Key factors for successfully implementing advanced control

ELSEVIER

ISA Transaction.~. Vol. 36. No. 4. pp. 267-272. 199,~ ~ 19t~8Published by Elsevier Science Ltd. All rights reserved Printed in The Netherlands, Pil: SII1119-11578197t110037-2 (tlii9-1)578/98 Slt).lX) + (tl)l)

Key factors for successfully implementing advanced control Fred F. Sanders St'nh~r Engineering 5"pet'h~iist. Monsanto. Pens, tcoht. FL 32560. USA

Abstract Pn,duct unifl, rmity in many plants is determined by the integrity of the systems that control key product parameters. Many of these control systems are multitiered, non-linear and often quite complex. Bridging the gap between thetwy and practical application is one of the most challenging tasks engineers and control professionals face. In addition, many advanced controls work well in concept or simulation but can fail for a variety of reasons when put into a real operating environment. This paper lbcuses on those factors that are critical to implementing and operating advanced controls. It will examine why many advanced controls fail and discuss ways in which we have successfully applied adaptive supervisory and model based control, as well as non-linear functions to improve controller performance. ~© 1998 Published by Elsevier Science Ltd. Kerwords: Supervisory control: Gain scheduling: Model-based control: Non-linear control: Adaptive control

will provide some insight from our experience on the arduous task of implementation on a day to day basis using real operators and real problem situations. For instance, at one plant site some of the key advanced controls incorporated as part of the upgrade to digital controls (DCS system) were not being used as originally envisioned. After installation of advanced controls, when the dust clears--the developers, perceivers, translation and programming is complete. and turned over to operations is when problems usually surface. This is the key acid test as to the effectiveness of the controls, it is easy lbr operators. uncomfortable with new systems, to shut them off and return to manual operation [!]. It is essential that any new advanced control systems receive at least some. long-term support. The assumption that a process will remain constant with time does not uneet real-world realities. The push for higher production rates and customerdriven product changes force many controls to operate in areas not envisioned by their developers. Self-tuning controllers [2] have gone through sub-

I. Introduction

When advanced controls are applied expectations are generally high. Under real operating conditions. however, the function of these controls is often in jeopardy--sometimes as a result of instrument inadequacy, instrument failures, or poor tuning. Finding the implementers or the individuals who developed the controls is often difficult or impossible. Many such individuals do not remain locally, or even retire, and reports and technical papers are usually published long before success can be demonstrated. Since documented failures make poor reports, success is often judged on the merit of idea and not the result. This paper will focus on the ways to up-front insure the success of controls, in anticipation of the departure of the developer, and answer the question of what may have gone wrong during the transition from development and simulation to deployment, it

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stantial development and improvement in the last decade, and can be helpful in improving the performance of many control systems. Most are available

Changes in operating conditions also demands that control systems have extended flexibility. This lack of flexibility is one of the major reasons tbr the operational failure of many advanced control systems. and pushes the need for developers to consider process non-linearities up-front. The classical way [3] of implementing non-linear control is to compensate lbr the process non-linearities via changes in the controller functions, i.e.. changing the controller gain. A gain-scheduling controller is therefore a linear regulator whose parameters are changed as a function of varying operating conditions [4]. A we/i-known example is adjusting controller gain to compensate for the non-linearities of a pH system titration curve. Much theoretical study continues, related to widely applied methods of gain scheduling from linear parameter-varying controllers to families of plant linearizations about constant operating points [5]. If an accurate nominal model of the plant can be obtained, an inverse-model based controller can be implemented. Process models can now be obtained through a variety of methods including first-principles, statistical methods, and neural networks. All-inclusive, model-based controllers may handle process non-linearities as well as provide anticipatory or feedforward type action [6]. Model mismatch can be handled continuously by adjusting a model parameter. Since most modern controllers consist of logic surrounding a core of feedback control algorithms. one problem that persists is integrating logic with existing controllers and designing controllers which are compatible with higher level algorithms [7]. In addition to gain scheduling, one practical solution to non-linearity may be the introduction of function(s) at the input or output of a PID controller. Results can be similar to the selection of a particular valve characteristic to offset the effect of line resistance to flow. One of the most promising applications for improving control is the application of fuzzy logic. This technology is practical in non-linear processes where human experience has the edge over mathe-

matical models [8] or where prior knowledge suggests division into "fllzzy regions" [9]. Statistical Process Cor|trol (SPC) has long been used as a tool for detecting statistical abnormalities and averting production problems [IO]: in addition, there is reason for inclusion of SPC in advanced control strategies by better linking laboratory data to processes and controlling these processes directly via statisticallybased supervisory control strategies. Exponentially Weighted Moving Average (EWMA) algorithms have been used successfully for this [I I]. Because of the intermittent nature and higher cost of laboratory analysis, there is often a need to inferentially determine a process parameter which agrees closely with laboratory analysis. EWMA strategies can often be used in conjunction with these inferential strategies to verify or re-calibrate on-line measurements in cascade fashion. All measurements, including laboratory analysing, are subject to some error, and it is important to understand the sources of error to insure tile success of the control.

2. Implementation Ill one situation when a new manager was assigned to oversee technical a~eas--the first thing noticed was the nunlber of advan~zed loops either bypassed or run ill M A N mode. in one area only 20~£ of the advanced control designed as part of the upgrade to digital controls on the DCS were being utilized. As an example, one system depended on the ability to preeisely measm~ steam flow. Unfortunately, the steam quality, condensate return system, as well as flow meter installation prevented this from oeeurring. As a result most of the advaneed capability was non-funetional and disabled. Another example, was a simple feedforward level control application. This system lacked the tuning needed, partieulady to handle different rates. When these tuning adjustments were made, utilization went from 15c~ to 100c,~. Both of these examples are simple and relatively easy to fix, when attention is focused in the proper areas. Consider the more diMeult example shown in Fig. I. It eonsists of a three-tier eontrol system: (a) The lowest level, the regulatory eontrol, consists of a simple feedback controller whieh measures and con-

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should be recognized, for instance, that secondary loop sensor errors in cascade control systems are normally passed on. sometimes unrecognized, as disturbances to the next higher control level [12]. It is particularly difficult to recognize these errors in fast loops such as flow. Flow signal comparisons to valve positions often provide the best clue. 2.2. On-line i!![erential control

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trois the flow rate of material into a reactor. (b) The 2nd level consists of an On-line inferential controller, consisting of multiple process measurement inputs which provides an on-line modeled estimate of a key laboratory parameter. The controller also contains an internal model to handle process dead time. This inferential controller provides the set poim for the regulatory controller. (c) The top control level consists of an EWMA supervisory controller which uses sampled laboratory analysis of the key product parameter and continuously makes adjustments to the calibration of the On-line controller. This control scheme was conceptually correct: however, most of the development effort was lbcused on the EWMA strategy and the integration into the DCS. yet most problems were at lower levels. After departure of the developers, utilization of the strategy went to z e r o ~ a n operational failure. never used.

When laboratory measurement of a key product parameter is diMcuit or time consuming, an on-line inferential measurement can be a good choice. The Neural network approach, using predictive models, is gaining accep!ability and has excellent potential, in this area. The models for this parameter, however. were developed using classical first principle approaches. Again. measurement integrity plays a key role in the success of the control system. 2.2.1. A c e , racy and reliability o f on-line estimate Consider Fi,,~ . . "~ wb.ich shows the relationship of an on-line inferential parameter and its corresponding laboratory result (period one month). The poor agreement between the two was. of course, recognized by the operators, without data analysis. This discrepancy supported their lack of system confidence and their election for manual control, usin- the laboratory data. In addition, operations, anticipating better control, increased the lab sample frequency

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six-Ibid. While inferential measurement can be a most useful tool for assessing analytical data [13]. there is a enormous tendency for confidence in laboratory results. After mathematical verification of model accuracy it is important to verify the accuracy and reliability of the key measured variables. A major deficiency found in our example was over-spanning critical pressure sensors. For instance. the range of some of the measured pressure transmitters was 0-1.000 psig. yet a change of 5 psig (0.5~)~ of range) was considered important. In addition some faulty wiring had been installed, undetected, durinan upgrade, Both these conditions produced unacceptably high error in parameter estimation. The on,line estimate was further improved through other instrument: upgrades and the use of mid-select methods for improving measurement accuracy. In addition, many of the sensors were automatically recalibrated, on-line, using filtered mid-selected readings. Figure 3 shows improvements realized fi'om improving the on-line estimate.

2.2.2. Controller i)eJfosvnance Controller performance is another key reason controls~ in general~ fail and are operated in Manual. To be effective controllers are required to handle process disturbances, dead-time and non-linearities over the operating range. We will discuss each of these areas relative to the example described above as it relates to controller performance.

(!) Process disturbances: if the proper variables can be selected, feedforward control is the m o s t common method of improving control loop performance in continuous process applications on distributed control system [14]! When disturbances are well understood and can be appropriately modeled, feedforward becomes a credible addition. The performance of the control system described above was improved through the addition of feedforward control. (2) Process dead-time: Handling process dead-time is a consideration in many controller designs and can be critical in obtaining acceptable pertormance. The process response characteristics will ultimately determine if model-based control is required or which type of model-based controller is most appropriate. The inferential controller described above utilizes a Smith Predictor [15] model to handle the dead-time, with acceptable performance. (3) Non-linearities: Non-linearities over the operating range affects the performance of many controllers and was. perhaps, the most important single l'actor to consider for improving the perlonn.:mce in out" example. Non-linearities affect the controller performance in two ways: (a) Degradation of controller performance for large disturbances, when the manipulated variable is extended beyond the linear controllable area. (b) Degradation of performance usually occurs when a controller is routinely operated outside its normal range. This is because controller tune settings and the internal model parameters are normally set up tbr one set of conditions. When a product change required operation over a different range, controller tuning and Internal Model Control (IMC) parameters had to be adjusted to maintain acceptable controller performance. This required local expertise in tuning and IMC parameter adjustment~often twice a month. This frequent returning discouraged operations and reinforced their "justification" lbr manual operation. Figure 4 shows the inferential loop process gain as a function of the set point (reactant flow) of the reguhltory co,urol loop. Note th:it the experimental data represents approximately three product groups and either an exponential or logarithmic curve can be fit to the data. This application would normally be ideal tbr gain scheduling, but was rejected becaw;e of the inability to programmaticaily change the IMC

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The EWMA worked well with the system described above, once these systems were corrected. Again. there were problems with the integrity of the laboratory results. The major problem was the lack of a sample "standard" for the analysis. This created drifts in the reported laboratory results as a function

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of varying laboratory conditions. Once corrected, acceptance of the control strategy was realized with 100% utilization.

3. Summary The development of all advanced control strategies should consider factors, up-front, that are critical to their operational success. These factors should include the following. (a) Long-term support: Almost all advanced control systems need some degree of long-term support. Since most control system developers do not remain locally, consider sufficient training locally of technical and operational persons to some minimum standard. This should be supplemented with expertise available non-locally if required from individual(s) familiar with the specific systems. (b) Regulatory control integrity: Advanced controls are usually built in layers. A common mistake is to assume that associated regulatory control work well. Insure, up-front, the integrity of all associated controls, dead-time compensation, tuning, equipment reliability, etc. (c) Measurement integrity: This is a "killer'. Check the accuracy and reliability of the instrument signals. Many advanced controls lose out here and control strategy is often blamed, Remember to check the integrity of sample-based htboratory results if they are incorporated into the control strategy. (d) Process non-linearity: Ensure adequate linearity over the expected range of operation. Anticipate future changes in the operating range, and how it might affect the integrity of control. Consider gain scheduling or other non-linear options for incorporation into the control strategy.

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[5] Lawrence. D.A.. Rugh. W.J.. Gain scheduling dynamic linear controllers for a nonlinear phmt. John Hopkins Technical Report 93-09. 1993. [6] Paruchurri. V.P.. Rhinehart. R.R.. Model-based flow control boosts accuracy, eases tuning. InTECH. Vol. 42. No. 4. April 1995. pp. 52-56. [7] Murray. R.. Top ten research problems in nonlinear conlixd. lnternet~Murray (q?indra.caltech.edu.. January 1996. [8] Rhinehart. R.R.. Murugan. P.. lmplxwe process control using fuzzy logic. Chemh'al Enghwering Progress. November 1996. pp. 60-65. [9] Qin. S.J.. Borders. G.. A multi-region fuzzy h,gic controller for controlling processes with p,onlinear gains. In Proceedings of IEEE Instrument Symposium on Intelligent Control. Chicago August 25-27. 1993. [10] Wolske. B.K.. hnplementing SPC concepts on a real-time basis. Chemh'al Processhtg. March 1988. [I I] Baxley. R.V.. Application of the EWMA for algorithmic statistical princess control. Qmdity i-ogim'ering. 1994-95. 7 (2). 397-418. [12] Sanders. F.F.. Watch out f'l~r instrtnuent errors. Chemical Eoghleering Progress. July 1995, pp. 62-66. [13] Gans. M.. Palmer. B.. Take charge of your phmt laboratory. Chemical Eoghwering Progress. September 1993. pp. 26-33. [14] Tolliver. T.L., Feedlbrward control implementation on a distributed control system. Spring Symposium Instrumentation in the Chemical Process Industries. Edmonton 1991. Vol. 22. pp. 9 9 - I I 7. [15] Vandoren. V.J.. The Smith predictor: A process engineer's crystal ball. Control Eogineering 1996. pp. 61-62.