englishtranslation amultivariablepulpbrightnesscontrolof

English Translation

A Multivariable Pulp Brightness Control ofECF Bleaching Process by Model Predic-tive Control＊１

Yoshitatsu Mori＊３

Takahiro Yamamoto

Pulp and Paper Innovation Center, Oji Holdings Corporation.＊２

Masahiro WatanabeAdvanced Process Control Section, Vigilant Plant Services Center,Yokogawa Electric Corporation.

Takahiro YamamotoTechnical Solution Dept.４, Technical Solution & Engineering Division,Yokogawa Solution Service Corporation.

AbstractIn the pulp and paper processes, to improve more cost reduction and more quality stability have been re-

quired at any time. However the pulp process response has long delay time and many mutual interference ele-

ments that affect each other between control variables’ responses；therefore it is well known that Kraft pulp

bleaching process is one of the most difficult processes to control automatically.

In the meanwhile,“Model Predictive Control（MPC）”has been developed as a new control function to oper-

ate complex multivariable processes systematically in good conditions from days of１９８０s, and is called and

known as“Advanced Process Control（APC）”, too. And, this new control system is reported a lot, it brought

fruitful economical outcome by optimizing its process conditions and by controlling multiple points reasonably as

observing whole process conditions.

In this paper, we show an example to apply MPC system to the hardwood pulp（LBKP）brightness control

for ECF（Elementary Chlorine Free）bleaching process of Kraft pulp and, at the same time, new“K value control”

utilized for to prevent serious“discoloration trouble”of paper sheet which has been recognized recently. The

trouble is, to break out strong“yellowing trouble（color reversion）”when paper rolls are exposed in the heat

and high moisture environment during long period for example in warehouses etc. after paper making.

Furthermore, in addition to use many special sensors in this control system, they are“hard sensors”, such as

pulp brightness sensor, residual chlorine consistency sensor and pulp Kappa number sensor etc.,“Soft sensor”

functions are also used together with MPC at the same time.

In this system, eight“soft sensors”which utilized multiple linear regression equation models for pulp

brightness and residual chlorine consistency etc. are installed in the points where sensors are not equipped or

where measurement is done by hand analysis, at fixed intervals, for the reasons of the requirement of more high

―precision measurement values and so on.

＊１This article was presented“The５６th―２０１３JAPAN TAPPI Annual Meeting”（９―１１October,２０１３）＊２１―１０―６Shinonome, Koto―ku, Tokyo１３５―８５５８, Japan＊３E―mail：morq１yoshitatsu@oji―gr.com

Yoshitatsu Mori Masahiro Watanabe

１１８４

― 88 ― JAPAN TAPPI JOURNAL Vol.６８, No.１０

１． Introduction

In the pulp and paper process operations, it is usually re-

quired to improve the stability of pulp stocks’ quality and

to reduce the production costs further. In this paper, we

show a multivariable pulp brightness control system that

we developed utilizing the“Model predictive control

（MPC）”and about the result of the control effects. Inciden-

tally, in this system, we targeted a Kraft pulp’s hardwood

ECF（Elementary Chlorine Free）pulp bleaching process

（LBKP）.

Although this MPC control system mainly consist of the

conventional pulp quality functions of pulp brightness, re-

sidual chlorine consistency and pH controls functions etc.

that have been known from long before, in addition to

those we introduced new function utilizing“K value con-trol”to prevent serious discoloration trouble（color rever-

sion trouble）of paper sheet that has been recognized re-

cently, after the bleach method had been converted to

“ECF”from conventional“Chlorine bleaching”.１），２） The

trouble breaks out strong“yellowing trouble”of the paper

products when paper rolls had been exposed in heat and

high moisture environment during long periods after pa-

per making, for example, in warehouses of the summer.

２． Kraft pulp bleaching process

Kraft pulp stocks have dark brown colors. To attain

higher brightness of pulp stocks, they are bleached in oxy-

gen delignification stage first and next in the multi―se-

quential bleach processes using several bleach chemicals.

Recently, as the multi―sequential bleach process, ECF

bleach method has been adopted in general. The flow dia-

gram of the multi―sequential bleaching targeted for hard-

wood（LBKP）is shown in Fig．１, and this bleach sequenceis described as D０―Eop―D１, shortened. At the first D０

stage, colored components in the pulp stocks are decom-

posed partially by chlorine dioxide strongly acidic solution

（ClO２），first. After that, those components are extracted

by sodium hydroxide solution（NaOH）from pulp stocks,

and they are bleached further by the cooperative reac-

tions of oxygen gas（O２）and hydrogen peroxide solution

（H２O２）at the next Eop stage. And finally, they are

bleached to targeted pulp brightness by chlorine dioxide

solution（ClO２）again at the final D１stage.

Although pulp stocks that enter into D０stage have only

４０％ ISO brightness or so and the color is still dark brown,pulp brightness increase gradually with passing through

each bleaching stages, and reaches to about８４％ ISObrightness at the end.

The pulp temperatures of every bleaching stages are

maintained at about６５℃ to advance the bleaching reac-

tions. The total retention time of pulp stocks that run

through the bleaching process is relatively long and nearly

from３to５hours, though it varies depending on the pro-

duction rates significantly. The made up pulp stocks are

stored in some storage tanks, and sent to final paper mak-

And these soft sensor’ values are linked with MPC system and monitoring system, and it had been able to

make use of well, in actual plant operation.

Finally, applying MPC system, we could control pulp brightness, K value, residual chlorine consistency and

pH etc. of the ECF pulp bleaching process in good conditions. Furthermore we refer to the detailed analysis re-

sults obtained from long run operations, the control stability for pulp brightness quality, the plant operability and

the reduction effect of the bleaching chemical cost.

Keywords：Pulp bleaching process, Pulp brightness control, Model Predictive Control, Soft sensor

Fig．１ ECF bleaching process and sensors

１１８５

― 89 ―October２０１４

ing processes according to demands, afterward. In pulp

bleaching process we are required to stabilize pulp quali-

ties, and lots of efforts are paid to reduce chemical costs as

much as possible, at the same time.

３． Model predictive control

We applied“Model predictive control（MPC）”method

to this pulp bleaching control.

MPC has been developed as a new control function from

days of１９８０s by Dr. C.R. Cutler who had an engineer

working in Shell Oil co. of U. S. A. et al. to control and opti-

mize targeted complex processes in good conditions. MPC

is a multivariable control system that aimed to stabilize

multi―input and multi―output complex process, controlling

multiple operation points at the same time considering the

whole process states. And this control method is known as

“Advanced Process Control（APC）”system, too.

This MPC system has been widely introduced in the

chemical industry in Japan from about２０years before,

and it had been known well that this new control system

brought fruitful economical effects by optimizing the proc-

ess conditions. However, the introduction of MPC to the

domestic pulp & paper industry has scarcely proceeded.

The variables of pulp bleaching process have long delay

times and many mutual interference elements that inter-

act each other with the plural control responses. However,

even though the bleach processes have such many difficult

problems, it was expected by introducing new MPC

method will bring lots of merits.

Fig．２shows the control diagram of MPC５）. The calcula-tions proceed as follows.

・After loading process variables：y（t）of present time

t, predicting the future movements trajectory（yp（t＋１），yp（t＋２）, ～, yp（t＋n））of manipulated variables：yp alongn―th unit time intervals from now（plus one）toward the

future（the period is called“Manipulated horizon”）, utiliz-

ing the control models which were build into the system

beforehand.

・Then calculate the movement trajectories（uf（t）, uf（t＋１）, ～, uf（t＋m）；m≦n）of control variables, so as to ap-proach the value of manipulated variables to the target

values as much as possible along m―th unit time intervalsfrom now toward the future（the period is called“Control

horizon”）by MPC optimal calculation functions that mini-

mize the“Performance function：J”value of Eq. �. Inci-dentally,“J value”consists of summation of control devia-tions terms and control variables variation terms and

chemical costs terms.

・At the end, repeat the action to set the value of the

first calculated manipulated variables：uf（t）only to thetarget process. At that time, the constrains of high and low

bound limit equations（Eq.�）are reflected against the re-strictions of manipulated variables and control variables’

movement. Those constraints are taken into account when

decide the control variable’s movement trajectory（uf（t）,uf（t＋１）, ～, uf（t＋m））by optimal calculations.Although Fig．２ showed a simple example, one―input

variable and one―output variable situation, more complex

control actions that considers multi―input variable and

multi―output variables are executed in the actual proc-

esses.

Fig．２ Concept of Model predictive control

１１８６


４． Pulp bleaching control functions

Pulp consists of lots of wood fibers taken out from natu-

ral timbers. Besides each pulp fibers are differ from the

shapes and chemical components that they involve. Fur-

thermore, the bleach reaction contains complex chemical

behaviors, and unknown phenomenons are remained yet.

On the other hand, recent pulp bleaching process is op-

erated by DCS（Distributed Control System）automatically.

In contrast, manipulated variable and control variable

number are many, and all bleach reactions have long time

delays. Consequently to control them are difficult. There-

fore if we try to operate those processes relying on the

conventional PID control functions of DCS only, we will ex-

perience the limits of the controllability of them.

Furthermore, if we change the dosage of bleach chemi-

cals only a bit without careful consideration intending to

control pulp brightness in the process, the unwished

bleach filtrates and pulp stocks pHs and residual chlorine

concentrations in the bleaching towers are moving to inap-

propriate zones at the same time.

As the result, the whole bleach process variables are off

balanced owing to the mutual interferences. Therefore the

control action may cause unfavorable effects on the pulp

bleach reaction efficiency and the bleachabilities.

In actual, the operation of pulp bleach process is not

easy and much knowledge is needed for it. We are re-

quired to consider about many states not only maintaining

pulp qualities such as pulp brightness, pH and residual

chlorine concentrations but also reducing the bleach

chemical costs and the loads for operating at the same

time.

On the other hand, although it is not experienced in the

conventional chlorine bleaching which had been used

widely before current ECF bleaching, we had been under-

standing through the recent studies, the importance of the

chemical content control, that the serious trouble of“heat

and moisture induced yellowing trouble of paper products

（discoloration trouble or color reversion trouble, the phe-

nomena that the finished paper product’s white colors are

yellowing gradually）”is break out when we used ECF

bleached hardwood KP（LBKP）with eucalyptus plantation

trees. It had been becoming clear that the trouble is in-

duced by residual hexenuronic acid（HexA）components

contained in pulp fibers when paper rolls are exposed in

the heat and high moisture environment during long time,

for example, in warehouses etc. after paper making３），４）.

In the ECF bleach process, chlorine dioxide（ClO２）solu-

tion is used as main bleaching chemical, and pulp bright-

ness is controlled by increasing or decreasing its additive

ratio. However the chemical cost of chlorine dioxide

（ClO２）is the most expensive. So, the action to replace ClO２

with hydrogen peroxide（H２O２）solution is often carried out

because H２O２ is also used in the same bleach process and

relatively low cost, although the pulp brightness bleach-

ability is inferior to ClO２. However hydrogen peroxide（H２O２）has no decomposition ability of HexA components,

therefore, the replacing action to H２O２ from ClO２may lead

to increase the risks of yellowing trouble, in the case not to

decrease HexA content satisfactory fully from pulp stocks.

To resolve this problem, though the exact analysis of

the minute amount of HexA contents in pulp stocks is

very difficult, we utilized another analyzed“K value”４）that

have been known recently that the measured value is pro-

portion to HexA contents（“K value”is equal to perman-

ganate number）. We used measurement functions utiliz-

ing the online spectrum sensor of this bleaching process

that can analyze K value contained in pulp stocks.

Although the conventional pulp bleaching control sys-

tem has been focused on only pHs, residual chlorine con-

centrations and pulp brightness as the quality control tar-

gets, in this new control system we added K value infor-

mation as the control variables that indicate the amounts

of HexA contents in pulp stocks.

We tried to construct the new control system that is

able to decrease the total pulp bleaching chemical costs to

the utmost limits maintaining all these pulp qualities, and

searching the appropriate replacing states for the plural

chemical dosages between chlorine dioxide and hydrogen

peroxide etc.

５． Hard sensors and soft sensors

In this bleaching process many kinds of sensors are in-

stalled as shown in Fig．１（chapter２）, and we used them

effectively to construct the pulp bleaching control system.

They are all online type special sensors；pulp brightness

sensors, pulp Kappa number sensor, K value sensor, resid-

ual chlorine concentration sensors and pH meters as

shown in Table１.First of all, the optical brightness sensor monitors pulp

brightness at the entry sides and the exit sides of each

bleach stages. The online type Kappa number spectrome-

ter sensor of Metso Automation co.（made in Finland）

monitors Kappa number at the entry side of this bleaching

process（equal to the exit side of oxygen bleaching tower），

and K value at two positions；the exit position of the ECF

bleach process and the exit side of Eop stage not to cause

the heat and moisture induced yellowing trouble of the

product papers in future. Incidentally, this K value sensor

uses the２３５nm waveband light of ultraviolet radiations.

Furthermore,“Soft sensor”is used together with“Hard

sensors”in MPC well.“Soft sensor”function is called“vir-

tual sensor”or“quality estimation sensor”, too.

However, those special sensors shown in Table１are all

１１８７


very expensive ones, therefore the numbers of hard sen-

sors installed in the process are limited by themselves.

Therefore, we introduced“Soft sensor”that utilized mul-

tiple linear regression model for the points that had not in-

stalled any hard sensors, or the hand analysis measure-

ment works are needed during periodic intervals for the

higher accuracy measurement value requirements.

At first, adopt the hand analysis measurements data as

the object variable values. Then decide the explanatory

variables. Find out some adequate variables that reveal

strong correlations among the whole process variables,

and at the same time, decide proper regression models

that the structure is as simple as possible, based on the sta-

tistics analysis, taken into account the characteristics of

physically and chemically properties of the process re-

sponses. Finally decide all explanatory variable coeffi-

cients of the regression models.

In this system, we had made eight points’ soft sensor

functions. Among them, six are no information points

where hard sensors are not installed and the hand analysis

works are necessary during periodic intervals, and the

rest two are the important points where more reliable and

more accurate measurement values are required from

process operation although hard sensors are installed as

the monitor sensor in actual（Table２）.For example, the first item of Table２is the multiple re-

gression model for“Eop stage exit side pulp brightness”.

This model used four explanatory variables, and considers

data time delays L１，２，３，４（min.）as shown in Eq.�. The pa-rameter t is time, and a１，２，３，４ are regression model coeffi-cients and b is constant term.

Eop stage exit side pulp brightness（t）＝a１・（D０stage entry side pulp brightness（t－L１））＋a２・（D０stage ClO２ additive ratio（t－L２））＋a３・（Eop stageNaOH additive ratio（t－L３））＋a４・（Eop stage H２O２ addi-tive ratio（t－L４））＋b ……�

Fig．３show, as an example, the comparison of transitionstates between predicted values and hand analysis meas-

ured data（mark ）. The data are plotted with the pulp

Table１ Pulp special sensors and install positions

Pulp Brightness sensor :

D０―tower entry―side，D０―tower entry―side（after chemical addition），D０―tower exit―side，D１―tower exit―side．

Pulp Kappa number

sensor：※interrval

measurement（４５min．）．

Kappa numberO２bleach―tower exit―side．※detection wavelength（２８０nm）

K―valueEop―tower exit―side．※detection wavelength（２３５nm）

residual Chlorine

concentration sensor :

filtrate of D０―Filter，filtrate of D１―Filer．

pH meter :filtrate of D０―Filter，Eop―towere entry―side pulp，filtrate of Eop―Filter，filtrate of D１―Filter．

Table２ Introduced Soft―sensors

Soft Sensors :

Eop―tower exit―side Pulp Brightness．Eop―tower Pulp K―value．D１―tower exit―side Pulp Brightness．D１―tower Pulp Brightness（after chemical treatment）．D１―tower exit―side Pulp K―value．D１―tower redidual Chlorine concentration．made up final Pulp Brightness（Storage―Tank exit―side）．made up final Pulp K―value（Storage―Tank exit―side）．

Fig．３ Calculated trend by soft―sensor vs. Lab. analysisdata（Eop―tower exit―side pulp brightness）

１１８８


brightness as ordinate axis and the date as abscissa axis

（one division is one day）. The predicted conditions calcu-

lated by the regression model are satisfactory.

However, the predicting accuracies that calculated by

the original regression models are gradually deteriorating

with time.

To prevent this problem, we integrated a correction

function for updating, to input hand analysis data with the

sampling time information to the soft sensor model from

DCS, which are measured during periodic intervals, for ex-

ample every few hours or every few days, in some cases

depending on the measuring species. And correcting the

regression model’s constant term value：b sequentially.Through this correction functions, we had been able to

maintain the accuracy of the regression model’s prediction

values in good condition always７），８）.

These calculated predict values by soft sensors are im-

ported to MPC system as control variables either to utilize

not only for monitoring system but for control MPC effec-

tively. By making use of soft sensor functions fully with

MPC, we were able to achieve this system as the usable

ones that were similar to the real process operation condi-

tions by operators from the aspects of both control and

monitoring functions.

６． System configurations of pulp bleaching control

In general,“transfer function”expressions are used well

for MPC control model. Utilizing the stable periods while

the process had been suitable for data gathering, we car-

ried out“step response tests”against each pulp bleaching

stages, and had been decided transfer functions. Changing

the individual dosages of bleaching chemicals（the change

width are only small）, shown as in Fig．４with appropriatestep―patterns with time（Input：u（t））against each pulp

bleaching stages, we had examined the movements and

transition patterns of plural variable responses（Output：

y（t））with time, such as pulp brightness, pHs, residual

chlorine concentrations and electrical conductivities and

so on, along the pulp flow of the bleach process.

By repeating these response tests several times, we

could recognize dynamic behaviors and characteristic of

the bleaching processes. Based on the series of step re-

sponse test information, we had decided finally the“matrix

table”consist of“manipulated variables：MV”,“con-

trolled variables：CV”and“disturbance variables：DV”

as shown in Table３which had become the fundamentalconfiguration for this MPC bleaching control system.

On the other hand, the transfer functions were decided

as the style of model shown in Eq. � by“system identifi-cation”works using statistical analysis. We adopted“first

―order delay plus dead―time model”for these transfer

functions. Analyzing gathered step response data, we had

been determined the each values of“process response

Gain：K”,“time constant：τ”,“delay（dead）time：L”through system identification works, and decided“transfer

functions：G（s）”which compose the individual dynamicmodels of this control system. Incidentally, U（s）is the in-put variable u（t）’s and Y（s）is the output variable y（t）’svariables that had been transformed by“Laplace transfer

function”along time axis：t. And“s”is“Laplace operator”that appear accompanied with the transformations of

Transfer function.

Table４ shows the overall structure view of transferfunctions matrix of MPC model that involved all pulp

bleaching stages, based on the control variable configura-

tion of Table３.

In the matrix table of Table４, each columns of the up-

per row show“controlled variable：CV”of the bleaching

stages, and each rows leftward shows“manipulated vari-

able：MV”and“disturbance variable：DV”of each

bleaching stages, and we decided finally２７elements’

transfer functions：Gi, j（s）of Eq. � which had been

judged as necessity functions trough step response tests.

For example, transfer function Gi, j（s）in the matrix tableindicates individual dynamic characteristics of each proc-

esses, that is, when manipulated variable MV of i―th rowfrom above the table is operated, how controlled variable

CV of j―th column will responds and changes.In MPC, preparing the transfer functions matrix table

beforehand such as Table４,（that is consist of controlled

variables, manipulated variables and disturbance vari-

ables）, the future movements of the whole process along

the time will predict, by superposing all required parts of

the transfer function responses using dynamic model in-Fig．４ Approximated Step response model for standard

process variable

１１８９


formation.

At the end, we have been deciding five control actions of

the manipulated variables：MV by the optimal calcula-

tions, those are four bleaching chemical dosages and one

pulp stock flow rate, in this MPC pulp bleaching control

system.

And“Cost―term”of Performance function：J（Eq.�）,which is used in this optimal calculations, involves total

summation of control value deviation terms and manipu-

late value deviation terms plus cost term that consist of

three main bleaching chemical costs；ClO２, H２O２, NaOH in

the bleaching process.

By the optimal function, the total bleaching chemical

costs are calculated by the unit price of each bleaching

chemicals（yens／kg）which were input to the system be-forehand, and then control system will calculate and decid-

ing the optimal control actions to minimize them automati-

cally.

List other characteristic points about this MPC pulp

bleaching control system, next.

① While an unique values are set as the target values

（SV）in DCS operations, in MPC system, the opera-

tively band width with higher limit and lower limits

have to be set against all controlled variables and ma-

nipulated variables by operators. Taking into account

all the restriction settings, MPC system searches opti-

mal points to minimize the Performance function

value：J within the movable areas, and approachingclosely to the optimal points little by little automati-

cally.

② We made“ON／OFF”switches against all controlledvariables：CV, manipulated variables：MV and dis-

turbance variables：DV shown in Table４that opera-

tors can set from DCS monitor windows.

By preparing these switch functions, operators

were available to use MPC as long as possible shut-

ting off only the relevant area function among the

whole control functions, avoiding complete control

shut downs, while even if unstable period, or partial

accident timing of hard sensors or inspection period of

the instruments and so on.

③ In pulp bleaching process, the pulp retention time

required to pass through each bleaching tower varies

considerably accompanied by the change of pulp pro-

duction amounts. However the values of time con-

stants：τ and delay times：L cannot edit directlywith the MPC tool. Therefore, we prepared two mod-

els, “high production mode”and“low production

Table３ An example basic configuration of advanced control system for pulp bleach process

Manipulated variables

（MV）Control variables

（CV）Disturbance variables

（DV）

D０―Stage ClO２additive rate． exit―side pulp Brightness，

exit―side filter filtrate pH．

O２Bleach―tower exit―sidepulp Kappa number，

entry―side pulp Brightness，pulp Temperature．

Eop―Stage NaOH additive rate，H２O２additive rate，pulp Quantity

send to Eop―Filter．

entry―side pH，exit―side pulp K―value，exit―side filter filtrate pH，pulp fluid Tower―Level，exit―side Pulp Brightness．

Pulp Temperature，Pulp production rate．

D１―Stage

ClO２additive rate．

exit―side filter filtrate pH，residual Chlorine

concentration，exit―side pulp Brightness，exit―side pulp K―value．

Eop―stage exit―sidepulp K―value，

pulp Temperature．

Storage―Tank

（send toPaper

Machines）

made up final pulp Brightness

（for monitoring），

made up final pulp K―value（for monitoring）．

D１―stage exit―sidepulp K―value，

residual Chlorine

concentration，pulp Temperature，Retention―Time（large volume storage tank）．

１１９０


Table４Configurationofmatrix―tableusingtransferfunctionsforECFpulpbleachprocess

１１９１


mode”into this system, so we were able to switch the

transfer function groups automatically, accompanied

by the operation conditions.

④ As the hardware for this control system, we intro-

duced“Windows―PC”that connected with DCS.

Installed MPC software tools that will show at next

⑤, we had integrated the control application system

in it. We had prepared MPC control and monitor win-

dows among DCS operation windows, so, operators

had been able to execute MPC functions as if they are

operating it from DCS directly.

Windows―PC is loading the process data from DCS,

and executing MPC control calculations. Setting the

plural optimal control output values（manipulated

variables：MV）as the SV―values to the PID control-

lers of DCS with set point control mode, and proceed

pulp bleaching control. In this MPC system, we

adopted every one minutes for the control intervals.

And the predictive control calculations are executed

as follows.

The prediction time length as the“control horizon”

that correspond to the dynamic trajectory of the”con-

trol amount uf（t）, uf（t＋１）, ～, uf（t＋m）”are calcu-lated３．７hours（m＝２２０steps）from now toward the

future, and as the”manipulate horizon”correspond to

the dynamic trajectory of the”manipulate amount：

yp（t＋１）, yp（t＋２）, ～, yp（t＋n）”are calculated about１５hours（m＝８７９steps）from now（plus one）toward

the future, as shown in Fig．２. However, notice that

the first calculated value of”control amount：uf（t）”only is output to the actual process as the exactly con-

trol value at the end, as shown in Fig．２with the dou-

ble circle. By repeating these MPC operations every

one minutes, the optimality of the control conditions

are renewed and maintained continually.

⑤ The packaged software tools for MPC functions are

provided by several venders. In this system, we used

MPC and Soft―sensor tools provided by Yokogawa

Electric Corporation and Shell Oil Company（Table５）９）.

７． Control effects and control conditions

We have been continuing to examine this pulp bleaching

control states by MPC through the daily reports data and

process trends. We summarize about the analyzed control

effects, the variances of pulp bleaching chemical costs and

about the stabilities of pulp qualities and control states.

７．１ Bleaching chemical costThe pulp bleaching chemical cost data that calculated

every day are checked as one of the important data that

are reported as the daily operational results, and they are

regarded as the most reliable master data in the mill. How-

ever, the simple arithmetic comparison of these data con-

tains serious problems in actual, because the pulp produc-

tion amount, the pulp brightness that enters to the bleach-

ing process and the target value of the final pulp bright-

ness etc. are varies frequently according to the daily plant

operations.

We shows some analysis result examples that compared

with histograms about the pulp bleaching chemical costs

between manual operation period（１１２days）and MPC con-

trol period（１９６days）in Fig．５. Incidentally, to avoid thoseproblems, the both data are analyzed and corrected by

PLS regression model taking into account of the several

operational variances factors１０）. The data in Fig．５are plot-

ted with pulp bleach chemical costs as abscissa axis and

Table５ Software tools for Model predictive control

used Software Tool

（trade name）Supplier

・Model Predictive Control（MPC）

ExaSMOCYokogawa Electric Corporation．※Licensed by Shell Oil．

・Soft Sensor ExaRQEYokogawa Electric Corporation．※Licensed by Shell Oil．

Fig．５ Comparison of chemical total cost distribution ofpulp bleaching（manual vs. MPC control）※After PLS corrective operation.

１１９２


the value of frequencies as ordinate axis. Additionally the

both display scales are all same.

In Fig．５ of the upper histogram of manual operation

modes, pulp bleach chemical cost data are distributed and

stretched wide around right side directions where costs

are expensive regions. On the other hand, for the lower

histogram of auto controlled by MPC, many data are dis-

tributed around from midst to left side areas, where costs

are lower regions. And the lower histogram’s cost reduc-

tion is recognized clearly. At the end, from０．００９to０．１１

yens／pulp―kg costs deduction of pulp bleach chemicalsduring auto―controlled by MPC had been analyzed

through the long―term investigations compared with man-

ual operation period, although the value fluctuates slightly

according to periods.

This reduction width is corresponding to about３．５％ ofpulp bleaching chemical costs, and the values agreed with

the investigated values of the other industries’ applica-

tions６）.

７．２ Stability of pulp qualitiesIn Fig．６, we compare with the stability states of pulp

qualities, that are final pulp brightness and K value that

exit from D１stage bleaching tower between manual op-

eration period data（２０９days and the number of data n＝

１，２９３）and MPC pulp bleaching control period data（２３３

days and the number of data n＝１，４４１）. They are com-

pared with utilizing many measurement data by hand

analysis. The data in Fig．６are plotted with pulp bright-

ness as abscissa axis and the value of frequencies as ordi-

nate axis. We can recognize from the shape of histograms

and the distribution values of pulp brightness written in

the statistic scores that were noted in the framework of

the figures, that both conditions are nearly same, when we

compare with the standard deviations（１―sigma）０．９for

manual operation,０．８for MPC control. No tendency be-

coming worse by MPC control were recognized surely.

Incidentally, it was also proved that the simple arithme-

tic calculated standard deviation when analyzed with

long―term data is nearly twice as large as compared with

when analyzed with short―term data. The reason is be-

cause many plural target pulp brightness data had been

included among the long―term data１１）.

The amount of hexenuronic acid（HexA）components in

pulp stocks that may cause the serious“yellowing trouble”

of the paper products are monitored and controlled mainly

by final K values. We can recognize from the statistic

scores that were noted in the framework of the figures,

both are nearly same, when we compare with the average

values in Fig．６,０．９９ for manual operation and１．０３for

MPC control, although the value increased０．０４for worse

direction when we used MPC bleaching control. However,

it did not any matter because it had been enough margins

against the upper bound.

７．３ Control resultsFig．７ shows an example of long―term trend during

twenty two days duration.

The one division of the abscissa axis indicates one day,

and each high and low value trends that were set by op-

erators are displayed into the graph at the same time.

Although this trend is an example that had been con-

tinuing to control by MPC during whole periods, it had

been recognized to be switched off some manipulated vari-

ables：MV by operators sometimes. Additionally, al-

though it is recognized from the trends that operators had

changed the setting values of high and low limit ranges of

D０and D１stages’ chlorine dioxide（ClO２）dosages fre-

quently, we understand that the control states had been

executed satisfactory on the whole.

From this long―term trend, we could recognize the fol-

lowing. At first, the pulp brightness of the exit side of D１

stage are keeping the restrictions of high and low con-

straints set by operators, approximately（Fig．７（�））. Next,the important minimization functions of MPC are working

properly, that pulp bleaching control functions by MPC is

working to decrease the dosages of D０and D１stages’

chlorine dioxide（ClO２）, the unit chemical cost is the most

expensive one, had been set toward the lower limit as

much as possible（Fig．７（�）,（�））, and on the contrary,to increase the dosages of hydrogen peroxide（H２O２）, the

unit chemical cost is relatively low, had been set toward

the upper limit as much as possible（Fig．７（�））.Incidentally, the frequent change operations of the dos-

ages of hydrogen peroxide（H２O２）are apt to cause the in-

stability of pulp stocks’ color problems, so switched off pe-

riods of MPC functions by operators are seen very often

disliked to cause the instabilities（Fig．７（�））. However thereduction effects of the total pulp bleaching chemical cost

were recognized as mentioned in Section７.１. This is un-

Fig．６ Comparison of final pulp brightness distributionstates and calculated K―value average and devia-tions（manual vs. MPC control）

１１９３


derstood as follows, the individual MPC optimizing func-

tions by each pulp bleaching chemicals contributed to con-

trol the dosages of chlorine dioxide（ClO２）and caustic soda

（NaOH）lead to reduce the chemical costs, respectively.

In actual operation, the pulp brightness that exit from

D１stage determines the final product quality, and the

value is important for the quality management. However,

the measuring pulp brightness value of hard sensors,

which are installed at the exit of D１stage, are treated only

as a reference value in actual, because the measuring val-

ues are often apt to occur sensor―shift phenomena, result

in indicate inaccurate values. Therefore, the data that had

been measured during periodic intervals in the same posi-

tion by hand analysis are regarded as the most precise

data, and used it for the control management data.

At the end, we constructed this pulp bleaching MPC

system to control ClO２ dosages by utilizing the predicted

values for“control variables：CV”, that are calculated suc-

cessively by soft sensors and had been modified based on

the hand analysis measurements.

In addition, any hard sensors, ex. pulp brightness sen-

sors, K value sensors etc., are not installed downstream of

the storage tanks described far right in Fig．１（Chapter２）.

Therefore, the final pulp qualities that are required the

highest accuracy had been managed by hand analysis data,

same as the exit pulp brightness from D１stage mentioned

before. We introduced soft sensor functions to predict

those important values online in a similar way（Table２）,

and by utilizing them effectively, we could be able to ob-

serve the targeted bleaching processes with a fairly close

states of actual manipulations with operators７），８）.

８． Conclusion

We developed multi variable control system to control

automatically pulp brightness and pulp K values and resid-

ual chlorine consistencies, pH and so on at the same time,

and further to prevent the serious troubles caused by K

values that may breaks out the“yellowing trouble”when

paper rolls are exposed long in the heat and high moisture

environment in future.

Although the works to keep pulp bleaching process con-

ditions to optimal states always is considerably difficult for

humans, MPC system is able to control it automatically,

moving plural chemical dosages to appropriate directions

maintaining all pulp qualities and reducing pulp bleaching

costs surely, and improving the economic efficiencies. It is

considered that they are achieved by specialized functions

of MPC.

The one is the optimizing function of Performance func-

tion：J. And the other one is the limits of high and low set-ting restriction functions for each control variables. Opera-

tors had been able to change the targets values succes-

Fig．７ Actual operation trends of pulp bleaching process by Model predictive control（abscissa axis：２２days operations and one block shows one day）※In the trend, upper and lower limits for each operation variables set by operators from MPC operation terminalare indicated.

１１９４


sively avoiding the break out of qualitative dangers with

evaluating the movable margins of lower and upper

bounds of chemical dosages properly. As the result, opera-

tors had been able to devote themselves with comfort to

other many important works to be done.

The cost reduction by conventional DCS control meth-

ods is approaching already close to the limits. MPC is a

method to have the possibility breaking through the cur-

rent situations in process control area. We are expecting

to be utilized this new control method effectively to some

appropriate applications in the domestic pulp & paper in-

dustries by all means.

In closing, the development of this MPC system was

done as joint research with Oji Holdings Corporation and

Yokogawa Electric Corporation. We should like to ac-

knowledge the invaluable assistance of the mill operation

staffs of Oji Holdings, and Mr. Jun Nishimura of former

Technical Division of Yokogawa & Co., Ltd.

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ence,２０１３.

２）Y. Mori et al.：Japanese Unexamined Patent Applica-

tion Publication No.２０１３―００７１３５,２０１３.

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８）Y. Mori et al .：Japanese Patent application No.２０１３―

２０９５０３, unpublished at this writing.

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aRQE Instruction manual, IM３６J０６D１４―０（３rd edi-

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１０）Y. Mori：A calculation method to evaluate the control

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nal, Vol.６７（１２）,２０１３.１１）Y. Mori：Statistics analysis for pulp brightness data

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（Manuscript accepted１４April,２０１４）

１１９５


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