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31 August 1999 22:57 CHAP-10.tex chap10.sgm MH009v3(1999/07/27)

PROCESS CONTROL IMPROVEMENT 10.61

CONTROL VALVE RESPONSE

by Mark T. Coughran∗

Control valve response strongly affects the performance of many process control loops. For throttlingcontrol, key performance issues are (1) the ability of the control valve to respond consistently tosmall input changes and (2) the installed loop gain resulting from the match of the valve to the otherloop components. The definition of small and the required valve gain depend on the process andother loop components, as shown in Fig. 1. Optimal selection of the control valve requires knowledgeof the control objectives of the loop; static characteristics of the process; dynamics of the process,transmitter, and controller sampling; controller tuning practices; source of the disturbances (load orset point); and the frequency distribution of the disturbances. Specification of control performanceis distinct from the mechanical configuration requirements typically seen in purchasing documents,such as materials compatibility, seat leakage, pressure class, and end connections. However, the effectof the control valve on process variability should be weighed equally with these other factors, sinceit may dominate the life cycle cost of the control valve.

In linear mechanical systems, errors between the percentage of input and the percentage of outputvary with the history of the input in a predictable manner. Control valves, on the other hand, exhibitnonlinear behavior, i.e., the errors also may vary with amplitude and direction of the input signalchanges. The user can specify allowable error in the input–output relationship with several open-loopperformance measures. Open-loop testing (also shown in Fig. 1) typically uses simple input signalsfrom a computer—or the existing process controller in manual mode—to the control valve. Whileopen-loop testing does not completely quantify the nonlinearities, it gives some indication whetherthe control valve will meet the user’s requirements for closed-loop control when the input signal isobviously more complex.

The first selection guideline is that the components of the control valve—valve, actuator, positioner,or other accessories—be designed for throttling (not on–off) service and tightly integrated together.This description fits design A in Fig. 2; process responses to 1% changes in the input signal can bedetected, despite the presence of process noise. In contrast, design B used a seal design better suitedfor tight shutoff, an actuator design that added friction and backlash, and a single-stage pneumaticpositioner. The process variable data in Fig. 2, recorded with the plant’s flow transmitters, show thatdesign B responded poorly to 5% changes and not at all to smaller changes. Automated block valvessuch as those in design B typically have lower initial cost but greater operating cost because of poorercontrol.

In most cases, good static performance can be obtained by attention to basic engineering issuessuch as minimizing friction in the valve and correct sizing of the valve and actuator. However, for theminority of control loops with fast response requirements and fast equipment surrounding the controlvalve, more complex analysis is needed. This section assumes that sound actuator sizing practicesare used, so that friction—not process fluid forces—is the main resisting force to be overcome by thepositioner/actuator servoloop.

STATIC PERFORMANCE MEASURES

Positioner test standards [1] define a variety of static performance measures; static means the dataare recorded after the device has come to rest. The full-scale calibration cycle familiar to instrumentengineers provides measurement of hysteresis plus dead band (combined) and linearity, which are

∗ Research Specialist, R. A. Engel Technical Center, P.O. Box 190, Fisher Controls International, Inc., Marshalltown, Iowa50158.



10.62 PROCESS/INDUSTRIAL INSTRUMENTS AND CONTROLS HANDBOOK

FIGURE 1 Most control valves are used in closed loop control systems.The control valve performance requirements and the appropriate open loop testmethods depend on the other components of the loop.

FIGURE 2 Example open loop data from adjacent pulp stock flow control loops in apaper mill; segmented ball valves. Design B used a high-friction seal, a rack-and-pinionactuator, and a low-gain positioner. Measurement time constant for the Process Variablewas 2.0 second (magnetic flow tube). Installed dead band was <1% for Design A and≥5% for Design B.

not the most relevant parameters. Although hysteresis and linearity are relevant for the process trans-mitter, the control valve plays a different role in the loop. Hysteresis and linearity are smoothly varyingerrors that accumulate significantly over only large strokes and have an impact on only open-loopsystems; for closed-loop control, the discontinuous errors (failure to provide any response over smallranges of input) are more important. Therefore the most useful parameter is dead band, measured withprocess loading. The benchtop dead band test specified for positioners [1] can be misleading because




of discrepancies between actuator position and flow coefficient and discrepancies between benchtopbehavior and installed behavior.

Dead band is the range through which an input signal may be varied, following reversal of direction,without initiating an observable change in the output signal [1]. For example, in Fig. 2, design B failedto reverse direction following 5% input reversals; therefore the dead band was greater than or equal to5%. A dead band of less than 1% can be expected in globe control valves with polytetrafluoroethylene(PTFE) packing, properly sized actuator, and reasonable positioner gain. However, in many processcontrol applications today, the control valve is required to move for signal changes smaller than 0.5%.The inability of some control valves to meet such requirements limits the use of advanced processcontrol strategies, which typically assume negligible dead band.

DYNAMIC PERFORMANCE MEASURES

Dynamic response is the time-dependent response resulting from a time-varying input signal. Possibleinput signal shapes include pulse, step, ramp, and sinusoid [1]. Step inputs have become popular forinstalled process testing [2] because of their simplicity. The output signal for dynamic response testsis usually position data measured at the actuator, not the closure member; the location where thepositioner receives its feedback is often most convenient. Actuator position data can be misleadingfor rotary valves [3].

Loop controllers that use proportional plus integral action are most easily tuned when there is adominant first-order lag in the loop. A dominant lag in the fluid process provides stability, ensuresthat the transmitter does not hide process events from the controller, and allows the highest con-troller gain setting—hence best loop response to set point and load disturbances. Relatively slowprocesses (in which the process dynamics are dominant in the loop) typically include temperature,level, large-volume gas pressure, mixing, and pH. Valve positioners are recommended for such loops[4].

Where the process dynamics are similar to dynamics of other loop components, stability can stillbe provided by installation of a dominant first-order lag somewhere else in the loop. In the past, acommonly recommended means to accomplish this was to connect an I/P transducer directly to adiaphragm actuator. However, several factors have led to an increasing use of positioners:

1. Popularity of digital controllers with sample intervals of 1 s or longer, resulting in increased focuson static performance and small-amplitude response of the control valve

2. Increased acceptance of springless actuators, which require positioners

3. Increased acceptance of rotary valves with the attendant need for feedback around the nonlinearrelationship between actuator pressure and rotation

4. Continued needs for split ranging and higher actuator pressure than commonly available from I/Ptransducers.

Although a dominant time lag is still desirable to stabilize fast process loops and loops with largedead time, today this can be accomplished with a software lag in a controller output function blockor a positioner input filter. Dynamic response requirements for the control valve typically focus onconsistent response at small amplitudes and minimizing dead time.

VALVE SIZE AND INHERENT FLOW CHARACTERISTIC

The design of the control loop for stability and best performance requires a dynamic analysis thatincludes all components [5]. However, dynamic analysis of many generic types of control loopshas led to the development of simplified guidelines [6]. Another approach that can prevent manyproblems—especially for slow loops—is to examine simply the static gain requirement.




FIGURE 3 Example of variation in open loop static process gain with an incorrectlyselected valve; cooling water flow control loop with 200-mm piping in a power plant.The 200-mm butterfly valve originally installed was oversized, whereas the 150-mmball valve provided a better size and inherent characteristic.

Loop gain is the product of the gains of the controller, control valve, process, and transmitter (Fig.1). The gains of the process and transmitter are usually fixed by the plant design. Some controllersprovide either gain scheduling or an output characterizer block; a static equivalent to the latter isan input characterizer in the positioner (signal-to-position characterizing).1 However, because theposition-to-flow gain of the valve occurs after the signal-to-position dead band, the most importanttask is selecting the best valve size and best shape of the inherent flow characteristic. High position-to-flow gain multiplies the effect of dead band; for example, if this gain is 4.0 and the dead band is 0.5%,the smallest process variable change that can be made reliably is 2%, regardless of characterizationin upstream blocks.

Figure 3 shows an example of a common situation: a greatly oversized valve. In this control loop,unstable behavior occurred at low flow rates. The installed process gain for the 200-mm (8-inch) valvein the 200-mm piping increased by a factor of 6 as the set point moved from high to low, indicatingthat a fixed-gain controller would give either sluggish response at high flow or excessively reactiveresponse at low flow. The 150-mm (6-inch) ball valve would be a better selection.

Standard control valves are necessarily somewhat oversized because of the availability of a limitednumber of trim sizes, the tendency of equipment (piping, pump) suppliers to add safety factors, andlimited accuracy of process information in the design stage. However, one preventable problem isdeliberate oversizing of the valve by the plant owner to allow for future production increases. This is

1 Twenty years ago, feedback cams were recommended for changing the signal-to-position characteristic. However, thismethod suffers from slope limitations, alteration of the positioner loop gain, and added maintenance issues.




false economy, saving a small amount on future capital costs relative to the continuous adverse effecton present operating costs. Control valves on critical loops should be sized for the present operatingconditions and with the most accurate data available for the pump, piping, valves, etc. Globe valveswith reduced trim may be the best solution when the user needs reserve capacity for future growth.Software is becoming more readily available for predicting installed characteristics and should seewidespread use as plant owners demand that valve size and inherent characteristic be selected to betterfit the control loop.

VALVE FLOW STEADINESS

Flow coefficients defined by standards [7] are customarily listed in vendor catalogs under the assump-tion that they do not vary with time. However, it is possible for a poorly designed valve to exhibitunsteady flow when the trim position is constant [8], and this phenomenon may become significantwhen the control objectives require small movements of the valve. The flow behavior is best capturedby dynamic flow coefficient measurements [9]. Figure 4 gives an example of liquid flow coefficientunsteadiness caused by a particular globe valve (lower graph). The possibility of this behavior is onereason that benchtop stem motion measurements are not a complete method of judging control valveperformance.

FIGURE 4 Examples of steady and unsteady flow patterns in 50-mm globevalves with advertised linear inherent flow characteristics. Both valves were testedin the same system with a flow coefficient measurement time constant of 0.2seconds. The unsteady flow pattern created by the valve in the lower graph wouldlimit the performance of fast control systems such as liquid pressure and flowcontrol loops. Q ≡ volumetric flow rate, �P ≡ valve pressure drop, G ≡ specificgravity.




FRICTION AND DRIVE SHAFT DESIGN

High friction in the valve and the actuator increases dead band and degrades the small-amplitudedynamic response. Increased friction on the sealing and guiding surfaces, caused by process temper-ature, pressure, or fouling, obviously is very specific to the application. In some valve designs, theoft-encountered requirement for tight shutoff adds friction, so that—for critical loops—separationof the control and shutoff functions should be considered (two valves with different purposes). Inall cases, attention should be given to the materials, loading, installation, and maintenance of thestem packing. Where temperature extremes would require high-friction packing materials, extensionbonnets may be a worthwhile investment. New elastomers with friction characteristics similar to thoseof PTFE but with higher temperature capability are also becoming available.

Currently no standards exist for defining and measuring friction in control valves. An explanationof the friction measurement typically provided by valve signatures is given by Jackson [10].

It is easy to see how friction opposes stem motion in linear motion valves. A more subtle effectoccurs in rotary valves because of finite stiffness of the drive shaft. Friction in the packing, bearings,and seals causes shaft windup [3, 10]. Figure 5 shows an example in which actuator position dataindicated dead band of less than one step, or < 0.13%, while the flow data proved that actual dead bandof the trim (ball) was approximately 0.8%. In the example of Fig. 5, most of the friction was causedby seal-to-ball contact, and the friction increased from 11 to 25 N m after the valve was installed andcycled. This type of friction can be eliminated if the closure element is mounted eccentrically so thatit moves out of contact with the seat as the valve begins to open.

The windup form of lost motion is in addition to the more obvious opportunity for backlash inrotary actuators that use linkages or in trim components that have more than 1 degree of freedom.This backlash can be prevented by careful design, manufacturing, installation, and maintenance. Manythrottling ball valves today use clamped splines to essentially eliminate backlash.

Although some butterfly valves have lower frictional torque than ball valves, they also tend tohave smaller-diameter shafts. Then, because torsional stiffness is proportional to the fourth power of

FIGURE 5 Example of shaft windup in a ball valve. From the advertised inherentflow characteristic, the expected flow coefficient change (right) was approximately0.10% for each 0.13% change in ball position. The flow coefficient measurementtime constant was 2.0 seconds in this installation. Note misleading indication ofdead band (<0.13%) from actuator position data (left). The benchtop torque of thenew valve, before cycling, was only 11 N-m.




diameter, butterfly valves are still susceptible to windup because of friction in packing, bearings, andliners.

SELECTION OF ACTUATOR AND ACCESSORIES

Manufacturers typically size the actuator to overcome (1) shutoff forces and (2) fluid dynamic forcesat higher lifts. Friction in the packing is considered only as it affects the ability to close the valve.However, sizing for best control may require a larger actuator for providing either more thrust (rela-tive to the friction) or more actuator volume; the latter allows for higher positioner gain in pneumaticsystems. Just as high gain is desirable for the outer process loop in Fig. 1, it is desirable in thepositioner servoloop to overcome friction and other nonlinear effects. Gain from proportional ac-tion is preferred; integral action in the positioner, combined with friction, tends to cause positioncycling.

The positioner design strongly affects dynamic response of pneumatic systems. For small-amplituderesponse, the positioner is more important than the actuator [11] unless the actuator adds friction.Figure 6 shows an example of small-amplitude response with three positioner designs on one valveand actuator. Positioner A gave dead time approaching 10 s when reversing direction, which wouldlimit the usable controller gain in many control loops. The design of positioner B resulted in muchshorter dead time following reversal. This valve and actuator with positioner B would be a good choicefor control loops requiring small moves with consistent, fast response.

FIGURE 6 Example of positioner design effect on control valve small-amplituderesponse. Positioners A, B, and C were installed to a globe valve that had steady flowand a diaphragm actuator. Positioner A was an analog two-stage design; PositionerB followed similar design principles but with adjustable gain and digital commu-nication. Positioner C used solenoid valves (supply and exhaust) with a duty cyclecontrolled by a microprocessor. Actuator pressure is shown in percent of supply.




FIGURE 7 Example of amplitude dependence in a control valve with a pneumaticactuator and positioner, illustrating that results from one amplitude cannot be extrapolatedto another amplitude.

The conventional design of high-performance positioners uses multistage amplification, typicallywith signal amplification in a nozzle flapper [12] followed by power amplification in either a poppet-type proportional relay or a spool valve. Within the past 10 years, an alternate scheme has appearedthat uses on–off piezoelectric valves whose input pulse width is controlled by a digital algorithm.Normally the bang-bang control scheme includes a dead zone to prevent chattering near the null state.Behavior with this scheme is irregular at small amplitudes, as the positioner C data in Fig. 6 illustrate.

DYNAMIC TEST SIGNAL AMPLITUDE AND SHAPE

Pneumatically actuated control valves tend to respond more slowly as the input amplitude approachesthe dead band (small signal changes) and also as the input amplitude approaches relay saturation (largesignal changes).2 Figure 7 illustrates both effects. The full-range tests, sometimes called strokingtime tests, are the only amplitude for which manufacturers make predictions available. In contrast topneumatics, electric actuators often provide a constant slew rate at amplitudes of 1% to 100%. Theuser must specify the amplitude for dynamic response tests if appropriate equipment is to be selectedbased on such tests.

Concerning the shape of the input signal, it is possible for open-loop step inputs to excite overshootthat either will not occur, or will occur to a much lesser extent, in closed-loop application. Figure1 shows that load disturbances pass through lags in the process, transmitter, and possibly in thecontroller antialias filter, before arriving at the control valve. Set point changes normally occur slowlyfrom either an outer loop in cascade control or from a supervisory control system; for these cases,tests should use slow input changes. For open-loop testing in a plant environment, the simplicity ofstep inputs is appealing but the above limitations should be kept in mind.

2 If fast response is desired for very large steps, a volume booster can be used between the positioner and actuator. The boostershould use a dead band relay [12], and its action should be limited to large-amplitude input changes.




PROCESS VARIABLE AS THE ULTIMATE OUTPUT

The most useful information arises from a test with process loading, since the most realistic frictionoccurs when the valve is exposed to the process temperature, pressure, vibration, wear, and possiblyfouling. Friction also may change during the break-in period after the valve is first installed; oneexample is metal-seated ball valves that lap in during service (see Fig. 5 notes and Ref. 10). For mostrotary valves, loaded tests that use process data are the only means of measuring true dead band. Evenfor globe valves, differences in flow behavior can negate the usefulness of benchtop stem motion tests.

The fundamental output of the control valve is the flow coefficient, which can be measured in alaboratory environment and has been used here in Figs. 4 and 5. However, the ultimate function ofthe final control element is to alter the process variable, and in many plants the process transmitterprovides sufficient information to diagnose control valve problems. Open-loop plant testing, in whichthe responses of the control valve, process, and transmitter are combined, is now widely used forloop tuning and diagnostics. The process data can be recorded at the controller terminations and, forself-regulating processes with adequate signal-to-noise ratio, used to estimate the control valve deadband (e.g., Fig. 2).

SUMMARY: CONTROL VALVE PRACTICES FOR BEST RESPONSE

� Use a control valve designed for throttling service.� Size the valve trim for present operating conditions; avoid oversizing.� Select the inherent flow characteristic by calculating the installed static characteristic.� The valve should provide steady flow when the trim position is held fixed.� Minimize friction in the valve and actuator (seals, bearings, packing).� Eliminate lost motion in linkages and maximize stiffness of rotary shafts.� Select the actuator type and size for best control with the positioner.� Select high proportional gain in the positioner.� Test with the appropriate amplitude and shape of input signal for the control loop.� Judge installed control valve response by change in the process variable.

REFERENCES

1. “Method of Evaluating Performance of Positioners with Analog Input Signals and Pneumatic Output,”ANSI/ISA-S75.13-1989.

2. “Control Valve Dynamic Specification,” Version 2.1, 1994, EnTech Control Inc., Toronto, Ontario, Canada.

3. Coughran, M. T., “Valves: Testing for Peak Performance,” Intech 41(10), p. 58, 1994.

4. Gassman, G. W., “When to Use a Control Valve Positioner,” Control, Sept. 1989.

5. Lloyd, S. G., and G. D. Anderson, Industrial Process Control, Fisher Controls, Publication Stockroom, P.O.Box 190, Marshalltown, Iowa 50158, 1971.

6. Adams, M., “Process Control Valves,” in Process/Industrial Instruments & Controls Handbook, 4th ed.,D. M. Considine, ed., McGraw-Hill, New York, 1993, p. 9.10.

7. “ Control Valve Capacity Test Procedure,” ANSI/ISA-S75.02-1988.

8. Dvorak, A. D., P. J. Schafbuch, and D. J. Westwater, “Flow Rate Stabilizer for Throttling Valves,” U.S. PatentNo. 5,765,814, June 16, 1998.

9. Coughran, M. T., “Measuring the Installed Dead Band of Control Valves,” ISA Transactions 37, p. 147, 1988.




10. Jackson, R. S., “Friction Effects in Control Valve Performance: Ball Valves,” ISA TECH/97, Volume 1, Part 5,p. 67, 1997.

11. Coughran, M. T., “Performance Influences in Globe Control Valves,” Intech 43(8), p. 44, 1996.

12. Lytle, R. F., and C.B. Schuder, “Pneumatic Components,” Section 42 in Mechanical Design and SystemsHandbook, 2nd ed., H. A. Rothbart, ed., McGraw-Hill, New York, 1985.

PROCESS IMPACT

by James F. Beall IV∗

The effect of process measurements, control valve performance, and controller tuning can have asignificant impact on process control. In some audits [1], improving the performance of the controlvalve and its related components (I/P, actuator, positioner, etc.) significantly decreased the variabilityof over 50% of the control loops. Case studies presented in this section show that, of the controlloops studied, up to 75% need improvements on the instrumentation or control valves in order to meetprocess control requirements. In most cases, improvements to the instrumentation and control valvesmust be done before better controller tuning will improve the process control.

A plant program to analyze and correct problems with process measurement instrumentation,control valve response, and controller tuning can significantly reduce process variability and increaseprocess availability. Reductions in process variability of individual control loops 10:1 or more arepossible in some cases. This, in turn, can result in a significant reduction in the variability of theprocess unit (e.g., the analysis of an intermediate or final product).

The economic benefit from these improvements is significant. One benchmark study [2] foundthat the average improvement of 12 companies with these types of improvements accounted for 25%of the economic benefit from all categories of process control improvements. In addition, good basiccontrol is required for obtaining the potential benefits from advanced control technologies.

PLANT PROGRAM FOR CONTROL OPTIMIZATION

The following is a guide to develop a plant program to decrease process variability and increase processavailability by improving controller tuning and the response of instrumentation and control valves.

� Research: Find out if anyone in your company has a control optimization program. Use theirexperience and track record to help start your program. Consider working as an extension of theirgroup. Start your program based on the success of their program. If there is not anyone in yourcompany with a control optimization program, talk to an industry peer to obtain their insight.

� Select a process analysis system: Consider the following characteristics:� Recording capability—number of recording channels, frequency of data sampling, types of signal

inputs, communication interface with control system. Although it is convenient to use a com-munications interface to record data from the control system, this method prevents analysis ofall types of control systems and may overlook some common problems. These problems include

∗ Eastman Chemical Company, Longview, Texas.




signal scan time problems, signal aliasing [3], control valve problems, resolution of instrumen-tation, and others. An analysis system that can record both the actual signals as well as data froma communication interface to the control system would be ideal.

� Accept the required types of signals (pneumatic, electronic, thermocouple, resistance temperaturedetector (RTD), of position, control valve or final element etc.).

� Suitable for the area electrical classification.� Analysis techniques—signal editing, simulation, power spectrum analysis, frequency plots, tun-

ing methodology, etc.� Plan: Develop a schedule of equipment purchases, training, and manpower. Usually it is best to

have a plan that is based on an expanding effort. Once the program has some success, the demandfor the program will grow.

� Sponsor: Sell your program to a member of your management. Utilize information on the successof similar programs. Make sure you get commitment for consistent, dedicated manpower for theprogram.

� Training: Obtain good training to use the process analysis system properly and to learn processanalysis techniques.

� Partnership: Develop a partnership with an operating department. Work hard to complete successfulprojects in this department.

� Expand: Use previous successes to expand program to other operating departments. Encourageoperating department supervision to share the success in their department with other operatingdepartments.

� Difficult cases: Do not be afraid to tackle control problems that no one else has solved. A moreconsistent effort and better tools such as a process analyzer may help you solve the problem. If youare successful, it will build credibility. If you are unsuccessful, it does not detract since the problemcould not be solved before.

� Unit versus single loop: In general, try to consider as much of the process unit as possible. Forexample, look at the complete distillation column rather than just the base temperature controller,or maybe look at the whole distillation train or the complete plant.

� Record results: Record before and after results. Try to determine economic benefit to justify andexpand the program. A data historian system that collects and stores process data can be very helpfulin comparing past and present performance.

� Performance monitor: Develop a program to monitor and maintain the performance improvements.This could be manual system or an automated system utilizing a data management system.

RESULTS OF CONTROL OPTIMIZATION---CASE STUDIES

These case studies are presented to give the reader examples of the magnitude of the benefits, the natureof the instrumentation and control problems encountered, and the value of a graphical presentationof the process performance [7].

Distillation Tower

This example shows how optimization of the instrumentation, controller valves and controller tuningresulted in 6:1 reduction in variability of key control loops on a typical hydrocarbon distillationtower. Figure 1 shows1 the trends of key control loops on a hydrocarbon distillation tower beforeimprovements were made. The large variability of the D-8 top temperature, trend 2, was the reason

1 All figures (1–20) are from the work of James F. Beall IV, Eastman Chemical Company.




FIGURE 1 Key control loops of a distillation tower, before improvements.

for a control optimization study. Note that the reflux flow, trend number 3, was making square-wave-type step changes that appeared to affect the D-8 top temperature. Also, the column base level wasvarying in a sinusoidal pattern. Further analysis revealed that the bottom flow out of the column,which was manipulated by the base level controller, also varied in a sinusoidal pattern. The bottomflow was the feed to the next column and therefore created a disturbance in it.

A process analysis system was used to record and analyze the response of the instrumentation andcontroller valves and to calculate controller tuning constants. Based on the analysis, the followingchanges were made:

� Positioners were added to two valves.� Trim size was changed in one valve.� Tuning constants were changed in four controllers.

Figure 2 show the trends of the loops after the improvements were made. A statistical analysis ofthese data, shown in Figure 3, reveals approximately a 6:1 reduction in standard deviation and rangefor the key control loops. Note how well the graphs of the data give a visual quantification of thecontrol improvement.

Pilot Plant Reactor

This example shows how optimization of the instrumentation, controller valves, and controller tuningresulted in up to a 19:1 reduction in the variability of key control loops on a pilot plant reactor.Once again, it was not just retuning the controller that provided the control improvement; it requiredimprovements to the instrumentation and control valves.

Figure 4 shows the trends of key control loops on the pilot plant reactor before improvements weremade. The large variability of the reactor temperature (trend 2) and the reactor pressure (trend 1) wasthe reason for a control optimization study.

The instrumentation and control system was analyzed and the following improvements were made:

� Positioners were installed on two control valves to reduce dead band.� The pressure control valve was replaced because it was oversized and had excessive dead band.� Three loops were tuned.




FIGURE 2 Key control loops of a distillation tower, after improvements.

FIGURE 3 Reduction in variability of key loops of a distillation tower.

FIGURE 4 Key control loops on a pilot plant reactor before improvements.




FIGURE 5 Key control loops on a pilot plant reactor after improvements.

FIGURE 6 Key control loops on a high-pressure reactor before and after improvements.

Figure 5 show the trends of the loops after the improvements were made. A statistical analysisof these data reveals approximately a reduction in standard deviation of 19:1 for the pressure loopand 6:1 for the temperature loop. Once again, note how well the graphs of the data give a visualquantification of the control improvement.

High-Pressure Reactor

This example shows how optimization of the control valve and controller tuning resulted in over a10:1 reduction in variability of key control loops on a high-pressure reactor. Figure 6 shows the trend




FIGURE 7 Poor performance of the control valve on a high-pressure reactor.

of the loop process variable both before and after the improvements were made. The traces are shiftedin time to be able to present both the before and the after improvement data on the same figure (bothtrends have the same span).

The instrumentation and control system was analyzed, and it was found that poor performance ofthe control valve was the major source of variability in the control loop. The piston actuator on thecontrol valve was worn, which caused the valve stem to have erratic movement when compared withthe control signal to the valve. Figure 7 shows the discrepancy between the signal to the control valveand the actual stem position. The actuator was replaced, the loop was retuned, and the variability wasreduced by a factor of 10 or more.

COMMON PROBLEMS

This subsection provides examples of some of the problems with instrumentation, control valves, andcontroller tuning as well as solutions for these problems. Also, these examples show the nature andcharacteristics of the problems in an effort to assist in finding other problems.

Improper Tuning on Level Loops

Improper tuning of level loops is a common problem found during control optimization. The typicalerror is that the integral action on the controller is too fast. Since a level process is usually an integratingprocess, too much integral action in the controller can cause control instability. Also, it is commonfor the purpose of a level control loop to allow the level to vary in order to reduce variation of the




FIGURE 8 Simulation of averaging tuning method for level control.

manipulated flow. However, it is a common problem to find that a level control loop has been tunedsuch that the level is held tightly and the manipulated variable is varied excessively. In some cases,the tuning is so inappropriate that both the level and the manipulated variable vary excessively.

A solution for this problem is to use a tuning method that results in averaging type response. Onesuch tuning method [4, 5] results in a response to a step change in process load stop or “arrest” therise or the fall of the process variable (level) in the arrest time and taking approximately 6 timesthe arrest time to return the process variable back to the setpoint. The selection of a larger arresttime results in more deviation of the process variable from setpoint and less aggressive movement ofthe manipulated flow (for a given size load disturbance). If the maximum disturbance is known orestimated, the required arrest time can be calculated for an allowable deviation of the process variable.Figure 8 is a simulation of this type of response.

Figure 9 shows the trend of a level control loop before and after the controller tuning was improved.Before the improvement, the level is held close to setpoint by aggressive manipulation of the flow.The flow is the feed to a process and the variability in flow causes process disturbances. After theimprovement, the variability of the flow is much less.

Dead Band in Control Valves

Dead band in a control valve can cause control problems. It can cause limit cycling of the processand limit control performance. Dead band creates dead time in the control valve response when thecontrol signal to the valve reverses its direction. Dead band will cause limit cycling of an integratingprocess.

A new control valve without a positioner can have a dead band of 25% or more because of normalfriction forces. A positioner without the integral action reduces the dead band of the control valve byapproximately its open-loop gain. For example, if a control valve has a dead band of 15% and the




FIGURE 9 Level control before and after averaging tuning is implemented.

FIGURE 10 Effect of dead band in control valve.

positioner has a gain of 50, the combined dead band of the control valve system will be approximately0.3%. If the positioner has integral action, the dead band of the control valve system theoretically is0% but this situation may result in a limit cycle of the valve even if the controller is in manual.

Figure 10 is a trend of control loops associated with the top temperature control of a distillationcolumn. Manipulating the reflux flow controls the reflux tank level. The trend shows the effect ofthe reflux control valve having approximately 10% dead band. The characteristics of dead bandin the control valve on an integrating process such as the reflux tank level is a triangular waveformon the controlled variable and the controller output; and a square wave on the manipulated variable.As shown in Fig. 10, the variation of the reflux flow in a square-wave pattern creates a triangulardisturbance on the top temperature on the column.

Figure 11 shows the control after a positioner is added to the reflux control valve to reduce thedead band. The improvement in control is obvious without a statistical analysis.




FIGURE 11 Result of adding a positioner to correct dead band in control valve.

Selection of Control Valve Trim Characteristics

The installed flow characteristics of the control valve directly affect the process gain. A variation inthe process gain results in a change to the closed-loop response of the control loop. If the changein the process gain is significant, the control loop can become sluggish or, in the opposite direction,unstable. For example, in many heat transfer processes in which the heating medium is throttled, anequal-percentage trim characteristic will help maintain a constant process gain [6]. However, considerthe application of a flow control loop with a control valve that has an equal percentage installed flowcharacteristics. In this application, the process gain of the flow loop can vary by a factor of 10 ormore.

The characteristics of the process and control scheme should be considered and the appropriatetrim characteristics should be selected to help achieve a constant process gain. If a control valve isinstalled with the wrong trim characteristics, there are several methods, besides replacing the trim, tomodify the valve response. One method is to characterize the output of the controller. If this methodis used, loop tuning methods that are based on the output controller should utilize the output of theproportional, integral, and derivative (PID) algorithm, before the characterization function. Anothermethod is to characterize the response of the positioner. Some of the new microprocessor-based smartpositioners provide a characterization function to achieve the desired valve response. Reference 6 isan excellent source of information on this subject.

Excessive Dead Time in Control Valve Response

Some positioner and control valve combinations exhibit excessive dead time when the signal to thepositioner is reversed by a small amount. The excessive dead time for this type of signal changecan create a region of loop instability if the dead time is large compared to the closed-loop timeconstant. This can create a cycle in the control loop. Figure 12 shows a control valve and posi-tioner with a dead time of 0.4 s when the signal is reversed by a 1% step change. Figure 13 showsthe same control valve and positioner with a dead time of 42 s when the signal is reversed bya 0.2% step change. The control loop was tuned based on the response to 1% change in signal;the closed-loop time constant was approximately 3 s. The 42 s of dead time in this small regionof signal change causes a region of instability that creates a cycle in the loop. This type of cycleis sometimes called a stick-slip cycle, and the variable dead time is one cause of the cycle. The mag-nitude of the cycle is limited because, as the output of the size of the change in the controller output



FIGURE 12 Medium-sized (1.0%) step change to positioner and control valve.

FIGURE 13 Small-sized (0.3%) signal reversal to positioner and control valve.

10.79




TABLE 1 PID Implementations

1. Method: ideal, parallel, series2. Output calculations: positional or velocity3. Proportional action on error or process variable4. Derivative action on error or process variable

FIGURE 14 Limit cycle in auto when positioner and control valve have large dead time.

increases, the dead time is reduced and the control loop is once again stable. Figure 14 shows thiscycle when the loop is in automatic. Figure 15 shows that a different positioner that does not haveexcessive dead time (same control valve) does not exhibit the limit cycle.

PID Implementation

The PID algorithm can be implemented in several ways. Each method has certain characteristics thatmay be better suited to particular applications [8]. Table 1 shows the possible combinations.

The most common PID implementation is a series with proportional and integral action on errorand derivative action on the process variable. For the same closed-loop control response, the tuningconstants must be modified based on the whether the algorithm is series, parallel, or ideal [8].

Implementation of the proportional action on the process variable prevents the output from makinga step change when the setpoint is changed. When the setpoint is changed, the integral action willbegin to ramp the output to attain the new setpoint. However, the control action for a load change isthe same as when the proportional action is on the error. This may be desirable on a level control loopfor which it is undesirable to make quick changes to the manipulated variable and it is not critical toget to the new setpoint quickly.




FIGURE 15 No limit cycle in auto when positioner and control valve have small dead time.

Figure 16 shows a simulation of a level control loop with the proportional action on the error.Note the large output movement when the setpoint is changed. Figure 17 is the same process and thesame tuning constants but with proportional action on the error. Note the smooth movement of thecontroller output and how long it takes for the process variable to get to the new setpoint.

Another application for the PID algorithm with the proportional action on the process variable iswhen the signal filter on the process value is significant compared with the process time constant.Special tuning techniques [3] combined with proper filter selection can be used to better control noisyprocess value signals.

If the derivative action is applied to the error, a setpoint change causes the controller output spikesin one direction and then spikes back almost immediately. In general, this response is not desirableand the derivative action should be applied to the process variable instead of the error. Figure 18 showsthe effect of this algorithm when the setpoint is changed. Figure 19 shows the response to a setpointchange when the derivative action is applied to the process variable rather than the setpoint.

Control Valve Performance Specification

In most control loops the final link back to the process is a control valve. For the control loop to meetthe required performance criteria, each component in the loops, including the control valve, mustmeet performance requirements. As shown in the case studies earlier in this subsection and in otherstudies [1], the control valve is a common cause of variability in the control loop. This has led tothe use of control valve selection and performance specifications [9]. The control valve performancespecification should be based on the process dynamics and the process control requirements. Thistopic is covered in more detail in the Plant Analysis, Valve Response, and Advanced RegulatoryControl sections.



FIGURE 16 Level loop with proportional and integral action on the error.

FIGURE 17 Level loop with integral action on the error, proportional and derivative on the process variable.

10.82



FIGURE 18 Simulated loop with derivative on the error.

FIGURE 19 Simulated loop with derivative on the process value.

10.83




FIGURE 20 New control scheme reduces variation by 5:1

Control Scheme

It is important to choose the correct control scheme to have optimum control. This topic is covered indetail elsewhere in this handbook but deserves to be noted here. Care should be taken in the designphase to select the proper control scheme. Figure 20 shows how improving the control scheme reducedthe variability of the product analysis of a distillation process by 5:1. This improvement was madeafter improvements to the instrumentation and control valves had resulted in a 2:1 reduction invariability.

Sometimes it may be apparent that the control scheme is not correct. Other times it is only afterthe instrumentation, controller tuning, and the control valves have been optimized that the controlscheme is suspected of being incorrect. Some of the potential control scheme problems, symptoms,and solutions are listed below.

� Nonlinear control scheme: Symptoms are that the process gain, dead time, and response time varyover the operating range. This causes a variation in the closed-loop response of the control loop.Solutions include changing the control scheme, changing the process, adaptive tuning, and signalcharacterization.

� Interactive control loops: Symptoms are when control loops affect by the action of each other.Solutions include decoupling techniques or completely changing the control scheme.

� Poor disturbance rejection: Some control loops correct for process disturbances only after theprocess has been affected. However, it may be possible to design a control scheme that correctsfor the disturbance before it affects the process variable. Examples of control schemes that helpcorrect for disturbances are reflux ratio control on a distillation column, cascade control, pressure–temperature-compensated flow, and feedforward schemes.

Other Problems

The following is a list of other instrumentation and control valve problems that can hinder optimumcontrol.




� Controller scan time: It may be too slow for good control. Generally, the scan time should be 1/5to 1/10 of the closed-loop response time.

� Signal resolution: Some control systems use a thermocouple or RTD module with a wide temper-ature range. The actual range of the control point is specified in software but the analog-to-digitalconversion is performed over the entire range of the module. This can result in a resolution for thecontrol point that is larger than desired.

� Signal filter: Proper filter techniques should be used [3].� Smart transmitter response: Make sure that the response time of the smart transmitter is adequate.

Find out the complete response (dead time, response time, resolution, etc.) of the transmitter, notjust the output update frequency.

� Split range of control valves: Make sure that the split range is correct. Determine if the split rangeis overlapped, not overlapped, symmetrical, etc.

� Level transmitter calibration: It is common to convert a field-mounted, proportional-only levelcontroller to a level transmitter with a separate controller. While functioning as a level controller,the gain of the controller is included in the calibration. Make sure the level controller is recalibratedproperly to function as a level transmitter.

KEY POINTS

� Improvements in process instrumentation, control valve performance, and controller tuning havebeen found to account for a significant portion of the economic benefit of all control improvementtechniques.

� Improvements in process instrumentation, control valve performance, and controller tuning areessential to obtain the full benefit of higher-level control improvement techniques.

� A plant program to optimize the performance of process instrumentation, control valves, andcontroller tuning will result in less process variability and higher process availability. The keypoints to implementing a control optimization program are the following.� Use the success of others to sell the program.� Select a process analysis system with the appropriate features and receive detailed training to

fully utilize the system.� Partner with an operating department to ensure early successes. Expand the program based on

previous successes.� Consider as much of the process unit as possible instead of individual loops.� Record before and after results and economic benefit to help justify and improve the program.� Develop a system to monitor performance to prevent loss of original benefits.

� There is a wide variety of problems with instrumentation, control valves, and controller tuningthat can increase process variability. A versatile process analysis system is needed to diagnose andcorrect many of the problems.

� Control valve performance should be specified based on the process control requirements.� Control schemes selection may limit control performance. Linearity should be a key consideration

in the design of control schemes.

REFERENCES

1. Rinehart, N., and F. Jury, “How Control Valves Impact Process Optimization,” Hydrocarbon Processing, June1997.




2. Tolliver, T., “Process Analysis for Improved Operation and Control,” Fisher-Rosemount Systems AdvancedControl Seminar, 1996.

3. Ender, D. B., Filter Application Manual, Techmation, Inc., Tempe, Arizona, 1993.

4. Ender, D. B., Protuner Application Manual, Techmation, Inc., Tempe, Arizona, 1993.

5. EnTech Control Engineering Inc., Course PCE-I, “Process Control for Engineers,” Toronto, Ontario, Canada.

6. Shinskey, F. G., Process Control Systems, McGraw-Hill, New York, 1996.

7. Beall, J. F., “Process Analysis, Diagnostics and Solutions,” North Texas ISA Instrumentation and ControlsExhibition, May, 1998.

8. Ender, D. B., Implementation of PID Algorithms, Techmation, Inc., Tempe, Arizona, 1993.

9. “Control Valve Dynamic Specification,” Version 2.1, 3/94, EnTech Control Engineering Inc., Toronto, Ontario,Canada.

BEST PRACTICES, TOOLS,AND TECHNIQUES TO REDUCETHE MAINTENANCE COSTSOF FIELD INSTRUMENTATION

by Gregory K. McMillan∗

BUSINESS IMPACT

Approximately 40% of manufacturing revenues are spent on maintenance according to the U.S.Department of Commerce [1]. For the chemical industry, the percentage can be larger because ofextreme pressures and temperatures, highly corrosive fluids, and exposure to the elements. One ofthe larger maintenance cost categories is instrumentation, and the single biggest subcategory costis typically calibration. If you take into account that most of the instrument maintenance and thecalibration checks are unnecessary, there is a significant opportunity to reduce manufacturing costs.In one large chemical plant, 35% of the instrument checks made for preventative maintenance and 28%of instrument checks from reactive maintenance found no problem [1]. One of the largest chemicalcompanies reports that 65% of calibration checks are unwarranted. If you also consider that mostcalibration shifts of smart digital instruments result from not using the best selection or installationfor the application, calibration should go from being the highest to the lowest subcategory cost.

The exponential increase in technology has led to a corresponding increase in the number oftypes and models and features of instrumentation. The stocking of spare parts has become an enor-mous, complex, and costly task to the point where the use of new technologies and manufacturers isdiscouraged despite performance and/or price advantages.

The other side of the story is that maintenance techniques to date have largely been ineffective ingetting at the source of the real instrument problems. Poor measurement and control valve consistency,sensitivity, and reliability reduce process efficiency and capacity. One production unit found that twothirds of the process upsets could be traced to instrument faults [2]. A study of the pulp and paperindustry by EnTech revealed that 80% of the loops did more harm than good by increasing process

∗ Senior Fellow, Solutia Inc., St. Louis, Missouri.




variability. If variability is high, the process has to operate further away from constraints such asequipment limitations and minimum product specifications, which causes lower yields and productionrates and more stress on the process equipment. Higher variability and frequency of instrumentationfailures mean more violations of constraints from environmental restrictions or interlock settings.The consequence is more waste and less on-stream time. Thus improvements in the performance offield instrumentation can not only reduce maintenance costs but can also reduce the cost of goods andincrease fixed cost dilution and revenue.

ENGINEERING PRACTICES

A review of the typical cause of errors and failures for measurements (see Table 1) or for control

Au: There isonly 1 Table

valves (Table 2), reveals that the performance and reliability of instrumentation is largely determined

TABLE 1 Categories and Classes for a Dynamic Specification for ControlValves (Four classes A–D for each of the four categories 1–4)

1. Minimum Step Classes 2. Maximum Step Classes

Class A: 3.0% +−0.3% Class A: 5% +−0.5%Class B: 1.0% +−0.1% Class B: 10% +−1.0%Class C: 0.5% +−0.1% Class C: 20% +−2.0%Class D: 0.2% +−0.1% Class D: 50% +−5.0%

3. Response T ime Classes 4. Minimum Positions Classes

Class A: 15 s Class A: 30%Class B: 5 s Class B: 20%Class C: 2 s Class C: 10%Class D: 1 s Class D: 0%

For example, a DACB class control valve will respond to signals larger than 0.2% andsmaller than 5.0% in less than 2 s above a positions of 20%. If you do not care, specifyclass AAAA—almost any valve will meet it. The idea is to require a control valve torespond by addition of class to valve specification.

� To choose a typical class, use the loop type that actually throttles valve. For cascadeloops, you should use the slave loop. Thus, if a column temperature loop sends aremote set point to a distillate flow loop, you should use the classes for a flow loop.

� Vessel or column temperature control => CABC� Exchanger temperature control => CACC� Pipeline temperature control => CCCC� Liquid pressure or flow control => CACC� Vessel level control => CABC� Gas or steam pressure control => CBDC� Compressor surge control => CCCD� Vessel pH control => DABD� Pipeline pH control =>DBCD� Pressure relief => BCCD� For split range, use class D for last category (minimum throttle position)� A general-purpose class would be BAAB

Note that the response time is the time for trim (not actuator) to stay within 10% ofstep or 0.1% span, whichever is largest (overshoot OK if recovery within this offset inresponse time).




during design and construction. The best practices for selection (Table 3) and installation (Table 4)eliminate the source of most maintenance problems and set the stage for developing the confidenceto eliminate unnecessary calibrations.

PROBLEMS AND CAUSES

The most common source of problems for pressure measurements is sensing lines and for levelmeasurements is equalization lines. The addition of these long and narrow passages filled with stagnantprocess fluid is begging for problems. Solids and coating buildup or freezing are likely to plug thelines sooner or later. Plus, the calibration of differential pressure measurement depends on the densityof the fluid in the sensing lines, which requires that the state and the composition of the fluid in thelines be constant. If the lines are assumed to be dry, condensate buildup causes a huge error. Similarly,if the lines are assumed filled but the liquid is vaporized or sucked into the vessel or pipeline, thereadings become meaningless. Sensing lines are expensive to install and maintain.

The next biggest cause of measurement errors and failures is not enough attention being given todetailing the process conditions and their adverse effect. The coating, fouling, and corrosion rate atdifferent velocities and temperatures should be estimated. The extremes in temperatures and pressuresduring abnormal operation and special modes of operation, such as defrosting and decontamination,should be identified and taken into account during instrument selection and installation.

Assuming that large case and pneumatic measurement instrumentation with levers, links, bellows,and flapper nozzles is a thing of the past, the final significant source of measurement problems is due toelectrical interference or grounding problems caused by poor wiring practices or improper enclosuresto protect the terminations. These problems were more predominant in installations completed beforecable tray, grounding, and enclosure practices were better defined in the 1970s. However, mistakesare still made, especially when new wires are installed to add a few signals without formal designprocedures. Figure 1 shows the erratic signal behavior caused by high signal wiring resistance. Thewire was pulled during a control system upgrade and never checked for integrity [3]. Fortunately,there was a redundant pressure transmitter, otherwise the large deviations might have been interpretedas process upsets.

FIGURE 1 Erratic signal behavior caused by a wiring problem [2].




Causes of Measurement Errors and Failures

1. Sensing lines that are plugged or that have liquid when they should be dry or vice versa

2. Sensing elements with excessive coating, fouling, or abrasion

3. Excessive bubbles or solids

4. Sensing elements with deformations, cracks, and holes

5. Low Reynolds numbers

6. Gaskets and O-rings that leak

7. Incorrect materials of construction

8. Sensing, pneumatic, and electronic components affected by process or ambient temperature

9. Moisture on the sensing element or signal connections

10. Electrical interference

11. High connection or wiring resistance

12. Nonrepresentative sensing point

13. Inadequate straight-pipe runs for flow sensor

14. Nozzle flappers that are plugged or fouled

15. Loose feedback linkages and connections

16. Incorrect calibrations

17. Electronic component failures

Unfortunately, control valves are mechanical and use pneumatic components. Their initial perfor-mance and degradation rate is coming under closer scrutiny because, next to poor tuning, they are thebiggest cause of variability in control loops because of their inability to respond to small changes incontroller outputs. Until recently, there were no requirements on a valve specification that the valveactually stroke when asked and no feedback of actual valve position in the control room. A combi-nation of practices on the part of the manufacturer and the user in the past two decades set the stagefor the sad situation in which a loop is better off in manual than in automatic. The manufacturer wasnot asked to make a valve responsive, the user did not know when a valve was sticking or slippingsince there was no position feedback in the control room, packing friction increased from the needto reduce packing leaks to meet new environmental regulations, higher sealing friction appeared asusers became enamored with tight shutoff and tried to use isolation valves as throttle valves or viceversa, and positioners and actuators became less sensitive from the desire for simpler and cheapercomponents.

When rotary valves, such as ball and butterfly valves, have high friction and long shafts and justone or two key lock connections between the actuator stem and the valve shaft, the actuator willmove, but the ball or disk will not, for small changes in signal that are to twisting of the shaft andplay in the connections [3]. Since the feedback of actual valve position is from the actuator stem, thepositioner, the user does not know the valve is not moving. Also, the installed valve characteristicbecomes very flat at large valve positions to the point where the change in flow per change in strokeapproaches zero. Diagnostics and indication of actual valve position in the control room will not showeither of these problems. Thus the use of a smart digital positioner is not the final solution. The usermust pay attention to valve construction and pressure drops and use a sensitive flow measurement asthe ultimate proof that a valve actually changes the flow for small changes in the controller output.Figure 2 shows the response in actuator stem position and flow for various step sizes [4]. It should benoted that the change in controller output per scan typically varies from 0.2% to 1.0% so if the flowdoes not change for these small steps; the control loop will hunt from reset action and loop variabilitywill be noticeably worse in automatic than in manual.

When the control valve does not respond, the controller continues to increment or decrement itsoutput each scan. The result is a ramp rate of controller output. The time it takes to get beyond theresolution limit sufficiently for the valve to respond is dead time. The rate of change of the loop output




FIGURE 2 Response of a control valve to various step sizes (R points to the first reversal of flow andG points to the first good response in flow) [4] (courtesy of Fisher-Rosemount).

is slower if the loop has low gain and reset action, and the peak error from load upsets is larger becauseof this additional dead time. If a valve is oversized, the resolution as a percentage of flow and theassociated dead time are both larger [5]. The practice of using line sized control valves is expensivefrom both initial investment and loop variability viewpoints. The permissible stroke range dependson the installed characteristic and the amount of friction near the closed position. Generally the sizingshould keep globe valves between 10% and 90%, ball valves between 20% and 80%, and butterflyvalves between 25% and 65% open for minimum to maximum flows. The exact ranges depend uponthe installed characteristics.

Causes of Control Valve Errors and Failures

1. High trim seating friction for sliding stem valves and high ball, plug, or disk friction for rotaryvalves

2. High packing friction

3. Loose shaft connections on rotary valves

4. Long shafts or linkages with gaps on rotary valves




5. Single-stage positioners

6. Electrical interference

7. High connection or wiring resistance

8. Nozzle flappers that are plugged or fouled

9. Feedback linkages or connections that are not tight

10. Piston actuators with high sliding friction, wide or worn gear teeth, or yoke slots with play

11. Trim that is plugged or has excessive coating, fouling, abrasion, or erosion

12. Flashing and cavitation

13. Changes in pressure, temperature, and composition

14. Low Reynolds numbers

15. Leaking packing

16. Pneumatic and electronic components affected by process or ambient temperature

17. Incorrect calibrations

18. Pneumatic and electronic component failures

SELECTION

The biggest leverage on maintenance cost is the selection and the installation of instrumentation thathas streamlined passages with no restrictions and cavities for stagnant process fluid to accumulate,and utilization of principles of measurements that have low drift, hysteresis, environmentally inducederrors, and process-induced errors but high sensitivity. This is true for measurements and controlvalves. The optimal situation is to have an instrument that is reliable, repeatable, and sensitive.Absolute accuracy is not as important, in that bias errors can be zeroed out for transmitters andcorrected by loop reset action for control valves. It is most important that a measurement consistentlyrespond to small changes in the process variable (PV) of interest and that a control valve consistentlyrespond to small changes in the controller output. The requirement for an instrument to be consistentis more inclusive than for it to be repeatable in that it specifies a limit to the variation in the gain, deadtime, and time constant of the response.

Field process pressure and temperature switches are generally mechanical in nature and provide nocontinuous information on their health to the control-room-like transmitters. They are not exerciseduntil needed, which further decreases their reliability. Plant operators are essentially flying blind withthese devices, not knowing the value of the PV and never being sure if the device will operate whenrequired.

Orifices are fine as long as you do not need a long-term accuracy of better than 5% and you donot use sensing lines for the differential pressure transmitter. Venturi tubes and flow tubes offer lessobstruction, require less upstream diameters of straight run, have a more constant meter coefficientover a wider range of Reynolds numbers, and have a lower permanent pressure drop than orificemeters.

Coriolis, vortex, and magnetic flow meters have minimal or no obstruction to flow and havehigh consistency and low drift when applied properly. Diaphragm seals and remote heads thatcan be flush mounted eliminate restrictions and cavities where process fluid can accumulate. Di-aphragm seals must be precision filled with hydraulic fluid at a factory or service center whoseprocedure has been verified to eliminate bubbles. The capillary lengths must be short, shielded fromthe sun, rain, and other sources of temperature changes, and equal for differential pressure trans-mitters. Remote heads should use digital signals to prevent the introduction of analog-to-digital(A/D) error into the computation of differential pressure. Also, the absolute pressure should be lessthan the differential pressure so that the error introduced by separate pressure measurements is notexcessive.




Radar devices offer an extremely accurate surface detection and are generally not affected bychanges in the vapor or liquid temperature or composition. Nuclear level measurements are non-intrusive and are thus attractive for very nasty fluids and extreme operating temperatures in whichnothing else will work. There is a considerable amount of paper work, but this can be contracted out.However, since the level is inferred from the attenuation of radiation by the mass between the sourceand the detector, nuclear devices depend on the liquid density’s being relatively constant. Radar willmeasure foam level whereas nuclear will not. Radio frequency (RF) admittance level probes can alsomeasure foam. New RF sensors have been developed that have a second probe to measure changes incomposition to make the level measurement independent of changes in the electrical properties (e.g.,dielectric constant) of the process.

Smart transmitters are available for a slight increase in price. The fact that this investment wassmart is evident from the beginning. A plant reported in 1992 that the installation of smart pressuretransmitters reduced unplanned maintenance calls by 90% and the average time to diagnose a problemdropped from 2.5 h to 10 min [6]. The estimated savings for 100 transmitters in a more recentimplementation of smart transmitters and HART communication protocol was $16,300 for stand-alone HART and $26,050 for HART diagnostics available on the operators’ console [7]. The biggestsavings of $10,000 and $15,000 was due to the reduction in time required for calibration checks.

Smart transmitters also offer the opportunity to sense, use, and transmit auxiliary PVs. These mul-tivariable transmitters provide more accurate signals by activating alerts or correcting for significantenvironmental effects and changes in process operating conditions. For example, temperature andpressure sensors integrated into an averaging pitot tube assembly can do pressure and temperaturecompensation to provide a mass flow measurement for a constant composition that is particularlyattractive for large pipelines where the use of Coriolis flow meters is not possible [8]. The additionof temperature and pressure sensors to a Coriolis meter, in combination with the existing mass flowand density measurements, can provide a viscosity measurement. The incorporation of temperatureand pressure sensors into a control valve with position feedback can compute flow for a constantcomposition. The addition of an accelerometer to a photocell can measure vibration that causes falseinformation [9]. Even if the additional information is not used for compensation but is needed in thecontrol room, the savings in wiring and installation is impressive.

The use of digital outputs, such as frequency and pulse count outputs from flow meters, can increasethe accuracy of the measurement by more than a factor of 2 by elimination of the digital to analog(D/A) converter in the transmitter and A/D converter in the distributed control system (DCS) or theprogramable logic controller (PLC). The use of HART frequency outputs for the PV adds a dead timeto the loop that could be a concern for antisurge control and some pressure control loops. Fieldbuswill reduce this delay by supporting much higher information communication rates.

It is essential to use HART and eventually Fieldbus to get the diagnostics into the control roomand part of a system that can document and analyze failures. The use of hand-held interrogatorsand hand-written reports keeps the knowledge to be gained localized at best. While the savingsin paper work for meeting ISO 9000 and FDA requirements is impressive, it is exceeded by thepossible improvements from the resolution of process and application problems. Fieldbus will greatlyincrease the speed and the quantity of diagnostic information available in the control room and enablethe interchange of data between instruments to expand the scope of diagnostics. For example, pressureand flow measurements could be cross checked against control valve positions for signal validationbefore the signals even get to the control room. Fieldbus will dramatically increase the view and scopeof instrument diagnostics.

Resistance temperature detectors (RTDs) can respond consistently to changes as small as 0.1◦Cbut this great sensitivity is compromised by not recognizing that each RTD has a slightly differentcalibration curve and is altogether lost because of A/D error introduced from large spans. Unlike DCSor PLC RTD input cards, the calibration of transmitters can be matched to a sensor, and the spans canbe narrowed to cover an individual application. RTDs hold their calibration longer and have greaterlife expectancy than thermocouples unless there is excessive vibration. While a bare-element RTDmay be a couple of seconds slower than a thermocouple, this extra lag is small compared with thatof the thermowell and the rest of the temperature loop. Last, three- or four-wire RTD transmitters are




important to eliminate the effect of changes in lead wire resistance. Unless the wiring is very short(e.g., less than 20 ft.), a four-wire should be preferentially used over a three-wire assembly. The errorof a three-wire RTD for 500 ft of 20-gage wire can be as large as 4.7◦F since wires have a resistancetolerance of 10%.

Single straight-tube Coriolis flow meters are better for liquids with solids because the solids willnot equally distribute themselves in dual tubes and there are no bends that are particularly susceptibleto erosion. The higher excitation energies for thick-wall single straight tubes maintain their accuracyat high solids loading. Also, the resonant frequency is so much higher than other frequencies in theplant that vibration is normally not an issue.

Sliding stem (globe) valves have higher sensitivities, and the actuator stem feedback position moreclosely represents the final element position than rotary (ball, butterfly, and eccentric plug) valves.Above 6 inches, the economics of rotary valves is compelling, and process conditions such as foulingand solids may dictate a rotary valve. When rotary valves are used, low friction, tight connections,and short large-diameter shafts are essential to minimize gaps (backlash) and twist (shaft windup).Valves designed for on–off service and tight shutoff generally cannot be made into good throttlingcontrol valves.

Piston actuators require more maintenance and are less sensitive than diaphragm actuators. How-ever, pistons are less expensive and may be the only alternative for very large control valves. Whenpistons are used, the emphasis should be on low sliding friction, minimal gap in the slot for scotchyoke, and wear-resistant tight teeth for rack-and-pinion pistons.

A valve response requirement should be added to the control valve specification. This valvedynamic specification must have enough details to ensure meaningful results. It is critical to realizethat the manufacturer will naturally choose the step size (10%), the throttle position (45%), the friction(hand tight packing, special lubricants, and/or flow ring), valve size (small), positioner air consumption(high flow spool or relay), and feedback measurement (actuator shaft instead of trim position) thatgives the best response time. These test conditions may have nothing to do with the valve actuallysupplied or the results experienced in the field. The dynamic specification must specify the minimumstep, the maximum step, the response time of the trim, and the throttle position expected. The fourclasses of performance for each of these four categories are shown in Table 1 along with examples ofclasses for different types of applications. Note that an offset is permitted and that an overshoot is oflittle consequence if there is a recovery within the response time except where relief valves or rupturedisks or instantaneous pressure interlocks might be activated by a pressure excursion. The longest ofthe response times for the minimum and maximum step size determine the class for the response timecategory.

For pH and Oxidation Reduction Potential (ORP), either a liquid-filled reference, pressurized toprovide a small flow of electrolyte out of the reference junction, or a solid reference is needed toprevent internal contamination of the reference electrode with process ions for high concentrationsof salts or acids and bases. The flowing junction provides a more accurate measurement by ensuringa more constant diffusion potential, but it requires refilling.

The life expectancy of pH electrodes is the lowest of all of the common measurements and ishighly dependent on operating temperature. Figure 3 shows the average life to be 12 months at 25◦C,6 months at 50◦C, 3 months at 75◦C, and 1.5 months at 100◦C when there are not additional adverseconditions such as abrasion, chemical attack, or dehydration. Caustic solutions above 12 pH increasethe degradation of life with rising temperature dramatically. For high temperature and high causticconcentrations, it is essential to limit the exposure by using an actuated assembly to automaticallyretract the sensor. This is also very effective for coating problems that cannot be prevented by highvelocities since the retraction can be followed by an automatic wash of the electrodes with a cleaningsolution followed by a flush and soaking of the electrodes. Note that the cleaning solution does notget into the process so it does not have to be chosen to be compatible with the process. However, theretraction and cleaning of electrodes upsets their thermal and ionic equilibrium. So for more moderateconditions, the use of three electrodes and selection of the middle reading is preferred. The middleselection will inherently ignore a single failure of any type and reduces the error from the short-termexcursions that are characteristic of pH measurements.




FIGURE 3 Life expectancy of pH electrodes for variousprocess temperatures (Courtesy of Fisher-Rosemount).

Best Practices for Instrument Selection

1. Do not use pneumatic transmitters.

2. Avoid sensors with mechanical linkages or bearings that can wear out.

3. Avoid field pressure or temperature switches.

4. Avoid orifice meters.

5. Avoid piston actuators for control valves, especially scotch yoke or rack and pinion with wideteeth.

6. Avoid tight shutoff and automatic block or isolation (on–off) valves as control valves.

7. Use transmitters and positioners with digital rather than analog components.

8. Use smart HART multivariable transmitters and positioners with diagnostics in the control room.

9. Use instrumentation with modular designs to the reduce the time and the cost to replace oroverhaul.

10. Use precision filled diaphragm seals or remote heads for differential pressure measurements.

11. Use Coriolis, vortex, and magnetic flow meters.

12. Use radar or nuclear level measurements.

13. Use components that can withstand the extremes of the process and environment.

14. Use sensor matched transmitter calibrations for RTDs.

15. Use three- and preferentially four-wire transmitters for RTDs.

16. If you must use thermocouples, use premium-grade elements and extension wire.

17. Use narrow span transmitters instead of thermocouple or RTD DCS or PLC input cards.

18. Use digital instead of analog output signals for cases for which speed is less important thanaccuracy.




19. Use level switches (e.g., tuning forks) that ignore coatings and changes in material composition.

20. Use straight single-tube Coriolis or magnetic flow meters for solids.

21. Use diaphragm actuators. If you must use a piston actuator because of valve size, consider alow-friction vane or floating cylinder.

22. Use bellows seals and extension bonnets instead of high-friction graphoil or environmental pack-ing.

23. Use sliding stem (globe) valves. If you must use a rotary valve because of size or plugging, usesplined or quadruple keyed shaft connections, no linkages, short shafts, low breakaway torque,and low sealing surface friction.

24. Size the control valve to operate beyond the seating friction and on a portion of the installed valvecharacteristic where the slope is between 0.25% and 2.5% flow per percentage of stroke.

25. Use pressurized or solid reference electrodes for pH and ORP.

26. For sensors with low life expectancies such as pH, use autoisolation to reduce exposure time ormiddle selection of three sensors.

INSTALLATION

To date, most installations have concentrated more on accessibility than on reliability. The desire tolocate a control valve at a convenient platform level has resulted in long sensing lines, insufficientstraight upstream and downstream pipe runs, trapped solids, and longer process dead times fromgreater transportation delays. The practice is self-fulfilling in that mounting instruments for easyaccess has increased the maintenance problems to the point where mounting location for such accessis deemed essential.

While the need for straight runs upstream and downstream is well recognized for orifice meters, itis sometimes neglected for other instrumentation. Pitot tubes, vortex meters, and thermal gas metershave approximately the same run requirements as orifice meters. Magnetic flow meters and ultrasonicflow meters need approximately half of the run requirements of orifice meters. Ideally, control valves,which are variable orifices, should have approximately the same straight run as an orifice [10]. Themanufacturer’s test facilities where the control valve characteristics are documented meet or exceedthese requirements, but these are rarely adhered to in the field. When the pressure drop across thecontrol valve is small compared with the upstream pressure, which occurs for rotary valves, it is moreimportant; but this is exactly the situation in which it is least likely to be practiced because of thesize of the lines. Also ignored is the swage effect on rotary valve, which can reduce the capacityby as much as 40%. Insufficient straight runs cause excessive noise and a loss in consistency forboth measurements and valves because of a nonuniform velocity profile, pressure fluctuations, andconcentration or temperature gradients. Coriolis meters have no straight run requirements.

Temperature and pH sensing probes should extend into the middle of the pipeline to get the mostrepresentative measurement. Both temperature and pH sensors should be at least 10 pipe diametersdownstream of a mixer to allow sufficient recombination and dispersion of the flows. Since a thermow-ell suffers from conduction error where heat flows to or from the tip, depending on the temperaturegradient along the thermowell wall, it is also important to ensure that at least 10 diameters are im-mersed into the process fluid. When an elbow is used for the thermowell installation, most of theinsertion length is in the center of the pipeline. Figure 4 shows four different types of installations ofthermowells in order of preference to reduce profiling and conduction errors [11].

The most effective and least costly method of keeping probes clean is to increase the liquid velocityto more than 5 feet per second (fps). For pH electrodes, it is desirable to keep the velocity less than 9fps to reduce wear and noise. For temperature probes (bare elements and thermowells), it is importantto keep the velocity less than 30 fps to reduce erosion. The maximum velocity to minimize vibrationmay be lower, particularly for bare elements or long insertion lengths and RTDs. When velocity doesnot work for pH probes, jet washer nozzles aimed at the probe tip or autoretractable assemblies witha washing cycle are used [12].




FIGURE 4 Order of thermowell installations to minimize measurement error [11].

It is important that the transportation delay between the control point and the measurement pointin the process add less than 10% dead time to the loop so that additional variability is not appreciable.This means that pH electrodes should be mounted approximately 10 to 20 pipe diameters from thedischarge of a pump for vessel pH control or a mixer for pipeline pH control. Similar requirementsshould be used for the control of temperatures at the exit of vessels, desuperheators, pipeline mixers,and heat exchangers.

For control valve applications for which flashing is possible, a lower temperature and/or high-pressure point in the pipeline, a control valve with a lower recovery coefficient, or a staged pressurereduction can prevent cavitation. Special trim for staged pressure reduction has small graduatedpassageways prone to plugging, can be as expensive as the valve, and is the epitome of a specialpart. A combination of orifice plates and valves stroked simultaneously provides a lower total cost ofownership than special trim. When cavitation is inevitable, the control valve should be mounted on theinlet nozzle to a vessel so that cavitation changes to flashing. It is important to note that appreciabledamage usually occurs only for water streams.

Best Practices for Instrument Installation

1. Avoid sensing and equalization lines.

2. Avoid sample lines.

3. Close couple and preferably flush mount sensor diaphragms.

4. Use sufficient straight runs upstream and downstream of flow meters, probes, and valves.

5. Preferentially use pumped pipelines sized for 5 to 7 fps for probes to minimize coatings.

6. Mount probes and flow meters in vertical lines to prevent solids accumulation.

7. In vertical lines, use flow up to minimize buildup of condensate, solids, and bubbles.

8. In vertical lines, use flow down to minimize abrasion of pH electrodes.

9. Extend probe tip into the middle of a pipeline to get a representative measurement point.

10. Provide insertion length at least 10 times the outside diameter of thermowells.

11. Use spring-loaded tight-fitted, grounded, sheathed temperature sensors in thermowells.

12. Choose location for probes close to discharge of equipment to be controlled.

13. Choose location for control valves close to entrance of equipment to be controlled.

14. Choose location or size pipe line for probes so velocity is less than 1 fps to reduce abrasion.




15. For vortex and magnetic flow meters make sure the velocity is greater than lower limit (e.g.,1 fps).

16. For magnetic flow meters make sure the conductivity is greater than lower limit (e.g., 1 µ�/cm).

17. Size pipeline and meter to keep the Reynolds number in the range for the best flow meter accuracy.

18. Keep flow meters completely filled with fluid and keep probes completely immersed.

19. Choose location for control valves to prevent cavitation or ensure implosion in vessel vapor space.

20. Use enclosures/fittings that prevent water and corrosion on connections and electronic compo-nents.

21. Use separate cable trays and conduit drops to keep instrument signal wiring away from AC wiring.

22. Use separate cables for pulsed, frequency, and switched instrument signals.

23. Use twisted shielded pairs that are properly grounded for instrument wiring.

24. Use autoretractable pH probes for fluids that attack, coat, or dehydrate the glass to limit exposuretime.

Even if you have used the best design and implementation practices and there are no instrument-related problems, there are still plenty of equipment and process problems that can lead to unnecessaryinstrument maintenance requests. Thus reaping the entire potential benefit from better engineeringpractices requires a change in maintenance practices.

MAINTENANCE PRACTICES

It is better to find the cause than to treat the symptom. Calibration is the technique that tends to beused first when a instrument is suspect because it is the easiest to understand and do and is well docu-mented. Instrumentation instruction manuals have calibration procedures detailed, but troubleshootingpractices are either too vague or missing. The emphasis may stem from the days of pneumatic instru-mentation when instruments could not hold their calibration for more than 6 months. Now calibrationis done in response to application problems or lack of operations confidence. Calibration is at besta temporary fix and often camouflages the real problem. A particularly troublesome practice is theremoval of the instrument and calibration in the shop. The instrument is not at the operating or envi-ronmental conditions of its application. A calibration may actually introduce errors that will cause aneed for calibration again. The result can be calibrations chasing calibrations. This occurs wheneverpH electrodes are removed from the process for buffering. There is also the risk of damage and amistake in the reconnection of the instrument. With reasonably good selections and installations andsmart HART transmitters and positioners, most of the maintenance cost of instrumentation can beeliminated by simply not calibrating a measurement or a valve until it is within the wear-out phaseof its life expectancy unless the instrument’s temperature or pressure limits were exceeded. At thispoint, it might be better to replace the measurement or overhaul the valve.

Instruments used for process control are continually tested at operating conditions and the resultsand diagnostics are displayed as part of the normal operation of the loop and the operator interface. Incontrast, instruments for safety interlock systems that are exercised only when trip conditions occurand whose health and response at the more extreme operating conditions are not scrutinized have alower reliability and would benefit from an automated documented on-line testing program.

When the major components that distinguish the primary features of an instrument can be easilyconnected without special tools, the manufacturer gains from reduced manufacturing costs and theuser benefits from reduced maintenance costs. It is a win–win situation. Modular instrumentation,such as the ceramic pressure transmitter shown in Fig. 5, enables a huge number of combinationsof ranges, connections, and materials of construction to be assembled within a few hours from asmall inventory of spare parts. This means regional service centers can provide same-day delivery forreplacements. Next to stopping unnecessary calibrations, the next biggest savings obtainable by bestpractices is in eliminating plant repair and inventory of component parts.




FIGURE 5 Modular construction of ceramic pressure transmitters to reduce inventory (courtesy of Endress andHauser).

Best Maintenance Practices to Reduce Maintenance Costs

1. Do not calibrate a smart differential pressure transmitter for 2 to 4 years after last calibration.

2. Do not calibrate a coriolis or magnetic flow meter.

3. Do not calibrate a smart four-wire RTD transmitter.

4. Do not calibrate a smart thermocouple transmitter for 5 years after last calibration.

5. Do not calibrate a vortex meter unless the kinematic viscosity permanently changes.

6. Do not calibrate a radar level gage unless the vessel internals change.

7. Do not calibrate a smart nuclear level gage unless the process density permanently changes.

8. Do not calibrate a smart pH transmitter until 2/3 of the electrode life expectancy is reached (e.g.,9 months at 25◦C, 4 months at 50◦C, 2 months at 75◦C, and 1 month at 100◦C).

9. Do not calibrate a smart digital positioner unless the valve is overhauled.

10. Do not overhaul a control valve or replace a transmitter until diagnostics indicate a problem.

11. Do not inventory spare parts or repair field instrumentation.

12. Use manufacturer’s on-site or regional service centers for replacements and overhauls.




INSTRUMENT-KNOWLEDGE-BASED DIAGNOSTICS

Diagnostics are the key to detecting real problems and eliminating time spent on pseudoproblems.In fact, it is the key to gaining confidence to leave instruments alone until they need to be replaced.However, foolproof diagnostics require the investment of a lot of expertise. A bad diagnostic or amissed diagnostic requires several correctly detected problems to reestablish operations confidenceand trust enough to rely on the diagnostics. Since real problems are few and far between when goodengineering practices are used, it may take years to recover from a false start.

The diagnostics must be presented near and preferably on the normal operator interface to get the24-h attention needed for effective use. However, if operators are barraged with false alerts, a validdiagnostic will be ignored. The problem is worse than with false alarms in that operators at first donot believe such expertise is possible, and diagnostic capability, like any new technology, is suspectuntil proven otherwise.

The investment to ensure reliable diagnostics has been underestimated to date. The investmentwill be made by manufacturers of instrumentation as an evolution of smart instrumentation to staycompetitive if the user recognizes the value of the additional capability. So far, the additional cost ofthese features is small because the manufacturer can spread the development cost over a large numberof units. If instrument-knowledge-based diagnostics can do the job, it is the most cost-effectiveapproach for the user.

One can be misled from the excitement surrounding smart transmitters and think that such trans-mitters can provide out-of-the-box completely self-sufficient diagnostics and calibration checks. Todetect or correct for a drift or span error requires comparing the transmitter output with known signalsor values at operating conditions [13, 14]. Some optical devices do this by inserting a known lightsource and filter in the path. Autoretractable assemblies for pH probes can provide autocalibration bya buffering cycle, but the installation costs of a buffer system are considerable and the calibration isnot done at process conditions. A control valve could automatically calibrate its positioner and ad-just its tuning (proportional and derivative settings) by comparing the requested and the actual valvepositions. Valve manufacturers have built this into their digital positioners as part of an initializationcycle. However, the supposed assessment of drift from comparing the change in transmitter signalfrom normal operation assumes that the PV is not controlled but is an indicator and that the excursionis not due to changes in process operating points or slow load upsets. To check or correct a calibrationrequires an internal reference or redundant measurements or inferences from plant-knowledge-basedtechniques.

A diagnostic check of integrity (e.g., good or bad status) without pinpointing the cause can be ob-tained from a reference state. For example, a reduction in the noise amplitude of a pressure transmittercompared with the noise during a representative initial 2-day period could be used to determine if thetransmitter sensor is coated or the sensing lines or connections are plugged. Transmitters are bettersuited to do this than expert systems or DCSs because they work on the raw sensor signal before filters,D/A, A/D, and scan times reduce the resolution to noise that is much slower and whose amplitudeis altered [13]. Diagnostics to detect the onset of coating will require some periodic adjustment orinitialization as operating points or equipment is changed.

Status checks of Coriolis mass flow meters can be made by comparing the excitation energy requiredfor maintaining oscillations, the output amplitude of the sensors, and the resonance frequency of thetubes [13]. Changes in these relative values can be used to detect entrained air or excessive solids.

The status of a tuning fork can be assessed on line by switching to a redundant circuit and drive, asshown in Fig. 6, every second to look for the divergence or nonappearance of the resonant frequency[13]. This essentially complete status check, combined with the tuning fork’s low price, simplicity,and ability to ignore nearly all coatings and density changes, makes it an extremely cost-effectivelevel switch. The only adjustment is a threshold specific-gravity adjustment to distinguish betweenliquid and vapor.

A differential pressure transmitter failure that gets too hot may get noisy before it fails. A di-agnostic that keeps track of high temperatures can also help find the source of the problem so thatthe replacement does not fail [14, 15]. Ideally, the transmitter should keep track of the number, timeduration, and magnitude of temperature violations. The same is true for overpressurization.




FIGURE 6 Tuning fork design to completely test its integrity every second (courtesy of endress andHauser).

The loss of fill in a differential pressure transmitter can be detected by comparing the measuredtemperature with the temperature computed from the changes in diaphragm capacitance [13]. Mostlosses in fill are slow, whereas methods of detection based on response time tests assume a relativelyfast loss and a single failure. Since the loss may be over a period of months, concurrent multiplefailures are possible and the degradation in response time is not discernible. Of 80 confirmed oil lossfailures during a response testing program, none were detected despite 4200 tests [16].

A loop current step response (LCSR) test has been used to determine the in situ response time ofthermocouples and RTDs. A small loop current is applied to the sensor leads, and the time for thetemperature to rise or decay is used to determine the response time. The method can detect foulingor any other changes in the heat transfer coefficient of the thermowell on line at actual operatingconditions [17].

A power spectrum density, combined with data qualification algorithms, can determine the responsetime of pressure transmitters. The fast Fourier transform (FFT) of noise data of each transmitter iscomputed to get the power of the fluctuating signal as a function of frequency. The plot of powerversus frequency is the power spectrum density [16].

Control valve manufactures have developed extensive diagnostic software. One package that addsmeasurements of stem position and I/P output and actuator pressures can quantify problems. Thecapability of the package, including pressure measurements and tests such as valve signature, dynamicerror band, step response, drive signal, and output signal tests has also been integrated into onboardadvanced diagnostics of a digital positioner so that the tests can be done on command [18]. The resultsare used to line up spare parts and service center time for repair and reconditioning of control valvesduring shutdowns or between batches. A savings of 75% in valve maintenance cost was reported bynot pulling valves that are fine and by more efficiently correcting actual problems. Sometimes thevalve can be fixed without being removed by tightening a bolt or changing a positioner part. Valvediagnostic software has shown the ability to pinpoint the problem. One plant reported a savings of$100,000 per year, which was a 60% reduction in valve maintenance costs. The diagnostic softwaredetermined that only 14 of 188 valves scheduled to be pulled during a shutdown for overhaul neededthat level of maintenance [19]. There is also the possibility of increased manufacturing revenues fromreducing down time.

Another valve manufacturer has developed qualitative diagnostics by noting relative changes inthe delay time (dead time) and run time (response time less the delay time) [20]. Depending on thedirection of the change in delay time and run time for filling and venting, higher friction, spring failure,


2 September 1999 16:23 CHAP-10.tex chap10.sgm MH009v3(1999/07/27)


air supply problems, air filter contamination, smaller closing force, and leakage in the actuator canbe diagnosed.

Some transmitters can be configured to go to a fail-safe signal value following detection of a failure.For decades, it has been possible to specify an upscale or downscale signal following thermocoupleburnout (open or high-resistance thermocouple junction). Now smart transmitters have added thecapability to hold the last value or to force its signal to a high or low value for a bad PV status thatcould be caused by a host of diagnosed problems [19]. In practice, signals forced to on-scale valuesfollowing failure have caused confusion and unforeseen consequences and can actually cause safetyissues. To prevent severe process upsets that are due to transmitter failure, hold the controller outputat its value from several seconds before the transmitter failure.

Asset effectiveness management software (AEMS) that displays and documents the diagnosticshas enabled companies to move to a predictive maintenance mode. The savings during startup alonehave been $150 to $200 per device. A pharmaceutical company reported savings of $100 per year perdevice for a total of $50,000 for 500 devices. A chemical company noted savings of $130 per yearper device [19]. Manufacturers are also offering AEMs coupled with on-site loop tuning tools andanalysis skills.

Diagnostic Techniques Used or Proposed by Instrument Manufacturers

1. Decrease in sensor signal noise amplitude or frequency to detect plugged, coated, or stuck sensor

2. Shift in sensor noise average to detect drift (highly dependent on numerous assumptions)

3. Change of diaphragm temperature to detect loss of fill fluid in differential pressure transmitters

4. Change in excitation energy, output amplitude of sensor, and resonance frequency of the tubes ofCoriolis flow meters to detect changes in the process fluid that affect measurement accuracy

5. Divergence or nonappearance of the resonant frequency of redundant tuning forks to problemswith level switches

6. LCSR to determine the in situ response time of thermocouples and RTDs

7. Power spectrum density to determine the response time of pressure transmitters

8. Increase in pH electrode resistance to detect coated or nonimmersed electrodes

9. Divergence of internal redundant reference electrode potentials to detect contamination of fill

10. Capture of pressure or temperature limits violations for almost any type of instrument

11. Totalization of cycles and amplitude to gain access to wear on mechanical components

12. Increase in delay time or run time of small perturbation of valve stroke to detect increase infriction, air supply pressure, clogged air filter, actuator leak, or seat loading

13. Increase in ratio of actuator pressure to drive signal to quantify increase in friction

14. Increase in dynamic error band to detect problem in positioner and valve combination

15. Increase in response time from closed-loop response test response to detect fouled temperaturesensors

16. Increase in recovery time of pH measurement after autowashing to detect persistent electrodecoating

17. Change in electrode efficiency from autobuffering to detect glass wear, abrasion, and dehydration

18. Fail safe inherently or by autoswitch for bad PV diagnostic

PLANT-KNOWLEDGE-BASED DIAGNOSTICS

Sometimes there are important failure modes that are best determined from material or energy balancesor correlation with other instrumentation. However, the user must go into such projects with eyes wide




open as to the initial and the continuing costs and commitment and make sure there is some sizablestake. Successful applications in one chemical company occurred when automatic actions were takenby the expert system or neural network that improved yield or capacity and there was strong technicalexpertise available on site. The greatest broad-based success of neural networks has been in providingon-line intelligent sensors for combustion stack gas analysis. These neural networks have been shownto be more accurate than industrial stack gas analyzers and much less expensive to maintain. Typically,the neural network needs to be retrained once a year by bringing in a validated skid-mounted on-line analysis system. Several states have accepted the results of these neural networks as proof ofcompliance. It is important that the real-time expert system or neural network have more than just anadvisory role in the plant to get the sustained level of attention that it needs.

Neural networks do well when there is a planned test program. For composition measurements,the rental of validated lab or on-line analyzers to generate lots of composition measurements duringthe test is extremely helpful. Neural networks would benefit from the rigor and the structure oftesting used for Constrained Multivariable Predictive Control (CMPC) model identification in whichloops are opened (run in manual), unusual operating modes are screened, and several steps in bothdirections of various sizes are made in all of the manipulated and disturbance variables. Effectively,this is what is done for training and verifying neural networks for combustion analysis. Unfortunately,users have been led to believe that process variables can be modeled from just dumping data froma historian into a neural network and adjusting the inputs, layers, and delays until there is a goodfit to the model. This is at best a snapshot in time, since many parameters, such as frictional losses,heat transfer coefficients, and transportation delays, depend on flow [21]. These parameters that areimportant should be computed as a function of flow before being used as inputs to the neural network.Nonstationary behavior and variables such as level, pressure, composition, and temperature of batchvessels that are the result of the accumulation of mass or energy should be excluded since thereis no steady state. Some exceptions are those for which level is determined by a measured gravitydischarge flow and pressure is determined by a measured vent flow. Even if integrators could be addedto the neural network, the inevitable error accumulation from the slightest measurement error andintegration step size would cause the prediction to drift away from reality. For continuous vessels,there may be a steady state, but there are important variable time constants. Neural networks can beused to predict the rate of change of level, gas pressure, temperature, and composition in vessels andthe temperature and composition of process equipment for which backmixing is negligible, such asplug flow reactors, static mixers, desuperheaters, stacks, dryers, and heat exchangers. In many cases anequilibrium relationship between temperature, pressure, and composition can also be used to predictone variable from the measurement of another. Also, for sequenced on–off feeds, batch end pointscan be predicted.

Generic signal validation rules have been developed for real-time expert systems that can de-termine if common measurement signals are failed (off scale), dead, or have an abnormal rate ofchange (blipped) or model error high or low (grossly out of calibration). There is also a check forvalve saturation because most problems in a control loop are first seen in the valve excursion unlessthe measurement is completely unresponsive. These rules can be generic in nature and applied to allinstrumentation [22].

The model rules in the real-time expert system uses material balances. Energy balances can beused but they are more complex and less generic. For level measurements, the rate of change of levelis compared with the net flow in and out of the vessel. These types of checks are done by passing thesignal through a large dead-time block so that the new value is compared with a value old enough thatthe actual level change is much larger than the A/D and measurement noise. If the large difference inage of the two signals used to compute the change is created by a large scan time or calculation interval,the diagnostic has a dead time equal on the average to approximately 1.5 times the calculation interval.This check has been able to determine if the level measurement is not responding or is responding inthe wrong direction.

The model for flow measurements is the computed flow through a control valve. This dependson the control valve’s having a good positioner and an identified constant installed characteristic.Otherwise, the upstream and the downstream pressures must be measured and used with the inherentcharacteristic to compute flow by means of the valve sizing equation. The accuracy of this check is




approximately 20% at best, mostly because of uncertainties in the valve characteristic and nonidealeffects in the valve passageways [22].

Principal component analysis (PCA) can be used to provide a simpler fault identification bycreating a new smaller set of uncorrelated variables with orthogonal properties from a much larger setof correlated variables. If you visualize a football in space, the axes of the three orthogonal variablesneeded to describe the football dimensionally would be rotated so that the long axis and the short axisof the football each lie on the axis of an orthogonal variable. An analogy would be the simplificationof instructions to a service person on how to locate an existing window to be repaired from usingbuilding number, street name, city, town, floor number, apartment number, room, and wall to justlatitude, longitude, and altitude.

In a PCA application to boiler measurements, a sensor validity index (SVI) was created that addsthe ability to distinguish abnormal operating conditions from a single sensor fault. False alarms areavoided by application of an exponentially weighted moving average to the SVI to act as a filter. Faultysensors were replaced with reconstructed values [23]. As more user-friendly software is developed,PCA methods will move from the lab and the university to the plant.

The data reconciliation step in real-time optimization (RTO) can identify instrumentation out ofcalibration in a few minutes by use of open equations and solving for the measurements with the lowestconfidence limits. Since heat transfer coefficients and frictional losses are updated, and the material,component, and energy balances are comprehensive, RTO has a good track record of detecting thedegradation of sensors. However, RTO depends on the process’s being at steady state, and successfulcomplete runs of the RTO may not occur when the process is upset by a faulty sensor.

The pattern recognition method used in tuning controllers could be used to detect fouled sensors.A coating of just a few millimeters on a pH electrode can cause the loop period to increase by a factorof 5 or more. Also a limit cycle may develop. While the changes are not as dramatic for temperaturemeasurements, the principle is still valid.

A closed-loop performance monitor has been developed that counts the number of loop cycles forwhich the amplitude or duration is large enough to exceed an integrated absolute error trigger point andbe registered as a loop cycle rather than as measurement noise. The cycle total has a forgetting factorthat causes the total to decay if the cycling stops. The algorithm is simple enough to be implemented inany controller with some math instruction capability. If the process gain is known, it can be determinedwhether the cycles are most likely due to a sticky control valve from the controller’s tuning settings[24].

Simple statistical measures, such as the process performance and capability indices, can determinehow close the control system is performing to its maximum capability and can estimate the benefitsfrom improvements that allow operation closer to constraints. The process capability index is inverselyproportional to the mean-square successive difference and the process performance is inversely pro-portional to the standard deviation. They are both proportional to the spread in specifications (e.g.,constraints) [25]. These indices are extremely useful and are simple enough to be installed on line todetect automatically a degradation in the control system.

Finally, a simple diagnostic procedure, whose logic is illustrated in Fig. 7, is helpful in determiningwhether oscillations are due to measurement noise, tuning, interaction, or a poorly responding controlvalve. If the oscillations disappear when the loop is in manual, they are due to tuning or a poorvalve. If the flow measurement does not change or jumps for a small change in controller output,the valve is sticking or slipping, respectively. If not, the tuning needs to be corrected, based onthe type of loop and whether this loop is upsetting other loops. If the oscillations remain with theloop in manual, then they are either measurement noise or caused by the cycling of other loops. Anoscillation period of less than 0.1 min is noise from electrical interference, pressure waves, insufficientmixing, or resonance unless there are some loops with unusually fast ultimate periods that are due toa variable speed drive instead of a control valve and a scan time of much less than a second. Slowoscillations are caused by interaction or on–off actions and by erratic action of regulators and steamtraps.

Which tool or technique is best depends on the type and the extent of on-site expertise available,the budget, and of course, the nature of the application. Since each technology has its limitations, acombination of methods might be best, such as neural networks embedded in expert systems or trained




FIGURE 7 Manual troubleshooting to locate the source of an oscillation (IVP refers to the implied valve position or controller output andPV refers to he process variable or controller measurement.

by first-principle models. The dominant method used at a plant site depends on resource requirementsand availability.

In general, RTOs are probably the most expensive and require the most outside expertise. Real-timeexpert systems and neural networks are a close second in cost if the time spent by plant specialistsfor implementation and maintenance of the system is included. The use of PCA techniques couldbecome less resource intensive as the technology is transferred from academia to industry. Thepattern recognition, closed-loop performance monitor, capability indices, and diagnostic proceduresare economical but are fairly limited in their ability to pinpoint problems.

Tools and Techniques that Use Plant Knowledge

1. Real-time expert systems for signal validation

2. Real-time neural networks for intelligent sensors

3. PCA for identification of faulty sensors

4. RTO data reconciliation to quantify instrument degradation

5. Pattern recognition to detect sensor fouling

6. Control loop performance monitor to detect control valve sticking

7. Process performance and capability indices to determine control system performance

8. Manual diagnostic methods to track down control loop problems

KEY POINTS

1. The cost of instrument maintenance is a significant portion of the bottom line.

2. The cost of reduced process efficiency and capacity, because of ineffective and reactive mainte-nance, can be greater than the instrument maintenance cost.




3. Most of the maintenance cost to date is unnecessary since it consists largely of calibration checksthat are unwarranted or of preventative maintenance that is premature.

4. Most instrument failures are caused by improper selection or installation.

5. Sensitivity, consistency, and reliability are of paramount importance for both measurements andcontrol valves.

6. Sensing lines are the biggest source of maintenance problems.

7. Most smart digital instruments do not need to have their calibration checked.

8. Calibration checks of transmitters are often not possible except through redundancy.

9. Control valves increase loop variability when they cannot respond to small changes in signals.

10. Control valves have the greatest need for diagnostics.

11. Control valves have the most diagnostics implemented.

12. pH needs diagnostics more than any other measurement.

13. pH leads the other measurements in terms of on-line diagnostics of coatings and other problems.

14. Most of the pressure and flow measurement diagnostics implemented to date are for detectingover range or violations of temperature or pressure limits.

15. Calibration checks are done in response to application problems and lack of operations confidence.

16. Instruments can analyze noise before it is filtered or aliased.

17. Diagnostics must be displayed in the control room.

18. Fieldbus will dramatically increase the view and scope of instrument diagnostics.

19. Instrument-based diagnostics are an excellent investment.

20. Plant-knowledge-based diagnostics require significant initial and ongoing internal or externalresources.

21. Neural networks and expert systems should have more than an advisory role to ensure ongoingsupport.

22. Neural networks and expert systems can predict the rate of change of variables that are the integralof mass flow and energy rates (e.g., backmixed volumes) and can predict the actual variable thatis the result of a steady-state relationship and a delay (e.g., plug flow volumes and equilibriumrelationships).

RULES OF THUMB

1. Engineer applications for the greatest sensitivity, consistency, and reliability.

2. Eliminate any areas of stagnant process fluid.

3. Use instruments that are least affected by the process that holds their calibration the longest.

4. Use smart HART modular digital instrumentation.

5. Phase in Fieldbus when its reliability is established.

6. Select measurements that do not need calibration and do not wear out (e.g., Coriolis meters).

7. Do not install pH electrodes unless you are ready to deal with the short life expectancy.

8. Stop making calibration checks and use field service centers for replacements and overhauls.

9. Use instrument-based diagnostics to develop a predictive maintenance program.

10. Implement on-line calculations of process performance and capability indices.

Most of maintenance is either unnecessary (e.g., calibrations) or reactive, in which action isnot taken until there is an actual failure and the damage has been done to the process efficiency andcapacity. Preventative maintenance programs suffer from a lack of knowledge as to when a transmitter




really does need to be replaced or a control valve overhauled. Most preventative programs are basedon time, and consequently most of the preventative maintenance is premature. What is needed is amove to predictive maintenance, in which replacements and overhauls are done based on diagnostics.Good engineering practices can greatly reduce the magnitude of the problems to the point wheremost of the diagnostics are associated with detecting quality control problems during manufacturingof the instrument or in the wear-out phase of the instrument rather than in application problems.The benefit-to-cost ratio of smart HART transmitters is tremendous because they are much moreaccurate and reliable and can provide the diagnostics important for predictive maintenance in thecontrol room. These diagnostics are based on many years of instrument knowledge. Sophisticatedplant-knowledge-based programs are costly, and their justification and impact on resources should bereviewed.

REFERENCES

1. Diagnostics and Reliability Based Maintenance, 5/8/98,http://www.rosemount.com/products/ams/rbmaint.htm (Internet).

2. Sanders, F. F., “Watch Out for Instrument Errors,” Chem. Eng. Prog., 62–66, July 1995.

3. Coughran, M., “Valves: Testing for Peak Performance,” InTech, 58–61, Nov. 1994.

4. Coughran, M., “Performance Influences in Globe Control Valves,” InTech, 44–49, Aug. 1996.

5. McMillan, G. K., “Improve Control Valve Response,” Chem. Eng. Prog., 76–84, 1995.

6. Feature Focus: Pressure Transmitters and Transducers, InTech, 26–29, July 1992.

7. 1996 HART/Fieldbus Investigation Team Tutorial, Monsanto.

8. Schnake, J. B., “Emerging Flow Technology Boosts Accuracy and Savings,” InTech, 52–56, Jan. 1998.

9. Dierauer, P., “Smart Sensors Offer Increased Functionality,” InTech, 60–63, May 1998.

10. Luna, S. F., “Installation Practices,” in ISA Handbook of Control Valves, 2nd ed., Instrument Society ofAmerica, Research Triangle Park, North Carolina, 1990, pp. 339–340.

11. McMillan, G. K., and Toarmina C. M., Advanced Temperature Measurement and Control, Instrument Societyof America, Research Triangle Park, North Carolina, 1995.

12. McMillan, G. K., pH Measurement and Control, 2nd ed., Instrument Society of America, Research TrianglePark, North Carolina, 1994.

13. Schneider, G., “Status Monitoring and Self-Calibration of Sensors,” Endress and Hauser Report FAR 507E,1998.

14. Berge, J., “Fieldbus Advances Diagnostics,” InTech, 52–56, April 1998.

15. Maintenance: Finding the Right Medicine, Chem. Eng., 119–122, May 1998.

16. Weiss, J., “Slow Oil Loss in Pressure Transmitters,” InTech, 40–43, Oct. 1992.

17. Peterson, K., “Testing Sensors for Accuracy and Speed,” Chem. Eng., 131–134, Feb. 1992.

18. FieldVue Instrumentation Fundamentals, Course 1750 Revision B , Fisher-Rosemount Educational Services,1996.

19. Giovannelli, S., “Controlling Maintenance Costs,” Chem. Process., 92–96, May 1998.

20. Kiesbauer, J., and H. Hoffmann, “Improved Process Plant Reliability and Maintenance with Digital Position-ers,” Automatisienungstechnische Praxis, 40(2), 1998.

21. Shinskey, G. F., “Modeling with Neural Networks—First Principles are More Reliable,” Chem. Process.,p. 87, June 1998.

22. Mertz, G., “Application of a Real Time Expert System to a Monsanto Process Unit,” Process Control Forumof the Chemical Manufacturer’s Association, 1986.

23. Dunia, R., et al., “Identification of Faulty Sensors Using Principal Component Analysis,” AICHE J., Vol. 42No. 10, 2797–2812, Oct. 1996.

24. Hagglund, T., “A Control Loop Performance Monitor,” Control Eng. Prac. 3(11), 1543–1551, 1995.

25. Shunta, J. P., Achieving World Class Manufacturing Through Process Control, Prentice-Hall, EnglewoodCliffs, New Jersey, 1995.

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by Mark T. Coughran - UFUftp.feq.ufu.br/.../Control/PROCESS_INDUSTRIAL...AND_CONTROLS_HANDBOOK/25821_10b.pdf10.64 process/industrial instruments and controls handbook FIGURE 3 Example