On-line monitoring of papermaking processes

This article was downloaded by: [University of Auckland Library]On: 26 October 2014, At: 16:25Publisher: Taylor & FrancisInforma Ltd Registered in England and Wales Registered Number: 1072954Registered office: Mortimer House, 37-41 Mortimer Street, London W1T 3JH,UK

Chemical EngineeringCommunicationsPublication details, including instructions forauthors and subscription information:http://www.tandfonline.com/loi/gcec20

On-line monitoring ofpapermaking processesK. Karen Yin a , Hongchuan Yang a & Ferris Cramer ba Department of Wood and Paper Science ,University of Minnesota , St. Paul, Minnesotab Boise Cascade Corporation , International Falls,MinnesotaPublished online: 09 Sep 2010.

To cite this article: K. Karen Yin , Hongchuan Yang & Ferris Cramer (2002) On-linemonitoring of papermaking processes, Chemical Engineering Communications, 189:9,1242-1261, DOI: 10.1080/00986440213883

To link to this article: http://dx.doi.org/10.1080/00986440213883

PLEASE SCROLL DOWN FOR ARTICLE

Taylor & Francis makes every effort to ensure the accuracy of all theinformation (the “Content”) contained in the publications on our platform.However, Taylor & Francis, our agents, and our licensors make norepresentations or warranties whatsoever as to the accuracy, completeness,or suitability for any purpose of the Content. Any opinions and viewsexpressed in this publication are the opinions and views of the authors, andare not the views of or endorsed by Taylor & Francis. The accuracy of theContent should not be relied upon and should be independently verified withprimary sources of information. Taylor and Francis shall not be liable for anylosses, actions, claims, proceedings, demands, costs, expenses, damages,and other liabilities whatsoever or howsoever caused arising directly orindirectly in connection with, in relation to or arising out of the use of theContent.

http://www.tandfonline.com/loi/gcec20

http://www.tandfonline.com/action/showCitFormats?doi=10.1080/00986440213883

http://dx.doi.org/10.1080/00986440213883

This article may be used for research, teaching, and private study purposes.Any substantial or systematic reproduction, redistribution, reselling, loan,sub-licensing, systematic supply, or distribution in any form to anyone isexpressly forbidden. Terms & Conditions of access and use can be found athttp://www.tandfonline.com/page/terms-and-conditions

Dow

nloa

ded

by [

Uni

vers

ity o

f A

uckl

and

Lib

rary

] at

16:

25 2

6 O

ctob

er 2

014

http://www.tandfonline.com/page/terms-and-conditions

ON-LINE MONITORING OF PAPERMAKINGPROCESSES

K. KAREN YINHONGCHUAN YANG

Department ofWood and Paper Science,University of Minnesota, St. Paul, Minnesota

FERRIS CRAMER

Boise Cascade Corporation,International Falls, Minnesota

As part of our continuing effort to improve monitoring and control in the

pulp and paper industry, this work is concerned with fault detection and

diagnosis in the papermaking process. It analyzes on-line data gathered from

the real industrial process, investigates the cause=effect relationships, identi-

fies latent variables responsible for paper machine operation and paper

quality, and constructs multivariate control charts for more effective super-

vision and control. Based on multivariate statistical analysis, a new diagnosis

method using the directional property of the observation residuals is also

developed.

Keywords: Papermaking process; Multivariate statistical analysis; On-line

monitoring; Fault detection and diagnosis

INTRODUCTION

This work is motivated by the increasing demand for better, safer, andmore reliable operation in the pulp and paper industry. Nearly all papermills are automated with various levels of DCS=QCS implementation,

Received 6 May 2000; in final form 14 March 2001.

Address correspondence to K. Karen Yin, Department of Wood and Paper Science,

University of Minnesota, St. Paul, MN 55108. E-mail: [email protected]

Chem. Eng. Comm.,189: 1242�1261, 2002Copyright# 2002 Taylor & Francis

0098-6445/02 $12.00+ .00

DOI: 10.1080=00986440290012609

1242

Dow

nloa

ded

by [

Uni

vers

ity o

f A

uckl

and

Lib

rary

] at

16:

25 2

6 O

ctob

er 2

014

and unified mill information networks have become a continuing trend.Other evident trends in the paper industry have included growing com-plexity of process plants, higher requirement of product quality, leanerorganizations driven by economic conditions, and increasingly stringentregulations. All these have necessitated the development of more reliableand more powerful monitoring and control techniques for the dynamicsystems.

This work concerns on-line monitoring and control in the pa-permaking process. It addresses two issues: paper machine breaks andpaper quality. By evaluating factors responsible for paper machine op-eration and paper quality and investigating cause=effect relationships, weaim to develop more effective supervision and diagnosis techniques.

Runnability has been a notoriously difficult problem in papermakingprocess. Although relatively infrequent, web breaks can cause a sig-nificant loss in production. As a result, predicting and preventing breakshave drawn growing attention in recent years. It has been observed thatspatial fluctuation in the local strength of paper and temporal fluctua-tions in local load cause the breaks. However, the real reasons for suchfluctuations are not well known.

Paper is characterized by its structural, mechanical, and appearanceproperties. A number of quality parameters such as basis weight, moisturecontent, filler content, formation, thickness, tensile properties, burst re-sistance, tearing resistance, stiffness, opacity, and brightness are measuredon-line or off-line. Many operating condition data such as power input,flow rates of various furnishes, steam pressure, machine speed, and tem-perature are also recorded in real time. Numerous factors have impact onthe final product quality. Three of them are summarized briefly as follows.

Rawmaterials. Similar to any other manufactured product, the propertiesof paper depend heavily on the composition and treatment of its fur-nishes. In addition to fibers, paper consists of fiber fragments, mineralfillers, and chemical additives. In the formation process, they settle ran-domly on the web. It is well known that fibers from various wood specieshave different sizes, different physical, and chemical properties, anddifferent structures, and that the more filler is used, the less expensive thefinal product is. However, grade specification, sheet strength, and certainoperating conditions limit the proportion of wood species and theamount of filler added.

Wet-end chemistry. Various chemical additives can facilitate the processand=or impart certain properties to the product. The mechanical strengthproperties of paper depend heavily on fiber-fiber bonding, intrinsic fiberstrength, fiber length, and fiber stretch properties, as well as paper sheet

ON-LINE MONITORING OF PAPERMAKING PROCESSES 1243

Dow

nloa

ded

by [

Uni

vers

ity o

f A

uckl

and

Lib

rary

] at

16:

25 2

6 O

ctob

er 2

014

density and structure. To enhance the strength property of paper requiresstrengthening fiber-fiber bonding and=or improving its stretch properties.Wet=dry strength additives are used for such purposes. The structural,optical, surface, and performance properties of paper also depend onwet-end chemistry. Responsible for process operation and productquality, the additive efficiencies vary with not only the type and amountused and stock factors, but also with the order of their addition. Thereare always a number of explanations for what occurs, but the predictivenature of today’s wet-end chemistry is very limited.

Operatingconditions. Operating conditions play a very important role inpaper quality. Poor paper quality may result from poor process design orpoor control loop performance in the wet end, poor headbox design oroperation, and=or process disturbances from various sources. It has beena well-known fact that paper quality and paper machine operation arestrongly affected by the first-pass retention. Important factors affectingretention include raw material and chemical additives, among others.

To eliminate machine breaks and to ensure end product quality re-quire good raw material, the proper amount of fillers and additives, andsatisfactory performance at each stage of the process. Changes in theproduct quality may result from one or multiple sources with similareffects. In addition, many factors are highly correlated to others, whichmakes it difficult to locate the real source of a web break or poor productquality. To develop more effective monitoring and diagnosis schemes, athorough understanding of the relationships among the factors and theirimpact on end product quality is necessary.

There has been continuing effort in improving paper machinerunnability and paper quality. In a recent work, Gess and Kanitz (1996)addressed stability problems in the papermaking process and suggestedthat the variation of the paper machine approach-flow system is key toreduce product variation. They examined the feedback control loop thatuses measurements of the basis weight downstream at the reel to adjustthe basis-weight valve and to control the amount of thick stock flowthrough the fan pump from the machine chest and stuffbox. The timedelay between the control action and its effect, i.e., the time lag betweenthe adjustment of the control valve and the observed basis weight change,creates a discrepancy between the measured and the actual stock con-sistency in the approach flow. They suggested that this discrepancy is theinherent source of variability in the product. In addition, any changes instock quality such as fines content, quality and ratio of furnish compo-nents, and the level of refining will change the relationship between themeasured and the actual consistency as well as the white-water loop’s fineand filler retention. The changes in retention over time will, in turn, cause

1244 K. K. YIN ET AL.

Dow

nloa

ded

by [

Uni

vers

ity o

f A

uckl

and

Lib

rary

] at

16:

25 2

6 O

ctob

er 2

014

a shift in the long-term thick-stock flow rate. Therefore, they con-cluded that the paper-machine stability can be monitored by trackingchanges in thick-stock flow. As also pointed out in their paper, thisidea can be used to develop a quantitative measure that should be veryuseful in process operation. Such measurement, however, has not beenavailable.

In response to the paper industry’s needs for more effective processmonitoring techniques and better product consistency, this work aims todevelop on-line monitoring, fault detection, and isolation methodologyfor the papermaking process. Our objective is to provide operators andprocess engineers with quantitative tools for timely process managementdecisions.

In this paper, we investigate the inherent relationship among theparameters of interest and identify latent variables responsible for papermachine operation by using on-line mill data. Data were acquired fromthe papermaking area, which includes more than 40 process variablesfrom stock preparation to the paper machine wet end. It is desirable tomake full use of the thousands of on-line data collected every few minutesand to gather as much information as possible. To this end, we need to‘‘compress’’ the data so that essential information is retained and easierto analyze than the original huge data set. Considering the correlationand redundant information in the measurement, we also need an effectivetool capable of handling noise and correlation. In this project we com-press the large number of process data and extract information fromthem with multivariate statistical methods. Applying principal compo-nent analysis and partial least squares method to the papermaking pro-cess, we are able to identify the latent variables responsible to theoperation. Multivariate control charts of the latent variables are pro-duced.

Multivariate statistical projection methods are capable of handlinghigh dimensional and correlated process variables and are powerful toolsfor revealing the presence of process abnormalities. However, they do notpossess ‘‘fingerprint’’ or ‘‘signature’’ properties for diagnosis. To find thecauses of a machine break or to diagnose the sources of poor paperquality requires certain pattern recognition techniques capable of iso-lating the many factors involved. In this work a new approach usable forfaulty component isolation and diagnosis will also be developed.

This paper is organized as follows. An outline of multivariate pro-jection methods and a brief discussion of the statistical process controlchart technique are given in the next section. The following section pre-sents our work on data analysis and multivariate control chart design forthe papermaking process. A new diagnosis procedure using the direc-tional properties of the residuals is proposed next. Finally a summary anddiscussion are provided.


Dow

nloa

ded

by [

Uni

vers

ity o

f A

uckl

and

Lib

rary

] at

16:

25 2

6 O

ctob

er 2

014

MULTIVARIATE STATISTICAL PROCESS CONTROL

With the continuing progress in information technology and increasingavailability and decreasing cost of digital hardware and software, mul-tivariate statistical methods have attracted much attention in recentyears. Statistical process control has evolved from charting methods forunivariate systems or sets of independent variables to multivariate qualitycontrol and has been applied to continuous as well as batch-type ope-rations. Despite its ever-growing popularity in other industries, however,the use of multivariate statistical process control in the pulp and paperindustry is still limited. This section reviews basic concepts for two of themost used multivariate statistical methods, principal component analysisand partial least squares. It outlines the basic idea used in the chartingmethods and summarizes the design and development of severalmultivariate control charts on the basis of multivariate projection ap-proaches.

Principal Component Analysis and Partial Least Squares

Initially proposed by Karl Pearson (1901) and later developed by HaroldHotelling (1947), Principal Component Analysis (PCA) has found wideapplications in many fields. Theoretically, PCA is based on an ortho-gonal decomposition of the covariance matrix of the process variablesalong directions that explain the maximum variation of the data(Jackson, 1991; Wold et al., 1987). The main purpose of using PCA is tofind factors that have a much lower dimension than the original data setbut can still properly describe the major trends in it. PCA transformsthe original correlated variables into uncorrelated new variables or theso-called principal components that are a linear combination of theoriginal ones.

Originated from the pioneering work of H. Wold between the middle1960s and early 1980s, Partial Least Squares (PLS) was further developedby S. Wold and coworkers (H. Wold, 1982; S. Wold, 1978; Wold et al.,1987) in the mid and late 1980s. Similar to PCA conceptually, PLS isuseful to reduce not only the dimension of the process variables but alsothat of the product quality variables.

Let p denote the number of measured process variables and Y be ann� p matrix representing the mean centered and scaled measurementswhose covariance matrix is S. The rows in Y are p dimensional vectorscorresponding to samples, whereas the columns are n dimensional vectorscorresponding to variables. Using a special orthonomal p� p matrixU¼ (u1u2 � � � up), S may be reduced to a diagonal matrix L

S ¼ ULU0: ð1Þ


Dow

nloa

ded

by [

Uni

vers

ity o

f A

uckl

and

Lib

rary

] at

16:

25 2

6 O

ctob

er 2

014

The ith column of U, ui is the ith normalized eigenvector of S, which areoften known as the principal component loading vectors. Note thatu0iui ¼ 1, u0iuj ¼ 0 for i 6¼ j and

L ¼ U0SU ¼ ðU0Y0ÞðYUÞ: ð2Þ

The diagonal elements of L; l1; l2; . . . ; lp are ordered eigenvalues of S.They define the amount of variance explained by each correspondingeigenvector.

The PCA method transforms the original p correlated variablesyi1; yi2; . . . ; yip into p unrelated new variables ti1; ti2; . . . ; tip. The trans-formation is given by

T ¼ YU or ti ¼ Yui: ð3Þ

The n � p matrix T ¼ ðt1t2 � � � tpÞ contains the principal component scoresor observed values of the principal components for all n observations.Note that ti, ui pairs are arranged in a descending order according to theirassociated li, i.e., the first PC of Y, t1, is defined as the linear combinationt1 ¼ Yu1 that has the maximum variance and is subject to ju1j ¼ 1; thesecond PC, t2, is the linear combination that has the next greatest var-iance, subject to conditions of ju2j ¼ 1 and being orthogonal to the firstPC, t1. Additional PCs are defined similarly.

As a result, Y is decomposed by PCA as

Y ¼ TU0: ð4Þ

Note that

S ¼

S11 S12 . . . S1p

..

.S22 . . . S2p

..

. . .. ..

.

Sp1 . . . . . . Spp

0BBBB@

1CCCCA

and L ¼

l1 0 . . . 00 l2 0 0

..

. . .. ..

.

0 . . . . . . lp

0BBB@

1CCCA;

where the covariance matrix S is not known and needs to be estimatedfrom the data given

S ¼ ðn � 1Þ�1Y0Y: ð5ÞIn practice, one rarely needs to compute all the p eigenvectors since thefirst two or three principal components are able to capture most of thevariation in the data. The decomposition can then be written as

Y ¼Xmi¼1

tiu0i þ e; ð6Þ


Dow

nloa

ded

by [

Uni

vers

ity o

f A

uckl

and

Lib

rary

] at

16:

25 2

6 O

ctob

er 2

014

where e is the error term. If m � p, the dimensionality is greatly reduced.It can be seen that using the principal component analysis method, datacompression is achieved by monitoring far fewer PC scores than theoriginal variables without significant loss of information. Furthermore,the linear combinations of variables produced by PCA can often provideuseful descriptions of the process events.

PLS is used to model the relationship between two blocks of datawhile compressing them simultaneously. It works on their covariancematrix and seeks directions in which the variabilities in both groups arebest correlated. It is often used to extract latent variables that explain notonly the variation in the process data but also the variation most pre-dictive of quality data. The first PLS latent variable is the linear com-bination of the process variables that maximizes the covariance betweenthem and the quality variables; the first PLS loading vector is the firsteigenvector of the sample covariance matrix.

Mill information systems are currently collecting and storing massiveamount of data from pulping and papermaking processes. This has re-sulted in databases with overwhelming sizes. Such databases will becomea gold mine of knowledge if accurate and relevant information can beextracted in a timely manner. Due to process disturbance and measure-ment noise from various sources as well as correlation among the vari-ables, however, simply examining each individual process variable one ata time will not be sufficient. More adequate and more efficient dataprocessing and interpretation techniques are necessary. There has beencontinuing effort to improve mill information systems and to developmore effective data analysis methodology. Recently multivariate statis-tical projection methods and related techniques have been applied in thepulp and paper industries. Dayal et al. (1994) used feedforward neuralnetworks and partial least squares regression to model the dynamic be-havior of a continuous Kamyr digester in a pulp mill, and diagnosed thereasons for poor Kappa number control by examining the loading plots.Rigopoulos et al. (1997) used adaptive PCA for control relevant dis-turbance modeling of paper machine profile properties. A data analysistool developed and marketed by Pacific Simulation, FactNet has beenused in the pulp and paper industry in recent years, and offers anotherexample of applications of such statistical methods as PCA and PLS inreal processes. In this work we apply multivariate projection methods tothe papermaking process and develop multivariate statistical processcontrol charts for supervision and diagnosis.

Statistical Process Control Charts

Statistical Process Control (SPC) methods have been widely used in themanufacturing and process industries for several decades. They are based


Dow

nloa

ded

by [

Uni

vers

ity o

f A

uckl

and

Lib

rary

] at

16:

25 2

6 O

ctob

er 2

014

on the assumption that a process subject to a common cause variationwill remain in a state of statistical control where the process and productvariables remain close to their desired values. By monitoring the per-formance of a process over time, abnormal events can be detected whenthey occur. If the causes for such events can be diagnosed and the pro-blem can be corrected, the process is driven back to its normal operation.

SPC charts such as the Shewhart chart, the CUSUM chart, and theEWMA chart are widely used procedures for monitoring key productvariables in stable univariate processes (Kresta et al., 1991; Shewart,1931). Such charting approaches require plotting process observations ora function of them sequentially on a graph that includes the target valueas well as the upper and lower control limits. A normal practice is to setthe upper and lower limits at three standard deviations about the target.By collecting and analyzing a set of reference data, the variability undercommon cause situations can be obtained. An observation is deemed tohave deviated from the targets significantly if it exceeds the limits. Thedeviation will trigger further diagnostic and correcting processes. Thesecharting methods were developed for univariate systems or sets of in-dependent variables. To handle the situation where the variables are notindependent, several multivariate charts have been proposed in the lit-erature. Although the basic concepts behind the traditional statisticalcontrol methods are still valid, they are not applicable to cases where theprocess dynamics are not negligible, or cases where the process variablesare highly correlated, and they cannot accommodate the tremendouschanges in data collection and processing technologies.

With advances in process automation and real-time data acquisitionand control in recent years, new SPC techniques based on multivariatestatistical projection methods have been developed for on-line processmonitoring and diagnosis (Kresta et al., 1991; MacGregor et al., 1994;Nomikos and MacGregor, 1994). For these analyses, data must becompressed so that essential information is retained and easier to analyzethan the original huge data set. Information must also be extracted withan effective tool capable of handling noise and correlation. This can all beaccomplished by multivariate statistical projection methods. Both prin-cipal component analysis and partial least squares have been shown to bevery effective in noisy data reduction and information extraction.

Ensuring paper machine stability and paper quality requires effectivetools for monitoring the process performance over time in order to verifythat the process is remaining in a state of statistical control, or to detectabnormal conditions in a timely manner. Given the huge number ofvariables and the massive amount of data involved in such a process, it isnecessary to use multivariate methods capable of treating all the datasimultaneously and describing the fewer major events driving the system.Our work is based on the belief that the observed variations in the large


Dow

nloa

ded

by [

Uni

vers

ity o

f A

uckl

and

Lib

rary

] at

16:

25 2

6 O

ctob

er 2

014

dimensional and highly correlated on-line data actually reflect differentmanifestations of the same underlying events. These events (latent fac-tors) have a much lower dimension than the number of the originalprocess variables and are not usually directly measurable. However, theselatent factors can be obtained with such methods as PCA or PLS.Transforming process variables to these dominant latent factors willallow us to monitor a large-dimension process in a more manageable,lower dimensional space. It will also enable us to handle correlation andredundant information.

An essential component of statistical process control is to constructcontrol charts to detect special events as they occur, and to diagnose theirpossible causes while the information is fresh. The philosophy in devel-oping multivariate SPC is the same as that in developing univariateShewart charts. First, choose a reference set from historical data collectedunder normal operating conditions, a critical step for any successfulapplication. Certain appropriate statistics, e.g., the latent variablesand=or some functions of them, and their in-control regions for theunderlying process are determined in the next stop. To accommodate thecommon cause variation in normal operations, the limits of theacceptable performance are set at such levels that the control region willencompass a certain percentage of the variation in the reference set. Thechart so constructed can serve as an on-line gauge or ‘‘soft sensor’’ forprocess monitoring.

Combining PCA models with traditional SPC techniques to formmultivariate SPC tools has been employed in chemical engineering as wellas in some other fields (MacGregor and Kourti, 1995; McAuley andMacGregor, 1993). Similar to other control chart approaches, This re-quires first establishing a PCA in-control model based on the historicaldata gathered where only common cause variation was present. Thedesign and development of such tools amount to selecting the statistics tobe monitored and their ‘‘in-control’’ regions. Using different statisticsand their corresponding control limits will lead to different tools. Pro-jecting new observations, Ynew ¼ ½y1;newy2;new � � � yp;new , onto the spacedefined by the principal component loading vectors yields new scores.

ti;new ¼ Ynewui i ¼ 1; . . . n ð7ÞIf the new scores are used directly for monitoring, the control region willbe an ellipse for the two-dimensional case or an ellipsoid=hyper-ellipsoidfor three- or higher dimensional cases. The contour of this elliptical re-gion can be delineated by a w2-statistic having m degrees of freedom at adesignated confidence level provided that an assumption of normal dis-tribution of the process data is valid. Alternatively, this region can also bedetermined based on the reference distribution of the historical data usedin the reference set.


Dow

nloa

ded

by [

Uni

vers

ity o

f A

uckl

and

Lib

rary

] at

16:

25 2

6 O

ctob

er 2

014

Using the sum of squares of the scaled scores

T 2m ¼

Xmi¼1

t0i;newti;new

lið8Þ

produces a one-dimensional control chart. Its upper control limit is de-termined by the Hotelling T 2 statistic

T 2UCL ¼ ðn � 1Þðn þ 1Þm

nðn � mÞ Faðm; n �mÞ ð9Þ

where Faðm; n � mÞ is the upper 100a% critical value of the F-distributionwith m and n �m degrees of freedom. Note that n and m in the aboveequations denote the number of observations and the number of theprincipal components retained, respectively.

The Squared Prediction Error (SPE), i.e., the differences between themeasured and the predicted values of the process variables, can also becomputed

Q ¼Xp

i¼1ðyi;new � yi;newÞ

0ðyi;new � yi;newÞ; ð10Þ

where yi;new is the ith new observation and yi;new its estimates obtainedfrom the principal component model as follows:

yi;new ¼Xmj¼1

tj;newui;j: ð11Þ

This squared prediction error can be used as an index for monitoring aswell. Often referred to as an SPE-chart orQ-chart, not only can it indicatewhether the observed variation results from ‘‘normal’’ causes, it can alsoreveal the occurrence of new events not covered in the reference set.

MONITORING THE PAPERMAKINGPROCESS

A millwide Process Information (PI) system used at the Boise CascadeInternational Falls Mill can collect and store thousands of ‘‘tags’’ thatcontain a number of operating data for the process. Our objective hereinis to design and develop tools for on-line monitoring of the papermakingprocess with the available historical data.

Factor Extraction

In the first phase of our work, we analyzed the process data with theFactNet software package. Used in pulp and paper mills in both North


Dow

nloa

ded

by [

Uni

vers

ity o

f A

uckl

and

Lib

rary

] at

16:

25 2

6 O

ctob

er 2

014

American and Scandinavia, FactNet is an analysis system based onmultivariate statistical projection methods, PCA and PLS. It extractsfactors (latent variables) from the original high dimensional process data.The factors obtained may bear certain physical meaning but cannot bemeasured directly. The mathematical models relating factors=PCs andprocess variables are often useful for revealing the underlying phenomenaof the process.

As in any other FactNet application, much time was needed in thepre-analysis states to gather, condition, and merge data. Data were ac-quired from No. 1 paper machine at Boise Cascade’s International Fallsmill, which has a capacity of 1000 TPD. A millwide process informationsystem currently in use can collect, display, and store large amounts ofprocess operating data. Our data set was extracted from the mill in-formation system from December 1, 1998 to March 31, 1999. This dataset contained 44 process variables, starting from refiners and continuingthrough the dryer section. Retrieved data contained on-line measure-ments collected every 15 min. Data conditioning is a necessary step priorto data analysis. It requires an in-depth knowledge of the real process inorder to decide, for example, the normal range of the data and to remove‘‘bad’’ (outliers, unrepresentative, and=or those taken when the processwas not operational) data. Our means for conditioning have includedvisual inspection to locate outliers and setting appropriate mini-mum=maximum limits. We have used a combination of time plots andhistograms as the primary tools to identify bad data and to set the limits.The data merging step takes care of the missing data and time lags. Ourpurpose is to make the data represent the process configuration closely.

Merged data was input into the FactNet system for analysis. Toinvestigate causes of breaks, 12 web-breaks in the four-month periodwere examined. Presorting, correlation studies, factor extraction andevaluation, factor network analysis, time-plot examination, and modelvalidations were carried out in each case. It was observed that in mostcases three factors could capture nearly 70% of the variance. It meansthat the large number of process variables can be replaced with only threeprincipal components without significant loss of information (Figure 1).The first factor was more closely related to process variables including thestock flow measured at the stuffbox, PCC (the amount of CaCO3 mea-sured at the inlet of the fan pump), headbox temperature and headboxconsistency, etc. The second factor was more closely related to the couchroll vacuum, softwood to blend chest, and softwood freeness. The thirdfactor appeared to be more closely related to the production rate at thereel. There was a web-break that occurred at around 6:00 a.m. the secondday as shown in Figure 2 and Figure 3. To investigate causes of thisbreak, however, we needed to examine, one by one, the time-trends of allthose variables more closely related to these factors.


Dow

nloa

ded

by [

Uni

vers

ity o

f A

uckl

and

Lib

rary

] at

16:

25 2

6 O

ctob

er 2

014

There were also cases where no obvious factor changes were observedbefore breaks. In our attempt to establish mathematical models fromPCA, we also found it difficult to build a good model for the entiresystem. The model established from one set of data cannot predict othersituations accurately. This, we believe, can be attributed to the highnonlinearity of the system. Therefore our latter analysis was restricted tothe area of the process from the headbox to the dryer section.

Multivariate Control Charts Based on Principal Components

Although FactNet is useful in analyzing large amount of interrelatedprocess data and in extracting information (factors) for interpretation, it

Figure 2. Time trends of main factors.

Figure 1. Factor network.


Dow

nloa

ded

by [

Uni

vers

ity o

f A

uckl

and

Lib

rary

] at

16:

25 2

6 O

ctob

er 2

014

does not provide quantitative limits=thresholds for supervision. Thissection describes our work of developing multivariate statistical control(SPC) charts with historical process data.

The multivariate monitoring procedure outlined in the previoussection was applied to the papermaking process. Given the requirementsof plant linearity and data normality, in this preliminary study weincluded only seven process variables from the stuffbox to the presssection.

It is understandable that the choice of the reference set is critical sincea PCA model is built based on data collected from various periods of realoperation. MacGregor and Kourti (1995) gave a detailed discussion onthe choices of reference data set when applying multivariate SPC toindustrial data. They pointed out that the data set should be chosenaccording to the needs of the intended application. The choices of dataset to build models for process supervision purposes are different fromthose for purposes of inferential modeling or historical analysis. Formonitoring purposes, an appropriate reference set is one that defines thenormal operating conditions of the underlying process. It is conceivablethat a reference set having smaller-than-normal variation will lead to atoo-small control region and an overly sensitive alarm system. On theother hand, a reference set having greater-than-normal variation willresult in a control region that is too large and will impair the performanceof the monitoring system. Therefore, it is necessary to incorporateknowledge of the specific process into the choice of reference data set.

After carefully examining the data available, we chose two data setsas our reference. A PCA model was then established from these referencedata. Only the first two latent factors were used in the monitoring pro-cess, which captured 60% of the variation in the system. Subsequently,

Figure 3. Main components of factor #1.


Dow

nloa

ded

by [

Uni

vers

ity o

f A

uckl

and

Lib

rary

] at

16:

25 2

6 O

ctob

er 2

014

we established the in-control area that was a confidence ellipse centeredat the origin of the T1�T2 plane and defined by the PCs’ loading vectors.Following the normal procedure in multivariate statistical control, wealso developed one-dimensional T 2 and Q control charts for the detectionof abnormal variation and for model validation. Having the ‘‘in-control’’regions available, we proceeded to check the future process condition byprojecting the ‘‘new’’ observations onto each individual control chart.

Figure 4 is a two-dimensional monitoring chart for this process. Thecontour of the in-control condition is an ellipse that was constructedfrom reference data with a 95% confidence level. The crosses, þ , re-present the PC scores of a period in the morning of December 10, when aweb break happened at about 7:30 a.m. Several scores are clearly outsidethe confidence ellipse, which indicates this abnormal condition.

The concept of the two-dimensional confidence region is very similarto that of the Shewart chart. However, this plot is not as convenient foruse due to the lack of time axis. This difficulty can be surmounted by aone-dimensional monitoring plot—the counterpart of the range chart inthe univariate case. Figure 5 shows an example of using a T 2 chart andSPE (Q-) chart for on-line monitoring. The upper half is a T 2 chart on thefirst two latent variables. The upper control limit was determined byEquation (9). The lower half is an SPE chart that can reveal not only

Figure 4. Two-dimensional control chart—confidence ellipse.


Dow

nloa

ded

by [

Uni

vers

ity o

f A

uckl

and

Lib

rary

] at

16:

25 2

6 O

ctob

er 2

014

process abnormality but also new events not covered in the historicaldata. The ‘‘new observations,’’ T 2

new and Qnew, are located on the chartsequentially in time. Observe that both the T 2 and the squared predictionerror charts indicate the abnormal events between the 162th and the170th time intervals, when a web-break occurred.

Figure 6 shows a 3-D monitoring chart for this process of an under-lying dimension of two. The horizontal plane is determined by the scores ofthe two latent factors of interest. The control region in the plan is a con-fidence ellipse (not shown in this figure) as described in Figure 4. Futureobservations are located on this plot via their Q values. The unusuallylarge values of several SPEs clearly indicate the occurrence of a new event.

USING DIRECTIONAL RESIDUALSFORDIAGNOSIS

The traditional control chart approach, univariate and multivariate alike,are useful for detecting abnormal events. Once an out-of-control signalpresents, the operators or process engineers will need to diagnose thecauses by combining their knowledge of the process with an inspection ofprocess variables one by one. PCA reveals correlations between theprincipal components and the process variables as well as those amongthe process variables themselves. It has facilitated the diagnosis process

Figure 5. One-dimensional control charts.


Dow

nloa

ded

by [

Uni

vers

ity o

f A

uckl

and

Lib

rary

] at

16:

25 2

6 O

ctob

er 2

014

by isolating those variables most closely related to the first few principalcomponents, thereby reducing the number of variables to be inspected.Miller et al. (1993) and MacGregor and Kourti (1995) proposed thecontribution chart approach that is capable of revealing the group ofprocess variables making the greatest contribution to the deviations inthe squared prediction error.

Methods such as contribution charts are useful in providing betterinsight into the possible assignable causes of abnormal situations. Sincethey cannot point to a specific cause unequivocally, more powerful toolsfor diagnosing=isolating the faulty component are desirable. We propose adirectional residual approach herein, which should prove to be a valuableaddition to traditional PCAmethods. For easy illustration, the notation inthis section is independent of that used in the previous sections.

Our basic ideas (Yin, 1998) in fault diagnosis are to compare theactual system behavior against its normal operating conditions for con-sistency, and to use the inconsistency to detect and isolate the faults. Inthis work, we define this inconsistency as residuals, which represent thedifferences between the current observables y(t) and their mean valuesobtained from a moving window including the k most recent measure-ments from t � ðk þ 1Þ to t� 1. Namely,

rðtÞ ¼ yðtÞ � �y; ð12Þ

Figure 6. Multivariate three-dimensional control chart.


Dow

nloa

ded

by [

Uni

vers

ity o

f A

uckl

and

Lib

rary

] at

16:

25 2

6 O

ctob

er 2

014

where

�y ¼ 1

k

Xt�1

j¼t�ðkþ1Þyð jÞ: ð13Þ

Note that yðtÞ ¼ ½y1ðtÞy2ðtÞ � � � ypðtÞ 0 and p is the total number of theobservables. Sample mean �y is used to ‘‘filter’’ out the noise. If the samplesize k is large enough, under steady state, sample average converges to thetrue mean of the underlying variable according to the Law of LargeNumbers. For practical purposes, the choice of k depends on each in-dividual case. We used 30-min averages of the mill data.

Computed from the observations, the residuals are measures of theinconsistency between the actual system behavior and its normal operat-ing conditions and are ideally zero. However, due to process faults anddisturbance as well as measurement noise, they are not zero in most cases.In the absence of faults, the series of the residual vector frðtÞg; t ¼ 1; 2; . . .has a normal distribution with zero mean if the observed process variablesy(t) are normally distributed. In response to a specific faulty componentfyiðtÞg, the residual vector will lie in a fixed fault-specific direction that isthe same as that of the faulty component in the sample space. As explainedin the previous sections, projecting the observed process variables onto thespace defined by the principal components yields their correspondingscores. Similarly, by projecting the residual vectors onto the same spacewe will obtain the scores of the residuals, which should be in the samedirection as the projection of the vector yiðtÞ ¼ ½0 0 � � � yiðtÞ 0 0 0. Owingto their directional property, such residuals can be used for fault diag-nosis. With directional residuals, fault isolation amounts to determiningwhich of the known fault projections the observed residual scores areparallel. Figure 7 illustrates the idea of using the residuals’ directionalproperties for diagnosis by a system of an underlying dimension of two. yi

and r represent the directions of the component of the process variables yi

and the corresponding residuals r in the original sample space. ~yi and ~r aretheir projections on the PC plane. The schematic shows that r k yi thus~r k ~yi, which are the bases of our approach. In fault-free case, the pro-jection of the residual series will distribute randomly around the origin ofthe PCs’ score space and will have no trend or directional properties. Inthe presence of fault i (reflected by the ith component yi), the series ofresidual scores will lie in the same direction as ~yi.

Given the fact that the process variables are often correlated, andthus changes resulting from a single fault are often reflected in multipleprocess variables, the ‘‘observed’’ residuals will represent the resultanteffect. Consequently, the resulting scores of the residuals in the PC spacewill be in a direction reflecting the contributions of all components attheir corresponding weights. Note that


Dow

nloa

ded

by [

Uni

vers

ity o

f A

uckl

and

Lib

rary

] at

16:

25 2

6 O

ctob

er 2

014

~r � ~yi ¼ j~rk~yij cos gi ð14Þ

where gi is the angle between ~r and ~yi. Therefore, the major ‘‘con-tributors’’ or the main faulty components can be identified from the sizesof these angles.

An application of the directional residual approach is given inFigure 8, which was constructed using the reference data set along withprojections of the seven possible faults on the PC plane. When the newmeasurements become available, we calculate their residuals and projecttheir scores onto the same plane. Diagnosis is made according to thelocation of these residuals. In the fault-free case, the residuals shouldconfine themselves to an elliptical region centered around the origin; e.g.,the dots in the figure represent normal operating conditions. If a faultoccurs, the residuals will be closer to the projection of that particularfault. For instance, the direction of the residuals, symbolized by ‘‘þ ’’ inFigure 8, is parallel to the line of Water Removal, which indicates thepossible cause of the web-break.

SUMMARYAND DISCUSSION

This paper concerns monitoring and diagnosis in the papermaking pro-cess. By analyzing data collected from a real industrial process and byfurther investigating the cause=effect relationships, we have identifiedlatent variables responsible for paper machine runnability and paperquality, constructed multivariate control charts for the monitoring and

Figure 7. Illustration of the directional properties of the residuals.


Dow

nloa

ded

by [

Uni

vers

ity o

f A

uckl

and

Lib

rary

] at

16:

25 2

6 O

ctob

er 2

014

control of the latent variables, and developed a new method for faultisolation=diagnosis.

This work is our preliminary study on the application of multi-variate SPC to the papermaking process. Further study has beendirected to increasing the number of process variables included andrefining the methodology developed. Incorporating such multivariatecontrol charts into the mill process information system will greatlyenhance the current monitoring and diagnosis capability. It has thepotential to significantly increase productivity and to further improveproduct quality by controlling the process more effectively and bycutting downtime, general operating cost, and the percentage of main-tenance employees.

REFERENCES

Dayal, B. S., MacGregor, J. F., Taylor, P. A., Kildaw, R. and Marcikic, S. (1994).

Pulp & Paper Canada, 95, 26�32.Gess, J. M. and Kanitz, R. A. (1996). TAPPI J., 79, 119�126.Hotelling, H. (1947). Multivariate quality control, illustrated by the testing of

sample bomb-sights. In Selected Techniques of Statistical Analysis, ed.

C. Eisenhart, M. W. Hastay, and W. A. Wallis, 113�184, McGraw-Hill,

New York.

Figure 8. Fault diagnosis using the directional properties.


Dow

nloa

ded

by [

Uni

vers

ity o

f A

uckl

and

Lib

rary

] at

16:

25 2

6 O

ctob

er 2

014

Jackson, J. E. (1991). A User’s Guide to Principal Components, Wiley-Interscience,

New York.

Kresta, J. V., MacGregor, J. F. and Marlin, T. E. (1991). Can. J. Chem. Eng., 69,

35�47.MacGregor, J. F., Jaeckle, C., Kiparissides, C. and Koutoudi, M. (1994). AIChE

J., 40(5), 826�838.McAuley, K. B. and MacGregor, J. F. (1993). AIChE J., 39(5), 855�866.MacGregor, J. F. and Kourti, T. (1995). Control Eng. Practice, 3(3), 403�414.Miller, P., Swanson, R. E. and Hecker, C. F. (1993). Paper presented at 37th

Annual Fall Conference of the American Society for Quality Control,

Rochester.

Nomikos, P. and MacGregor, J. F. (1994). AIChE J., 40(8), 1361�1375.Pearson, K. (1901). Phil. Mag. Ser. B, 2, 559�572.Rigopoulos, A., Arkun, Y. and Kayihan, F. (1997). Pulp & Paper Canada, 98,

42�45.Shewart, W. A. (1931). Economic Control of Quality of Manufactured Product,

Van Nostrand, Princeton, N.J.

Wold, H. (1982). Soft modeling: The basic design and some extensions. In

Systems under Indirect Observations, eds. K. Joreskog and H. Wold, North

Holland, Amsterdam.

Wold, S. (1978). Technometrics, 20(4), 397�405.Wold, S., Esbensen, K. and Geladi, P. (1987). Chemometrics Intell. Lab. Syst., 2,

37.

Wold, S., Hellberg, S., Lundstedt, T., Sjostrom, M. and Wold, H. (1987). Paper

Presented at Frankfurt Partial Least Squares Meeting, Frankfurt.

Yin, K. (1998). Chem. Eng. Sci., 53(16), 2921�2931.


Dow

nloa

ded

by [

Uni

vers

ity o

f A

uckl

and

Lib

rary

] at

16:

25 2

6 O

ctob

er 2

014

Documents

On-line monitoring of papermaking processes