Application of advanced control methods in the pulp and paper industry — A survey

0005-1098/86 $3.00 + 0.00 Pergamon Press Ltd. ,~ 1986 International Federation of Automatic Control

Automatica. Vol. 22, No. 2, pp. 143-153, 1986 Printed in Great Britain.

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Application of Advanced Control Methods in the Pulp and Paper Industry A Survey* GUY A. D U M O N T t

A survey of advanced control applications in the pulp and paper industry indicates that very jew industrial control schemes use advanced control methods, although there are some encouraging applications of adaptive control and signs of Jrui(ful cooperation between academia, industry and suppliers. Key Words--Adaptive control; control applications; multivariable control systems; paper industry; process control; pulp industry; state-space methods; stochastic control; system identification.

Abstract--Digital computers have been used for process control in the pulp and paper industry for more than 20 years. This paper reviews the applications of advanced control methods to pulp and paper unit process control reported during the last decade. Modern control theory and adaptive control theory are most often applied to paper machine and headboxes. However a large number of reports deal only with simulated or laboratory scale processes. Indeed very few industrial applications are reported. Some of the reasons for this are analyzed and future trends briefly discussed. i. INTRODUCTION

THE PULP and paper industry first initiated computer application to process control some 20 years ago (AstrSm, 1964). Today, the majority of paper machines in the world are computer controlled. Some of the major breakthroughs in advanced control theory have been first tested in the pulp and paper industry, e.g. the minimum-variance controller (MVC) (AstrSm, 1967) and the selftuning regulator (STR) (Borisson and Wittenmark, 1974). In the past, a number of survey papers have been published on various aspects of computer process control in the pulp and paper industry. Brewster and Bjerring (1970) give an overall view of the status of the field at that time. Church (1976) reviews the field with an emphasis on the problems that are specific to the pulp and paper industry. In Keyes (1977), the computer installations in the industry * Received 18 April 1984; revised 25 June 1985. The original version of this paper was presented at the 5th IFAC/IMEKO Conference on Instrumentation and Automation in the Paper, Rubber, Plastics and Polymerization Industries which was held in Antwerp, Belgium, October 1983. The Published Proceedings of this IFAC Meeting may be ordered from Pergamon Press Limited, Headington Hill Hall, Oxford OX3 0BW, U.K. This paper was recommended for publication in revised form by Associate Editor B. Wittenmark under the direction of Editor K. J. Astr6m. ?Pulp and Paper Research Institute of Canada and Department of Electrical Engineering. University of British Columbia, 2356 Main Mall, Vancouver, B.C., Canada V6T I W5. AOTO 22: 2-A

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are surveyed. In Perron and Ramaz (1977), a survey of control strategies available for chemical plants is presented. Gee and Chamberlain (1977) review the control algorithms in common use in the industry. Michaelson (1978) reviews computer control strategies used in thermo-mechanical pulping (TMP) plants. Battershill and Rogers (1980) present a survey of on-line computers installations in the pulp and paper industry. The present paper is concerned only with the control algorithm used in these systems, that too often overlooked "black-box". It is the opinion of this author that process control in the pulp and paper industry is at a turning point. Over the past decade, the major concern was to b.e able to "close the loop", control a unit process using a computer. Now that computer control systems are available for most process units, the emphasis will be on control performance. The objective will be to improve the efficiency of the control system in order to improve quality and productivity, reduce costs and satisfy stringent environmental constraints. At the same time pulp and paper mills are becoming more interactive due to increased internal recirculation, more demanding customers and a shortage of quality wood supply. In addition, the long established Western pulp and paper industry has to face the challenge of developing countries with their cheap and abundant fiber supply. Advanced control methods will help to make computer control systems more efficient. Almost all current computer control systems make use of long established methods. These methods, although often ingenious, were necessarily limited by the pneumatic or analogue electronic hardware used for implementation. For the most part, current systems ignore the control theory that has come out of universities and research centers over the past 20 years and also ignore the versatile signal processing

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possibilities of digital systems. However there are some exceptions and this paper surveys some of the advanced control applications in the pulp and paper industry. Many papers dealing with either simulated or pilot plant control experiments are included in this survey, since work on simulations or pilot plants indicates the potential of some of these methods and trends in computer control system applications. The concept of advanced control is relative and subjective. In the process industries, for example, dead-time compensation is often classified as advanced control whereas in the aerospace applications, Kalman filtering is considered ordinary. For the purpose of this review the following methods will be included: modern control methods rstate-space; linear-quadratic-Gaussian (LQG), pole assignment], system identification, stochastic and adaptive control and automatic tuning algorithms. The subject of production control and scheduling will not be addressed, as it would demand a survey on its own. The paper is organized as follows. In the next section some characteristics of the problem of process control in the pulp and paper industry are briefly touched upon. Section 3 constitutes the core of the paper. Section 4 discusses some of the findings of this review. Finally Section 5 draws some conclusions and gives a hint about possible future developments. 2. CHARACTERISTICS O F THE PROCESS CONTROL PROBLEM IN THE PULP AND PAPER INDUSTRY

Like chemical processing plants, pulp and paper mills are very interactive plants with extensive recycling. They consist of both batch and continuous processes. The nature of these processes is also very varied. For instance pulping processes can be mechanical, chemical or a combination of both.

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As an illustration of the complexity of a pulp and paper mill, Fig. 1 depicts a schematic diagram of an integrated kraft mill. The first step is pulping, that is the dissolving of the lignin, the material that binds the fibers together. The degree of delignification of a pulp is generally measured by a laboratory analytical test known as the Kappa-Number. Pulping is achieved by cooking the wood chips with a chemical solution, called liquor, under controlled conditions in a digester. There exist two types of digesters, batch and continuous. The latter is most often a Kamyr digester, i.e. a vertical reactor in which both chips and chemicals are fed at the top and move downwards as a plug, see Fig. 2. Bleaching can be regarded as the continuation of pulping, as it is essentially the removal of residual lignin and colouring compounds. A typical bleach plant consists of five or six chemical reactors with interstage washing. Each stage must be carefully controlled in order to preserve desirable pulp properties. Finally in papermaking, the most important piece of equipment is the paper machine, on which stock is drained and dried to produce a sheet of paper. The paper machine is a very complex system. Figure 3 outlines some features of a particular type of paper machine. The purpose of the headbox is to distribute the proper amount of fiber suspension on the paper machine wire at the right speed and in a uniform way. Most modern headboxes are pressurized, meaning that the air pad between the surface of the stock and the top of the headbox is kept under pressure. The total head, i.e. air pad pressure plus level, at the slice determines the velocity of the outlet jet. An extensive range of online sensors installed at the dry end of the paper machine is available to measure critical properties such as basis weight, moisture, thickness, etc. The paper machine is by far the best instrumented

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F1o. 3. Simplifieddiagram of a paper machine. process in the industry. The paper reel is then sent to the finishing area for rewinding and roll trimming. The control problem in the pulp and paper industry shares many characteristics of the control problem in the process industries, as discussed in B61anger (1980). First, because this is an old industry, many pulp and paper mills are old and were designed without much concern about their controllability. Raw materials characteristics vary, especially in the pulping plant and chemical recovery area. The processes in a pulping plant are poorly known. A noteworthy effort toward better understanding and mathematical modelling of kraft pulping is reported in Williams and Holm (1975).

Despite intense activity in the field, quantitatively accurate kinetic models of delignification are not yet available. Little is known of the mixing and dilution of gas in a fiber suspension, yet it has a direct influence on the bleaching reaction rate. Flow patterns in process vessels are generally poorly modeled. Processes in a chemical pulping plant are generally slow, with often large dead-times. Typically, the retention time in a Kamyr digester can be 4-5 h, in a chlorination tower 1,5-2 h. Deadtimes can vary due to changing mixing conditions, dead zones, channelling, or as in black liquor evaporators as the liquor viscosity changes. Processes in a paper making plant are faster, with

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time constants and dead-times that can vary from a few seconds to several minutes. Most processes are non-linear and multivariable. Very often, sensors are not available to measure key process variables. Although effective alkali sensors for the liquor are used, effective sensors to measure the degree of delignification of a pulp do not exist yet. Low consistency measurements are not properly solved yet. Many sensors use inferential measurement, and are affected by factors other than the variable of interest, and so require frequent calibration. Sometimes, the key variables are not clearly defined, let alone measurable. For instance, in mechanical pulping pulp quality is affected by factors like fiber length and its distribution and specific area but is not clearly defined. In general, more sensors have been developed for the paper mill than for the pulp mill. The applications of computer control systems have centred around the paper mill. In particular, many turnkey control systems are available for the paper machine for which a variety of sensors exist. Although fewer suppliers offer them, turnkey systems are also available for most processes in the pulp mill. 3. ADVANCEDCONTROL APPLICATIONS The past 20 years have been an era of rapid progress in control theory. The development of multivariable control methods based on the statespace approach were a major breakthrough. These methods, which form the backbone of the so-called modern control theory have been mainly applied in the aerospace industry. One reason is that they require good models of the system to be controlled, and in the aerospace field it is usually no problem. As said earlier, such is not the case in the process industries. Two other important developments took place in the early and late seventies. The development of sophisticated identification methods allows the designer to empirically obtain a good model of the process. And the development of applicable adaptive control methods eliminates the need for accurate models and allows control of timevarying processes. The applications of all these methods in the pulp and paper industry will now be reviewed. (a) M o d e r n control methods Representative of early effort in this area, are a series of papers presented at the BPBMA Conference in Oxford, 1969 (Bolam. 19691. The work in Belanger (1969) or color control is also typical of these early efforts. In Sandraz (1973), modern control theory is applied to control of basis weight, moisture and color on an experimental paper machine. In Halme et al. (1974) a quadratic state regulator

for a class of non-linear systems is derived and applied to control of level and total pressure on a pilot plant headbox. The performance achieved is slightly better than with PID control, at the expense however of significant computing time requiring a 10s sampling interval. Fjeld (1978) describes the application of leastsquare quadratic optimal control theory to the control of a pressurized headbox and thick stock consistency on a kraft paper machine. This system has been in operation for many years from 1972, one of the oldest continuing applications of modern control theory in this industry. The thick stock dilution system is modeled by 6 x 6 discrete-time state-space, where three states represent the transport delay between the mixing point and the consistency meter. The controller consists of a Kalman filter state estimator combined with a multivariable PID regulator designed using minimum-variance control in the presence of biased disturbances. The tuning constants are computed so as to keep a fixed assignment of eigenvalues each time mill personnel enter a new value for the process gain. This system is claimed to reduce the consistency standard deviation by 40 % compared to an "optimally" tuned PI regulator. The headbox is modeled as a 4 x 4 system where two states have been added to provide integral actions. A "modaloptimal" control design that computes the optimal LQG regulator with prescribed eigenvalues is used. A major advantage of multivariable headbox control is that it takes the couplings between liquid level and total head into account. This system is claimed to reduce short-term total pressure variations by 5 0 ~ while completely eliminating long-term variations. It is also claimed to have improved basis-weight and moisture control as well as having simplified the task of grade change. The work on application of modern control theory to headbox control that took place in France is presented in a series of papers: Nader, 1978; Nader et al., 1979; Lebeau et al., 1980; Lebeau et al., 1982. The headbox control system is patented under the name MUVAR and also makes uses of a process identification package PROCIDENT, described later. Their algorithm, based on formalism developed in Bornard and Gauthier (1977), uses two reference models, one for tracking, the other for regulation, and two internal models, one for manipulated variables, the other for measured disturbances. It is thus claimed that the use of an observer is not required and that zero steady-state error is achieved without use of integrators. In Nader (1978) and Nader et al. (1979) it is shown that the linear control system becomes unstable when the headbox is operating too far away from design point. This problem is the motivation of Nader's work on adaptive control. However, in Lebeau et al.

Survey Paper (1980), this potential problem of robustness is not mentioned. This system has been tested successfully on an experimental headbox as well as on an industrial paper machine in France (CTP, 1982a; Lebeau et al., 1982). Various options are proposed from a simple 2 x 2 system, i.e. level and total head to a more complex 4 x 4 system, i.e. level, total head, headbox consistency and basis weight either measured at the reel or calculated at the slice. MUVAR may become commercially available in the near future. The headbox application is also used as a benchmark problem in universities. Thus, Sinha and Rutherford (1976) present an application of the University of Manchester Institute of Science and Technology (UMIST) Computer Aided Design package (CAD) to the design of a controller for the UMIST pilot plant headbox. The design was performed in the frequency domain, using Inverse Nyquist Array (INA) technique. The controller consists of two PI regulators and a decoupling precompensator. Gunn and Sinha (1983) apply non-linear control to a simulation model of the UMIST headbox. The level is controlled by the stock flow with a PID, while the air flow is given as a non-linear function of total head, level and stock flow, to maintain constant total head at the slice. Abdel-Moniem and Sorial (1981) apply optimal control with pole assignment to a simulated headbox. After parametrizing the class of controllers yielding specified closed-loop eigenvalues, they use a gradient method to choose the condition that minimizes a time-dependent performance index. LQG theory has been used as the basis of paper machine control systems by Bialkowski (1978a, b, 1983). Single-input, single-output systems are considered and are described by first-order dynamics and dead-time. The dead-time is modeled by augmenting the state vector. The disturbance is modeled by white noise passed through a first-order filter. The steady-state Kalman filter gain is used for on-line implementation. Tuning rules for the various weighting matrices are given in Bialkowski (1983). In order to handle dead-time uncertainties, the residual error is passed through a dead band whose magnitude is changed on-line, thus increasing the robustness of the algorithm at the expense of performance. This algorithm is used in five paper machine basis-weight control systems, one paper machine moisture control system. In a bleach plant brightness control system, the performance of this system for basis-weight control is judged to be close to minimum-variance. Gorez (1981) applied optimal control theory to design a dead-beat controller for a simulated paper machine, using orthogonal transformations to solve the Riccati equation.

147

Few applications have been reported in the pulping area. Boyle et al. (1981) present an application of multivariable dead-time compensation to control a black liquor evaporation plant. The plant is described by a 2 x 2 transfer function matrix. The prototype system was successfully tested in a mill. Most batch digester control systems in use are model-based, e.g. Jutila (1980). A kraft-cooking model is used to predict the Kappa-Number at the end of the cook, using an effective-alkali measurement at a given stage during the cook, and an Hfactor calculation. These models are generally fairly simple and prone to modeling errors. Sometimes they are put in state-space form and a simplified Kalman filter used to update the predicted KappaNumber every time a Kappa-Number test result is available (Wells et al., 1975; Freedman, 1976; Powell, 1979). (b) System identification A major characteristic of modern control methods is that they rely on models of the process. Since accurate dynamics models are very rarely available from first principles, identification experiments have to be performed on the plant. Seminal work in this area was performed in Sweden in the mid-sixties by Astrbm and his colleagues, using a paper machine as a benchmark. In Gentil et al. (1973), the transfer functions between fiber flow, steam pressure and basis weight and moisture on an experimental paper machine, are identified using four different identification algorithms. Use of an experimental paper machine as a benchmark for a recursive on-line identification method based on a Model-Reference Adaptive System (MRAS) is reported in Landau (1976). This technique is used to identify the transfer function between the stock flow and the reel moisture. A pseudo-random binary sequence (PRBS) is applied to the stock valve and a second order model is used. Good results are obtained. More recent work is described in Mahig and BouGhannam (1980). There multivariable identification is applied to a kraft paper machine. A modified maximum-likelihood method is used to identify the parameters of an auto-regressive (AR) model representing the start-up transients of the machine. In Lindeborg (1981), an estimation scheme for the parameters and states of the non-linear moisture variation model is described. Variations in the machine and cross-directions are separated, with the purpose of improving control. In DeVries et al. (1977), multivariate time-series analysis is applied to study the stability of crossmachine direction basis weight profiles on a paper machine: Auto-regressive moving average vector (ARMAV) models are identified using maximum-

148

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likelihood technique. In DeVries and Wu (1978), the same technique is used to evaluate the effectiveness of basis-weight control schemes. Sud et al. (1977) report an application of identification to the UMIST headbox using a statespace representation. Two methods are compared, an extended Kalman filter and a two-stage estimation procedure where state and parameter estimation are done sequentially. Because MUVAR, the headbox control system previously described, requires an accurate model of the process, a companion identification software package called PROCIDENT was developed (CTP, 1982b). It combines a non-linear programming method with a least-squares algorithm. It is applicable to MISO processes that can be represented by first order ARMA models. Once the ARMA model is obtained, the corresponding statespace model is computed. This software package was tested during the implementation of MUVAR or an industrial headbox (Lebeau et al., 1982). In the recovery area, work on lime kiln and causticization control is reported in Uronen et al. (1976), Uronen et al. (1977) and in Uronen and Aurasma (1979). An off-line least-squares identification algorithm is used to estimate a set of MISO ARMA processes. First and second order models are tested. In total 9 transfer functions are identified. Control strategies are compared on computer simulations. Two industrial applications are reported and a commercial control system based on this development is now available (Elsila et al., 1979). In Kippo et al. (1981), SISO least-squares identification is applied to obtain a MIMO model of a recovery boiler. A multivariable PID regulator based on LQG theory is designed and tested on a simulation. Liao (1980) and Liao and Wu (1983) employ time-series analysis to identify a Kamyr continuous digester. Parameters in extended low order AR and ARMA processes are estimated using either leastsquares or non-linear optimization. Normal operating data is used. The derived model is used to design a control strategy for Kappa-Number and H-factor. The most original contribution of that work is a proposed digester hang-up detection scheme. Heating zone temperature is modeled as an ARMA (3,2) process. Such a system has two complex conjugate poles and a real pole. The complex poles are associated with shifting due to liquor extraction screens. The real pole is interpreted as representing the mixing of fibers and liquor. The temperature response time constant increases as the wood proportion increases. Such an increase in wood proportion in the heating zone signals the building of a plug. Hence it is proposed to monitor the magnitude of the real pole with an upper limit to signal the detection of a hang-up. The severity of the

hang-up can be deduced from the amplitude of the pole, i.e. the closer to one, the more severe the hangup. This proposed on-line technique for detection of hang-up does not appear to have been implemented. (c) Stochastic and adaptive control Pioneering work in this area was performed in Sweden in the late sixties and early seventies, using the paper machine as benchmark. However, since the now classical paper by Astrbm (1967), there has been no reported application of minimum-variance control (MVC) or its extended form (EMVC) in the pulp and paper industry. This may be due to the fact that these controllers cannot be tuned manually. This fact motivated the work on self-tuning regulators (STR) and its first two reported applications on paper machine, Borisson and Wittenmark (1974) and Cegrell and Hedqvist (1975). Despite the fact that the pioneering work on adaptive control used the paper machine as a benchmark, it is only recently that the industry has manifested interest to apply STR to a paper machine. The first application reported by a supplier of turnkey control systems to the industry can be found in Fjeld and Wilhelm (1981), Wilhelm (1982) and Kelly (1982). Application to MD control of moisture on a fine paper machine is presented. As in Borisson and Wittenmark (1974) and Cegrell and Hedqvist (1975), feedforward terms are included. The algorithm uses the Clarke-Gawthrop selftuning controller STC in velocity form and with fixed forgetting factor. Many proprietary modifications to the basic STC are claimed. This scheme is said to further reduce moisture standard deviation by 5 0 ~ compared to conventional control. A similar approach is taken to control the cross-machine direction weight profile, Wilhelm and Fjeld (1983), Wilkinson (1983). A space recursive model is used to represent the action of a slice actuator on the basis-weight profile. The three parameters characterizing this action are identified on-line. The results are used to compute the slice actuator movements to obtain the desired profile, solving the decoupling problem between the numerous actuators by means of generalizedminimum variance control theory. This control system is installed in a number of U.S. paper mills and is now commercially available. An application of STC to moisture control on a linerboard machine is reported in Sikora (1983) and Sikora and Bialkowski (1984). Once again Clarke-Gawthrop with forgetting factor is used. Plant trials are reported and it is shown that feedforward from couch vacuum, machine speed and moisture setpoint may improve the STC performance. Use of a variable forgetting factor may

Survey Paper also be considered in future work. Other examples of the current interest for adaptive control of the paper machine are found in Angelov et al. (1983) and Lirz et al. (1983). In many cases the headbox control problem is used as a benchmark for testing new developments in multivariable self-tuning control methods. The seminal work on this topic is found in Borisson (1979), where an extension of minimum-variance control to n x n M I M O systems with unique deadtime is applied to a simulated headbox. Later Koivo (1980) applies to the same system a M I M O extension of the Clarke-Gawthrop STC. Anbumani et al. (1981) use the same model to test a non-linear multivariable STR. Their approach is based on a multivariable Hammerstein model to represent a linear process with non-linearities in the actuators. Halme and Selkainaho (1982) describe an application of a non-linear filter coupled with a statespace controller to a pilot plant headbox. The nonlinear filter estimates both parameters and states. A multivariable PI regulator is then tuned to minimize a quadratic criteria. The non-linear filter is described in more detail in Selkainaho et al. (1983). In D'Hulster et al. (1980), the performance of several SISO adaptive controllers is compared on a simulated headbox. Decoupling is performed by adding feedforward compensation terms in the models, then using two MISO controllers. All adaptive controllers outperform conventional PID control. However, it is noted that oscillatory modes can sometimes be excited by the adaptive controllers. This problem could be serious on an industrial headbox. In D'Hulster and van Cauwenberghe (1981), a multivariable adaptive controller is applied to the same system. Model reference adaptive control has also been applied to the simulation of a pilot plant headbox (Nader, 1978; Nader et al., 1979; Nader, 1980). Perfect model following and decoupling are achieved on simulation results. In the pulping area, applications have concentrated around the continuous digester, the first work being reported in CegreU and Hedqvist (1974). A MISO STR is used to control chip level by means of the chipmeter and the blow flow. Cooking temperature is manipulated to control a predicted Kappa number. The parameters of the model, which is linear in the coefficients, are updated every time a new Kappa number test is available. This system was tested on an industrial Kamyr digester in Sweden. In Sastry (1978), a minimum-variance STR is used to control the chip level by manipulating the blow flow. Results of plant trials on an industrial Kamyr digester are presented. Because of lack of reliability and robustness of the STR, the application was abandoned after a few weeks. A more recent approach to that control problem is found in

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B~langer et al. (1983, 1985) and Dumont et al. (1984). The Clarke-Gawthrop STC with estimation of a bias term is used. Four separate strategies are evaluated, using blow flow, outlet device or some combinations as manipulated variables. Control of the chip level is much improved thus giving smoother operation of the Kamyr digester, and relieving the operators of one of their chief concerns. This scheme has been in continuous operation since March 1983 in an Eastern Canadian pulp mill. In Dumont (1982a), a STR is used to control the motor load on a chip refiner, as part of a TMP control system described in Dumont et al. (1982). The motor load control problem is interesting since the incremental gain of the process is subject to a slow drift due to plate wear and occasionally can change .sign abruptly. To solve that problem, a recursive least-squares estimator with variable forgetting factor is used in conjunction with a Dahlin regulator. Results of plant trials of probably the first industrial application of Fortescue's variable forgetting factor (Fortescue et al., 1981) are presented. Further work on this control scheme is proceeding and a continuous industrial application is expected. Similar approaches to the motor load control problem have stemmed from that work, e.g. Jones and Pila (1983). Recently, off-the-shelf adaptive regulators have become commercially available. One such system, the ASEA NOVATUNE is used to control reel density and web tension on roll winders, see Eriksson et al. (1983). Because the process dynamics change continuously during the winding operation, the self-tunit~g controller behaves much better than conventional, PI regulators. About a dozen applications of this system have been reported worldwide. Other potential applications of the NOVATUNE to pulp and paper production, i.e. consistency control and Yankee dryer moisture control are briefly outlined in Sinner (1983). The recovery area is generally neglected in control studies. An interesting example of advanced control in this area is found in Haataja et al. (1983). Adaptive filtering is used on both a lime mud filter and a lime kiln, to respectively estimate the mud alkali content from a filtrate conductivity measurement and to estimate the burning temperature from a pyrometer and a thermocouple. The least-mean squares (LMS) algorithm is used for the filter. (d) A u t o m a t i c tuning methods In some cases, there is no need to continuously adjust controller coefficients. However, if a complex controller is used, a tuner is necessary. With a PID regulator, a tuner can reduce the surveillance required and improve the control performance. In Astr~Sm (1980), a Swedish pulp and paper mill is said to use the STR as a tuner on paper machines, a

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recovery boiler and various low level loops. Recently, a supplier of turnkey control systems to the pulp and paper industry has introduced a package for the tuning of the computer-based controllers (Kunde et al., 1983). A recursive leastsquares algorithm is used to identify the open-loop process while the manipulated variable is changed in a step-wise manner. PID controller automatic tuners are also developed for implementation on distributed control systems. One such system is described by Halme and Ahava (1984). The plant dynamics are estimated via a modified recursive least-squares scheme and the regulator tuned to minimize a nonquadratic performance index. Results on a consistency control loop are presented. More recently, work has been performed at PPRIC and McGill University, Montreal to develop an automatic tuning method for PID regulators, for eventual implementation on distributed computer control systems. The step response of the closed-loop system is identified by means of a Laguerre expansion and the regulator is then tuned by minimization of a quadratic performance index. Trials on various loops in two pulp mills have been performed. For more details, see Dumont et al. (1985) and Zervos et al. (1985). 4. D I S C U S S I O N

As seen in Fig. 4, the literature on applications of advanced control methods to pulp and paper processes has been growing at an increasing rate, during the past five years. However, this requires cautious interpretation. Table 1 shows that in many papers surveyed in this report, advanced control methods have been applied either to simulations of pulp and paper process units, or to small scale pilot plants. Industrial applications include cases where only short plant trials were performed on an industrial scale unit. In fact very few permanent industrial implementations exist. For most of those industrial implementations, close interaction with university existed at some stage of the project.

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FIG. 4. N u m b e r of publications on applications of advanced control to pulp and paper processes.

Many reasons exist for this present lack of industrial applications of advanced control methods, not only to the pulp and paper industry but to the process industries in general. First, there is the problem of confidence development, i.e. clearly demonstrated success and reliability of these methods. Process industries will use these methods only if they are proven. The difficult question to answer is whose responsibility it is to prove the reliability of advanced control methods. Second, the complexity of modern control theory cannot be grasped without sufficient mathematical background. The design of controllers based on modern control theory requires accurate models and complex and time-consuming calculations, which necessitates highly specialized software. This discourages the potential industrial user. Fortunately, this problem is now disappearing with the advent of Computer-Aided Design (CAD) packages for control systems. A good example of this is the use of the Lund Institute of Technology's IDPAC and SIMNON packages by the Swedish Forest Products Research Laboratory (STFI) (Lundqvist, 1978; Lundqvist and Nordstrom, 1980). The need for accurate models can be eliminated by the use of adaptive control methods. This explains why most recent papers deal with application of these methods. However, these methods are still highly mathematical and the fact

TABLE 1. DISTRIBUTIONOF THE APPLICATIONS Paper Machine

Headbox

Digester &Washer

SIMULATIONS

3

7

PILOT PLANT

4

9

INDUSTRIAL

10

2

4

2

2

TOTAL

17

18

5

2

3

TMP

I

Recovery &Kiln

1

Total

12

13

20

Survey Paper that controller parameters are changing on their own introduces a new dimension to the way control engineers must view the control scheme. In an effort to accelerate the use of these methods, the Pulp and Paper Research Institute of Canada has issued several reports to introduce the practising engineer to these new concepts (Dumont, 1981, 1982b, 1983). The availability of "general-purpose" self-tuning regulators will also accelerate the adoption of these techniques. As noted in Wittenmark and AstriSm (1982), present adaptive control methods require some a priori knowledge of the process. It is felt that those methods will become widely used only once the process-related parameters are replaced by performance-related parameters, thus making their choice goal-oriented. Often, the need for more advanced control schemes is not perceived by the user. For example, a survey showed that users rated modern control theory advances as next to lowest priority, among the requirements for further advancement of control systems (Battershill and Rogers, 1980). Despite the extensive developments on headbox control, many headboxes are controlled by two PI regulators without decoupling and, for example on a fine paper machine, with the same tuning constants for all grades. Maybe control researchers are to be blamed partly for not getting the message across, i.e. not explaining clearly the advantages of more advanced control methods, using language that may seem esoteric even to the practising control engineer. University researchers are not sufficiently interested in getting their control scheme all the way to the plant floor. This is one of the often heard criticisms from the industry to the university. Industry may also be blamed for not hiring, training and motivating personnel capable of adapting university work to solve its practical problems. A third and major component in this are the suppliers of turnkey process control systems. Process control in the pulp and paper industry is supplier dominated. Suppliers have traditionally devoted more efforts to the development of sensors. Also, this is a market where an attractive color graphic display is a better selling argument than an advanced control scheme. Nevertheless, as seen in this survey, some recent supplier papers are encouraging. This is however only with increased demand by the industry for more advanced control that this trend will accelerate. The market for distributed computer control systems is typical of this situation. Although those systems use state-of-the-art technology for hardware and graphic display, they use PID regulators that still have to be tuned manually. These distributed control systems could make good use of automatic tuning algorithms.

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The majority of applications reviewed took place on paper machines or headboxes, either simulated or real. It is interesting to note that paper machines were used as benchmark in the sixties for testing use of stochastic control theory and in the seventies for the SISO or MISO self-tuning regulator. The headbox is now used as benchmark for MIMO STR. Thus, some of the most advanced control theory is tested on pulp and paper processes. This is bound to be beneficial to the state-of-the-art of process control in this industry and should encourage more cooperation between university and industry. Not many applications are reported in the pulping area whereas it is felt that it is probably there that adaptive control will have the largest impact. Indeed pulping processes are subject to raw material variations and wear and their kinetics are generally poorly defined. 5. CONCLUSIONS

This survey has shown that although the literature on the subject appears extensive, there exist only few applications of advanced control methods to industrial pulp and paper producing processes. Many reports come from academic researchers using simulated pulp and paper processes to test new control methods. This is a healthy sign and the industry must learn to exploit this source of know-how. Another good sign is that the number of papers on the subject has been increasing at a faster rate over the past five years. The author thinks that adaptive control methods, together with new sensors will form the basis for the new generation of control systems for pulp mills. Techniques like pattern recognition and image analysis may be used in the future to obtain on-line information on fiber morphology, then providing fiber quality measurement for mechanical pulping, or to detect foreign material like bark on a log or bark or plastic in pulp. Sensor failure detection techniques may increase usability of present and future sensors. Failure detection may be used to detect hang-ups in a Kamyr digester. Estimation techniques may be used to get continuous estimates of plant flows and inventories. Thus leaks could be detected, sensor calibration could be automated. Such techniques would also improve the efficiency of present production control schemes. Finally, looking further ahead, use of fuzzy logic control may help to control processes difficult to model while much touted expert systems may help in the management of mill-wide control systems. Commercial systems using these techniques are becoming available, see Bejder (1984) and Moore (1985). REFERENCES AbdeI-Moniem, T. H. and N. Sorial (1981). Design of optimal regulator for z pressurized flow-box using time-multiplied

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