Theory and Tools for Overall Process Control System Design

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THEORY AND TOOLS FOR OVERALL PROCESS CONTROL SYSTEM DESIGN G. W. Barton and [)''1)(i/'/II/I'II/

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D. Perkins

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Abstract. This paper reviews the current status of control system synthesis for continuous processing plants. The need to integrate control considerations into the overall plant design from an early stage is discussed. The role of structural analysis, interaction measures and singular value analysis in control system synthesis are outlined. Several important areas requiring further research are identified. A systematic approach to the development and implementation of optimising control schemes is still unavailable as are simple, reliable techniques for assessing the economic benefits of advanced, as opposed to multi l oop, control. Features desirable in a control system synthesis package are identlfled .

Keywords. Review; control system synthesis.

INTRODUCTION The design of a process plant's control scheme is today an aspect that is considered relatively late in the overall design process. Control considerations are certainly rarely taken fully into account during the development of the (steady-state) flowsheet. Attitudes to when and how control considerations should be incorporated are, however, changing. One reason for this is the increasing complexity (e . g. high energy integration) of process plants and the r ealisation that approaches to control system design that had coped quite adequately for less complex plants were today neither rigorous enough nor flexible enough. A more complete integration of control considerations into the overall plant design certainly offers real beneflts. However, it also requires new tools - both analytical techniques and computer based design packages incorporating these techniques. To gain widespread acceptance by industry, any such package must display comparable useability to the flowsheet simulators, such as PROCESS and DESIGN 2000, that are so widely used in steady-state deSign . The obJectives of this p3.per are , thus, twofold : (1) TO review recent theory pertinent to control system synthesis. (ii) To look at how such techniques may be packaged for easy usage by industry.

CONTROL SYSTEM OBJECTIVES If one were to poll industry on what features an

"ideal" control scheme for a continuous processing plant should have, then the following would probably cover most of the replies. Such a control scheme WOUld : (a)

Hold the plant close to ltS current economic optimum, while providing good regulatory control at this operatlng pOlnt.

(b)

Allow rapid, minimum cost movement to a new optimum once such a new set of operating conditions had been identified.

(c)

Ensure safe operation in the face of a wide range of failures - both within the process and the control scheme itself.

(d)

Handle automatically a wide range of abnormal situations such as start-up and shut-down (both normal and emergency) .

In short , what industry would like is safe, steady, econom i cally optimal operation. Before turning to the objectives outlined above, let us brlefly examine each of these three industry requirements from the point of view of current lndustrial practice.

safe Operation The consequences of failure are sufficient to ensure that safe operation commands high priority among design engineers, plant operators and control system vendors alike. However, because safety and safe operating practice is so process specific , it is an area for which the available techniques are very general. For example, HAZOP provldes a framework within wh ich a given flowsheet and control system may be assessed on a "what if" basis. s'l,Ch a technique is time consuming to employ wh ile its results are qualitative in nature. In lt S present form, HAZOP provides a final check on a proposed control sche~e rather than offering a control scheMe design procedure. The use of dynamic simulation to assess control schemes from a safety point of view is becoming :r,ore common , particularly for known operatlonal problems in commonly used equlpment (e.g. anti-stall control schemes for large compressors) . Largely as the result of process specificity, safety is an area that has, wlth few exceptions, received scant attention to date from academia. One recent area of both academic and industry lnterest is the application of expert systems to complex problems where a wholly deterministic modelling approach is inappropriate. Expert systems are certainly being used increaslngly in the field of alarm analysls (Moore and Kramer,

216

C. \\' . Barton and.f. D. Perkins

1986) and may eventually provide the general framework within which plant safety is monitored and assessed.

dissimilar to the response time of major process eqUipment. Probably the most familiar example here in the petrochemical industry is the mode change in oil refineries, associated with changed feed-stocks and/or required product splits.

Steady operation The current norm within the process indu~t ries is still the familiar multiloop PI control scheme. However, the advent of microprocessor based distributed control schemes has brought with it the potential for a quantum leap in the degree of sophistication of control schemes employed in industry. Initially, advanced control implementations consisted of enhancements to a basic multiloop control structure that were possible (at least in some form) using analogue control eqUipment, e.g. decoupling and feedforward control. Recently, however, the flexibility of distributed control schemes has seen the implementation of a wide variety of techniques that would have been difficult, if not impossible, using analogue hardware. Typical examples here include more sophisticated measuremen t techniques (e . g. state estimators), other than simple control variables (e.g. the use of ratios in distillation column control) and predictive control laws (e.g. Dynamic Matrix Control). The widespread employment of advanced cont rol techniques is being hindered to some extent by the question of economic justification. The benefits associated with the replacement of an analogue multiloop scheme by direct digital control (i.e. increased productivity through steadier operation and reduced operator costs) are well documented and understood. Similarly , the economic benefits of steadier operation, through advanced process control, for certain classes of process eqUipment (e.g. distillation) are also readily calculable. The wider problem of identifying where in any process plant advanced control techniques would be of economic benefit and, then, quantifying these benefits has been solved only in the sense that methodologies have been developed by some companies, generally contractors and control/instrumentation vendors. With an ever increasing range of advan ced process control options being put forward and the potential for their implementation becoming mOre readily available to industry, the question of economic justification is going to loom large in the years ahead.

Economically Optimal operation Ensuring steady operation in the face of disturbances to a process is the role of the regulatory control system. The option has always existed to drive the set -poi nts of the regulatory controllers so as to meet some wider objective. Such an objective may be associated with a single process unit, a group of interconnected units or indeed the plant as a whole. Increasingly, the trend will be for (relatively) simple supervisory control schemes to be replaced by, or made subordinate to, optimising control schemes. Provided a realistic steady-state model is available, process optimisation can be carried out using robust, non-linear, constrained optimisation packages, such as MINOS (Murtagh and Saunders, 1980) . Whole plant optimisation is typically performed once a month, frequently as a means of scheduling plant operation to maximise profit while satisfying markets and operational constraints. However, although recognised as a problem, very little attention has been paid to cases where the economic optimum changes on a time sca le not too

SYSTEMATIC DESIGN OF MULTILOOP CONTROL SCHEMES As an int roduction, it is well worth pointing out that the control literature literally abounds with papers demonstrating quite clearly that advanced control techniques can result in significantly better regulatory performance than multiloop control . Despite this, multiloop control is going to remain the industrial norm into the foreseeable future, primarily because it has been shown to provide acceptable control in the majority (i.e. of the order 90%) of cases, to date. Even in cases where it is suspected that multiloop control will prove inadequate in some way, the challenge is still to find the best - or, at least, one of the better - multiloop control scheme as the base case against which to compare various advanced control options on a cost-benefit basis. An additional factor in their favour, is the range of techniques available for improving the performance of multiloop cont rol schemes. These include cascade, decoupling and feedforward control, as well as model based techniques such as dead-time compensators (Palmar and Powers, 1985). The remainder of this section will be devoted to the basic question of how to design systematically multi loop control schemes.

Structural Analysis Probably the most fundamental question that can be asked in developing a multi loop control scheme is - "What alternative schemes are feasible?" This basic question can be solved using structural information alone, i.e. information on whether one variable affects another, not on the magnitude of the effect. Johnston, Barton and Brisk (1985) have recently shown how all feasible alternative multi loop control schemes for an entire plant could be generated in a non-iterative fashion. They demonstrated their approach using an extended version of the Williams -O tto (1960) plant. The great strength of structural analysis is that it requires no numerical data. This , unfortunately , is also its great weakness, in that it can provide feasible alternative control schemes but provides no means of ranking them. Atkins, Barton and Johnston (1986) used structural analysis to generate alternative, to the conventional, control schemes for a mineral separati on plant involving 11 control objectives and 15 possible manipulated variables. Engineering heuristics (e.g. mlnimising dead-times in control loops) were used to reduce the number of alternatives which could then be assessed using dynamic simulation. This exercise clearly demonstrate d that dynamic simulation is a poor mea ns of choosing between even a relative ly few alternative control schemes, if the process is

complex. The question of how to assess alternative control schemes more rapidly than via dynamic s i mulation is discussed later.

Control Loop Interaction once any excess inputs or outputs have been eliminated tv leave a square system, the question arises - "Which input should be used to control which output?" The structure of the process

Chcrall Process COlltrol S\S\t'11\ Desigll

eliminates certain possibilities (i.e. an input should not be paired with an output it has no effect on) and forces certain pairings. In cases where choice is possible, undoubtedly the most widely used criterion for loop pairing is the minimisation of control loop interaction, Since its inception (Bristol, 1966), relative gain analysis has been widely used as a measure of control loop interaction. Although widely used,the re l ative gain array (RGA) may give misleading pairing information due to its reliance on steady-state gain data only. Since a control system must cope with the process dynamics, this dynamic information should ideally be included in any interaction measure. Various workers, for example McAvoy (1983), have proposed dynamic extensions of the RGA. Stanley, Marino-Galarraga and MCAvoy (1985) recently introduced the concept of a relative disturbance gain (RDG) and showed how it can be e mployed in conjunction with the more familiar RGA. The basic premise underlying the RDG is that for some disturbances, interaction between control loops can be advantageous. While it is possible to criticise the RDG approach on the grounds that it does not take into account dynamics, ( being based on the steady-state gain matr1x relati ng outputs to inputs and disturbances), it would appear to have a role to play for processes where the major disturbances are known and quantifiable.

~ 1 7

Apart from regulatory control quality, SVA can be employed to quantify many other aspects of control system performance, such as the ability to cl osely track set-points, the likelihood of manipulated variable saturation and the performance and stabili ty sensitivity of the control system to plant-model mismatch (Johnston and Barton, 1985; Doyle and Stein, 1981; Arkun, 1986). For a given process, there appears to be a close correspon dence between the results of closed-loop SVA a n d dynamic simulation. The two techniq u es should be regarded as complementary . SVA of fers a mean s whereby a range of alternatives can be rapidly evaluated and the less promising discarded. Dynamic simulation, taking into account process and controller non-linearities, can then be u sed to compare the more promising options, open-loop singular value analysis. As discussed in the Introduction, one of the trends in con trol system design, is to have control considerations taken in t o account at an earlier stage in the overall design process. In the lim1t this would involve looking at alternative process designs and comparing them on the basis of characteristics known to be related ultimately (i.e. when a control scheme is finally installed), to good controllab ility. The most promising single indicator appears to be the process co ndi tion number, 1 ~ u·(G)/u.(G). As a general rule, the smaller 1 is, the more control labl e a process will prove t o be.

Alterna tive Criteria for Assessing Multiloop Control Schemes Improving Multiloop Performance Minimising loop interactions is on ly one way of designing or assessing a multiloop control scheme. Via RGA, it was certainly popular when the only alternative was dynamic simulation. In recent years, however, various alternative indicators of control system performance have emerged. Closed-loop singular value analysis (SVA). Papers describing both the theory behind (Doyle and Stein, 1981) and applications of singular value analysis to the design of multiloop control schemes (e.g. Yu and Luyben, 1986) have appeared fr equently in recent years, so onl y a brief description 1S necessary here. Fr om the block diagram of a general multivariable feedback system (see Fig. 1 ), various matrix quantities related to the control system perf o rmance may be obtained. For example, the closed-loop relationship describing the dynamic response of the output vector ~ to the external d i sturbance vector w is

For high quality regulatory control, all outputs sh ould remain close to their set-points. In other words, any measure of the size of the vector denoted by

~~~,

~,

should remain small under the

1nfluence of externa l

disturban c es. If, in the

fr equen cy (w) d omain, the size of vectors is measured by the Euclidean norm (Stewart, 1973), it can be shown from Eq.

(1) that

where a* denotes the maximum singular value of a matri x quantity . Fr om Eq. (2), it foll ow s that for high quality regulatory control, the control system K(s) should be designed such that

while preserv ing closed - loop stability.

Hav1ng selected the most appropria te control objectives and manipulated variables, having paired them up into a multiloop control scheme and tuned each loop, on whatever criteria, one can still be left with a problem - poor control quality. The simplest response to this problem is to implement enhancements to the basic multiloop scheme, such as cascade, feedforward or decoupling control . I f the problem is principally due to large dead-times then one could implement one of the increasingl y robust dead-time compensa tors which have been developed since the advent of the Smith (1957) predictor. Process non-linearities, either manifested at a given operati ng point or becoming importa nt as the oper at ing conditions are changed (possibly under the control of a unit or plant op t imiser), are a major cause o f poor multiloop performance. At a given operating p oint , one can incorporate i nformation about t~e local non-l1nearit y 1nto the appropriate PI contr oll er. This is the bas is of the gain scheduling algorithm examined by Tsogas and McAVOY (1985) for controlling top composition in high purity distillation columns. As the operating range expands, it becomes increasingly unlik ely that one multiloop control system (i .e. one set of input-output pairings) with fixed controller parameters will provide acceptable performance over the whole range. The problem of frequent controller retuning is to s ome extent overcome by the recent emergence of cor~erc i al adaptive controllers, such as the Foxboro "Exact" and the ASEA "Novatune". However, it is quite possible (Lau and Jensen, 1985) that the most econ omic al l y profitable part of the ope rating region also happens to be the regi on where poor dynamic performance could be expected. Ther e are several alternative solutions to this problem of wid ely varying process dynamic characteristics within the feasible operating

218

C;. W. Bartoll and.J. D. Perkins

range. Some of these retain a multiloop structure while others employ some form of advanced controller. (i)

Employ non-linear controllers. While such an approach has promise (Economou, Morari and palsson, 1986), it is still very much in its infancy.

(ii)

Employ s ome f o r m of multivariable linear control. NOw, perfect control can be achieved if a controller that "inverts the plant" could be implemented. This simple realisation has been the starting point for the Internal Model Control (IMC) approach to c ontroller deSign, developed, primarily by Morari and co-workers, in recent years. Despite the obvious benefits of multivariable controllers, such as IMC, their widespread adoption by the process industries is primarily dependent on their implementation within vendor control eqUipment as easy to use options.

(iii) Restructure the multiloop control scheme to ensure the best alternative is being used for the current operating point. While possible with current control technology, and necessary if the regulatory control scheme is being dr1ven by an optimiser, it represents a formidable challenge for real processing plants . (iv)

It is well worth emphasising the trade-off that often exists between good steady-state performance and good dynamic (in terms of controllability) performance. AS mentioned previously, Lau and Je n sen (1985) have demonstrated that regions of the feasible operating space that are economically most attractive can also be highly undesirable fr om a controllability point of view. A similar situation can arise when comparing alternative process flowsheets. Barton and others (1986) compared 11 mineral flotation cirCUits designed to separate a water/crUShed ore slurry into valuable mineral and a worthless gangue . Table 1 gives both the results from steady-state optimisation (set up to maximise valuable m1neral recovery (R), subject to operating constraints) and steady-state open-loop controllability analysis. The conclusion that a flowsheet may not exhibit both good steady-state performance and good dynamiC performance was confirmed by closed-loop dynamic simulation of each flowsheet. TABLE 1

Flowsheets Flowsheet

The concept of constra i nt control (e.g. running a compressor as close as possible to its surge line) is familiar in the process ind u stries. For most plan t s a n d indeed mo s t p i eces of eqUipment, their operation is constrained not by one but by many constraints. In such c a ses, the economic optimum often lies at the intersection of as many constraints as there are degrees of freed om ( Maarleveld and Rijnsdorp, 1970 ) . The aforementioned workers s ho wed via a constraint diagram how the e conomi c optimum moves as a f u nction of thr o ughp u t f o r a deisopentaniser column. They also reported the successful i mplementat i on of optim1sing c ontro l , via con straint intersecti on con trol, on an act u al deisopentaniser.

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AN "IDEAL" CONTROL SYSTEM SYNTHESIS PACKAGE In concept, an ideal control system synthesis package is one that is comprehensive enough and flexible enough to allow the complete integration of control considerations into the overall process des1gn by supporting a control eng i neer from his early conceptual doodlings through to the completed design of a tuned control scheme, possibly operating under an optimiser. While by no means comprehensive, any such ideal package wou l d need the ability to carry out the following (i)

Steady-state design and performance calculations using a built - in library of models.

(i1)

Steady-state, constrained optimisation.

(iii) Structural analysis. (iv)

The most complete attempt to put optim i sing control (as opposed to mere plant optimisation) on a sound mat h ematical bas1s was by Morari, Arkun and Stephanopoulos (1980) and Arkun and Stephanopoulos (1980,1981). In brief, their approach can be reduced to two concepts :

R

94.7

Retain the one multiloop control structure but modify the process so that the resulting process is easy to control over as wide a range of operating conditions as possible. The major advantage of this approach is that as far as operators are concerned the process is being controlled via a familiar multiloop control stru c t u re. For example, the use of " internal control loops" is a technique (Johnston and Barton, 1984) whereby excess manipulated variables can be employed to improve process controllability.

COMBINING REGULATORY AND OPTIMISING CONTROL

Comparison of Alternative

(v)

Open - loop controllabil i ty analysis. Closed-loop controllability analysis . Here, this should be taken not as being merely closed-loop SVA but as including all relevant aspects of closed-loop analysis and deSign, including the ability :

(a)

DecompOSition, of both the control structure and the process.

(a)

to carry out mUltivariable Closed - loop stability calculations;

(b)

Constraint following, as a means of moving from one optimum to another.

(b)

to determine, at least approximately, a set of controller parameters for a

219

Oyerall Process Control SYStem Design

multiloop control scheme (Marino-Galarraga, McAvoy and Marlin, 1986a) ;

(cl (d)

(vi)

to chose, incorporate and tune a wide range of multi loop enhancements; to determine the benefits of implementing multivariable rather than multiloop control (Marino-Galarraga, McAvoy and Marlin, 1986b) and design the former if required.

Dynamic simulation using a built-in library of models to which a user can add more rigorous or specialist models.

obviously, no such idealised control system synthesis package exists today, although a conceptual union between two currently available packages would not only provide many of the desired features but also a framework within which to incorporate the rest. The union required would involve a flowsheeting package incorporating both steady-state optimisation and dynamic simulation capabilities and a linear control system design package . It 1s felt that an equation-oriented package, such as SPEEDUP (Perkins, 1983) would be more applicable to this role than a sequential modular package, such as dynamic FLOWPACK 11 (Aylott, Ponton and Lott, 1985). Compared with the number of suitable flowsheeting packages presently available , there are a relatively large number of possible linear control system design packages, of which Matrix X is a good example.

Arkun, Y., and G. Stephanopoulos (1980). studies in the synthesis of control structures for chemical processes: Part IV. A . I.Ch.E.J., .?i, 975-991. Arkun, Y., and G. Stephanopoulos (1981). Studies in the synthesis of control structures for chemical process : Part V. A.I . Ch.E . J . , 12, 779-793. Atkins, J.E., G.W. Barton, and R . D. Johnston (1986). Synthesis and evaluation of alternative control schemes for a chalcopyrite flotation circuit. Int.J.Min.Processing, l£, 29-42. Aylott, M.R., J.W. Ponton, and D.H. Lott (1985). Development of a dynamic flowsheeting program. In Process Systems Engineering PSE·85 . Pergamon Press, Oxford. pp. 55-66. Barton, G.W., W-K . Chan, J.D. Perkins, and R.G.H. Prince (1986). Controllabil i ty analysis of alternative process designs. CHEMECA 86, The Fourteenth Australasian Chemical Engineering Conference, Adelaide , 19-22 August, pp. 200-205. Bristol, E.H. (1966). on a new measure of interaction for multivariable process control. IEEE Trans. Autom. Control, 11, 133 - 134. Doyle, J.C., and G. Stein (1981). Multivariable feedback design: Concepts for a classical/modern synthesis. IEEE Trans. Autom. Control, .?i, 4-16.

CONCLUSIONS In summary, the process industry wants control schemes that will provide safe, steady, economically optimal operation.

Economou, C . G., M.Morari, and B. O. Palsson (1986). Internal model control. 5. Extension to nonlinear systems . Ind. Eng. Chem. Proc. Des. Dev . ,12 , 403-411 .

safety, being highly process spe c ific, is extremely difficult to incorporate in a meaningful way into any generalised approach to control system design .

Johnston, R.D., and G.W. Barton (1984). Improved process COndItioning using internal control loops . Int. J. Control, ~, 1051-1063.

In terms of regulatory con trol, multiloop structures are going to remain the industry norm into the foreseeable future, provIding as they d o acceptable control in the majority of cases. In recognition of this, there has been significant progress in recent years in develop i ng techniques for the systematic design, assess ment and enhancement of multiloop control schemes. Calculating the optimum set of operatl n g conditions for a process plant is relatively straightforward provIded a realis t ic plan t mo del is available . However, the Widespread implementation of optimising co n trol is still s ome way off. Finally, the realisation that control system design should be more fully integrated into the overall design process is s l owly being accepted and put into practise . This integration process requires paCkages that are more comprehensive than anything currently available commercial l y although no doubt, such packages will be developed as the demand for them grows.

REFERENCES Arkun, Y. (1986). Dynamic process operability. Important problems, recent results and new challenges. In M. Morar i and T.J . McAvoy (Ed.), Chemical Process Control - CPC Ill. Elsevier, Amsterdam. pp. 323-367.

Johnston, R.D., and G . W. Barton (1985 ) . Control system development without dynamic simulation. In Process Systems Eng i neering PSE·85. Pergamon Press, oxford. pp. 443-456. Jo h n sto n , R.D., G.W. Bar to n, a nd M. L . Br i sk ( 1985). Sin gle-inp ut sing l e- o utput control syst em sy n thes i s. Computers and Ch em. En g., 547-566.

~,

La u , H., and K.F. Jensen (1985) . Evaluation of cha nge o ver contro l polic i es - effects of scaling . A . I.Ch .E .J., 21., 135 -1 46. 4

Maarleveld, A . , and J.E. Rljnsdorp ( 1970). Constra i nt control o n dist il lation columns. Au t omati c a, ~ , 51-58. Marino-Galarraga, M., T.J. MCAVOY, and T . E. Marlin (1986a). Short-cut operability analysis Part II-Estimation of f. Detuning parameter for claSSlcal control systems. submitted to Ind. Eng. Chem . Proc. Des. Dev. Marlno-Galarraga, M., T . J. McAVOY, and T.E. Marlin (1986b). Short-c ut op erability analysis Part Ill. Short-cut meth od ology for the assessment of process c on tr ol des i gns. Su bmitted to Ind. Eng. Chem. Pr o c. Des. Dev.

(;. W. Barton and .J. D. l'erkillS

McAvoy, T.J. (1983). Some results on dynamic interaction analysis of complex control systems. Ind. Eng. Chem. Proc . Des . Dev., ll, 42-49 . Moore , R.L., and M.A. Kramer (1986). Expert systems in on - line process contrOl. In M.Morari and T.J. McAvoy (Ed.), Chemical Process Control CPC Ill. Elsevier, Amsterdam. pp . 839-867. Morari, M. , Y. Arkun, and G . Stephanopoulos (1980). Studies in the systhesis of control structures for chemical processes: Part I. A.I.Ch.E.J., ~, 220-232.

Smith, O. J.M. (1957). Closer control of loops with dead-time. Chem. Eng. Prog., ~, 217-219. Stanley, G., M. Marino-Galarraga, and T.J. McAvoy (1985). Shortcut operability analysis.l. The relative disturbance gain. Ind. Eng . Chem. Proc . Des. Dev . , ~, 1181-1188. Stewart, G.W. (1973). Introduction to Matrix computations. Academic Press, New York . Tsogas, A., and T.J. McAvoy (1985). Gain scheduling for composition control of distillation columns. Chem. Eng . Commun., 22, 275-291.

Murtagh, B . A., and M.A. Saunders (1980). Technical report SOL 80 - 1, systems optimization laboratory, Department of operations research, Stanford Un:versity, Stanford, U.S . A .

Wil liams, T.J., and R . E. Otto (1960). A generalized chemical processing model for the investigation of computer control. AIEE Trans., 2i, 458-473.

Palmor, Z . J., and D.V. Powers (1985). Improved dead-time compensator controllers . A.I.Ch.E.J., ll, 215-221.

Yu, C., and W.L . Luyben (1986). Deslgn of multiloop SISO controllers in multivariable processes. Ind. Eng. Chem. Proc. Des . Dev., ~, 498-503 .

perkins, J.D. (1983). Equation-based flowsheeting. In Proc. 2nd. Int. conf. on Foundations of computer-aided Process Design, CACHE, pp. 309 -367 .

y

K(s)

Fig.l.

G(s)

Block diag ram of multivariable feedback control system.