regulated than withthe current technology.

On themeasurement system part a constriction in the blow-linemay have increased the information content related to pulp quality.We have seen on another refiner with a control valve placed directafter the refiner that the flow was more stabile and that the lowfrequency content of the signal was reduced. The valve wasnormally in regulation in the start-up stage and then went to fullyopen. Due to the valve design a control valve and a constrictionhas almost the same function when the control valve is fullyopened. Further, having a larger frequency region, e.g. using amore broad-band vibration/acoustic emission sensor and collect-ing vibration data at higher sampling frequency would probablyhave given better information about the fiber content.

5. Conclusions

From validated length fraction curves obtained by continuouswavelet transform-fiber length extraction, CWT-FLE we havesuccessfully made PLS2 models linking the distribution curvesobtained to pulp tensile strength and optical properties. However,the results from models built on curves from a FiberMaster showlower prediction errors. Still the prediction error is likely to be lowenough to be useful for on-line use. The resulting RMSEP for allpulp quality parameters using CWT-FLE is in the range 120–200% of the pooled standard deviations from the different trials.We believe the difference in prediction error between the two typesof data is caused by the difficulties in the physical sampling and

sed data. For each property the bars are from left to right: Bar 1, CWT-FLE PLS2-FLE PLS2 models using 2 PCs. Bar 4, corrected CWT-FLE PLS1 using 2 PCs.nd fiber width PLS2 model using 1 PC.

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sample handling. The rapid measurement and the high samplingfrequency attainable with the acoustic technique make it a viablealternative. This method could constitute a new tool for improvedprocess control and aid optimization of the fiber characteristics in aspecific process and for a specific purpose. The technique could beimplemented in a PC-environment at a fairly low cost.

Acknowledgement

SCA Graphic Sundsvall AB is acknowledged for making dataand pulp samples available for further investigations. Site per-sonnel at the Ortviken mill are greatly acknowledged for theircontributions. Special thanks go to Curt Edström, Anders Gannå,Joar Lidén, Patrik Lindblom and Gert Skoglund for their supportand assistance at the Ortviken Mill. The Center for ChemicalProcess Design and Control, CPDC is acknowledged for eco-nomic support during the preparation of this paper.

The Bo Rydins foundation for scientific research and HelgeAx:son Johnsons foundation are acknowledged for providingfunds for extensive pulp testing.

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