All Search Results matching “disturbances”

To view the recording of Deminar #8, click on the above picture. If you want to just view the slides click on Deminar #8 - PID Control of Runaway Processes

Self-regulating processes are the easiest to control given similar dynamics (e.g. delays, lags, and gains), nonlinearities, and upsets. In manual, the process variable will eventually reach a steady state for a self-regulating process. Integrating processes are the next most difficult to control because in manual the process variable will always be ramping even if there are no disturbances. Runway processes are the most challenging and potentially the most dangerous because in manual the process variable is always moving and can accelerate in its divergence even if there are no disturbances. Runaway processes are termed "open loop unstable." The acceleration is characterized by a positive feedback time constant. Both integrating and runaway processes have a low gain limit that causes slow rolling oscillations and a divergence off-scale, respectively. Integrating processes are more sensitive to integral action and secondary lags than self-regulating processes and runaway processes are more sensitive to integral action and secondary lags than integrating processes. The most common problem with integrating and runway processes is too much integral action (too small of a reset time) and the omission of derivative action for secondary lags (rate time should be set equal to largest secondary lag). Some highly exothermic polymerization reactors have proportional plus derivative control to avoid the potentially unsafe situation of someone adding too much reset action. I have been in the control room when an exothermic reactor has reached a point of no return where the temperature acceleration was so high despite full cooling, the only thing the operators could do was prepare for the rupture discs to burst and the reactor contents blow over to the flare stack tank. Highly reactive chemicals lead to rapid and complete reactions but can also lead to an uncontrollable temperature rise since the reaction rate and hence heat release doubles for every 6 degree increase in temperature. Runaway processes can look like integrating processes unless the temperature controller is left in manual long enough for the temperature change to be large enough.

Deminar #8 shows the dramatic correction needed for the tuning settings. The factors used in the short cut tuning method for near-integrators in Deminar #6 and the classic Ziegler Nichols ultimate oscillation method are detailed and demoed. Equations are offered to predict the ultimate gain and ultimate period showing the dramatic effect of a secondary process or thermowell lag and loop deadtime. If a secondary lag or the loop deadtime approaches the positive feedback time constant, the window of allowable controller gains closes and the loop is unstable for all tuning settings. The virtual plant is where you want to learn about runaway processes. You can't experiment much or have the loop in manual for more than a few deadtimes with a true runaway process.

To view the recording of Deminar #7, click on the above picture. If you want to just view the slides click on Deminar #7 - PID Control of True Integrating Processes

Time is money. If you can get to optimum setpoints faster during fed-batch operations and for startup and product transitions of continuous operations, the increase in production revenue can be significant. For continuous operations there may also be an appreciable decrease in the processing, recycle, and waste treatment costs of off-spec material.

For cascade control, the speed of the secondary PID setpoint response largely determines the ability of the primary PID to get to its setpoint quickly and reject disturbances in the primary loop. A slow secondary PID setpoint response may require detuning of the primary PID to prevent interactions between the secondary and primary loops.

In Deminar #7 we explored how we could use PID structure options, setpoint feedforward, and bang-bang control to improve the setpoint response for integrating (e.g. batch) processes. The concepts are also applicable to the continuous process startup and transitions. The demos showed a big reduction in rise time (time to reach setpoint) by the use of "PID on Error" instead of "I on Error, PD on PV." The benefit of the additional bump from derivative action on error is rather marginal for the small rate setting used. In other words most of the speedup in the setpoint response could be achieved by "PI on Error, D on PV" unless there is a large secondary lag and hence a large rate time setting. The use of setpoint feedforward helped reduce overshoot, rise time, and settling time by about 25%. For deadtime dominant self-regulating processes, the improvement would have been more impressive. The most dramatic improvement occurred for full throttle bang-bang control. With some adjustment of logic and resting value as noted on slide 6, the bang-bang logic can also be effectively used for self-regulating processes. You can try out setpoint feedforward and bang-bang control on the virtual plant website starting August 20.

Whenever I see real control valves with digital positioners and diaphragm actuators, I get a bit giddy with excitement. If on the other hand I see on-off valves installed to perform the role of process control, I just shake my head in dismay. If flows are turned on or off, there is very little process control opportunity. Flows, whether process or utility, are the levers for the process. If we can only jerk the levers around, we will have a jerky process. The Feb-Mar 2010 InTech article "Key Design Components for Final Control Elements" details this perspective as well as the essential design features needed. If you have throttled flows not only do you have a means of affecting but also a way of optimizing the process. It would be a rare coincidence if the flows were exactly at their best value at the right time. There is almost assuredly an opportunity to increase capacity or yield or decrease energy use by changing the flow to reduce variability and/or moving a measurement closer to it optimum operating point. Sure there are options to sequence the turning of flows on and off but such pre-programmed actions lack the feedback correction needed to deal with disturbances, non-idealities, and unknowns in industrial processes. Unfortunately, graduates from chemical or biochemical engineering programs may mistakenly be thinking they can set the flows per the process flow diagram and process design simulation program. Sure they probably had a course on control theory, but maybe all they got was a mathematical view of process control isolated rather than integrated with process research, development, and design.

If the fixed flow mindset results in the use of on-off valves and missing feedback measurements, the opportunities are difficult to identify and may require years and a bunch of money not only for the field instruments and valves but also for the piping and equipment modifications. Just think if you want to install a thermowell and there is no nozzle on the vessel or column in the right location? Also, on-off flows create the step disturbances you would hope would be relegated to control theory textbooks.

Dynamic simulations can show the way but a large expensive automation project can be a hard sell without an installed example. If on the other hand there are sensitive throttling valves and process measurements, opportunities can be trialed and implemented by taking advantage of the ever increasing incredible capability being built into the modern DCS. The key characteristic is sensitivity, which is the smallest change in the controller output or process variable that the valve and sensor, respectively will consistently respond to. Once the sensitivity threshold is reached the output will change by the full amount whereas the output will only change by a quantized amount that is a resolution limit, the other major component of precision. Often the term "resolution" is mistakenly used instead of sensitivity. Resolution, which has a stair-case response, was mostly an issue with rack and pinion actuators and older A/D converters with wide signal ranges (e.g. 1980s generation DCS thermocouple input cards). The resolution today of digital I/O far exceeds the sensitivity capability. The consistent precise response to change is more important than an exact match between input and output for valves. For example, valve span or bias errors (offsets) are clearly not much of an issue because the feedback loop will correct for them provided there is a full range of control possible. Measurement span and bias errors can also be corrected by upper loops or operating procedures, but accurate besides precise measurements are important for closing material balances for process analysis, diagnostics, and optimization as discussed in the Jan-Feb 2010 InTech article "Advances in Flow and Level Measurements Enhance Process Knowledge, Control"

Wireless measurements offer the opportunity to move the transmitters to find opportunities and the optimum location if the process and equipment design engineers had the understanding to provide the connection options. Wireless pH offers the ability to develop inferential measurements and prove the best electrode technology as revealed in the Jan-Feb 2010 InTech WEB Exclusive article "Opportunities for Smart Wireless pH, Conductivity Measurements"

Higher value added products are generally produced by batch operations. Often these products are sold out and extra batches translate to significant increases in revenue. Prime opportunities are specialty chemicals and drugs, especially new biopharmaceuticals where optimization took a backseat to time to market in the initial plant and automation system design.

I looked over my past experience with Monsanto, Solutia, and Emerson and have come up with myriad of methods to reduce batch cycle time. I have divided them up into opportunities to help feedback loops to get to setpoint faster that are important for Fed-Batch operations and for startups and transitions of continuous operations (Part 1) and opportunities to shorten phases and holds that are important for Pure-Batch operations (Part 2). These techniques like all new configurations and strategies should be thoroughly tested by simulation and closely monitored and adjusted for safe and efficient operation. Today's blog is a preview of Deminar #7 on July 14.

Fed-Batch Opportunities

1) PID on Error Structure - This structure maximizes the kick of the controller output for a setpoint change. The overdrive (driving of output past resting point) is essential for getting slow loops, such as temperature, to the optimum setpoint as fast as possible.

2) SP Track PV - With this control option the setpoint is changed to its optimum with the controller in automatic providing the kick from the PID structure (1). For batch operations this option is commonly used. For continuous operations with few setpoint changes (no grade transitions) and extremely long run time (e.g. years), the setpoint is held at its last value. However, even here loops with slow reset action (large reset times), such as level, the use of the SP Track PV option can prove useful when putting these loops back in service after maintenance.

3) SP Feedforward - For low controller gains (controller gain less than inverse of process gain), a setpoint feedforward is useful. The setpoint feedforward gain is the inverse of the dimensionless process gain minus the controller gain on a percent basis. If the setpoint and controller output are in engineering units the feedforward gain must be adjusted accordingly. The feedforward action is the process action, which is the opposite of the control action, taking into account valve action. In other words for a reverse control action, the feedforward action is direct provided the valve action is inc-open or the analog output block, I/P, or positioner reverses the signal for a inc-close (fail open) valve.

4) Output Lead-Lag - A lead-lag on the controller output or in the digital positioner can kick the signal though the valve deadband and sticktion, get past split range points, and make faster transitions from heating to cooling and vice versa. When combined with the enhanced PID algorithm described in Deminar #1, the lead-lag can potentially provide faster control when online analyzers are used for closed loop control of the integrating response associated with batch operations.

5) Deadtime Compensation - The simple addition of a delay block with the deadtime set equal to the total loop deadtime to the external reset signal for the positive feedback implementation of integral action (see Deminar #3). The controller reset time can be significantly reduced and the controller gain increased if the delay block deadtime is equal or slightly less than the process deadtime (see Advanced Application Note 3 entry March 25, 2009 on this website).

6) Full Throttle Batch - The controller output is put at its output limit to maximize the rate of approach to setpoint. When the projected PV equals the setpoint less a bias, the controller output is repositioned to the final resting value captured from the last batch. The output is held at the resting value for one deadtime. For more details, check out the Control magazine article "Full Throttle Batch and Startup Response."

7) Feed Maximization - Valve position control, Model Predictive Control (see Advanced Application Notes 1 and 2 entries March 25, 2009 on this website), or override control is used to maximize feeds to limits of operating constraints (e.g. maximum vent, overhead condenser, or jacket valve position with sufficient sensitivity). Alternatively, the limiting valve can be set wide open and the feeds throttled for temperature or pressure control. For pressure control of gaseous reactants, this strategy can be quite effective. For temperature control of liquid reactants, the user needs to confirm that the inverse response from the addition of cold reactants to an exothermic reactor and the lag from the concentration response does not cause temperature control problems. All of these methods require tuning and may not be particularly adept at dealing with fast disturbances unless some feedforward is added. Fortunately the prevalent disturbance is a feed concentration change that is often slow enough due to raw material storage volume to be corrected by feedback control.

8) Profile Control - If you have a have batch measurement that should increase to a maximum at the batch end point (e.g. maximum reaction temperature or product concentration), the slope of the batch profile of this measurement can be maximized to reduce batch cycle time. For application examples checkout "Direct Temperature Rate of Change Control Improves Reactor Yield" in a Funny Thing Happened on the Way to the Control Room E-book April 3, 2009 entry on this website and the Control magazine article "Unlocking the Secret Profiles of Batch Reactors"

This blog was kind of fun to write "With A Little Help from My Friends" (beer and music). By the way, the album "Joe Cocker" was apparently only produced on vinyl and 8-track tape. I fondly remember riding in my roadster with the top down listening to "Saint James Infirmary Blues" on the way to Monsanto's New Orleans plant. There are great songs on this album that carried me through startups that never made it onto a CD.

You can click on the above to view and hear the recording of the Deminar.

In Deminar #5 we first show that for a self-regulating process, the process variable will line out (reach a steady state) when the controller is in manual unless there are continual disturbances. The self-regulating response is most commonly encountered response because there are more flow loops than any other type of loop. Liquid pressure loops and temperature control loops in continuous operations have a self-regulating response. Level normally has an integrating response but in the Deminar we show test results for a conical tank level with self-regulating response due to gravity discharge flow. The flow across the discharge valve is proportional to the square root of the liquid head as the level increases, the discharge flow increases and vice versa. The self-regulating or steady state process gain increases with level as a result. The significant increase in cross sectional area with level due to the conical shape causes a dramatic increase in the process time constant that creates a stabilizing effect. The process response at high level is much slower enabling the use of more aggressive tuning settings. However, the test results show these settings at low level cause excessive oscillation. The adaptive level controller is able to keep the set point response smooth and consistent over the level range. For more details you can check out the Control magazine article "Adaptive Level Control"

Most of the Deminar focuses on how an auto tuner, adaptive tuner, and adaptive controller can be used to improve the response of liquid flow and liquid pressure loops. The principle nonlinearities are the control valve characteristic for the flow loop and pump curve for the pressure loop.

I got an excellent question during Deminar #3. An attendee asked how fast does the readback of actual valve position need to be as a secondary variable from a smart positioner. I said it depended on the speed of the valve. For flow loops, I thought once per second would be fast enough. However, since the communication of the actual valve position is not synchronized with PID module execution, there needs to be more than one communication per module execution time. Also, for very fast valves, the valve response time could be much less than the module execution time. The dynamic reset limit needs to know the valve is actually moving or it will slow down the change in controller output. For wireless communication of position measurement, exception reporting could be used where the deadband for updating the position readback is the resolution limit of the valve.

A guideline for the conventional PID could be:

When the controller output changes by an amount greater than resolution of the valve, the communication of the valve position for the dynamic reset limit of a conventional PID should be less than ½ the module execution time and less than ¼ the valve response time.

For an enhanced PID as described in Deminar 1, it is possible that valve position only needs to be communicated when a new measurement value is communicated.

The response time per the ISA-75.25.01-2000 (R2009) standard Test Procedure for Control Valve Response Measurement from Step Inputs is the time the valve takes to reach 86% of the final stroke. As noted in slides 12 & 13 in Deminar 3, the response time for small signals and small actuators is a second order exponential response (response time is approximately twice the sum of the time constants) whereas the response time for large signal and large actuators is a ramp (e.g. response time is 86% of the step change in signal (%) divided by the slewing rate (%/sec)). For valves with hydraulic or digital actuators or small valves with a negligible deadtime from backlash and stiction and with a high sensitivity actuator and positioner (e.g. sliding stem valve diaphragm actuator and digital positioner), the response time could be less than a second. For extremely large valves with excessive deadtime from backlash and stiction and with a low sensitivity actuator and positioner (e.g. piping valve with scotch yoke actuator and pinned shaft connections) the response time could be more than 100 seconds. Thus, we have the ironic situation, where if we have a poor valve choice, the resolution and update rate of actuator position communication can be decreased and the filtering of noise can be decreased to keep fluctuations in controller output from measurement noise less than valve dead-band and resolution. If you don't do small step tests or have no communication of actual valve position, the poor loop performance from a piping valve posing as a control valve may be attributed to disturbances or noise.

The accuracy of the valve position communicated is not as important as precision since it is the change in valve position rather than the value of valve position that is important. The bias and span errors in valve position are corrected by feedback control of the process loop. Since even the best valves with pneumatic actuators do not respond to changes in signal less than 0.1%, the greater resolution of digital values of valve position communication is unnecessary. Consequently, to get faster communication for fast valves and small signal changes, analog signals of valve position should be used for the dynamic reset limit even though they may not be as accurate as digital signals.

The precision of the valve position communication should be better than resolution limit of the control valve (e.g. 0.1% for sliding stem valves with diaphragm actuators and digital positioners).

All of what I have presupposed here needs to be tested and investigated. There is no shortage of interesting scenarios to investigate via dynamic simulation.

You can click on the above to view and hear the recording of the Deminar. The second Deminar answers two questions. The first question "Why? (Why do I write so much stuff and why I am I doing these Deminars and setting up free worldwide access to generic loop and unit operation labs?) is answered on slide 4. The virtual plant used in these Deminars that creates a non DCS specific control room type experience is the most exciting thing I have done in years. This is either a commentary on my sedate existence or is an indication of the possibilities for an interactive opportunity assessment that could provide the knowledge and justification for process control improvements.

The answer to the second question that is actually a list of questions on slide 8 about the source of oscillations that cannot be tuned out is, as you might expect, the subject of the Deminar.

I think there are 8 main concepts not widely known that one can take away from this Deminar to provide guidance for a wide variety of applications.

(1) Valve stick-slip will create a limit cycle in any control loop where there are one or more integrators. The integrators can be via the integral action in the PID controller(s) or in the process (an integrating process type such as level and batch temperature). Some of the implications are as follows:

a. For a self-regulating process, integral action in any PID controller in the control loop will cause a limit cycle from stick-slip. In order to eliminate the limit cycle all PID controllers must have their integral action turned off either by a I-deadband setting bigger than the limit cycle amplitude or by using a structure with no integral action (e.g. "P on error, D on PV, no I").

b. For an integrating process, the limit cycle from stick-slip cannot be eliminated even if the integral action is turned off in all PID controllers.

(2) The limit cycle amplitude from valve stick-slip is set by the process gain and hence cannot be altered by changing the controller gain. For nonlinear processes and nonlinear valve characteristics, the amplitude changes with operating point.

(3) The limit cycle period from valve stick-slip is proportional to integral time. Slowing down the reset time will make the period larger. Thus to increase the filtering effect of process time constants in the primary loop or downstream processes, a tuning strategy would be to decrease reset time and if peak error for load disturbances is not important to decrease the controller gain to allow a further decrease in reset time.

(4) Valve deadband will create a limit cycle in any control loop where there are two or more integrators. The integrators can be via the integral action in the PID controller(s) or in the process (an integrating process type such as level and batch temperature). Some of the implications are as follows:

a. For a self-regulating process, a single loop with integral action will not develop a limit cycle from valve deadband. A cascade loop with integral action in both controllers will develop a limit cycle from deadband.

b. For an integrating process, the limit cycle from valve deadband can be eliminated if integral action is turned off as seen in slide 1 in: NonSelfRegulatingProcessDeadbandLimitCycle.pdf

c. For a runaway process (exothermic reaction) I expect the behavior to be similar to an integrating process but to a greater extreme (larger amplitude for limit cycle and larger offset for no integral action in PID controller) as seen in slide 2 of NonSelfRegulatingProcessDeadbandLimitCycle. The lack of process self-regulating in both integrating and runaway processes causes similar problems for a non-ideal valve response.

(5) The limit cycle amplitude from valve deadband is inversely proportional to controller gain.

(6) The limit cycle period from valve deadband is proportional to the integral time and is inversely proportional to the square root of the controller gain.

(7) The limit cycle amplitude in the primary process variable or in downstream process variables is proportional to the period of the limit cycle of the secondary process. The ratio of the primary or downstream amplitude to the secondary limit cycle amplitude is determined by the filtering effect of the time constant in the primary or downstream processes. When the period is smaller than the primary or downstream process time constant, the attenuation of amplitude can be approximated by the equation in: LimitCycleAmplitudeAttenuation.pdf

(8) The offset created by the use of I-deadband or selecting a structure with no integral action is less disruptive to downstream processes because a constant load upset is readily corrected by downstream loops. Periodic disturbances are more disruptive and can be amplified if the period is close to the period of loops. An offset rather than an oscillation causes less interaction between loops. One of the ways to reduce interaction is to remove integral action and decrease the gain in the least important controller.

The PID I-deadband setting should be larger than the maximum amplitude allowing for measurement noise. Note that the valve stick-slip and deadband will vary with time and operating point. The stick-slip and deadband is generally greatest near the closed position and when process material coats or corrodes the closure element seal, seat, and stem. Any addition of I-deadband or change in PID structure should be carefully monitored. Of course, the best solution is to correct the root cause of the problem and select a control valve per the "Best Practices for Valve Performance" on slide 27 of Deminar 2.

The next Deminar on "PID Control of Slow Valves and Secondary Loops" is set for May 12 Wednesday 1:00 pm Central Daylight Time.

The first Deminar is history. The seminar-demo showed how an enhanced PID controller can reduce cycling caused by sampled measurements. The benefits are not only the obvious one of less process variability but includes extending valve packing life by reducing the accumulated valve travel and battery life of wireless measurements by reducing the number of communications. The name of this series of live meetings was the result of me mistakenly saying "Deminar" when I meant to say "Seminar-Demo."

To keep the demo fast enough the process dynamics were in seconds instead of minutes. In other words, the 1 second deadtime and 10 sec time constant of the primary process were chosen to be indicative of a well mixed vessel with a mixing delay of 1 minute and a residence time of 10 minutes. Setpoint changes were made to show the response of a standard PID and an enhanced PID (DeltaV PIDPLUS). In future labs, the testing and importance of dealing with load disturbances will be discussed and demoed. Even though the process dynamics were relatively fast, I did not want to waste precious viewer time or risk viewer boredom staring at a trend chart waiting for the response to develop. Consequently, I shuffled back and forth between the demo and the seminar presentation WebSeminarDemoLab01.pdf and user screens to discuss the concept of the enhanced PID and flexibility of the lab and virtual plant to explore, test, and quantify process control improvements. I could have presented comparison trend charts of a traditional versus enhanced PID as typically seen in most presentations but choose to make the demo more interactive and show the dynamic transition when the enhancement was turned on.

The demo started out with a controller tuned for composition control of a self-regulating process with an online analyzer providing a continuous measurement of vessel composition by means of a probe (e.g. NIR probe in a circulation line). The setpoint response of the standard PID for the continuous measurement was fast and non-oscillatory with almost no perceptible overshoot.

I then set the sample time to be twice the primary process time constant and made another setpoint change. If the time scale was minutes instead of seconds, the 20 minutes sample time would be typical for a chromatograph. Now the setpoint response exhibited a significant overshoot and oscillation. I then cut the reset time in half, a common scenario because of tuning misconceptions or change in process dynamics. The setpoint response developed severe and persistent oscillations . When I switched on the PID enhancement, the oscillations quickly died out. A subsequent setpoint change showed that the enhanced PID response had no overshoot or oscillation.

The last test involved the removal of the sample time and the addition of a 2% sensitivity limit to show the result of an analyzer or wireless measurement with a detection or reporting threshold (called deadband for wireless measurements). The sensitivity limit was purposely chosen to be larger than typically expected to show a clearly recognizable oscillation. I had intended to switch back right away to the traditional PID but instead made the setpoint change to the enhanced PID. I wondered why the response did not show the expected cycling until I realized I had forgotten to switch back to the traditional PID. When I did make the switch to the traditional PID, the cycling started but we ran out of time to show the subsequent limit cycle (perpetual constant amplitude square wave cycle in the process variable and saw tooth cycle in controller output).

For your viewing pleasure, checkout the ScreenCast courtesy of Jim Cahill.

We expect to have the audio glitches worked out for the next Deminar on "PID Control of Valve Sticktion and Backlash" set for April 21 at 1:00 Central Daylight Time - my personal apologies to Europe about the time.

Let's pull together this series on errors and conclude with a check list. The idea was prompted by perusing a popular book written on just the value of check lists. I didn't think you could write a book on just one concept but the result of saving lives for surgical procedures is impressive. I know as I have gotten older, check lists are essential to just remember what I am suppose to be doing. I have found checklists to be helpful for me from both a practical and psychological viewpoint when rushed or overwhelmed with details, tasks, and objectives.

In the following list, increases in on-stream time can increase efficiency besides capacity by eliminating the time and off-spec and waste associated with abnormal operations, startup, and shutdown. An increase in yield or decrease in recycle can be taken as a decrease in raw material costs (same production rate for lower feed rate) or an increase in production rate (higher production rate for the same feed rate). The order of the list is in order of things to check and somewhat in the order of priorities.

Check List to Improve Process On-stream Time, Production Rate, and Efficiency
(composition measurements include conductivity, dissolved oxygen, pH, and turbidity)

1. Use smart transmitters with the best sensor technology and integration of process and ambient conditions compensation.

a. Avoid older technologies particularly ones with mechanical elements

b. Seek sensor and transmitter with the best sensitivity and repeatability

2. Pick sensor location and installation method to provide the most representative measurement in process with no stagnation, best velocity, fastest response, and least noise.

a. For DP and pressure transmitters, avoid impulse lines (sensing lines) by direct mounting transmitters or using diaphragm seals and filled systems

b. For DP and vortex flow meters insure uniform velocity profile

c. For thermowells and electrodes increase velocity to reduce response time and coatings but not so high to cause abrasion, static charge, or vibration

d. For thermowells and electrodes pick location with good mixing, minimal transportation delay, and least bubbles, slime, and solids

3. Use real throttle valves with smart positioners.

a. Avoid on-off and isolation valves posing as throttling valves. Go to a control valve manufacturer instead of a piping valve manufacturer

b. Seek actuator, positioner, and valve type with best sensitivity of installed flow characteristic and signal response with least stick-slip and backlash

c. Verify positioner feedback measurement is representative of internal closure member (e.g. ball, disk, or plug) and not just actuator position

4. Tune control loop with on-demand auto tuner or adaptive controller to meet loop objectives. Tuning speed is chosen to:

a. Insure an exceptionally smooth PV and output response by decreasing transfer of variability from PV to output (increasing Lambda) for:

i. level loops on surge tanks to minimize feed upsets
ii. deadtime dominant loops (deadtime >> process time constant)
iii. interacting loops (e.g. headers)
iv. loops on piping or equipment with no back mixing (e.g. blenders, heat exchangers, extruders, static mixers, sheets, webs, and yarns)

b. Provide good load rejection of moderately fast disturbances by increasing transfer of variability from PV to output (decreasing Lambda) for:

i. Fed-batch and continuous agitated vessel and column composition, level, pressure, and temperature loops

c. Provide good load rejection of extremely fast disturbances by setting the gain and reset as a factor of deadtime rather than the time constant for:

i. Continuous agitated vessel and column composition, pressure, level, and temperature loops

d. Provide minimal overshoot of setpoints of slow lag dominant loops (process time constant >> loop deadtime and slower than 10 minutes) by tuning the loops as near-integrating processes for:

i. Fed-batch and continuous agitated vessels and column composition, pressure, and temperature loops (setpoint changes occur at startup or for changes in batch phase and product grade)

e. Provide minimal peak error by maximizing controller gain even if it requires increasing reset time to maintain robustness for:

i. Prevention of SIS activation
ii. Prevention of pressure relief
iii. Prevention of environmental violation
iv. Prevention of equipment damage

5. Add DCS signal filter or damping adjustment to keep loop output fluctuations from noise less than the valve deadband to prevent excessive valve packing wear and inflicting disturbances on loop. For wireless transmitters use damping adjustment to reduce keep transmitter output fluctuations from noise less than wireless deadband to eliminate unnecessary communication and extend battery life.

6. Eliminate on-off actions

a. Replace on-off control by switches with loops.

b. Eliminate manual actions by adding loops, keeping loops in highest design mode, adding feedforward, and automating and tuning loops to handle startup and abnormal operating conditions

c. Replace pure batch with fed-batch automation by replacing discrete sequential actions (e.g. stepping feeds) with loops (e.g. throttling feeds)

7. Tune loops that create feed disturbances (e.g. surge level loops) to provide a smooth slow transition in feed rate.

8. Add cascade control to compensate for nonlinearities and pressure disturbances (e.g. secondary flow loop and secondary coolant temperature loop).

9. Add feedforward control of measurable fast disturbances not compensated by secondary loop.

10. Optimize setpoints by operating closer to constraints for production rate or product quality spec.

a. Eliminate operating margin imposed by shift's perceived sweet spot or operating margin caused by process variability from not doing check list items 1-9

b. Find more efficient operating points based on R&D reports and virtual plant exploration - confirm with process tests

b. Add model predictive control to optimize setpoints as process conditions and market requirements change.

At my recent presentation to the ISA Saint Louis section meeting on "pH measurement", I had several people around my age say how nice it was to see me still involved in advancing our profession. Maybe it was the beer and the top ten lists but just maybe it was also that I represent a generation of expertise rapidly disappearing via retirement. The ability to still learn and share keeps me going but I realize time is running out so I intend to take this blog to the next level by coupling it with a web lab series to provide an interactive self-learning experience for exploring process control improvements (PCI). I intend to start the web lab series on April 7. Recordings of the PCI topics and demos along with instructions on using the associated labs will be viewable anywhere anytime.

In the meantime, we need to finish up this series so let's see what we can do as automation engineers to minimize loop errors.

The first thing is to make sure the measurement is fast and precise enough. So far as loop performance is concerned, precision is more critical than accuracy. The bias or offset in a measurement and control valve position can be corrected by feedback control. The offset in valve position is eliminated by the process loop. Similarly, the offset in a process loop is eliminated when the loop is in cascade or remote cascade modes. For loops operated in the auto mode, operations have often compensated for the measurement offset by tweaking the set point. This is not to say that measurement accuracy is not important.

Improving the loop's speed of response often comes down to keeping sensors clean (e.g. electrodes and thermowells), minimizing signal damping and filtering, selecting sensor locations that eliminate transportation and stagnation delays, using boosters for big valves, maximizing positioner sensitivity, minimizing deadband, and maximizing the controller gain (last week's blog).

Control loops have a difficult time dealing with the poor precision experienced as excessive stick-slip and backlash (deadband) in control valves and insufficient resolution, repeatability, and sensitivity in measurements (older measurements technologies, such as floats and rotameters can also exhibit stick-slip and backlash). Fortunately, an increase in A/D input card bits have greatly improved the resolution of transmitted signals so that sensitivity and repeatability is the remaining focus. This is unfortunately not the case for variable frequency drives manufacturers whose standard input cards have only 8 bits. A resolution limit is more degrading than a sensitivity limit. For example for a 1% resolution and 1% sensitivity and a change in the true process variable of 1.5%, the changes in measurement would be 1% and 1.5%, respectively. The deadband setting in wireless transmitters is really a sensitivity setting. When the change in a wireless transmitter measurement exceeds this setting regardless of the direction, the full change in the process variable is communicated.

Pages 12 through 15 of EffectsLoopTuning&Dynamics-KPI.pdf show the relative effect of measurement accuracy and resolution on variability. For control valves, process variability is introduced when excessive slip-stick and deadband causes an appreciable limit cycle in loops that have single and two or more integrators, respectively (pages 19 and 20).

The total loop deadtime can be approximated as the sum of all the delays and small lags in the loop whether they are in the DCS, valve, process, or measurement. For flow, pressure, level, and inline temperature and pH loops, most of the loop deadtime comes from the automation system. If you consider that the remaining loops that have significant process deadtime, such as vessel or column temperature, have seriously detuned controllers that create an effective deadtime per Advanced Application Note 5, you realize you have the opportunity as a process control engineer to make big reductions in loop deadtime that are also low cost and quick compared to changing process piping or equipment to reduce transportation or mixing delays.

Fast disturbance originate from manual operation, on-off actions, sequences, or setpoint changes. The elimination of operator actions, on-off control (e.g. level switches), and the use of set point rate of change limits and fed-batch rather than pure sequential batch, can dramatically slow down disturbances since throttling control by intention is smooth. If we keep all loops in their highest design mode and limit on-off valves to SIS actions and isolation, we could eliminate step disturbances. Page 22 shows how slowing down the disturbance dramatically reduces the peak and integrated errors for an integrating process. Not shown here is the fact that slowing down disturbances can also reduce interaction between loops. This phenomenon explains why it is difficult to get pharmaceutical companies excited about doing a better job of bioreactor control after reaching setpoint. The disturbances from cells are incredibly slow (e.g. process time constants of days).

Maybe we should not slow down disturbances because all of our control texts are based on step disturbances. Slowing down the upsets relegates us to improving the set point response in the startup of a continuous process or for changes in phase in a batch process. Whoops, even here we could use strategies such as "Full Throttle Batch and Setpoint Response" to eliminate most of the job of the loop.

There are always opportunities to make us more appreciated even when we are not improving loops. Since spouses were at the ISA Section Saint Louis Meeting, I interjected the following list. The spouses laughed although a second opinion was suggested for some of the items. See what your spouse or significant other thinks.

Top Ten Reasons Why an Automation Engineer Makes a Great Spouse or at Least a Wedding Gift

(10) Reliable from day one
(9) Always on the job
(8) Low maintenance - minimal grooming, clothing, and entertainment costs
(7) Many programmable features
(6) Stable
(5) Short settling time
(4) No frills or extraneous features
(3) Relies on feedback
(2) Good response to commands and amenable to real time optimization
(1) Readily tuned

How does controller tuning affect on-stream time and environmental costs?

The basic process control system (BPCS) forms the inner protective layer for safety instrumentation systems (SIS) as shown on page 5 of EffectsLoopTuning&Dynamics-KPI.pdf. The performance of the BPCS loops must limit excursions to be well within the operating limits that correspond to the trip points of the SIS. Specifically, the peak error for the largest and fastest disturbance should not cause a trip. The SIS should only be activated for failures or extremely abnormal conditions. The trip of a process unit not only causes downtime but can cause off-spec and additional waste during the shutdown and startup of the unit. The start-up of the process unit is often the most operator intensive and hazardous time. The importance of minimizing peak errors to prevent shutdowns can involve all types of loops (e.g. flow, level, pressure, and temperature). We normally think this is important only for continuous loops but I have been able to increase a fed-batch reactor capacity by 25% by eliminating level, pressure, and temperature trips by a series of override controllers tuned to minimize peak errors.

The peak error from the closure of a downstream valves (e.g. trip of reactor feed valves) on the discharge of a compressor controller must not cause an excursion of the operating point of the compressor to the left of the surge curve. If the operating point reaches the negative slope of the characteristic curve, it is like the compressor is falling off a cliff. The operating point jumps to a negative flow operating point in 0.03 seconds. This precipitous drop rivals water hammer in disturbance speed (both phenomena involve momentum balances that are orders of magnitude faster than material balances). Once a compressor gets into surge, the feedback controller is helpless and needs an open loop back-up (e.g. kicker) to get out of trouble as detailed on pages 6 - 8. Surge cycles can cause a decrease in compressor efficiency and damage by excessive vibration. I have also seen where surge caused a runaway speed response.

RCRA environmental regulations may classify a pond as hazardous waste if the pH of an effluent stream going into the volume momentarily ventures outside the permissible 2 to 12 pH range. Even though a short term excursion can not possibly change the pH in the volume and is effectively filtered by the volume where the change in pH is not detectable, the volume may still be classified as hazardous. For these systems, peak errors are incredibly important and kickers are used as shown on page 10 to prevent RCRA violations that not only can cause excessive fines but necessitate the process unit to apply for a new permit that might not be approved. A violation could result in the permanent shutdown of a unit because operation is no longer economically feasible or even allowed under new permit requirements.

Many process units have relief devises (e.g. relief valves and rupture discs) to prevent the over pressurization of piping and equipment. Often, pressure letdown and vent loops are the first line of defense. The peak error for the largest and fastest disturbance should be sufficiently away from the relief device setting to prevent fatigue and activation of the relief device taking into account setting tolerances and fatigue that cause a premature relief. The activation of a relief device is hazardous and causes downtime and waste burned in a flare stack or at best in a waste heat boiler.

So how do we minimize peak error? Given a set of dynamics and disturbances, the solution is to maximize controller gain even if it means increasing the reset time. This is seen in the first equation on page 1 but also intuitively from the realization that gain provides an immediate response whereas reset provides a gradual response. In the fed-batch reactor example cited above, the override controllers were proportional-only with their gains set high enough to cut back the reactant feeds immediately when the reactor pressure and level from the gas released as a byproduct or the temperature from the exothermic reaction approached settings that would cause a trip and the associated delay and disruptive restart of the feeds.

We can reduce the peak error per the first equation on page 2 by increasing the process time constant and decreasing the loop deadtime which increases the maximum allowable controller gain. We can also decrease the open loop error in the time frame of the controller's response by increasing the disturbance time constant. The fastest possible tuning should be able to stop the excursion from a disturbance after the loop deadtime. Thus, slowing down the disturbance slows down the excursion and reduces the peak reached in one loop deadtime (more on this next week). The process time constant is typically set by process equipment size and design, but we as automation engineers can greatly affect the disturbance time constant and the loop deadtime and sensitivity. We can iimprove the degree of automation, interaction, speed, reliability, and precision in automation systems. The opportunity may be larger than we realize. Up to 50% of downtime is attributable to instrumentation problems as noted in the March 2010 Control magazine article "Look to Valves for More Uptime"

Next week we will look at how the dynamics and precision of measurements, valves, and disturbances affect peak and integrated errors. We conclude this series with a check list for improving loop performance (Part 4).

It is well known that measurement noise reduces or eliminates the use of derivative action. Since rate is not popular (another story), the exclusion of rate is not seen as a significant disadvantage even though temperature loops could benefit from rate since it can compensate for thermowell and heat transfer surface lags and reduce overshoot. In the 1980s and 1990s many temperature loops suffered from the prevalent use of 12 bit I/O and wide range thermocouple input cards that caused a resolution error of 0.25 degrees in a signal whose true rate of change of temperature was usually much slower than 0.25 per minute. The result was a poor signal to noise ratio. We tried to filter the heck out of the signal so we could use rate but this added another lag. Fortunately, today we have 16 bit I/O systems and smart transmitters so that signal resolution is better than the sensitivity of the sensor - just one of the many reasons to get your automation system into the 21st century.

A wider consequence of measurement noise not so readily recognized is the reduction in permissible controller gain. For loops with a true integrating or "near integrating" response where the process variable ramps when the controller is put in manual, the high limit for controller gain is way above the normal range of consideration. For example, level and batch temperature loops normally have a ramp rate so slow (0.000001 %/sec), that the controller gain could be higher than 50 if there was no measurement noise and the reset time was not too small (a big "if"). Since the peak and integrated errors are inversely proportional to the controller gain, these and other loops could significantly benefit from a smoother signal and better tuning.

What is measurement noise and where does it come from? In my book, measurement noise is any fluctuation in the measurement signal that should be ignored by the controller. If the controller reacts to a fluctuation it really cannot correct, the loop inflicts a disturbance upon itself. If resolution problems are behind us, the biggest sources of measurement noise are inadequate axial (back) mixing, bubbles and foam in liquids, liquid droplets in steam or gas, inconsistent profiles, lqiuid and pressure waves, and insufficient measurement rangeability. Measurement noise is amplified by high process gains (e.g. steep titration curve for pH control) and sensitive measurement ranges (e.g. - 0.25 to 0.25 inches of water column for draft pressure control). The Table in MeasurementNoiseSourcesControlBandAmplitude.pdf provides a summary of my assessment of noise sources, control bands (allowable control error), and noise amplitude (peak to peak) for common loops. The noise amplitude should be less than ¼ the allowable control band for fast disturbances. A reduction in noise amplitude is ideally achieved by eliminating the source of the problem. If the correction is not practical or is not yet implemented, a signal filter is often used to attenuate the noise. The ratio of the amplitude of the filtered signal to raw signal is roughly proportional to the ratio of the period to the filter time when the filter time is greater than the period (simplification of the Bode plot attenuation equation). The filter time becomes effectively additional deadtime in a loop when it is less than the process time constant. If the filter time is considerably greater than the process time constant, the measured process variable amplitude may look better but the real amplitude is worse because you are seeing a very attenuated version of the real world. I have seen where an ISA conference speaker said he almost did not get permission to give his presentation because the improvement was so great it was considered proprietary. He had increased the measurement filter so much he was drawing a straight line no matter what was happening in the process. I have seen where a biochemist withdrew a temperature sensor halfway in its thermowell and proudly said this was the way to run the bioreactor because the temperature reading was so much smoother. Then there were the cases of sand in thermowells and the mounting of extruder temperature sensors in massive blocks of metal giving the illusion of smooth temperature. These are all old stories but I am sure people are being fooled today especially since one can so easily add a filter via the damping setting in the transmitter, the analog input block, and the PID block. Provided the filter setting is not so large it eliminates any recognition of process variability, the key symptom of too large of a filter setting is a long control loop period or recovery time if the controller gain is not so detuned you can't see the effect of more loop dead time (see Advanced Application Note 5 for estimation of how the detuning of a controller is equivalent to additional deadtime in the loop). To prevent the loop from inflicting disturbances upon itself by reacting to noise, the filter time should be set just large enough to keep the fluctuations in the controller output smaller than the resolution (stick-slip) of the final control element (e.g. control valve). A less desirable but widely used way of keeping the fluctuations in the controller output small enough is to reduce the controller gain.

Simulation was such an integral part of my job it is difficult for me to visualize a process control career without models. I was asked to join Engineering Technology (ET) at Monsanto in 1976 because I had developed a dynamic compressor model as the lead Instrument and Electrical engineer for what was the largest Acrylonitrile plant in the world. I developed the model in order to understand more about the incredible surge phenomena where reversals of flow could occur in less than 0.01 seconds leading as a minimum to a loss in efficiency and in some cases to the damage of shafts and seals of large and expensive compressors from the extreme momentum swings and vibration. In most plants the ability to initiate and explore abnormal situations is severely limited or not allowed. A dynamic model allows you to readily and quickly try out "What if Scenarios" whose only limit is your imagination.

ET developed FLOWTRAN, a process simulator that was directed by the government to be sold to Aspen institute. Several key specialists left with the FLOWTRAN to develop the process modeling software that eventually was the state of the art process design modeling software by AspenTech. In the ET process control groups, we used FLOWTRAN to get the process gains and then used IBM's Continuous System Modeling Programs (CSMP) followed by Raytheon's Advanced Continuous Simulation Language (ACSL), and ultimately HYSYS Plant for dynamic simulations. After retirement from my career in ET, I focused on using the DCS as a Virtual Plant for simulation and control. The graphical configuration environment where function blocks are equipment and wires are streams (e.g. DeltaV Control Studio and MiMiC) allows the development of dynamic process models in the same familiar way as the configuration of control strategies.

My vision of a virtual plant has a simple first principle model that starts with one component (e.g. water and air) that is corrected by an experimental model automatically generated by a simple test that takes less 10 minutes to execute for most loops. The result is a plant wide simulator. As more information is available and desired, the process knowledge embedded in the model grows but the fundamental basis is the same. No re-write is required. The opportunities and associated fidelity needed are as follows:

1. Control system set point optimization - Fidelity 5

2. Control strategy analysis and R&D - Fidelity 4

3. Root cause analysis and data analytics R&D - Fidelity 4

4. Operator training for abnormal situation management - Fidelity 4

5. Controller tuning and PID structure and options analysis - Fidelity 3

6. Batch configuration checkout and operator training for system familiarization - Fidelity 2

7. Loop configuration checkout - Fidelity 1

Fidelity 1: loop process variables respond in the proper direction to their loop output

Fidelity 2: measurements respond in the proper direction when control and block valves open and close and prime movers (e.g. pumps, fans, and compressors) start and stop.

Fidelity 3: loop dynamics (e.g. process gain, time constant, and deadtime) are sufficiently accurate (e.g. 50%) to tune loops and see process interactions

Fidelity 4: measurement dynamics (response to valves, prime movers, and disturbances) are sufficiently accurate (e.g. 25%) to track down and analyze disturbances

Fidelity 5: process metrics (e.g. yield, raw material costs, energy costs, product quality, production rate, production revenue) are sufficiently accurate (e.g. 5%) to find optimums

In the ISA New Orleans section short course I am teaching on March 3 and 4 titled: "Exceptional Process Control Opportunities - An Interactive Exploration of Process Control Improvements", I will use a virtual plant suitable for process control research, development, and education. I will demonstrate how a user can perform a 10 minute test of a manipulated process flow to provide a fidelity level 3 and 4 model. The contact for the course is Robert Deeb (ISA New Orleans section education chairman).

In the InTech Jan-Feb 2010 Web Exclusive "Advances in Flow and Level Measurements Enable Dramatic Improvements in Process Knowledge and Control", the following perspective was offered on the importance of flows for many types of process models including the following:

• Projection to Latent Structure or Partial Least Squares (PLS)
• Model Predictive Control (MPC)
• PID Adaptive Controller Tuning
• Neural Network
• First Principle

Flows determine what is going on in a process. If you don't get the flows right, not much else matters. Because of valve backlash, stick-slip, nonlinearities, and variable pressure drop, all types of process models have suffered from the use of valve positions rather than flow measurements. PLS, MPC, and PID performance assumes dynamics that are linear and independent of direction and size, all bad assumptions when valve positions rather than flows are used as inputs. Additionally, the valve nonlinearity from the installed characteristic varies with pressures at the inlet and outlet of the valve.

Pioneering advances in dynamic modeling by Alex Muravyev offer a next generation of pressure-flow solvers that will be robust and flexible enough to provide flows from valve positions. The solver is expected to handle complex piping networks and the discontinuities from batch and startup sequences (AdvancedSimulationPressureFlowSolver.pdf). The ability to consistently and comprehensively compute flows for all streams will enable dynamic models to reach the highest levels of fidelity required for research, development, and design of automation systems for nearly all applications. Presently, models can only move up in fidelity when flow control loops are installed on the key streams so that feedback action removes the nonlinearity and unknowns of the valve and piping system. New pressure-flow solvers can eliminate this precondition. A side benefit will be the demonstration by these models of the improvement in process performance that can be gained from cascade, feedforward, and ratio control. The quantifiable benefits from demonstrable test cases can justify new flow devices to provide missing flow measurements or improve the accuracy of existing flow measurements.

The figures in the attached VariableSpeedDriveRangeability.pdf and the following discussion is an excerpt from the ISA book The Essentials of Modern Measurements and Final Elements - A Guide to Design, Configuration, Installation, and Maintenance.

The 4 main practical reasons that variable speed drives (VSD) drives are not used as extensively as one might think for pump control are as follows [35].

1. Drives are generally not built just for pumps. They handle conveyors, extruders, etc. There are a lot of VSD menu choices and options not pertinent to pumping applications.

2. Users don't like the complexity of the VSD. The user must address setup, maintenance, and design issues. Special practices are needed to prevent EMI in instrument signals and from getting harmonics back into the power supply.

3. Someone needs to do the right calculations on dollars saved. Typically calculations don't take into account the drop in drive efficiency at low speeds. The duty cycle (amount of time speed is really turned down) is not known in advance. If there is a high static head, the energy savings of a drive disappear.

4. It is rare to compare a VSD and valve. There are generally no decision points in the project for this comparison.

Is a Valve or VSD Faster?

Exceptionally fast loops (e.g. furnace pressure, liquid pressure, and surge control) can ramp off-scale in milliseconds. These loops have essentially a zero process deadtime and may have a high process gain due to a narrow control range (e.g. fractional inches of water column for furnace pressures). These loops require DCS scan times of 0.05 to 0.1 seconds. Special fast scan rate digital controllers or analog controllers are needed. DCS scan time requirements of 0.2 seconds or less signify a VSD opportunity. A properly designed VSD has no measureable dead time while control valves and dampers take anywhere from 0.2 to 2.0 seconds to start to move. For example, an incinerator pressure and polymer pressure loop that could get into trouble in less than 0.1 second required a VSD and analog controller to stay within the desired control band [20][23][35].

The VSD has a negligible time delay unless a deadband or dead zone is introduced in the drive electronics to reduce reaction to process measurement noise or a low resolution input card is used. A control valve or damper has a deadtime that is proportional to the resolution limit (sticktion) or deadband (backlash) divided by the rate of change of the process controller output. For large or fast changes in signal this deadtime disappears.

A pneumatic actuator has a pre-stroke deadtime that is the time it takes for the actuator pressure to change enough to move the actuator shaft. For large actuators, the pre-stroke deadtime can be several seconds unless a booster is added.

The inertial time constant of liquid flow response is inversely proportional to flow. Consequently, the process lag at low flow rates and at the initial start of flow can be quite slow (e.g. 5 seconds) compared to the process lag at normal flows (e.g. 0.5 seconds). The comparison between VSD and control valve response should be at normal flows.

In a published comparison of the dynamic response of a control valve and a pump for flow control for a system with negligible static head, the integral times were about the same for the VSD and valve loops. However, the controller gain could be increased by over a factor of 6 for the VSD loop. As a result, the set point response was faster [38]. In this test the valve deadband was about 8% and there was no static head. In unpublished lab test results of control valves with low sticktion, low backlash, and a digital positioner and a VSD with a volts/hertz PWM drive for liquid flow control, the speed of response of the valve and VSD were similar.

Variable speed drives, control valves, and dampers have a velocity limited exponential response. The velocity limiting in a drive depends upon the available motor torque and the inertia of the motor rotor, the pump shaft, and the pump impeller. The exponential term is generally much smaller for a VSD than for a control valve or damper. On the other hand, the velocity limiting is slower for a VSD unless the actuator size is large and boosters are not used. Consequently, for small changes in signal, a well designed VSD is faster. Conversely, for large changes in signal, a small control valve is faster (see section on dynamics). This leads to the conflicting statements about whether a VSD or control valve is faster. Which final element is faster often depends upon the size of the change in signal.

VSD Best Practices

To summarize, a VSD is most likely to offer energy savings or better loop performance as a final element for the following types of applications:

• Loops that require 0.2 seconds or faster scan time
• Valves and dampers with 0.5% or more sticktion or backlash
• Large utility flows
• Integrating and runaway processes without a secondary flow loop
• Low static head processes requiring frequent turndown

A tachometer or inferential speed feedback signal should be sent to the process controller in the DCS that is sending the signal to the drive. The speed feedback should be used in a similar way to the position feedback from a digital positioner to prevent the process controller output from changing faster than the final element can respond. The use of the dynamic reset limit option for the loops in the DCS can automatically prevent the process controller from outrunning the final element response (see section on dynamics).

For best performance users should consider the following during the specification and implementation of variable speed drive systems:

• High resolution input cards
• Pump head well above static head
• On-off valves for isolation
• Design B TEFC motors with class F insulation and 1.15 service factor
• Larger motor frame size
• XPLE jacketed foil/braided or armored shielded cables
• Separate trays for instrumentation and VFD cables
• Inverter chokes and isolation transformers
• Ceramic bearing insulation
• Pulse width modulated inverters
• Properly set deadband and velocity limiting in the drive electronics
• Drive control strategy to meet rangeability and regulation requirements
• Dynamic reset limiting using inferential speed or tachometer feedback

VSD Response

The response of variable speed drives more closely resembles a pure ramp with no rounding or time delay provided a filter or deadband has not been added in the drive electronics to attenuate process noise in the process controller output signal. The ramp time in the VSD depends upon the size of the load compared to the available torque from the motor. In general, the ramp time of a VSD is longer than the stroking time of a control valve but is shorter than the stroking time of a large damper. Longer than necessary VSD ramp times may inadvertently be imposed in the drive electronics.

There is essentially no sticktion or backlash in variable speed drives for axial and centrifugal blowers, fans, and pumps but this does not necessarily mean there is no resolution limit or deadband in the VSD response.

Controller outputs invariably have fluctuations that originate from process or sensor noise and transmitter resolution limits. These fluctuations are not representative of the actual value of the process variable and are best ignored. These fluctuations are particularly large and fast for flow and pressure loops. A deadband is sometimes introduced in the VSD electronics to prevent changing the speed. The effect may be a true deadband where the desired speed does not change upon a change in direction until the change in signal is larger than the deadband setting. The effect here is similar to backlash in a control valve. In other cases, it may be a deadzone setting, in that the desired speed does not change until the accumulated change in signal since the last change in speed is larger than the deadzone setting. Here the effect is similar to a resolution limit.

If there is no deadzone setting, the resolution limit in a VSD is largely determined by the input card. Assuming there is no sign bit, the VSD resolution limit is simply 100% divided by 2 raised to the number of bits (n) of the input card. Unfortunately, VSD manufacturers did not understand the limit cycle that would result from the resolution limit and offered an 8 bit input card (0.4%) as the standard card. Higher resolution input cards (e.g. 12 bit and 16 bit) should be specified to make the VSD I/O resolution comparable to the DCS I/O resolution.

VSD Installed Gain

In a variable speed drive for liquid flow, the pump characteristic curve shifts with pump speed. Since there is no control valve, there is no valve drop and the flow is at the intersection of the pump curve and the system frictional loss curve.

For a negligible static head and an idealized pump, motor, and VSD, the change in flow with speed is linear. If the static head is negligible, the loss in pump efficiency and the increase in slip at low speed, cause a decrease in gain (sensitivity) at low speed. This loss of sensitivity is seen as a flattening at low speed in the plot of flow versus speed.

If we ignore the loss in pump efficiency and increase in slip, a pump curve that approaches the static head will show a sharp bend downward to zero flow at low speed. The plummet of the speed at low speed causes a significant increase in gain and a nosier flow at low speeds [46].

A flat pump curve will cause almost a quick open type of flow characteristic. The high gain (sensitivity) at low speed can cause cycling [46]. Operation on a relatively flat pump curve can occur from improper pump selection or over-sizing.

VSD Rangeability

For variable speed drives, estimating rangeability gets tricky. The decrease in process gain from speed slip offsets the increase in process gain as the pump discharge head approaches the static head. If there are no overheating or cogging problems as suggested is the case for a pump and valve system with a well designed open loop (volts/hertz) PWM drive, high resolution input card, and negligible static head, the rangeability is normally 40:1. When the pump head is operating near the static head, the minimum controllable flow is set by rapid changes in the static head and frictional loss. These rapid changes could be due to noise and sudden or large disturbances. The speed can not be turned down below the amplitude of these fast fluctuations.

The rangeability of a VSD could drop to 4:1 for the following systems:

(1) Older VSD technologies such as 6-step voltage (excessive slip at low speed)

(2) Systems with a high static head (flow plummets to zero at a low speed)

(3) Operation on the flat portion of the prime mover curve (cycling at low speed)

(4) Hot gases (motor overheats at a low speed)

In the process industry, what a control loop eventually manipulates in nearly all applications is a flow via a final control element such as a control valve, damper, or variable speed prime mover (pump, fan, or compressor). Dampers and variable speed prime movers are commonly found in utility systems. Peristaltic pumps are used in labs and positive displacement pumps are used for extremely low additive flows in plants. In instances, mass flow controllers (thermal mass flow meters with an integrated PI controller and valve) and remotely set pressure regulators are used. However, in production units, control valves are used as the final element in 95% or more of the loops.

Do we know for a change in controller output, did the valve actually move and if so when? Do we know when the control valve is the source of process variability? Do we know what makes a valve "Good" or Bad" in terms of its ability to do its job?

In valve selection and specification, a lot of effort is put into making sure the valve passes the required flows, has minimal leakage, no plugging, and has materials of construction and packing that withstands process composition and conditions. The dynamic response is often neglected possibly because response criteria and requirements are not well understood. Since most loops are digital, the question comes down to whether the change in controller output in a given scan results in a change in position of the internal trim (closure component such as a plug, ball, or disc). Of course most valves will eventually re-position, but the internal trim may not move until the total accumulated change in the controller output is large enough to

(1) Exceed the sensitivity of the positioner and actuator
(2) Change the pressure in the actuator enough to move its shaft
(3) Work through the play in shaft/stem linkages or connections (backlash)
(4) Break free the internal trim from packing, seating, and sealing friction (sticktion).

The result is a delay and a jump followed by a slow transition to a new position. The jump from sticktion causes a limit cycle in any PI or PID control loop. The deadband from backlash causes a limit cycle in any PI or PID control loop on an integrating process (e.g. level or batch temperature). The delayed and slow response adds pure and effective deadtime, respectively, to the loop.

The ultimate question is what should a user specify in terms of valve response? The table ControlValveResponseCriteria.pdf provides a summary of the parameters that makes a valve rated "Great", "Good", "Fair", "Poor", and "Bad". For most loops where process variable deviations of 0.5% are tolerable, a "Fair" valve will suffice. For loops where tighter control is needed (e.g. column, crystallizer, evaporator, or reactor temperature), a "Good" valve is needed. For loops with high process gains (e.g. pH), a "Great" valve is required to prevent self-inflicted oscillations from limit cycles being larger than the allowable deviation around set point (pHControlValveSizeandResolution.pdf). For tight control in loops with extremely fast dynamics (e.g. polymer pressure and incinerator pressure) a "Great" valve or a special variable speed drive may be needed (see "Analog Control Holdouts" on this website).

The ISA-75.25.01-2000 (R2006) draft standard "The Test Procedure for Control Valve Response Measurement from Step Inputs" and ISA-TR75.25.02-2000 (R2006) draft technical report "Control Valve Response Measurement from Step Inputs", use the time to reach 86% of the final response as a major criteria. This assumes the step input size is larger than the valve resolution and deadband for steps in the same direction and reverse direction, respectively. This 86% response time for small steps can be estimated as the sum of the pre-stroke deadtime and secondary lag time plus twice the primary lag time. For example, the 86% response time of a "Good" valve would be about 1.3 seconds for a 0.5% step (0.2 sec + 0.1 sec + 2*0.5 sec). For large step sizes encountered in surge and vessel pressure control systems, the 86% response time can be estimated as the sum of the pre-stroke deadtime and secondary lag time plus the stroking time to reach 86% of the step size. For example, the 86% response time of a "Good" valve would be about 2.45 seconds for a 50% step (0.2 sec + 0.1 sec + 0.86*0.5*5 sec). Note that the actuator size, pneumatic connections, and accessory (e.g. booster, positioner, and solenoid valve) flow coefficient determines the pres-stroke deadtime and stroking time, The pre-stroke and stroking values are based solely on actuator shaft movement and are determined by the manufacturer for tests of an actuator not connected to a valve. The sensitivity of the actuator and positioner is the minimum change in signal that causes a change in shaft position within a reasonable time frame (e.g. 10 seconds). Diaphragm actuators and digital positioners have the best sensitivity. Rack and pinion actuators and spool positioners have the worst sensitivity. Pneumatic positioners and scotch-yoke actuators are also bad news. The deadband from backlash in stem and shaft connections and the resolution from friction in packing, seats, and seals are determined after the actuator shaft moves. For practical purposes, the sensitivity of the actuator and positioner can be combined with the resolution limit of the valve for a total resolution of the package.

I have been particularly sensitized to valve response due to working on pH, furnace pressure, and compressor control. To add insult to injury, a proliferation of piping valves with piston actuators and spool positioners developed as a result of the emphasis on tight shutoff and low cost rather than response. These on-off valves posing as throttling valves created a problem for all types of loops. The idea was if the on-off valve worked well for sequencing and safety systems and was already in the piping spec, why not slap on a positioner and make it a throttling valve. Often the process variability from valve limit cycles was attributed to unknown process disturbances since there was no readback of actual closure component position.

This blog is getting long so I will just close with some figures on valve dynamics (ControlValveDynamics.pdf) from my new book The Essentials of Modern Measurements and Final Elements - A Guide to Design, Configuration, Installation, and Maintenance.

In upcoming entries we will seek to sort fact from fiction and hopefully provide some insight on valve rangeability and variable speed drive dynamics and rangeability.

Startups, grade transitions, and abnormal conditions are the most difficult, operator intensive, hazardous, and inefficient periods of plant operation. Operators often believe these conditions require operator evaluation and action. The conditions are thought to be too special and the response too situation dependent to automate. The operators are right in saying these periods of operation require the best in operator expertise. However, case histories show that the power of the PID can be used to automate the best operator responses and build on them to provide faster, safer, and more efficient plant operation during these difficult process conditions. For some specific examples dealing with compressors and reactors check out the two chapters "Wally and the Beave Automate Reactor Startups" and "Wally and the Beave Return to Automate Another Reactor Startup" in my E-book on this website A Funny Thing Happened on the Way to the Control Room. For impressive examples for chemical, mining, and pulp and paper operations, check out the Control Talk columns "Show Me the Money - Part 1" (November 2009) and "Show Me the Money - Part 2" (December 2009) in Control magazine.

An extensive interview of the operators and process engineers is necessary to capture the best responses for a preliminary functional description of the control system. There are often a lot of surprises hidden by the diversity of actions that are inevitable from human responses. Free will implies these decisions are basically unpredictable. The operator actions consistent with first principles and process knowledge offer a good starting point but not the final strategy. During the commissioning of the control system, the plant response must be carefully observed and the best operator actions verified and improved by the use of the many options built into a PID loop to deal with rampant problems as the plant goes from zero to full rate, or vice versa. For example, output tracking, dynamic reset limiting, set point ramping, PID structure, gain scheduling, adaptive control, and override control can be used to deal with the problems at low rates such as noisy or inaccurate flow signals, excessive valve stick-slip near the closed position, larger transportation delays, and unrepresentative measurements. One of the common solutions is to head start (initialize) the controller output via output tracking to the best valve position for startup, transition, or abnormal situation. The initial position can be a "Full Throttle" position for fastest set point response. When the set point approaches the set point, the controller output can be momentarily set to a resting value based on experience or average position captured from a representative operating point from the last run. For fast loops such as flow and pressure, the resting value can be used as the "head start". One of the common mistakes is for process engineers to get carried away with trying to sequence the PID controller output too much or hold the controller output in the track mode for too long. For shutdown, the output must normally be held but otherwise the PID controller should be returned to automatic as soon as possible to deal with disturbances, unknown process effects, raw material variability, and nonlinearities. The process is not known or measured well enough to sequence flows without feedback control. It is particularly important to return pressure loops to automatic as fast as possible. Smart techniques for startup, transitions, and abnormal situations that take full advantage of the flexibility of the PID controller have been the source of the most impressive benefits in process control improvement. In general, these were also "quick hits" in that they were implemented in a matter of a couple of weeks by just configuration changes and controller tuning.

After all is said and done, articles and books have been the main method of advancing and sharing the technology for industrial process control.

I don't know of an undergraduate degree in process automation. Chemical, electrical, mechanical, and systems engineering programs offer an undergraduate course or two on process control. However, the typical university control course needs to spend most of the time on Laplace transforms, frequency response, and state-space to provide a theoretical understanding and groundwork for graduate courses. Outside of chemical engineering the focus is more on set point response and signal noise for servo mechanism and aerospace control. Consequently, the student doesn't learn about the critical characteristics of control for the process industry where nonlinearities, deadtime, valve stick-slip, unmeasured load disturbances, and incredibly long time frames are the cause of most tuning and control loop performance problems. Throw into the mix the unknown features of proprietary PID algorithms, and you have a script for islands of expertise. I personally like tropical islands so maybe this is OK. I could retire to one and conduct web based courses instead of doing cross word puzzles.

Courses may not be the whole answer considering that more than 80% of the details presented are forgotten. The PowerPoint slides often don't tell the real story. In my days, professors used the chalk board with only passing references to a book so my only record of knowledge is in notes long gone. Maybe the best way to make courses have a greater long term value is by providing labs for hands-on learning and refresher exercises, key memorable concepts, and resources for reference and further investigation. Audio should be combined with the presentation as exemplified by the slidecast of my Boston ISA presentation Exceptional Process Control Opportunities.

Considering that people don't have time to read books maybe courses and seminars and the structure of books themselves could provide better direction to areas of specific interest to solve problems. This is an argument for electronic books with interactive queries and demos.

For process automation, the articles and books written by practitioners are our best way of capturing and advancing the technology. Unfortunately users are not given the time or priority to write and most companies are reluctant to disclose information that could be considered to provide a competitive advantage for manufacturing. Consequently, suppliers of automation systems and services write most of the magazine articles and books on the practical application of process control. University professors write most of the journal articles and technical conference papers on the theoretical advancements in process control. The two groups don't talk much to each other. The use of industrial control systems for labs is one glimmering area of hope for the meeting of minds from universities and industry (see my last entry on "Exceptional Opportunities in Process Control - Expertise Development" and the June 1, 2009 entry "What I have Learned? - Bridging the Gap between Universities and Industry").

For me writing books was a way of organizing and expanding knowledge gained on the job. I found it allowed me to put technologies to bed (at least temporarily) so I could clear my head for the next area of expertise. My serious technical books in order of oldest to most recent publication date are: Axial and Centrifugal Compressor Control, Biochemical Measurement and Control, Continuous Control Techniques for Distributed Control Systems, Tuning and Control Loop Performance, Advanced Temperature Measurement and Control, Process/Industrial Instruments and Controls Handbook, Good Tuning - A Pocket Guide, Advanced pH Measurement and Control, Advanced Control Unleashed, Models Unleashed, New Directions in Bioprocess Modeling and Control, and The Essentials of Modern Measurements and Final Elements. My favorite book, which is a mostly serious collection of case histories written in a humorous way, is A Funny Thing Happened on the Way to the Control Room. My mostly humorous books in order of oldest to most recent publication date are: How to Become an Instrument Engineer - The Making of a Prima Donna, Logical Thoughts at 4:00 am, How to Become an Instrument Engineer - Part 1.523, Dispersing Heat Through Conviction, The Life and Times of an Automation Professional - an Illustrated Guide, and The Funnier Side of Retirement for Engineers and People of the Technical Persuasion. The last two books were written solely for comic relief.

While I had to largely write the books on my own time (except for the last serious one), the companies I worked for were supportive in terms of approval and recognition. In the end I expect books helped me along with my heroes Shinskey and Liptak to be the first group of inductees into Control magazine's Process Control Hall of Fame.

I think the following message titled "Why Books" from Ted Stillwell who is of the same vintage as me concisely offers "memories of the way we were."

Because I learned process control on the job books provided the only formal learning environment. Starting with the first treatment plant, with a control panel that would not fit through the door, I began my knowledge quest about instruments and process control. Chemical Engineering published Process Automation a 14-Part Series. My first book purchase was Liptaks' Instrument Engineers' Handbook that I read commuting back and forth to the office. The process control companies offered a great training ground for young engineers. Highly experienced application specialists at these companies wrote most of the articles and books on process control. I have five books by Shinskey, the most recent being Feedback Controllers for the Process Industries (McGraw-Hill 1994).

I hesitated at first to include sample time as one of the exceptional opportunities in process control because in most loops it is not issue. Then I realized I should give my perspective on the effect of sample time for the following reasons:

(1) Since we live in a digital world, sampled data is the norm. Just from the volume of applications, the opportunity is large

(2) There are no clear guidelines for various types of process control applications

(3) In some applications conventional sample times can cause severe safety and performance issues

(4) In most cases the tuning of the controller dictates that sample times could be significantly slower. If DCS module execution times and wireless communication time intervals could be increased, controller loading is reduced and wireless battery life is prolonged, respectively

(5) If we want more at-line analyzers to provide measurements of stream compositions that tell us what is really going on in the process and offer the opportunity for a more advanced level of control, we need to understand and address sample processing and analyzer cycle times

(6) If we want to move to more wireless measurement that give us the flexibility and intelligence for process control improvement, we need to understand and address wireless communication intervals

I am considering sample time as the time between updates in sampled data in the broadest sense. The following discussion should be useful for determining whether DCS scan or module execution times, wireless communication time intervals, model predictive control execution time, and at-line analyzer cycle time will affect control system performance.

If you are pressed for time you can skip the discussion below and just check out ProcessControlSampleTimes.pdf

There is considerable confusion as to when sample times affect the ability of a control system to compensate for unmeasured disturbances. The following is my quick attempt to provide some concepts to sort out fact from fiction and provide some guidance.

The performance of a control loop depends upon the tuning. Specifically, the peak and integrated errors are inversely proportional to the controller gain. The peak error is not affected much by the integral time setting. However the integrated error is proportional to the integral time. Thus, a loop with good dynamics can be made to perform as poorly as a process with bad dynamics by sluggish tuning. The effect of slow sample times is hidden by large integral times or small controller gains. Thus, it is critical for any comparison, that tuning criteria be specified. In fact there is an implied deadtime as a result of the tuning of the loop as derived and discussed in Advanced Application Note 5. The tuning of the controller puts a practical limit on how fast the sample time must be for the effect to be negligible.

If a controller is tuned for maximum performance, the peak error is proportional to the loop deadtime to process time constant ratio. The integrated error is proportional to the deadtime squared. These statements are strictly true only when the process time constant is large compared to the loop deadtime. The loop dead is the sum of final element deadtime (e.g. valve pre-stroke time delay, deadband, and sticktion), process deadtime (e.g. mixing, thermal, and transportation), automation deadtime (e.g. sensor lag, transmitter damping, and sample times), and small process time constants. All of the time constants smaller than the largest time constant become effectively deadtime in the first order plus deadtime approximation used in industry. Process and automation system dynamics places an ultimate limit on loop performance. There is a corresponding ultimate limit on the sample time.

The relationships between process dynamics (e.g. total loop deadtime), controller tuning, and loop performance is detailed in the Theory section in Chapter 2 of Advanced Control Unleashed, and Appendix C in New Directions in Bioprocess Modeling and Control. All of my books and many of my articles take advantage of the fundamental understanding gained from these relationships.

The effect of sample times can be accessed in terms of practical and ultimate limits on performance. Critical loops where peak errors can cause destruction or environmental releases such as compressor surge control, furnace pressure control, exothermic reactor temperature control, and RCRA pH control, the tuning is necessarily aggressive. As a result the practical limit is much closer to the ultimate limit. For a discussion of cases where exceptionally fast sample times are needed, checkout the April 2, 2007 entry "Analog Control Holdouts."

For excellent final elements, clean sensors, and transmitter damping settings of 0.2 sec, we can suggest practical and ultimate sample times for different types of processes with typical dynamics. The ultimate limit (fastest conceivable sample time requirement) is set to be less than 1/10th of the sum of the minimum loop deadtime and minimum process time constant with some consideration as to maximum practical controller gains to reduce valve cycling and noise amplification. For any loop with a a large control valve, the minimum loop deadtime is about 1 second for an unmeasured disturbance (unless volume boosters have been added to the output of the positioner) so the ultimate limit on sample time is about 0.1 second. The practical limit reflects current tuning practices (much slower tuning to insure a smooth gradual response despite unknowns and nonlinearities). For integrating processes, the process time constant shown is the inverse of the integrating process gain (denoted by single exclamation point). The double exclamation point denotes a runaway (positive feedback) process time constant. Consultants says it is impossible to generalize but I think some guidance is helpful to the user with the realization there are always exceptions and the actual process dynamic and tuning should be identified by automated online tuners and adaptive controllers (e.g. DeltaV Insight). I didn't consider ultimate sample times slower than 60 sec. Note that slower sample times will affect the deadtime identified. A Rough Guide to DCS and Measurement (e.g. Wireless) Sample Times is offered in ProcessControlSampleTimes.pdf

For many digital devices the update is available near the beginning of the sample time (latency is negligible), which means the average deadtime from the sample time is about half the sample period. For at-line analyzers (field analyzers with automated sample systems), the result is not available until the end of the sample processing and analyzer cycle time, which translates to an average effective deadtime that is about 1.5 times the time interval between updates in the analyzer output signal. Theses deadtimes determine the minimum peak error for an unmeasured step disturbance at the input to the process.

The detrimental effect of sample time is greater than deadtime in that for continuous sources of dead time such as process transportation and mixing time delays and small process time constants, there is a continuous train of updates. For sampled data there are no intervening values. Consequently, the effects can be worse. For example, there is aliasing of oscillations where the indicated amplitude is smaller and the period is larger than actual. There can be jitter due to variations in latency and lack of synchronization of digital data that introduce variable time delays and noise for rapidly changing signals.

The PIDPLUS modification of the traditional PID developed for wireless applications helps the PID deal with the sample time from digital devices and communication, and at-line analyzers. The improvement is most dramatic for self-regulating processes but is also significant for integrating processes as seen in the tests documented in ControlStudiesPIDPLUS1.pdf. The PID-Plus algorithm also breaks the limit cycle from the resolution limit from the deadband setting for exception reporting of wireless devices because integral action is only done when there is a measurement update.

Unlike self-regulating processes that will line at a steady state after disturbances have died out, integrating processes will ramp until a physical limit is hit. The ramping response is caused by the lack of negative feedback (e.g. self-regulation) in the process as defined in Advanced Application Note 4. In other words an increase in the process variable does not increase a counteracting effect to make the response bend over and reach a equilibrium.

The most common integrating process is level. Since the discharge flow is not appreciably affected by level (except for the rare case of gravity flow), any difference between the feed and discharge flows causes the level to ramp. The low limit is the vessel running dry and the high limit is the vessel spilling over or flooding a vent system.

Other common examples are

(1) Gas pressure control of columns, furnaces, and vessels when changes in operating pressure does not appreciably affect the vent flow rate

(2) Batch temperature control when changes in vessel temperature does not appreciably change the heat transfer rate

(3) Batch pH control when there is no reagent reaction or consumption or reagent concentration does not appreciably change reagent reaction or consumption rate

(4) Batch dissolved oxygen control when the change in oxygen absorbed does not appreciably change the oxygen transfer rate

(5) Batch product composition control when a change in product concentration does not appreciably affect side reaction or degradation rate

(6) Vessel solids concentration control when changes in solids concentration does not affect the evaporation or precipitation rate

(7) Bioreactor biomass or cell density control before the stationary and death phases

Many processes due to a long process time constant or large process gain, will appear to ramp because the steady state is beyond the time range or control region, respectively. What the user sees on the trend charts and what the controller sees as a response from the process variable is a ramp. These processes called "near-integrating" or "pseudo-integrating" processes are better analyzed and tuned as if they were integrating rather than self-regulating processes. Temperature control of any continuous process with a large residence time (volume/flow) can be treated as a "near-integrating" process.

Most of the more important loops have an integrating or "near-integrating" response. Furthermore the ramp rate (%/sec) for a % change in controller output (integrating process gain) is often incredibly slow. These slow ramp rates require exceptionally high controller gains and large integral times.

The test results for a single use bioreactor (SUB) with what would appear to be a small volume (100 liters), revealed an integrating gain of 0.000008 %/sec/%, that was 30 time slower than a bench top bioreactor. The SUB volume was about 30 times larger than the bench top bioreactor volume. The relative size of the volumes is a strong factor but the relative size of other parts such as heat transfer area play a role. This was the first time temperature control was tried on a SUB in this lab. Fortunately an adaptive controller was in service that identified the unexpectedly slower integrating process gain. The best response was achieved with a controller gain of 80 and an integral time of about 10,000 seconds. A Lambda factor of 0.05 was needed. The test results are shown in "BioreactorTemperatureTuningTestResults.pdf."

The principle opportunity for integrating processes is realizing and using higher controller gains and larger integral times. We tend to use too much integral action (too small of an integral or reset time) because we are impatient and integral action provides a continual driving action to eliminate error. We don't normally think of using higher gains because the problem of instability from high gains is drilled into us in all our courses and books on process control, our older measurement systems often gave flaky signals, and before we had structure and set point filter options, high controller gains caused the controller output to peg on a set point change. Properly installed smart transmitters with integral sensors and primary elements have a noise level that is low enough and a sensor sensitivity and repeatability high enough so that the amplification of small changes provides corrective actions rather than amplification of noise or extraneous actions. The proper use of the many PID parameter, control options, and structure today allows the user to minimize the disruption to the operator and other loops.

Most people don't realize there is a window of allowable controller gains. As I mentioned we all know too high of a gain causes instability. For many integrating processes, this controller gain is way above our comfort level (e.g. gain > 100). More often we run into the low limit for controller gain (e.g. gain < 10). Too low of a controller gain causes overshoot and slow rolling oscillations. The correction is non intuitive. You need to increase the controller gain. Even with a high gain and integral time and rate action, it is difficult to prevent overshoot with an integrating process unless you take a very slow approach by using a PID structure that provides no step change in the controller output on a set point (e.g. proportional and derivative action on PV and integral action on error). The overshoot and speed of approach problem was the primary motivation for the simple control strategy for making a temperature go as fast as possible and then stop right at set point as discussed in the article "Full Throttle Batch and Startup Response"

The Lambda tuning equations for integrating processes automatically makes the controller gain large enough to stay above the low limit in the window of allowable controller gains. This is accomplished by keeping the product of controller gain and integral time to larger than 4 divided by the integrating process gain as seen the last slide of "LambdaTuningEquations.pdf." However to get an acceptably fast enough response, Lambda factors much lower than the user is accustomed to must be used. Not shown is the fact that derivative action is helpful. The rate time should be set to the next largest time constant for a self-regulating process and the largest time constant in an integrating process. These rules are consistent for a "near-integrating" since the integrating process gain is the process gain divided by the largest process time constant leaving the next largest time constant as the one used to set the rate time.

Temperature control of exothermic reactors where the reaction rate increases with temperature and particle or crystal size control where the formation rate increases with particle or crystal size can have an integrating followed by a runaway (positive feedback) response where is it is critical to maximize the controller gain and integral time.

My publications are notorious as "no-fluff" zones. My articles "Life's Batch" and "Maximizing PAT Benefits from Bioprocess Modeling and Control" should have been a 5 part series. After 120 blogs, 84 Control Talk columns, and 14 articles since I retired from my full time job, you might think I might be running out of ideas. I wonder myself when I sit down to write but once I feel a flow with the music, the main constraint is time. There is always something to say even if it is just shedding more light on an old subject. It is kind of surreal since I am a quiet guy. As I get older I am going to have to make sure I don't repeat myself, repeat myself, repeat myself.

Here are the key points for my 2005 - 2006 articles

"Life's a Batch", Control, May, 2005
(Click "Download Now" button at end to get Equations and Figures)

1. The key to good batch temperature control is the secondary loop setup and tuning

2. An inlet or outlet secondary temperature loop linearizes the process gain of the primary batch temperature loop and makes the primary loop dynamics faster

3. An inlet jacket or coil temperature can correct for coolant disturbances before they appreciably affect the batch temperature

4. An outlet jacket or coil temperature can correct for heat transfer surface disturbances before they appreciably affect the batch temperature

5. The use of a heat exchanger in a recirculation loop instead of a jacket or coil creates a delayed integrating response in the secondary temperature loop that is problematic if much integral action is used (not discussed in this article)

6. The difference between an inlet and outlet jacket or coil temperature multiplied by coolant flow provides a measurement of heat release and hence reaction rate. The inlet temperature should be delayed by the transport time through the coils or jacket (Volume/flow) to match up the inlet time wise with the outlet temperature

7. If the jacket or coil flow rather than a makeup flow is throttled, the increase in the process gain and process delay of the secondary loop can causes oscillations

8. The secondary loop should be tuned with mostly gain action for a fast response otherwise disturbances start to affect the batch temperature and an exothermic reactor can develop a runaway response

9. Coolant valves should be judiciously sized sliding stem (globe) valves with digital positioners to reduce the limit cycles from stick-slip and deadband

10. Most batch temperatures will oscillate across the split range point because of the dramatic difference between the installed valve characteristic curves and the increase in sticktion near the closed position

11. Trim coolant valves should be considered to reduce oscillations around the split range point and provide fine adjustments (see the March 16 and March 24 entries on this site on the "Manipulation of Multiple Flows")

12. The integrating response of batch temperature will cause a limit cycle from deadband even if the secondary temperature loop has no integral action

13. A highly exothermic reactor can runaway if the secondary temperature measurement or heat transfer rate is too slow

14. To reduce the batch cycle time for to reach a batch temperature end point, the jacket and coil valve can be set wide open and a control strategy such as the following used where appropriate:

a. A temperature rate of change calculation multiplied by the deadtime triggers the shutoff or positioning of the coil or jacket valves. If the feeds are to continue or there is some residual heat generation, the batch temperature should be put in automatic (see 2006 article "Full Throttle Batch and Startup Response" for details)

b. A reactor temperature controller can throttle the reactant feed rates nut there may be an appreciable inverse response from the dilution and cooling effects of increasing a reactant feed rate

15. Model predictive control is more effective approach where there are multiple constraints for batch reactors being pushed beyond their nameplate capacity

16. Coriolis mass flow meters can correct of reactant concentration and provide a model of reaction product concentrations

17. Equations can estimate the ultimate gain of self-regulating, integrating, and runaway process for process gains, lags, and dead times and provide a deeper understanding of what affects performance and why batch reactor temperature loops require higher controller gains and lower integral times

18. The primary temperature controller integral time setting should be scheduled based on totalized feeds and the secondary temperature controller gain and integral time setting scheduled based on the position of split ranged valves

"What If? Virtual Plant Reality", Control, Aug, 2005
(Pages 3 and 4 of "How to Survive the Oncoming Train of Technology")

1. Process flow diagram (process design) simulations circa 2005 that are made dynamic

a. Can provide a reasonably accurate steady state process gain and the residence time based process lag time if the physical properties are well known

b. Generally do not model mixing lags, transportation delays, installed valve characteristics, valve backlash or sticktion, mixing or sensor noise, and sensor lags, or bubble or particle distribution and size

c. Have trouble simulating batch operations, startups, and shutdowns because equipment instantaneously go to equilibrium conditions and the program can develop numerical instabilities for extreme conditions and zero flows

d. Cannot possibly emulate all of the batch and loop control capability in a DCS and thus must relay upon being interfaced to a DCS which is problematic in terms of running faster than real time (synchronization and acceleration issues)

2. Dynamic simulations that focus on the dynamics of interest can focus on the details important for process control

"Model Predictive Control can Solve Valve Problem", Control, Nov, 2005

Advanced Application Note 002

I don't need to say anything here since it is covered in the application note and the March 16 and March 24 entries on this site on the "Manipulation of Multiple Flows." Dare I repeat myself?

"Maximizing PAT Benefits from Bioprocess Modeling and Control", Pharmaceutical Technology, IT Supplement, Nov, 2006

There are so many uses of a virtual plant it is mind boggling. Just search for Virtual Plant on this website. In particular, check out the Oct 8, 2008 entry "High Fidelity"

"Full Throttle Batch and Startup Response", Control, May 2006

This article shows a simple calculation when the reactor temperature will reach set point based on rate of change and deadtime can minimize the time to reach set point. The calculation is particularly appropriate for the integrating response encountered in a batch operation or in the startup of a continuous piece of equipment where the discharge flow has not started. It is important to remember for integrating processes, the controller output must be driven past the balance point (resting valve position) to make the process variable move. With self-regulating processes, you can go to the balance point directly but even here you get there faster if the output is initially drive past the balance point.

I really like blogging. The only reason the blogs are fewer these days is that my time is consumed with finishing up the "Essential Book" so it is available in time for ISA Expo. What free time I have is spent taking advantage of Austin being the "Live Music" capital.

As I reflected on my career, I reaffirmed that what drives me is gaining a deeper understanding and sharing what I have learned, hopefully with a few laughs along the way. Throughout my career I sought with an open mind the knowledge and insights of the leaders in process modeling and control. I then used simulations to rapidly explore process relationships and to prototype control improvements that incorporate process understanding. The knowledge prepared me to solve tough plant control problems.

During my career at Monsanto I wrote a bunch of articles in the 1980s for InTech on my time in the plants with some humor introduced to help make the material more accessible and memorable. These articles were compiled and published in the book A Funny Thing Happened on the Way to the Control Room available for viewing as an E-book in the April 3, 2009 list of my books on this website. This is my favorite book, I didn't write much in the way of articles or books in the 1990s. I was on the road most of the time.

When I retired from Monsanto-Solutia in 2001 (sans package), I taught at Washington University. The students were great but after the course and lab was developed, it became routine. Also, I felt isolated.

I tell people I flunked retirement. I moved to Austin in September 2004 and started a second career as a part time consultant at Emerson Process Management. This gave me a chance to keep up to date with the latest new tools besides continue my exploration of process control opportunities. Plus it felt like home since Monsanto and Fisher Controls were one for most of my career.

I have been blessed with access to the best minds. In Monsanto's Engineering Technology I got to work with the leaders in process modeling and control. Some went on to distinguished chairs at prestigious universities, several were inducted into the Process Control Hall of Fame, some served as presidents of ISA and AIChE, and others left to become the principal technical resources for leading simulation companies. Here in Austin in Applied Research I get to work with the brains behind DeltaV. Plus my second career is more balanced. Except for the spike in work this year, I take a total of 4 months off each year to travel to see relatives, friends, and neat places and to write books.

Key points of my articles written in my post retirement years provide a quick overview of what I have been doing. The entries on this website in July will focus on the dozen articles I have written since retiring from my full time job. Here are the articles from 2003-2004.

"Has Your Valve Responded Lately", Control, May, 2003
"What is Your Flow Control Valve Telling You", Control Design, May 2004

Putman publications decided to do an encore publication in a second magazine. Some nomenclature typos were corrected in the reissue of the article in Control Design.

1. Deadband originates from backlash in the linkage and connections between the actuator and the plug, disc, or ball. Stick-slip comes from friction in stem packing and seals around the sealing of the plug, disc, or ball for process isolation

2. Deadband from linkage and connection backlash and stick-slip from trim and packing friction create deadtime for slowly changing controller outputs

3. Deadband will create a limit cycle in any control system where there are two integrators in series, such as a PI controller on an integrating process (e.g. level)

4. For deadband, the limit cycle amplitude is the ratio of deadband to controller gain

5. For stick-slip, the limit cycle amplitude is the product of the open loop gain and the stick-slip

6. For both deadband and stick-slip, the limit cycle period is proportional to the controller integral time and inversely related to the controller gain

7. Large actuators can have a large stroking time for a large change in signal

8. The size of the changes signal typically used to checkout control valves will not reveal the deadband or stick-slip and make all but the largest valves look good

9. A volume booster can reduce the stroking time of big actuators but has a large deadband. The booster should be put on the positioner output to quickly drive through this deadband. The booster bypass must be opened enough to prevent fast cycling from the positioner output looking into the booster's small inlet volume

10. Unstable oscillations can break out for large disturbances when the integral action in process loop becomes faster than the valve response. The integral time must be greater than the product of the valve slewing rate, disturbance size, and controller gain. (Not mentioned in the article but frequently discussed on the this website is that position read back from digital positioners and the PID dynamic reset limit option can automatically prevent the controller output from outrunning the valve)

11. Limit cycles are attenuated (filtered or washed out) by vessels or columns. The ratio of the attenuated to original amplitude is proportional to the period of the oscillation and inversely proportional to the residence time (volume/flow)

12. The control valve with the best response is a sliding stem valve with a digital positioner. If one must use a rotary valve, avoid tight shutoff and high friction packing and use a diaphragm actuator with a short shaft and splined connections between the actuator shaft and the stem of ball, disc, or plug. Make sure the stem is cast with the ball, disc, or plug to avoid another connection with backlash

Postscript: Rotary valves designed by piping manufacturers have a lot of deadband and stick-slip as discussed in the July 2009 Control Talk column "Downturn Turndown" in Control magazine.

"The Next Generation - Adaptive Control Takes a leap Forward", Chemical Processing, September, 2004

1. Nearly all controllers are detuned (backed off from maximum performance) to some degree to provide a smooth response and to deal with the inevitable changes in the process dynamics

2. Older technology adaptive controllers had these undesirable features
a. The process had to be disturbed or oscillated (e.g. patter recognition)
b. The dynamics were embedded in tuning settings
c. No real insight as to where the process has been or where it is going
d. Tuning method was fixed
e. Always playing catch up even if same situation was seen a thousand times

3. The next generation adaptive controller can
a. Normal changes in a controller's set point or manual output are used
b. The process dynamics are displayed and historized
c. From changes in the process dynamics, plant problems can be diagnosed
d. Several tuning methods are available
e. Tuning settings identified can be scheduled for preemptive action

4. "The information on changes in the process model may be directly used to monitor loop performance and to provide more intelligent diagnostics. The models can provide the dynamics for simulations and identify candidates for feedforward control and advanced control techniques. For example, loops dominated by a dead time or exhibiting disturbance models for multiple variables, are prime candidates for model predictive control. The dynamic process models in general can be used to create or adapt real time simulations for prototyping new control strategies, exploring "what if" scenarios, and training operators. Process gains that decrease or time constants that increase with feed totals are ripe for real time optimization of the run time between defrosting or cleaning and catalyst reactivation or replacement. The beauty of this route is the models and tuning settings are available from the adaptive controller for a higher level of control by a better knowledge of the topology"

"Advanced Control Smorgasbord - A Lot of Tasty Choices", Control, May, 2004

The online version is missing the following introductory sentences at the beginning of the first paragraph.

"By the time I was assigned to my first electronic control room project, some very smart engineers had already developed most of the techniques to exploit PID controllers.
Relative gain arrays and simple decoupling of the controller output were used to analyze and deal with interaction on a steady state gain basis. The outputs from PID controllers, whose process variable was a constraint variable, were sent to a signal selector to form an override control scheme to maximize or minimize a manipulated variable."

1. Previously, advanced process control (APC) required software packages at $100K a clip, separate computers, special interfaces, and consultants to do the studies and implementation. The total bill could easily approach or exceed a million dollars for a medium project, the biggest chunk being the consultant's time charges. Even a greater consideration was that the process knowledge to exploit or to just maintain the system disappeared when the consultants left the site

2. At the turn of the century, APC technologies were integrated into the basic process control system. License fees were minimal and whole cost of implementation decreased by a factor of twenty or more by the automation of the configuration, displays, testing, simulation, and tuning

3. In the time it takes to read this article, a model predictive controller or neural network could have been configured

4. Perhaps the biggest opportunity for driving the application of APC is the development of online process performance indicators

5. The key variable for process performance monitoring is the ratio of the manipulated flow to the feed flow

6. The controlled variable is best expressed and plotted as a function of the flow ratio (e.g. pH versus reagent to feed ratio, column temperature versus reflux to feed ratio, exchanger temperature versus coolant to feed ratio, and stack oxygen is versus air to fuel ratio)

7. The process efficiency is seen in difference between the actual and optimum ratio rather than in the gap between the actual and optimum controlled variable

8. A novel method has been developed to use model predictive control (MPC) to simultaneously adapt multiple first principle process model parameters

9. For closed loop process control, consider
a. PID for tight control of integrating or runaway processes
b. MPC for multivariable control, interactions, and optimization

10. For online property estimators for continuous processes, consider
a. ANN for highly nonlinear predictions with uncorrelated inputs
b. LDE for lag dominated linear predictions with uncorrelated inputs
c. PLS for steady state predictions from large number of correlated inputs

ANN is an artificial neural network, LDE is a linear dynamic estimator, and PLS is a projection to latent structures or partial least squares prediction discussed in Chapter 8 of Advanced Control Unleashed

If you want to know how to minimize oscillations from final elements and don't have time to read the supporting information you can use the following rules of thumb and move on to more important tasks like reading email. The final elements considered here are throttling control valves and variable speed drives (VSD) on pumps or fans.

• Use a sliding stem throttling valve with a properly tuned digital positioner (position feedback) or a VSD with a properly tuned speed controller (tachometer feedback) to minimize the amplitude of the limit cycle from a final element
• Make sure the DCS and final element I/O cards have at least 12 bits
• Enable "Dynamic Reset Limit" in PID block and use position or speed feedback as PV for BKCAL_OUT of AO block to prevent a burst of unstable oscillations when PID reset action is faster than valve or VSD response
• Set IDEADAND in the PID block equal to the limit cycle amplitude from the final element to kill the limit cycle during quiet periods of operation (e.g. periods when there are no disturbances or set point changes) for a self-regulating loop

Resolution is the minimum change in the element's output. Changes in the output smaller than the resolution cannot be made. For a control valve, the resolution limit is the result of friction in the packing, seat, and seal. For a VSD, the resolution limit is the result of an artificially imposed deadband, which is really a dead zone or from a speed sensing element resolution limit. Resolution can also result from a quantize limit from the number of bits in a microprocessor or I/O card. The number of bits in A/D and D/A cards for most DCS has increased from 12 bits to 16 bits. In both cases, the resolution limit from these I/O cards is negligible. However the standard input card of some VSD manufacturers is only 8 bit causing a significant resolution limit. The resolution in the stroke of a control valve or in the speed a variable speed drive will cause a limit cycle in any loop with integral (reset) action.

The term deadband is often used in automation systems to specify a dead zone (a bandwidth around a reference value where there is no response). Examples are deadband (dead zone) specifications in VSD configuration for noise rejection and in a PID configuration for integral action suspension.

For final elements, deadband has a significantly different definition. Here deadband is the change in signal required upon a reversal of direction to get a change in the element's output. Once the output reverse direction, deadband places no limit on how small a change can be made in the same direction. In reality, valve deadband is usually accompanied by a resolution limit. In the stroke of a control valve, deadband is the result of backlash from gaps or play in linkages and shaft or stem connections. Deadband normally doesn't exist in a VSD. Deadband will cause a limit cycle if there are two integrators in series in the control system. Multiple integrators in series can occur from a PID with integral action on a process with an integrating response such as level. Alternately, the limit cycle can occur if there is a cascade control loop where there is integral action in more than one controller. If both the temperature and flow PID blocks have integral (reset) action in a temperature to flow cascade control system, then deadband can cause a limit cycle. Most people forget that a positioner or digital valve controller creates a cascade loop where the positioner controller is the secondary loop. Positioners until recently were proportional only controllers.

The amplitude of the limit cycle is the smallest change in flow associated with the smallest possible change in valve position or speed multiplied by the process gain (change in process variable in engineering units divided by the change in flow). To get the smallest possible change in flow of a control valve, multiply the valve's resolution limit in % of stroke by the installed characteristic curve for the valve at its operating point. Note that valve stick-slip and the resolution gets worse near the seating or sealing surface. The manufacturer's quoted numbers are at a 50% throttle position. To get the smallest possible change in flow of a VSD multiply the resolution limit of the input card resolution of the tachometer sensing element, or noise deadband, whichever is largest, and convert to flow based on the interpolated shift in the installed characteristic curves with speed for the pump or fan. Be careful, many VSD have an adjustable deadband (dead zone) to prevent the VSD from responding to noise. This adjustment is often set with no regard to the effect on loop performance.

Resolution limits and deadband add dead time to the control loop for slow disturbances because it takes time for the PID output work through the zone of no final element response. The dead time is the resolution limit or deadband divided by the rate of change of the controller output. This additional deadtime increases the peak and integrated error for the upset. Note that step changes in the controller output larger than the resolution limit or deadband will not reveal the deadtime.

Control valves have an inherent velocity limit from the limitations imposed by actuator fill and exhaust rates. VSD have an application set velocity limit from the motor load limitations imposed by the impeller inertia. Make sure the valve actuator and VSD motor have enough muscle for the valve sticktion and pump inertia, respectively or you can get into poor valve position or speed control and hence even bigger loop problems.

Use the "dynamic reset limit" option of a PID block in a DCS, such as DeltaV, where the PID uses a positive feedback network for its integral action. The BKCAL_OUT for the AO block which in connected to the BKCAL_IN of the PID block should be actual valve position or VSD speed. Select the PV (position or speed) option in the AO block for the BKCAL_OUT. This feedback of actual position or speed to the PID enables the PID algorithm to curtail its integral contribution to the PID output so that the PID output from reset action does not change faster than the valve or drive can respond. If this protection is not in place, everything may look OK until the loop gets a disturbance large enough PID to cause the PID output to change faster than the final element. The mysterious bursts of instability for big load upsets often go unresolved.

Set the IDEADBAND option in the PID block to a value about equal to the limit cycle amplitude. IDEADBAND will suspend the integral action when the PID error is less the IDEADBAND. This suspension will stop limit cycles from a resolution limit or deadband for a self-regulating process at a steady state. It will not stop the limit cycle on a process with an integrating response because the process has no steady state and will continue to ramp until the process variable exceeds the IDEADBAND.

For more info on final element response, check out the "Deal or No Deal" Control Talk column in Control magazine, the article "What is your Valve Trying to Tell You" in Control Design magazine, and "Improve Control Loop Performance" in Chemical Processing magazine.

Ratio control provides coordination of multiple flows. One flow is an "independent flow" that is used to set production rate. Sometimes this flow is also termed a "wild flow" when the availability of this flow is not determined by the production unit. In a ratio control system, the process variable (PV) or set point (SP) of the independent flow (leader) is multiplied by a ratio factor and becomes the set point for the dependent flow (follower). Slide 1 in RatioControl.pdf shows two flow loops in a ratio control system.

If the flow is noisy, the SP of the independent loop may be preferred. Flow transmitter damping or signal filtering can be used to smooth out the noise but this adds a lag that reduces the ability of the flow loop to deal with pressure disturbances and valve issues. If pressure swings and valve response problems are negligible, the slowing down of the independent loop (leader) by the use of a signal filter may be useful in allowing the dependent flow loop (follower) to catch up with changes in production rate. If this is not the case, then the signal filtering is only put on the independent flow PV passed for multiplication by the ratio factor. I favor using whatever means possible to eliminate noise so the ratio control can use the PV rather than the SP of the independent flow loop to reduce the downstream errors from the transient response of this loop.

Regardless of whether the PV or SP of the independent loop is used, the measurement should have good repeatability and rangeability, the control valves should have minimal backlash and sticktion, and the controllers should be tuned so the follower can keep up with the leader to minimize the errors downstream.

Some blend tanks totalize the ingredient flows and use a tank blend controller to correct the input ratio to keep the blend composition in the tank closer to its target. The total in the tank for the independent feed is multiplied by the ratio, which is the set point for the total in the tank for the dependent feed. The actual total of dependent feed is the process variable for a tank blend controller to correct the ratio control system on the tank's input flows. A proportional only controller may be desirable. The totalization of flows can be done on a batch or continuous basis. For a continuous blend tank, the material balance Equation 4-7f (without the reaction rate) in the Advanced Application Note "First Principle Process Gains ...." posted March 25, 2009 on this website is integrated. For this blend system, achieving a particular ratio is the final objective. For most ratio control systems, the target ratio changes with the composition, physical properties, and temperature of the input flows.

When a critical process variable loop is used to provide feedback correction of the target ratio, the independent flow multiplied by the ratio factor is called flow feedforward and the ratio factor may be called a feedforward gain. Some people reserve the term "ratio control" to the case of no feedback correction of the target ratio.

There are many examples of ratio control and its extension to flow feedforward control. A simple example is the inline control system where ingredient flows (main and additive flow) are added to a pipeline mixer as shown in slide 2. Often this pipeline mixer is simply a baffled piece of pipe called a "static mixer". The combined stream coming out of the mixer is at the current ratio set by the inputs to the mixer. Sometimes the real intent is to provide a specific viscosity, density, percent solids, or consistency. In these cases, online measurements of these critical process variables at the exit of the static mixer are used in a loop whose output provides feedback correction of the target ratio.

Another examples of ratio control is catalyst to reactant feed ratio control as shown in slide 3. An enhancement used for this application is a correction for catalyst activity, which is particularly important when the catalyst is recovered and recycled. Property estimators based on batch conditions and completion times biased by at-line or lab analytical measurements are used to provide feedback correction of the target ratio.

Reactors typically use ratio control of reactant feeds. It is desirable to have an online analyzer to provide automatic correction of the target ratio of reactants as shown in slide 4. The independent flow may be the main reactant feed or a recycle reactant feed.

Neutralizers often use flow feedforward where the pH controller corrects the target ratio of reagent to the main flow (e.g. influent flow) when accurate flow measurements with sufficient rangeability are available. For food sweetener production it was found that the mass flow ratio control by the use of coriolis flow meters was tighter than pH control. The pH was then relegated to indication only. This was an extreme case where the feed compositions had tight specs and the set point was on the flat part of the titration curve so that the error in the pH measurement corresponded to a greater error in the ratio than what was achieved with the coriolis flow measurements.

Temperature control of heat exchangers is often improved by flow feedforward where the coolant flow is ratioed to the feed flow and corrected by the temperature loop. Feed forward control of columns has saved millions of dollars in many plants by a straightforward ratio of the reflux or distillate and/or steam flow to the feed flow and correction of the target ratio by a tray temperature control loop.

Combustion control of boilers and furnaces rely on air to fuel ratio control. In some cases, stack or combustion zone oxygen analyzers are used to correct the target ratio for the changes in mixing efficiency and heating values of waste fuels.

Have you ever wondered why so many ratios exist? Is it just convention or is there a fundamental underlying reason? Why do some users prefer feedforward summers over feedforward multipliers for target ratio correction? Why do oxygen controllers provide a correction of a calculated air flow rather than a target ratio? If waiting on the answers is going to keep you awake at night, you can call me at 512-832-3029 and I will tell you an answer that will put you to sleep. Warning from the Automation General: "Calling Greg McMillan while driving a car is hazardous to your health."

If you have manipulated flows with counteracting effects (application 5), such as steam and coolant or acid and base reagents, your most straightforward solution is split range control because split ranged control prevents a loss in efficiency from both streams flowing at the same time if there is no overlap at the split range point and no low limits in the manipulated flows.

What about applications to increase plant turndown and capacity (application 1), reduce process variability (application 2), and improve plant efficiency (applications 3 and 4)?

(1) Extend rangeability
(2) Improve resolution
(3) Enable preferential use of flows based on cost
(4) Send flows to multiple destinations possibly based on priorities
(5) Provide counteracting effects

If the manipulated flows had perfect valves and no discontinuity at the split range point, we could use split range control for applications 1-4 if we addressed the tuning considerations for the different dynamics of the manipulated flows. If the manipulated flows had the same time constant and deadtime, compensation would reduce to setting the split range point to compensate for the different process gains for each manipulated flow as mentioned in Part 1. When the speed of response is different, a more effective technique may be to schedule controller tuning settings based on which flow is being manipulated. Scheduling of the gain, reset, and rate time will take into account the changes in the process time constant and deadtime as well as process gain. For example, if a loop is manipulating waste bark feed and natural gas flow to a boiler, the response of steam generation to waste bark flow will be much slower than to natural gas flow. Often the less expensive manipulated flow is the one with the slowest and most variable response. An adaptive controller, such as DeltaV Insight, can continuously update the scheduling of the tuning of the settings for a manipulated flow with variability, such as the heating value of waste fuels, the acid and/or base concentrations of waste reagents, the composition of recycle flows, and the temperature of heat recovery streams.

What are the options for dealing with the specific problem of a single critical process controlled variable and two manipulated flows with different costs, dynamics, stick-slip, and backlash? Can we mitigate the consequences of non-ideal valves? Can we avoid the nasty discontinuity of the split range point and limit the need to schedule PID settings to the effect of just one manipulated flow on the critical process variable?

A solution in the regulatory control world is to continuously manipulate the flow with the faster and fixed dynamics (FFD) for tight control of the critical process variable and only move the flow with the slower and variable dynamics (SVD) when absolutely necessary.

This strategy uses a PID to tightly control the critical process variable by directly and rapidly manipulating the FFD flow. A valve position controller (VPC) keeps the FFD flow from getting too high or low by slowly manipulating the SVD flow. The valve position control (VPC) is an integral-only controller that is optimizing the FFD flow. Proportional and rate action are not used in the optimizing VPC because fast and abrupt changes create interaction and disruption. A description of VPC starts on slide 25 in ControlUsingTwoManipulatedVariables.pdf

Control valves, particularly rotary valves, lose their sensitivity at high positions (installed valve characteristic flattens). Consequently, there is a maximum throttle position for good control. At the other end, it is undesirable to ride the seat of any control valve. Many develop more stick-slip and backlash as you approach the closed position (< 20%). As a result, there is a minimum throttle position for good control.

The VPC set point is the optimum desired FFD flow. If the FFD flow is more costly, the VPC set point is a minimum FFD flow that still enables good control. A minimum FFD flow may also exist for stability, such as a minimum gas natural flow for flame stability. If the FFD flow is less costly (less common case), the VPC set point is a maximum FFD flow that still enables good control. If there is no cost difference between the FFD and SVD, the VPC set point is the mid throttle range of the FFD (e.g. 50%). Whenever, small and large valves are used on the same stream to increase rangeability and resolution, the small valve is considered the FFD flow because the smaller valve generally has a faster response and a finer resolution in terms of total flow. In this case, the critical PID directly throttles the small valve (fine adjustment) and the VPC throttles the big valve (coarse adjustment). The VPC set point is the best mid throttle position of the small valve. The best mid throttle position is a function of the room to roam on the best part of the installed valve characteristic and keeping away from the seat.

The VPC process variable is the FFD flow. Typically, the critical PID controller output is used. Since the VPC response is intentionally slow and the optimum VPC set point knowledge is rarely better than 1%, the use of actual flow or valve position read back is unnecessary as the PV of the VPC. There might be some advantage in using actual flow in terms of linearization, but there are bigger issues like what is the ball park for tuning? The good news is we have only one VPC tuning setting, integral time. The bad news is this integral time tuning is not defined for applications. We know the VPC should be slow enough to prevent interaction with the PID but fast enough to allow the PID to do its job. The best paper I have seen on VPC tuning is "Analysis of Valve-Position Control for Dual-Input Processes" by Cheng-Ching Yu and William L. Luyben published in the American Chemical Society journal in 1986 (0196-4313/86/1025-0344$01.50/0). The conservative tuning in this paper appears to me to be the best and simplifies to the integral time setting being approximately the ratio of the SVD process time constant to the FFD process time constant for stable (self-regulating) processes. For unstable (runaway) processes, a satisfactory integral time is about half the ratio. For the exothermic reactor example cited, the integral time is about half of the ratio of SVD heat removal time constant to the FFD heat removal time constant. This article implies an independence of the VPC integral time from other process dynamics. This independence should be confirmed through more analysis and testing. The VPC integral time might also be a function of the ratios of process gains and dead times in the response of the critical process variable to the manipulated flows.

It is important that the critical PID be tuned first for tight control. For unstable processes this PID must have enough gain and rate action to prevent a runaway. The VPC is then tuned next and any fighting between the loops or oscillations created in the PID loop for a set point change in the VPC loop must be prevented by increasing the VPC reset time. For large and fast disturbances that drive the FFD flow out of the good control range, it is important to add feedforward control to put the valves in the right position without having to wait for the slow VPC loop to respond. If we are doing the small valve PID and big valve VPC control deal, it may be useful to turn off integral action in the VPC when the fine valve is within an acceptable throttle range (e.g. 40-60%) so the big valve ("Mr. Big") with its big problems is only asked to move for a big disturbance. This eliminates a big limit cycle from the big stick-slip and big backlash of "Mr. Big."

Stay safe. Always monitor and test any new strategy or tuning for worst case scenarios.

We conclude with a ten step implementation procedure for helping you get the most out of your cascade control system.

(1) Pick a fast secondary measurement with enough rangeability to correct for nonlinearities and disturbances. Flow is the most popular secondary measurement because it is relatively fast and can compensate for nonlinear valve characteristics and pressure upsets. However differential head meters may lack sufficient rangeability for some applications. A common triple cascade loop is vessel temperature to jacket temperature to makeup coolant flow, which makes the primary loop linear and corrects for coolant makeup temperature and pressure upsets and non-ideal control valve behavior. If you have a positioner on the coolant valve (highly recommended), you have a quadruple cascade. If you have a digital valve controller (DVC) as your positioner with an inner loop of actuator pressure, you have graduated to a quintuple cascade control system.

(2) If you have a positive feedback network for the integral mode in your secondary PID and have fast reliable feedback of the variable that the secondary loop is manipulating, enable external feedback ("Dynamic Reset Limit") in the secondary PID and provide the manipulated variable for external reset feedback. Fieldbus read back is fast enough for any valve with a pneumatic actuator whereas HART read back is fast enough for very large pneumatic actuators. Some variable speed drives (VSD) have tachometer feedback creating an inner speed loop. The use of speed for external reset feedback is particularly useful for dealing with overly conservative maximum ramp rate settings in the VSD.

(3) Remove set point filtering on the secondary loop.

(4) Tune the secondary (inner) loop first for a fast response to set point changes. Consider set point feedforward in the secondary loop for a low secondary PID controller gain (< 0.5) to make the secondary response to setpoint changes faster.

(5) If you a positive feedback network for the integral mode, enable external feedback ("Dynamic Reset Limit") in the primary PID and provide the secondary loop PV for external reset feedback.

(6) Put the secondary loop in the remote setpoint cascade (RCAS) mode.

(7) Make sure the output limits of the primary PID match up with the setpoint limits of the secondary PID.

(8) Add a PV noise filter to the primary loop just large enough keep from unnecessarily moving the secondary loop set point.

(9) Tune the primary (outer) loop for a smooth response. The primary closed loop time constant must be at least five times larger than the secondary closed loop time constant to eliminate any interaction between the primary and secondary loops. If the secondary loop time constant cannot be made faster, you must slow down the primary loop time constant by decreasing the primary controller gain.

(10) Add feedforward signals as necessary to the primary controller output to improve its response to measured disturbances. Add a PV noise filter to the feedforward signal just large enough to prevent unnecessary movement of the secondary loop set point. Add dynamic compensation (delay and/or lead/lag) to the feedforward signal so that the correction by the secondary loop doesn't arrive too soon or too late relative to the disturbance at the same point in the process.

In the February 9 entry on "Unexpected Wireless Benefits" we saw how the positive feedback implementation of the integral mode enabled an enhancement of integral action had benefits that extended beyond wireless devices to any loop with appreciable measurement delay. The positive feedback network sends the controller output or an external reset feedback back through a filter and adds the result to the controller output from the proportional mode as shown in PIDPLUS_Results.pdf. The positive feedback network also offers a convenient method of deadtime compensation by just inserting a deadtime block in front of the filter as shown in Advanced Application Note 3.

Additionally the positive feedback network provides a significant improvement for batch control, override control, and cascade control as described in the article "The Power of External Reset Feedback." In cascade control the use of the secondary process variable (PV) as the input to the filter (external reset feedback) enables the primary controller to deal with a poor (e.g. slow) response in the secondary controller. To set up external reset feedback of the secondary loop PV, the "Dynamic Reset Limit" must be enabled in the primary (outer) loop PID and the secondary (inner) loop PV must be selected for the BKCAL_OUT of the secondary PID as shown in Cascade_Control.pdf. The use of external reset feedback prevents the primary loop from acting faster than the secondary loop can respond, which could occur if the secondary loop has a slow reset setting or has a slow valve. To include the effect of the valve response, the valve position read back must be selected as the PV for the BKCAL_OUT of the AO block. Without this configuration, the primary controller does not know a valve has excessive stroking time, stick-slip, or deadband, a loss of signal, solenoid valve failure, or unexpected de-energization of the solenoid due to a discrete process action, sequence, or interlock. The loop may seem OK for small changes load if it is just a slow valve or slow variable speed drive without external reset feedback, because the velocity limiting (rate limiting) in the final element has little effect for small changes in the controller output. However, a large disturbance or set point change will trigger oscillations when the controller output outruns the response of the final element.

Did you know the peak error for cascade control decreases for an unmeasured disturbance as the size of the secondary process time constant (lag) increases? In the single loop, the secondary process time constant is detrimental because it creates dead time whereas putting the secondary time constant as the largest time constant in the secondary loop is beneficial because it allows a high secondary controller gain and slows down process disturbances entering the secondary loop. The ratio of cascade to single loop peak error goes from about 0.25 to 0.1 as the ratio of the secondary (inner) to primary (outer) loop time constant increases from 0.2 to 1.0 for a 0.6 inner to outer loop dead time ratio as shown in Tuning_and_Control_Loop_Performance_Figure_11.2.pdf. The figure also shows how a smaller deadtime in the secondary loop compared to the deadtime in the primary loop decreases this same peak error ratio. The improvement is even more dramatic when the primary loop has an integrating or a runaway process.

A cascade control system has a secondary (inner or slave)) loop that gets a remote set point that is the output of a primary (outer or master) loop. The set point of the secondary loop is driven to meet the needs of the primary loop. Most of the benefits stem from the secondary loop correcting for disturbances, nonlinearities, and non-self-regulation before they affect the primary loop. A more obscure benefit is the speeding up of the primary loop by decreasing its natural frequency, particularly when there is a secondary process lag. All of these benefits depend upon the cascade rule that the secondary loop be sufficiently faster than the primary loop. The trend plot SlowSecondaryLoopOscillations.pdf shows how oscillations break out when the secondary loop is slowed down by a factor of five.

Where do you cascade control systems and how many do you already have in service?

How many control valves do you have? Would you believe you should have as many cascade control systems as you have control valves?

Every control valve connected to a digital control system should have a digital valve controller (DVC). The DVC is a high gain fast secondary loop that takes care of most of the non ideal stuff that can occur with valve position due to backlash, friction, and actuator response plus give you diagnostics on the valve's health and dynamic capability, and feedback (readback) of the actual valve position. In the old days, with analog loops, putting a pneumatic positioner on a fast loop was stated to be a "no-no"! The solution (the use of a booster instead of a positioner) was worse than the problem as discussed in the chapter "Compressor Surge Control - Traveling in the Fast Lane" in the E-book A Funny Thing Happened on the Way to the Control Room and in the Chapter "Instrument Requirements" in the E-book Centrifugal and Axial Compressor Control. In reality, even in the old days, the analog flow loop was usually tuned so slow, the cascade rule was not really an issue. Of course, some positioners were poorly designed, particularly the spool type single stage positioners slapped onto on-off valves posing as throttle valves. Every now and then I see the question still asked; when should you use a valve positioner on a control valve? Academics and people stuck in the mindset of the days of analog controllers will say "important slow loops." The right answer in my book is "every loop" if you are talking about an electronic high performance positioner (e.g. DVC) unless you really don't care what the valve is doing.

The next most common secondary loop is the flow loop, which corrects for pressure upsets and valve characteristic nonlinearities before they affect the primary loop. Most of the common primary loops (e.g. composition, pressure, level, and temperature loops) can benefit from cascade control. If you are going to do flow ratio or flow feedforward control, secondary flow loops are almost essential. Most secondary flow loops should have secondary valve position loop forming a triple cascade control system.

There are exceptions as to when a secondary flow loop is useful. If the flow measurement has significantly less rangeability than the control valve or excessive noise or failure rate, a secondary flow loop can do more harm than good. In 3 element boiler drum level control, the level controller output switches from cascade control of a secondary flow loop to direct manipulation of boiler feed water valve at low loads because of the insufficient rangeability of the differential head flow meter on the feed water.

Liquid or polymer and some gas pressure loops are too fast to have a secondary flow or valve position loop. In general, the controller output of these extremely fast pressure loops should go directly to a variable speed pump via a high resolution input card with a suitably designed variable speed drive with minimal velocity limiting and no deadband. In some cases, the pressure loop should use an analog electronic controller or a DCS with special fast scan and execution time.

Inline (e.g. pipeline or static mixer) pH loops have a response almost as fast as the flow loop. The pH loop must consequently be detuned to be slowed down enough to satisfy the cascade rule. Also, the flow loop often lacks the rangeability needed for pH control and flow ratio control is inexact at best due to the extreme effect of immeasurable changes in feed concentration. Most inline pH loops perform better if their output goes directly to a final element with good resolution and minimal deadband. The exception is when there are Coriolis feed and reagent flow meters, a relatively constant feed composition, and the pH set point is on the relatively flat part of the titration curve making mass flow ratio control more sensitive than pH. If there is no flow feedforward, a "head start" to momentarily preposition the valve or the use of signal characterization helps the pH loop deal with startup and large load disturbances.

In some cases, the process gain of an equal percentage valve characteristic, which is proportional to throttle flow rate, compensates for a process gain that is inversely proportional to load (e.g. feed rate). The most common cases are inline concentration and pH control and heat exchanger temperature control. The use of a secondary flow loop removes this compensation of the process gain making the primary loop more nonlinear.

In part 2 we look in greater detail at the cascade rule, the use of reset in the secondary loop, and how dynamic reset limiting with external reset is a powerful tool for cascade control. In part 3 we conclude with the "Rules of Thumb" summary for cascade control.

The idea of a lag in the control loop just sounds bad but are lags always bad news? The popular consensus is yes. Could a lag could be your best friend despite its bad rep?

I was instructed in a graduate class on distillation column modeling and control decades ago that the big problem with columns is the big process lag. This didn't sit well with me but I didn't stand up and object. I concluded there seems to be a lag in the understanding of lags.

If the lag is a process time constant in the input path of the disturbance into the process, it is actually beneficial. This process time constant slows down the effect of a disturbance and gives a chance for controller to catch up. The controller gain for the ultimate performance of most tuning methods is proportional to the ratio of the largest time constant to the total loop deadtime as seen in Appendix C of New Directions in Bioprocess Modeling and Control. BioprocessModelingControlBookAppendixC

Furthermore, the peak error from a disturbance for this tuning is inversely proportional to this ratio of time constant to total loop deadtime as shown in Equation 2-40 in Chapter 2 of Advanced Control Unleashed. This ratio is about 5:1 for distillation column temperature due to interactive process lags. In other words, you can anticipate a 5:1 reduction in error by closed loop control for a step disturbance. For well mixed crystallizers, evaporators, and reactors this ratio could be 50:1 or more. This leads to permissible controller gains much larger than we are accustomed to using.

Often there is a similar type of process lag on the path of the manipulated flow into the process used for correction of the disturbance. When reflux rate is adjusted directly or indirectly to compensate for a feed disturbance and the temperature used for control is about half way between the feed tray and the top of the column, the process lags could be about the same. If the tray for temperature control is closer to the feed tray, the feed upset would be seen before the correction can arrive, not a good deal.

For continuous composition and temperature control of well mixed volumes, the process lag is approximately the residence time (volume/flow). This process lag is in the path of both the disturbance and the manipulated flows and temperatures. The process delay (process deadtime) is usually quite small relative to the process lag except for neutralizers where small reagent flows cause incredibly large injection delays.

When the process lag also exists in the path of the manipulated variable, it is important to use a high controller gain to overdrive the controller output so the loop can catch up to the disturbance.

Large process lags from large process volumes smooth out oscillatory disturbances from poor control or limit cycles and are the principal reason why we don't see as much variability in storage tanks as we might expect. Equation 3-4 in Chapter 3 of Advanced Control Unleashed can be used to predict this attenuation of oscillations by process volumes.

If there is a final element lag (e.g. slow valve or positioner) or there are volumes in the path of the manipulated flow that don't exist in the path of the disturbance, then the controller can't react to a disturbance fast enough. If there is a measurement lag due to a sensor lag (e.g. thermowell lag) or DCS filter (e.g. AI or PID PV filter time), then the controller can't see the disturbance fast enough. What is hideous and not well recognized is that the time constant in the tuning equations is for the largest time constant in the loop and doesn't matter where it is located in the loop. If the largest time constant is in the measurement, the user is seeing an attenuated version of the real process variable. An increase in the measurement lag allows the user to increase the controller gain. The oscillation amplitude may also look smaller due to filtering. The key indicator is an increase in the oscillation period. Not all measurement lags are bad. A small judiciously set PV filter to keep measurement noise from causing fluctuations in the controller output greater than the valve resolution can prevent self-inflicted disturbances from reaction to noise.

While control textbooks show step disturbances, most process disturbances have a process lag because they are the result of control loop reset action and valve throttling and are smoothed by intervening volumes. The worse case disturbance is a manual action by an operator, a discrete action by a batch sequence or interlock, and an on-off flow from level switches or an overly aggressive level controller that directly feeds into an important unit operation. The best bet is to slow down the disturbance, and then use a properly tuned PID and MPC and add feedforward control.

Lags slow down the set point response and make tuning a test of patience. However, if you tune the controller with a gain close to the maximum permitted by the use of small Lambda factors or simplified internal model control tuning per Appendix C, the closed loop time constant can be made much less than the process time constant by overdriving the output past its resting point. This is only true if the PID structure chosen has proportional action on error so the loop kicks the output from the set point change. If the setpoint change is large enough to saturate the loop output, you don't see the full boost in the response from the higher controller gain. If rapid changes in controller output upset the operator or another loop, set point velocity limits or filters can be employed but these limits or filters should not be used on the secondary loop of a cascade control system.

To summarize; a lag in the disturbance path on the input to the process should be maximized and lags anywhere else should be minimized for disturbance rejection. If the largest lag in the loop is much larger than the total loop dead time, the Lambda factor should be set less than one to give higher controller gains for a faster response. Tuning tests take a long time for long process lags and people get frustrated but if the higher controller gains that are permitted are used, the results can be great.

Line "D" of a pet food plant never operates as well as the other lines. Line "A" has the best performance. The operators for line "D" say that line "D" is different and it can't do better. When a line "D" operator gets sick, a line "A" operator fills in on Line "D". Line "D" begins to do as well as line "A".

A builder and operator of ethanol plants puts process metrics on the operator screens for each plant that are viewable by operations at all of the plants. The competitive nature of people kicks in and all of the plants start to do better.

The energy cost for a lime kiln is displayed online. Model predictive control (MPC) is installed and the energy costs drop by 10%. Projects are started to install MPC on all of the lime kilns.

Online process metrics can blow away war stories, motivate operators, increase the on-stream time of advanced controls, justify process control improvements, and develop correlations between key performance indicators and operating conditions. For example, processes may show daily and seasonal performance variations because of the change in feed and cooling water temperatures. Also, process may run better or worse at night and or weekends and holidays depending upon whether automation, maintenance, and process engineers are supporting or distracting and interfering with operations.

However, the implementation is not necessarily straightforward. Process metrics can show us something essential but we may not always like what we see. The president of an MPC company years ago was unequivocally against online process metrics because they may initially take a dive when the MPC is turned on.

I installed online metrics of base reagent cost to show the advantage of adaptive pH control for neutralization of an acidic waste stream. The tighter control increased the reagent costs for disturbances that drove the pH below set point or for increases in the pH set point because the addition of caustic was larger and sooner driving the pH to the set point faster. For disturbances and set point changes in the opposite direction the tighter control decreased the reagent costs. So is tight control right or wrong and are process metrics in this case not useful? If there is a penalty for being below set point, it should be added to the online cost metrics. If not, the controller should be tuned with a lower gain when the pH is below the set point.

Consider a batch operation where the process must be heated up before a reaction occurs. A control system that gets temperature to set point fast will increase the steam use per batch by overdriving the control valve past its resting position. The question is whether the reduction in batch cycle time is worth more than the increase in steam per batch.

You cannot control what you can't measure. To control plant profitability we need to have the automation system and computations to put process metrics online. Undoubtedly, improvements will be needed to the metrics and to the automation systems that affect them. Filtering and averaging will be needed to screen out noise and delays added to make process inputs coincide with process outputs. New measurements and valves will be needed. Throttling valves with better deadband and resolution can reduce limit cycles. Coriolis meters can provide accurate flow measurements and inferential measurements of stream compositions important for yield, quality, and production rate calculations. Ambient, piping and equipment wall, feed, and coolant temperatures can help provide indications of previously unknown adverse effects.

I see a future where the cost and revenue per production rate, batch, shift, day, night, week, month, and season besides yield and on-stream time are displayed for each production line. Data analytics will be used to develop correlations for projections to latent structures or partial least squares (PLS) to provide predictions of process metrics online and to provide a drill down to contributions most affecting the metrics for better process understanding. The trends and future predictions of these metrics immediately translate to improvements and eventually "closing the loop" for plant profitability. I expect an MPC will be developed to use process metrics as controlled variables and the principal components as manipulated variables.

It seems to me online process metrics are the key for a manufacturer, process control group, and automation company to thrive in a competitive worldwide economy. Loop tuning and performance is just the beginning. As automation engineers we tend to think of the loop as the "end all." We need to get outside of the box that is the loop to prevent islands of automation. We need to think in terms of unit operation control and how these units interact to affect the process as a whole. We need "oneness" guided by process metrics as introduced in my control talk column. This is the moment.

http://www.controlglobal.com/articles/2008/287.html

For a list of some items we need, see slide 57 in my presentation.

http://www.emersonprocessxperts.com/archives/2008/11/assessing_oppor.html

I think the future is advanced process control (APC). My definition of APC is any technology that puts process knowledge on the line online. Feedforward control is APC when the feedforward gain and dynamic compensation are based on process knowledge. On-demand and adaptive auto tuners, such as DeltaV Insight, are APC tools because these tuners identify the process dynamics that are useful for process diagnostics and training besides model based tuning. For example, the process deadtime can be monitored as an indicator of heat transfer surface fouling in temperature loops and the dynamics can be inserted in simulations for operator training and scenario testing and prototyping of PID enhancements (e.g. set point filtering and structure) or Model Predictive Control (MPC). There are many higher level technologies. In a recent presentation I made to a major chemical company I showed these technologies, the results from a benchmarking study of the top ten companies in the use of process control, and practical tips on how to conduct an opportunity assessment. The presentation can be seen at:

http://www.emersonprocessxperts.com/archives/2008/11/assessing_oppor.html

Slide 8 shows the pyramid of technologies that includes process performance monitoring (data analytics and process metrics), abnormal situation prevention, property estimators (inferential composition or quality measurements), model predictive control (MPC), rampers and pushers to maximize or minimize a controlled variable (e.g. feed rate), linear programs (LP) for optimization given defined constraints and economics, and real time optimization (RTO) for variable constraints and economics. The importance of process knowledge in all of these technologies is obvious. Slide 9 gives a straightforward "easy to remember" relationship between controller tuning for loop performance. The equation indicates before, during, and after APC implementation, the controllers should be tuned.

The amount of effort and the performance of the upper level technologies rest upon the strength, breadth, and integrity of the foundation of basic control. As you improve the number, type, and sensitivity of the measurements and control valves, the performance of these systems improve by reducing the number of unidentifiable disturbances and enabling more first principle calculations and inferential measurements, such as frosting rate, fouling rate, crystallization rate, and reaction rate important for diagnostics and batch profile control as discussed in a recent article in Control magazine.

http://www.controlglobal.com/articles/2008/230.html

Decades ago, field pressure and temperature gages were installed. These were not very accurate. prone to be broken, and obviously were not visible in the control room or historized. With wireless, we can afford to get many more measurements into the control system. Wireless measurements offer the opportunity to provide many of these missing measurements at a reasonable cost. However, the choice of measurements for data analytics (principal component analysis and projection to latent structures) must be judicious. Randy Reiss, the developer of online data analytic algorithms for Emerson, says "more measurements for analytics means more correlations. However, it introduces the possibility of dominate correlations that do not relate to product quality. That would skew the model for the worse. So there is a double edge sword there."

For portable bioreactors, laboratory analyzers, and sterilization systems, wireless adds flexibility and utility. Wireless access to process and loop performance monitoring systems in the field makes troubleshooting much smarter. Wireless access anywhere to virtual plants with process performance scores for university courses on process control makes learning almost like a video game. There are many more applications for wireless than the monitoring of remote tanks and pipelines. The following Control Talk column slated for the December issue of Control magazine discusses the role of wireless in APC.

WirelessControlTalkColumn

Randy Reiss's list of the "Top Ten Reasons You Will Go Wireless Next Year" in the above column provides a reality check in case we are thinking of making everything wireless. This list has the insight, bite, and humor typical of the lists Randy has contributed to my column in recent months. Upon reading the draft of the column, Randy said "it's the best argument I have heard for wireless." Randy agreed to the post of this quote after checking with his PR agent.

Scott Broadley, the president of Broadley-James, is participating in a beta test with Emerson on the use of wireless transmitters on portable single use bioreactors (SUB) whose size is steadily growing from pilot plant (100 liter) to production (1000 liters) scale. Scott is also looking forward to the elimination of ground loops and noise by wireless pH transmitters particularly where the solution ground is not used or where AC noise gets through the power supply. Scott says, tongue-in-check, "we could hook the pH and DO transmitter up wirelessly to a Twitter account so your cell phone is getting constant text updates on how your bioreactor is "feeling" . Scott offers the following additions to the top ten list for going wireless.....(11) Each bioreactor can have its own Face book page where operators from different shifts can leave their comments......(12) Each transmitter can be on Twitter and send you instant text messages on your phone when it is moody..."

This week I completed a model with the help of Roger Reedy that allowed me to confirm some concepts besides detail how the design of the control system can cut a project cost almost in half by the use of 10,000 gallon instead of 40,000 gallon neutralization tanks. It wasn't an easy pH control application but not many of them are. The titration curve slope and the hence process gain changed by a factor of 1,000:1 from the extremes of the pH scale range to the neutral point. The influent pH could swing from 12 to 2 pH during the regeneration of a demineralization system or an area pump out. The disturbances could be fast because of plug flow, batch sequences, manual operations, and the stick-slip action of control valves. If pH control is not your thing and it is "High Time We Went" per the Joe Cocker song I am listening to, here is the escape clause.

"Besides embedded process models saving projects a chunk of money, improving plant performance, and justifying better controls and valves by studying the dynamics and integrated functionality of the process and automation system design, you can learn neat stuff like:"

(1) Speed besides size is important
(2) Feedforward signals can do more harm than good
(3) Feedforward head starts based on deltas can help
(4) Linearization of the process variable can be robust and useful
(5) Valve stick-slip can be the upset that keeps on giving

If you are caught within the gravitational pull of this study, I can't guarantee it is not a black hole that sucks you into another dimension.

A process model constructed and embedded in the DCS was used to study a conventional pH and a reagent demand control system with and without feedforward control. In all cases the control loop was in the recirculation line of a vessel to provide a fast feedback correction of abrupt and large disturbances. The feed and reagents were injected at the inlet of a static mixer just before the recirculation stream reentered the vessel. Middle signal selection of 3 pH electrodes was used to inherently ignore a single sensor failure of any type, reduce measurement noise, ignore spikes and slow sensors, and facilitate online diagnostics and calibration. The inline control loop was extremely fast. The transportation delay was only about 2 seconds. The largest potential source of deadtime was injection delay associated with opening and closing of the reagent control valves but this was minimized by coordinated action of close coupled isolation valves at the injection point. Insuring model fidelity for a pH system simply came down to matching the slopes of the model's titration curve with the slopes of the plant's lab titration curve. The following file shows the model and lab titration curves on slides 1 and 2 and the control system on slide 3. Not readable is the slope of 0.015 at 2 and 12 pH.
pH System02 Study Results

First you need to get good lab curves by taking samples of the influent at key times such as steps in a batch sequence when acids or bases are used or during unusual operations such as the pump out of containment areas. The samples should be at the process temperature and titrated with the same reagents used in the automation system. The sample time, temperature, and volume and reagent type and strength must be noted and reagent addition volumes and pH must be tabularized. The typical graphical plots of titration curves showing a vertical line between 3 and 11 pH are next to useless.

The feedforward signal and linearized process variable for reagent demand control were created by use of the same signal characterizer block where the input array was pH values and the output array were corresponding X-axis values per the titration curve. The X-axis was scaled 0 to 100% for the Y-axis and the pH measurement scale of 2 to 12 pH. The first input to the signal characterizer for feedforward control was influent pH. The first input to the signal characterizer for reagent demand (feedback control) was static mixer outlet pH. The second input to both signal characterizers was the pH set point.

Since influent pH measurement errors as small as 0.04 at 2 and 12 pH can cause feedforward errors of 20% or more per the titration curve, it was decided that continuous adjustment by means of a pH feedforward signal could be making large incorrect changes in the reagent flow. It was reasoned that large changes computed in feedforward signal due to large changes in influent flow or pH could be useful as a delta head start to pre-position the valves for the start of a large upset and then let the feedback controller do its thing. This proved to be the case although the feedforward was complicated by the blend of the recirculation stream with the influent at the inlet to the static mixer. Unfortunately, the accuracy of the feedforward curve depended on the accuracy of the titration curve.

Reagent demand control does not deteriorate significantly for changes in the titration curve because only relative changes in the slope are important for linearization and any information is usually better than no information about the shape of the curve. Reagent demand control uses the X-axis of the titration curve scaled as a 0-100% process variable and set point. This control ignores the pH fluctuations near neutrality because these correspond to very small changes in reagent demand due to the steep slope. Reagent demand control also recognizes the true distance of the influent from the set point, which is important for startup and well as disturbances.

Results of the auto tuner showed that the pH controller gain needed to be very low (e.g. 0.02) because of the high process gain from the steep slope of the titration curve at the 7 pH set point. The reagent demand controller gain could be 10 times larger (e.g. 0.25) - see slides 5 and 6 for screen prints of auto tuner results.

A comparison of the conventional pH and reagent demand control is shown on slide 7. The spikes in the static mixer pH are caused by 0.4% stick-slip of the water valves. If the resolution of the water valves was improved from 0.4% to 0.1%, the spikes went away. If the resolution of the acid and base valves upstream or at the static mixer deteriorated from the specified 0.1% to 0.4%, there were many more spikes from the limit cycles of these valves. Normally, a 0.5% resolution control is consider good. This is not so for high process gains. Neutralization systems with pH set points near neutrality are excellent indicators of actual valve resolution and a perpetual stick-slip limit cycle. If you want to know more, check out "Improving pH System Design and Performance" at the Emerson Global Users Exchange this September and the Chemical Processing article on control valves last October.
http://www.chemicalprocessing.com/articles/2007/200.html

I dug up the following myths from my April 2006 Control Talk column in Control Magazine. I am into recycling and going green. In fact these myths may be a bit moldy.

(26) Auto tuners can compute controller tuning settings with an accuracy of more than one significant figure. Act surprised when unmeasured disturbances, load changes, valve stick-slip, and noise cause each result to be different. Look forward to the opportunity to play bingo with the second digit.

(27) You can just dump all your historical data into a neural network and get wonderful results. Forget about the same stuff that cause auto tuners to have problems and use variables drawing straight lines because anything that smooth or well controlled must be important. Use the controlled variables (process variables) instead of the manipulated variables (controller outputs). Don't try to avoid extraneous inputs or identification of the control algorithm instead of the process. If you want to purse a career in data processing, use every variable.

(28) Models can predict a process variable that is not measured in the field or lab. Great way to spur creativity in training a neural network and validating a first principal model plus it has the added bonus of the model never being wrong. Wait till your customers figure out something is wrong with the composition of your product. Discount as hearsay any suggestions that even the best models need periodic correction.

(29) To reduce variability in process outputs (temperatures and compositions), keep all the process inputs (flows) constant. Keep believing that you can fix both the process inputs and outputs and don't accept the notion that process control must transfer variability from process outputs to process inputs to compensate for disturbances.

(30) Positioners should not be used on fast loops. This was true for the good old days of pneumatic positioners and analog controllers. Surely, digital positioners with tuning settings and digital control system scan times can't make the original theoretical concerns less important than the practical issues of real valves. If you would rather believe the controller outputs are the actual valve positions, and just want valve problems to slip by, save some bucks on your project and only put positioners on slow loops. Just don't stick around for start up.

Myths are a fertile topic maybe because of all the fertilizer in process control. You can make almost any point you want, by changing what are often obscure details on process and automation system dynamics. For example, you can show a variable speed drive can do better or worse than a control valve. The results can easily be swayed by VSD settings (e.g. deadband and rate limiting), VSD options (e.g tachometer feedback and vector control), and valve type\accessories (e.g. throttling sliding stem or rotary isolation valve and digital dual relay positioner versus pneumatic spool positioner). For insights into the relative merits of the VSD versus control valve in terms of control loop performance, check out the February Control Talk column in Control magazine titled "Deal or No Deal."

I promised to post this week the development of equations that are a myth buster. The equations show there is an implied dead time greater than the actual dead time in most loops because the controller is tuned to be slower than what is shown in academic articles and papers. Control loop performance does not appreciably deteriorate until the actual dead time exceeds the implied dead time. The equations go on to provide an estimation of the peak and integrated absolute errors for the implied and actual dead times for step disturbances. The effect of the slowness of real life load disturbances can be roughly included by adding the load disturbance time constant to the process time constant in the equations for the peak and integrated errors. The first page appeared in a blog and Control Talk column in 2006. This updated document has better explanations/nomenclature and adds a second page for the estimation of the peak and integrated errors. When I bounce out of negative free time, I will do an application note to study the accuracy and implications of the equations. Next week we will continue on with mythology.

ScanTimeEffectonPeakandIntegratedErrors

Process control is rich with mythology probably because what happens in the field is pretty remote from what was described in control text books. It is best summed up by a button given to me by my daughter 25 years ago that says "Reality Reeks." Here are some myths that come to mind this Friday evening after updating a simulation library whose main threat is reality.

(1) Decreasing the scan time will improve control - for slow processes and older DCS with 12 bit A/D for I/O, the faster scan reduces the signal to noise ratio. This was particularly a problem for temperature loops that used thermocouple input cards with large spans. Often the noise from A/D chatter precluded the use of rate action even though these loops had significant second order time constants. The more prevalent reason a reduction in scan time may have no impact on control is the implied dead time from the use of current tuning practices as seen in the next myth. You can estimate how much dead time you can add before you see an increase in integrated absolute error for a load disturbance. Next week I will show the development of the equations that predict the implied dead time and the impact on peak and integrated error when the dead time added causes the total actual dead time to exceed the implied dead time. The dead time for a load upset from an unsynchronized scan time can be estimated to be on the average the latency plus one half of the scan time.

(2) Controllers are tuned for rapid set point response - controllers are tuned slower than what is shown in nearly every academic paper and book. This slower tuning creates an implied dead time that is greater than the actual dead time. Intuitively you can visualize this effect by considering as the tuning is slowed down more and more, the loop approaches manual control where the dead time for automatic corrective action is infinite. Whenever articles show the improvement from reduced dead time, the controller is retuned for best response to take advantage of the better dynamics.

(3) Unmeasured disturbances are a side issue - if there were no unmeasured disturbances, control would be a non issue because you could home in on the controller output that corresponds to the desired set point for a process variable. You would just need to run some data fitting algorithm one time and the loop would be set for the life of the process. In reality, there are always unmeasured disturbances.

(4) Disturbances enter directly into the measurement - in almost every process I have worked on the disturbance gets into the loop via a process input. For example, changes in raw materials to a reactor are feed inputs that go through the mixing and reaction process before they appear in the reactor temperature. If the upset enters downstream of the process, it is noise to me.

(5) Disturbances are step inputs - this is the case for almost every published analysis of control loops but in the real world, except for on-off control, there is nearly always a load disturbance time constant whether it is due to reset action in the culprit controller or the mixing time of a volume (even unagitated vessels have some degree of dispersion even if it just from temperature or concentration gradients).

Who is living with the process every minute? Who changes the feed rates or charges? Who changes the modes and set points of the control system? Who starts or stops batches or unit operations? In most plants, it is the operator, yet the displays and education of the operator haven't changed much in the last 20 years. We still have faceplates, trend charts, and digital values of process variables, and changing or flashing colors or shades. We still have minimal operator training based more on tiebacks and interface familiarization than on first principles and process understanding.

If the operator knew the yield and cost per pound of product for the last eight hours of each shift, the operator could be more recognizant and probably more competitive. This could be achieved by flows that are synchronized, shift totalized, and ratioed with dollar amounts assigned for each flow. Consider a reactor and an 8 hour shift. Here the total flow of each reactant and utility for the last 8 hours would be ratioed to the total product flow for the last 8 hours for each shift. Each flow total would be multiplied by the cost of the stream ($/lb) to provide cost to product ratios for the last eight hours. The reactant and utility flows could be delayed to match them up time wise with the product flow. The use of totals for the last 8 hours reduces the accuracy requirement of this synchronization besides decreasing noise. The use of ratios decreases the effect of production rate on metrics. Also, changes in ratios offer keys to tracking down disturbances and changes in concentrations of feeds (e.g. raw materials, intermediate, or recycle streams). Both totals and ratios for each shift could be indicated. Shift metrics could be treated similar to batch metrics where each shift is like a different piece of equipment running the same batch process. The shift metrics could be plotted similar to batch metrics.

For waste pH systems, it would simply be the total reagent flow ratioed to the total effluent flow ratio for the last eight hours. I developed a real time virtual plant in DeltaV using this concept a couple of years ago to show the value of adaptive controller tuning for pH control. If you want a copy, contact your rep.

The concept could be expanded to use totals to cover the last week or month or the last "n" number of batches for each shift and all shifts.

If the operator could plot these ratios versus changes in operating points, what insight could be gained on process nonlinearity and for process optimization? What if the operator had XY plots, worm plots, and 3-D plots built into the operator graphics for all historized variables like what engineers generate in Excel and statistical packages?

When comparing the performance of similar plants in the USA and Belgium, it was found that the Belgium plants had consistently better yields. The Belgium operators lead the design of experiments and guided the process improvements. Could better online performance metrics and process training be the key for operators to perform roles of the increasingly scarce process and process control engineers?

In analyzing loops in the control room, the story for me was more in the controller output. Yet data analytics tend to focus mostly on primary process variables.

The clues to the significance of the controller output as a source of information are in its job and its action. The job of a feedback controller is to transfer variability and offsets in the controlled variable (process variable) to its manipulated variable (controller output) whether they originate in the sensor, process, or valve. The PID controller output is the result of proportional, integral, and possibly derivative action. Thus the trend of the PID output can contain information on the duration of a shift, approach to set point, and the rate of change of the process variable. Several examples help illustrate this concept.

A trend of a pressure controller's output showed it varied significant from day to night. It was later confirmed that there was a day to night temperature induced shift in the calibration of the transmitter.

The shifts in the steady state value of the reflux to feed ratio manipulated by a column temperature controller and the reagent to influent feed ratio manipulated by a pH controller were found to coincide with the replacement of the sensors.

For a batch reactor, a larger and earlier dip in manipulated jacket inlet temperature and peak in manipulated vent flow corresponded to a higher heat release and secondary product vapor flow from a more concentrated reactant. In other cases, a higher makeup coolant flow manipulated by the jacket temperature controller coincided with a higher cooling tower temperature.

For a continuous reactor, a larger variability in the vent valve position at higher rates was discovered to be caused by a significant flattening of the installed characteristic of the butterfly valve above 50% open.

Sustained equal amplitude saw tooth and sinusoidal oscillation in the controller output were deduced to be indicative of a limit cycle from stick-slip in a self-regulating loop and deadband in an integrating loop, respectively.

A study of the control of reactor feed flow showed that an inadvertent change in the time interval used for the calculation of the loss in weight flow measurement created a shift in the feed controller output (manipulated speed of the positive displacement pump).

If the controller gain is higher than one or rate action is used, noise in the process variable will be amplified in the controller output. If the peak to peak noise in the controller output exceeds the dead band and resolution of the valve, the controller is inflicting disturbances upon itself or other loops.

Noise also makes it more difficult to see the change in the pattern of the controller output due top changes in process inputs. Thus, whether the analysis of the controller output is done visually or by multivariate statistical process control, the reduction of noise by better doing tuning and filtering is important for batch and continuous analytics.

When there are set point changes, there is also significant information in the pattern of the process variable (e.g. approach, overshoot, and settling of reactor temperature). In a way this consideration is consistent with the above concept in that the set point and thus the process variable is being manipulated by a batch or startup sequence. Similarly, the process variables of loops manipulated by cascade or model predictive control are important.

Unfortunately most of the examples in literature for batch analytics are for process variables of manual or missing controllers. For example, a significant downward trend in dissolved oxygen (DO) is often shown for batch analytics of a fermentor when in fact DO would be controlled at a set point and the story would be in the manipulated air flow.

The concept of transfer of variability from controlled variables to manipulated variables for analysis of batch profiles is emphasized in the book New Directions in Bioprocess Modeling and Control (ISA, 2006).

The process variable of greatest interest in a process stream is generally concentration. If the final concentration is not right, not much else matters. Yet online analyzers are few and far between. Plant analyzer groups have been cut back or allowed to disappear through attrition. While I think the staffing of these groups would be more than justified, given the current situation analyzers are needed more than ever that can be maintained and supported without special expertise.

Coriolis mass flow meters offer exceptionally accurate mass flow and density measurement with minimal maintenance when properly selected and installed. Many compositions can be better controlled by a more accurate mass flow ratio and the stream density can be used to provide an inference of changes in the stream composition. For specialty chemical and pharmaceutical, the potential number of Coriolis flow meters is quite large because the pipe sizes are small and the value of the product is high.

We loose sight figuratively and literally that the concentration in any stream does not usually match what is listed on the process flow diagram. The concentrations are quite variable due to fluctuations or unknowns in the raw materials and in the unit operations and from cycling introduced by poor controller tuning and final element resolution. The effect of most of these disturbances is typically unmeasured. Often not considered is the capability offered by the Coriolis meter to track down a disturbance. For example, if the temperature corrected density of a feed has changed, it probably means the concentration of a raw material or intermediate has changed.

The extended use of Coriolis meters on feed, recycle, and product streams in any industry with reasonable pipe sizes for both continuous and batch operations seems to me to be one of the biggest straightforward opportunities.

One of the simplest and easiest techniques to evaluate and implement is set point feed forward. The maximum feedforward gain is the inverse of the process gain. You just need to make sure the process gain is converted to the same units used for the feedforward gain and only a fraction of the maximum is used as the actual feedforward gain to allow for nonlinearities, errors, and PID action. New adaptive controllers such as DeltaV Insight can find and schedule the process gains and hence the set point feedforward.

If the controller gain is large (> 1.0) and the controller structure has proportional action on error, set point feed forward has little value because there is already a sizeable step in controller output from a set point change. However, large dead times can cause low controller gains. Here, set point feedforward can get you to a set point much faster, which can be useful for loop set points driven by batch, cascade, or advanced control.

In many of the plants I worked in the production capacity had been increased over the years by a series of debottlenecking projects. Unfortunately the surge tanks volumes were not increased probably because of a lack of understanding of dynamics. Consequently, unit operations upstream and downstream of the surge tank had to be decreased because of high and low levels, respectively. Also, abrupt changes in the surge tank's discharge flow which are unavoidable as these level limits are approached were disruptive to nearly every type of unit operation.

If batch units or continuous units that are going up and down are dumping into a surge tank, you have a tough scenario to achieve both maximum availability of the surge volume and maximum smoothing of the outlet flow by feedback control alone. Notch gain and error squared level controllers can help but are difficult to tune. Also, low controller gains cause slow oscillations from reset action unless the reset time is also increased so that the product of the reset time and controller gain stays above a minimum. The fact that a low PI controller gain for an integrating process, such as level, can cause oscillations is not well recognized. For more details on this source of oscillations see the equation on page 109 of Good Tuning - A Pocket Guide (2nd Edition) and Equation 3-3j on page 81 of New Directions in Bioprocess Modeling and Control published by ISA. These equations are consistent once you consider the maximum integrating process gain is the inverse of the fastest full scale ramp time.

One solution is to add a velocity limited feedforward. For a surge tank level controller that manipulates the tank's discharge flow, the total flow of all units that are dumping into surge tanks is a feedforward signal to set the discharge flow. If the flow engineering units are consistent and there is cascade control of level to discharge flow the feedforward gain is one. The big question is what is the velocity or rate limiting needed to spread the disturbance from batch and on-off operations over the available surge volume.

A material balance and dimensions of the tank can be used to compute the velocity or rate limit on a first principle basis. The attached file shows the calculation and implementation in a graphic representation of a Function Sequence Table (FST). Furthermore, the calculation offers continuous directional adaptation of the velocity or rate limit. The only adjustment is to set a filter time for the feedforward measurement that is equal to the normal time that the feedforward flow could be zero. For a single batch operation upstream, this time would be the batch cycle time plus the normal time between batches. For more info on this technique see Appendix B - Batch to Continuous Transition in Advanced Control Unleashed published by ISA.

BatchToContinuousTransition

Maybe I have just been unlucky or maybe the plants I worked in were as stressed as the typical project schedule these days but often for column temperature, boiler level control, and neutralizer pH, feedforward control didn't live up to expectations. The problem was abusive unmeasured disturbances. The feedforward signal is often flow, which doesn't tell the whole story. If there were only flow disturbances, life sure would be simple.

Consider a distillation column with a feedforward of feed flow corrected by a tray temperature controller output that manipulates steam flow. For an increase in feed flow, the feedforward initiates an increase in steam. Seems great but what if the tray temperature is rising because of a change in feed concentration Adding steam adds to the rate of rise of temperature toward a possible product spec limit plus wasting steam.

Consider a boiler drum with a feedforward of steam flow corrected by a drum level controller that manipulates feed water flow. For an increase in steam flow, the feedforward initiates an increase in feed water flow. Technically sound but what if the drop in drum pressure causes a swell from the expansion of bubbles that is headed for the high drum level trip point set to prevent liquid carry over into the steam header. Adding hot feed water adds to rate of rise of level and the possibility of boiler shutdown.

Consider a neutralizer with a feedforward of acid waste flow corrected by a pH controller that manipulates a basic reagent flow. For an increase in waste flow, the feedforward initiates an increase in base flow. Seems smart but what if the pH is rising because of a decrease in acid concentration in the feed. Adding base adds to the rate of rise of pH toward a possible environmental limit plus wasting reagent.

A smart technique would preemptively correct the feedforward signal by subtracting a signal that is the rate of rise of the filtered rate limited controlled variable multiplied by an adjustable factor. Here, the correction for a positive rate of change only occurs when the controlled variable is above the set point plus some noise band. The correction for a negative rate of change only occurs when the controlled variable is below the set point minus some noise band. In each case, the feed forward is corrected to help deal with an unmeasured upset. If the controlled variable is near the set point, the controlled variable is lined out, or the unmeasured upset is driving the controlled variable back to set point, the feedforward correction is zero. The adjustable factor like the feed forward gain can be initialized based on first principles (e.g. material, component, and energy balances). Note that the above scenario is for a reverse controller and a direct feed forward action.

Concentration and temperature measurements of the feed may help make the feedforward signal calculation inherently smarter and reduce the number and size of unmeasured disturbances. But, there can be extenuating circumstances. For example, cold feed water would cause bubbles to collapse and the inverse response that might counteract steam flow induced shrink or swell. Also, a pH feedforward based on a pH measurement of the incoming waste may do more harm than good because the electrode error and failure rate in low or high pH streams is larger and error in reagent demand greater because the titration curve is flatter. Testing is always a must before putting even the best idea online.

When does feedforward control do more harm than good? Are there smart techniques to deal with these situations so feedforward is not permanently disabled in a PID controller?

If the feedforward correction arrives too soon, there can be an inverse response where the initial reaction seen in the controlled variable is in the opposite direction of the effect of the disturbance. This causes the feedback controller to make a move in the wrong direction. The solution is to add a delay to the feedforward signal so its correction arrives at the same time or a little bit later than the disturbance at a common point in the process. If the feedforward arrives way too late (after the feedback controller has returned the controlled variable back to set point), the feedforward creates a second disturbance. If the lateness is due to a lag in the feedforward path, a lead-lag can be added to the feedforward signal for dynamic compensation. If the lateness is due to a transportation delay or dead time in the feedforward path, the delay or dead time must be reduced by making changes to the process or the feedforward measurement choice or location.

Excessive feedforward measurement noise can show up as an increase in variability of the controlled variable. A simple fix is to add a filter to the feedforward signal with the filter time set to keep the fluctuations from the feedforward noise in the controller output within the resolution limit of the control valve. If the feedforward measurement is below its low rangeability limit, its signal can become bizarre. This is a common problem with flow measurements. The best solution is to use a better sensor and transmitter technology and scale range, but given you are stuck with the situation, the feedforward action can be programmatically turned off when too erratic. Sometimes flow controller set points instead of flow measurements are used to get around flow measurement noise and erratic behavior.

A more interesting problem is when unmeasured disturbances have caused a deviation in controlled variable that is in the same direction as the feedforward correction. Here a smarter technique would programmatically turn off the feedforward when its correction would make the existing control error worse. Next week I will propose some ways to predict this scenario.

It is important that the turning "off and on" of the feedforward action be bumpless, automatic, and tested. A dead band in the trigger for "off and on" is advisable. Finally, model predictive control inherently deals with many of these issues through its use of disturbance variables.

Last week we discussed how a resolution limit (stick-slip) in a control valve can cause a saw tooth oscillation in the controller output for a self-regulating (steady state) process. For a flow or liquid pressure loop where the process time constant is small, the oscillation in the process variable (PV) is a square wave. For a gas pressure loop where the process time constant is significant, the oscillation in the PV is rounded. Note that if the resolution limit was zero, dead band in itself would not cause this oscillation for a self-regulating process. In real systems, the resolution limit is never zero, so oscillations exist but may be so small that they are lost in the noise or upsets.

For integrating processes such as level, a dead band will create a limit cycle independent of whether a resolution limit exists. In an integrating process, there is no steady state. The PV ramps unless the controller output exactly balances the load, which only occurs for a perfect valve and no disturbances or noise. The lost motion of the control valve from dead band (backlash) causes the PV to ramp until it has worked through the dead band. The result is a saw tooth in the PV whereas for self-regulating processes the saw tooth was in the controller output. While the dead band is never zero, the amplitude of the saw tooth of the PV can be so small it is lost in the noise or upsets.

Whether a valve limit cycle affects the product quality depends whether there is a back mixed volume down stream that filters (attenuates) the oscillation. The analogy in circuit theory works well here where the filtered amplitude for large filter times is proportional to the period of the oscillation and inversely proportional to the filter time.

For a well agitated vessel, the filter time is the vessel residence time (volume divided by throughput flow). Even if the vessel does not have agitation, turbulence of boiling mixtures, the entrance and recirculation of flows, and the migration of compounds from low to high concentrations results in significant smoothing of the oscillation. Thus, for chemical processes involving blend tanks, columns, evaporators, and reactors, the limit cycles typically have little economic impact for reasonably good valves (e.g. resolutions and dead bands less than 0.5%). The exception of course is pH, where the process gain and thus the amplification of a resolution limit can be extremely large for strong acids and bases. In fact, a reagent valve with exceptional resolution combined with advanced control techniques can eliminate a stage of neutralization and the associated equipment, piping, and instrumentation costs.

For pipeline composition control or sheet thickness control, limit cycles are not attenuated because there is essentially no back mixed volume. Oscillations readily appear in the final product and the impact of the valve response plays a more important role. Consequently, the pulp and paper industry is much more sensitive to valve problems.

For split ranged valves, the topic for next week, all bets are off.

If a control loop is oscillating, would it be best to increase or decrease the controller gain?

The standard answer of decreasing the controller gain is right for a decaying or growing oscillation in a relatively fast self-regulating loop (loop whose PV quickly goes to a steady value when in manual and disturbances have dissipated). If the oscillation is banging between set point limits of a secondary loop or output limits of any loop, then you could end up with an equal amplitude oscillation for an unstable loop and the best thing to do is to first decrease the controller gain until it settles down.

If the oscillation amplitude does not decay but is relatively constant and the loop is staying well within its set point and output limits, the oscillation is probably a limit cycle caused by stick-slip, or a resolution limit in the control valve. Decreasing the controller gain will not reduce the oscillation amplitude but will make its period longer. Over a narrow time range, this may make the trend appear smoother but the longer oscillation period is less filtered out by downstream volumes and is consequently more likely to appear in the product. Here a well mixed downstream volume divided by the throughput flow acts like a filter time constant.

If you have an integrating loop (a loop whose PV ramps away from the set point when in manual) or a runaway loop (a loop whose PV accelerates away from the set point when in manual), decreasing the controller gain can make the oscillation worse if you were below the low controller gain limit. Note that the oscillations are extremely slow and may not be noticeable over a trend for a single shift. The minimum controller gain for an integrating loop is approximately 4 divided by the product of the reset time and integrating process gain. The minimum controller gain for a runaway loop is approximately the inverse of the process gain.

For integrating loops, if you are near the limit, the controller gain should be increased if the reset time is decreased to prevent an oscillation, which is counter intuitive. With real processes, the dynamics can change so any tuning should be thoroughly tested and the user must be well below the high gain limit that causes instability. Lambda tuning prevents violating the low gain limit for integrating processes. To avoid getting too close to the high gain limit, Lambda must be larger than the largest possible total loop dead time.

There are many important types of loops that have an integrating response besides level, such as batch chemical and fermenter dissolved oxygen, pH, overhead pressure, and temperature. Extremely exothermic batch and continuous reactors (e.g. polymerization reactors) can have a runaway response.

We wind up our series on measurement and valve dynamics with the timely question do we want a large time constant anywhere?

A large time constant in the measurement or valve slows down what the controller can see and manipulate, respectively. A large time constant in the process slows down the effect of disturbances at the input to the process. It gives a chance for the controller to catch up. In fact the ultimate integrated absolute error is proportional to the dead time squared divided by the process time constant. Is this the whole answer?

The process time constant must be downstream of the manipulated variable otherwise this process time constant acts to slow down the effect of the controller's reaction to the upset similar to a slow valve. You can spot a slow valve or large intervening slow process time constant by a fast initial excursion from a disturbance followed by a slow recovery.

We have been talking about open loop time constants (time constant for an output change for a controller in manual). There is also a closed loop time constant (time constant for a set point change to a controller in automatic). We may want a fast closed loop time constant if this loop is a critical loop (e.g. reactor pressure) that doesn't upset other loops or this loop is a secondary loop (e.g. flow) in a cascade control system. If the action of this loop upsets other loops, then you can reduce the interaction by increasing the closed loop time constant of the loop.

Is smooth good? We like smooth trend charts but is that what is really going on in the process? Do we want a smooth talking measurement or the straight story?

My first clue dates back to a startup of a world class intermediate plant when smooth temperature recordings were traced back to sand in thermowells from when the pipelines were sandblasted during construction. Then, in a downstream plant a report came in that temperature sensors on extruders were to be now installed in large blocks of metal rather than the melt because the trends were smoother. Many years later in a lab, a biochemist proudly showed how he had smoothed out his temperature recording on the bioreactor by partially retracting the sensor in its thermowell. Finally, I heard horror stories about thermocouples installed in glass lined thermowells on exothermic reactors.

The concern is not restricted to temperature. Rugged (thick glass) and most high temperature electrodes are extremely slow. pH electrodes installed in overflow lines and behind baffles in a vessel have an environment so still that process buildup makes the electrode smooth out changes even when the flow restarts or the agitator speed is increased. Just a 10 millimeter film on a pH electrode can increase its response time from 10 seconds to 100 seconds. Coated electrodes are slow electrodes. Multiple electrodes should expose the foul up but then again the smoothest response I have seen was for 3 electrodes all installed with their protective caps still on.

The easiest way to slow down a measurement is to increase the filter time constant in the DCS. Here the sky is the limit particularly for pressure systems that blow their rupture disks. For some fast gas pressure systems, putting in a faster transmitter will make the trend recordings look worse even though the pressure loop is doing a better job because it is seeing the disturbances better.

For pressure, flow, and inline composition and temperature control, the measurement time constant is probably already the largest time constant in the loop. An increase in this time constant due to coatings or filter times not only makes the trend chart smoother but allows the user to increase the controller gain which furthers the deception. You and the controller are seeing an attenuated version of the real world.

Other time proven ways to make trend charts look smoother to impress friends and relatives is to increase the process variable scale range, decrease the time scale range, and increase the compression, update time, and exception trigger for data reporting.

A smooth loop could be good news or bad news, which leads me to my Top Ten List.

Top Ten Good News Bad News

(10) The good news is that smart instrumentation has been approved. The bad news is it is a dumb installation.
(9) The good news is that the control valves are not oscillating. The bad news is the loops are all in manual.
(8) The good news is the new project manager is a process control engineer. The bad news is you are the project manager.
(7) The good news is that all the process variables are drawing a straight line. The bad news is they are off scale.
(6) The good news is that digital positioners have been added to all of the control valves. The bad news is the position measurement is a "smooth talker".
(5) The good news is that the loops are no longer oscillating in automatic. The bad news is the plant is shutdown due to the loops being in automatic.
(4) The good news is that your group's name has "advanced" in it. The bad news is the name is "Advanced Aged Engineers".
(3) The good news is that you have reached the level to work through "others". The bad news is there are no "others".
(2) The good news is that you will have a creative new office. The bad news is it has virtual walls.
(1) The good news is that you have been offered a retirement package. The bad news it is a gift certificate.

If I had just 12 wishes for practices to improve pH control systems they would be:

1. Review and improve electrode design (glass thickness, shape, and formulation and reference type and electrolyte)

2. Check and improve electrode location for dead time (transport delay) and velocity

3. Review and improve calibration practices

4. Check and improve upstream loop tuning and valve resolution to reduce size and speed of pH disturbances

5. Verify valve resolution by small step tests

6. Improve valve resolution (add digital positioner, reduce seating and packing friction, and verify positioner feedback mechanism to ascertain it actually tracks internal trim position)

7. For great reliability, maintainability, and onstream time, consider middle signal selection of 3 electrodes

8. Check dip tube and injector design for time delay for emptying and refilling upon closure of reagent valve (consider reagent injection into recirculation line)

9. Check for fully filled reagent pipeline downstream of valve

10. For flow disturbances and startup, consider flow feedforward

11. For steep titration curves, consider linear reagent demand control

12. If tank is not mixed well enough for pH, consider adding inline system upstream or in a recirculation line

I originally considered the first item further down on the list because it takes time for a plant trial to confirm improvements for radical changes in electrode construction but then I considered the system is only as good as the measurement and the trial could be started while the other items are pursued. Electrode construction is particularly important for high temperature, high ionic strength, low water content, high pH, and low pH streams.

In my younger days I was presented with the critical need to make air compressor surge and electrical phosphorous furnace pressure control loops able to handle very abrupt and extreme disturbances. I vastly prefer the present to that present. These applications offered more excitement than engineers should be allowed to have.

The compressors provided the air feed to multiple exothermic reactors whose flow could drop enough on a reactor trip to trigger a surge in about 2 seconds. A surge every month would cause the other reactors to trip and cause accumulating damage and loss of efficiency in the compressor besides reactor downtime and a subsequent challenging startup of reactors and the associated waste oxidation boilers.

The phosphorus furnaces had to deal with what was called "controlled explosions" from sudden shifts in the ore around the electrodes (slag slides) that caused bursts of water vapor and CO2 besides phosphorus vapors and particles. The slag slides caused a pressure spike large enough each shift to blow the water out of the electrode seals. There were tubs of water around the furnaces to jump in if the hot phosphorous landed on you. Little fires would break out when you walked by the furnaces from your shoes scraping the phosphorous residue on the floor.

These were big problems in terms of both size (18 to 24 inch pipelines) and the safety implications besides the process efficiency and capacity considerations.

High speed recorder measurements of the of the compressor flow and furnace pressure response confirmed that the process dead time in both cases was essentially zero and the observed dead time was due entirely to the components in the automation system. I installed transmitters with a sensor response time constant of less than 0.1 seconds and controllers with a special scan rate of 0.05 seconds. I had to take some special precautions in the configuration to insure the controller loading would never have negative free time (a lesson as well for our personal lives).

The control valves were the largest source of dead time. The pre-stroke dead time and stroking time for the big actuators were estimated as the fill or exhaust factor for the actuator supplied by the valve manufacturer divided by effective fill or exhaust flow coefficient of the existing positioner. This yielded pre-stroke dead times ranging from 1.0 to 2.0 seconds and stroking times of about 10 to 20 seconds. A booster had a fill and exhaust flow coefficient that was 10 times larger than the positioner and therefore offered dead times and stroking times that were 10 times faster. However, I knew the actuator connection and air tubing would then become the restricting limitation, so I had these sizes judiciously enlarged in the field, I also added a position transmitter (before the days of Hart and Fieldbus positioners with position read back).

Armed with the rule "boosters instead of positioners should be used on fast loops" and a copy of the theoretical frequency response studies to back it up I arrived onsite for the compressor application and boldly insisted against the advice of the well seasoned instrument maintenance technician to replace the positioner with a booster on the compressor vent valves.

My confidence was shattered the morning the first surge valve was put in service. The flow transmitter showed the impending surge and the controller asked the valve to open. The valve responded by doing the worst possible thing. It slammed shut before the forward flow to any of the reactors had been established.

The technician who wanted the positioner took me to the surge valve and showed that he could move it to any desired position by tugging on the actuator shaft. Obviously, the buffeting action of the turbulent flow could cause the disc to wander and eventually close. The actuator size was checked and found to be adequate; the spring rate was increased but the results remained the same. Subsequent tests showed that the stem resisted movement considerably better if the actuator was fed directly from an I/P transducer and that it could not be budged at all if a positioner was installed.

We still needed speed, so I installed the booster on the outlet of the positioner. Unfortunately the positioner looking into the small inlet port volume of the actuator can change the pressure here much faster than the booster can change the actuator pressure. The consequence is an audibly and visually impressive 1 cps limit cycle. The booster had a built in bypass whose restriction was then adjusted so that the positioner could see part of the actuator volume. Of course, the more you bypassed the booster, the slower the valve got so the restriction was opened just enough to reliably prevent the limit cycle.

On the furnace pressure control application, I put my pre-stroke dead time and stroking time requirements on the control valve specifications along with a test to be witnessed by me at the valve manufacturer. When I arrived at the valve factory, the control valves each had a booster instead of a positioner. I walked up to the valves and showed them how I could stroke these big butterflies by grabbing the shaft. Needless to say they were astonished. The actuator sizing and spring rating was rechecked. We put on the same booster and positioner combination with a tuned bypass and the problem was solved.

There is no official explanation but obviously since neither one of us had the strength to move the shafts of these big boys at will, the booster was doing something to assist us. Possibly the extreme outlet port sensitivity of the booster (fractional inch of water column) provided positive feedback in that a slight change in the diaphragm actuator volume would cause a change in the booster outlet port pressure and hence booster flow.

These valves were designed for throttling with minimal packing and sealing friction so the dead time from deadband and resolution limits were small and in fact less than the booster because the inlet sensitivity of the booster was reduced by design to work better on piston actuators. Thus, the positioner had less dead band than a booster and the combined use of them meant that source of the most of the loop dead time was relegated to actuator pre-stroke dead time. This is not the case for isolation (block and interlock) valves masquerading as control valves so here as promised last week is the help you need for this bigger problem discussed in my upcoming Control Talk column (May 2007 issue of Control magazine).

Top Ten Signs Your Control Valve is an On-Off Valve in Disguise

(10) Valve body looks suspiciously like the block valve next to it
(9) Actuator looks suspiciously like the one on the interlock valve
(8) Process engineer is seen going out to lunch with the on-off valve supplier
(7) The valve deal is a steal
(6) Your flow is on-off
(5) Positioner measures actuator shaft instead of ball or disk stem position for feedback
(4) Limit cycle amplitude exceeds largest data historian compression setting
(3) 360 degree feedback in your loop becomes 360 degree feedback in your performance review
(2) The valve package is nicknamed "Sloppy Joe"
(1) No leakage till the controller output is greater than 40%

I have seen two control loops that did not go digital during the migration to a DCS. These electronic analog controllers stick out like a sore thumb in a modern day control room. The user would like to get rid of them along with the parts, maintenance, and operator interface issues. What keeps these relics from the 1970s hanging around?

The two analog control holdouts I have seen had 4 things in common: a variable speed drive, zero process dead time, a critical process impact, and an inability to run in manual.

If you don't have time to read on to get details on the particular loops, the most important "insights" are:

(1) Controller tuning tools and methods that rely on an open loop test cannot be used
(2) Digital adaptive controllers that identify tuning from set point changes are needed
(3) Must faster measurement update, communication, and controller scan and execution time intervals must be developed for valid holdouts to go digital
(4) If a loop has a control valve, it is rarely a valid holdout

The first application was polymer melt pressure controlled by the manipulation of melt pump speed. The melt pressure was important for throughput and relative viscosity control. An analog trend chart recorder showed what would appear to be a lot of noise. However, if the loop was taken out of auto, the amplitude of the fluctuations got so much worse you could not afford to stay in manual for more than a few seconds. The loop was reacting and compensating for incredibly fast disturbances. The process time constant can be estimated from the fluid inertia and viscosity and typically varies between 50 and 500 milliseconds. The process dead time can be estimated as the time it takes a pressure wave traveling at the speed of sound in the fluid to propagate from the final element to the first major resistance to change the pressure difference that is the driving force for the acceleration of a basically incompressible column of fluid. In other words, the process dead time was essentially zero. The dead time in the loop was all due to the automation system. The dead time of a variable speed drive (VSD) is nearly zero if the following conditions are met in the VSD application.

(1) The change in speed is larger than the resolution limits of the VSD A/D card
(2) The change in speed is larger than any dead band introduced by the user into the VSD configuration to suppress reaction to noise
(3) The rate of change of speed is smaller than any rate limiting introduced into the VSD configuration to reduce motor load and upsets to down stream equipment
(4) The rate of change of speed is smaller than any rate limiting from rotor inertia

These conditions are met for a well designed VSD for liquid pressure control, which leaves the measurement and controller as the sources of dead time. The process is self-regulating but it takes a high speed recorder to see any sort of time constant unless a signal filter is added. Note that I am not advocating replacing control valves with a VSD. There are practical problems when a control valve is omitted, such as the reversal of flow and the creation of incredibly fast flow upsets to other loops and unit operations.

There is an important exception to zero process dead time for liquid flow control. For highly viscous flows, a "ketchup bottle effect" has been observed where there is a huge dead time to initially start a flow through a small injection orifice as described in the first chapter of my book titled A Funny Thing happened on the Way the Control Room available at http://www.modelingandcontrol.com/FunnyThing/.

We all know about aliasing from digital communication, even more important here is introduction of a delay into a control loop that has essentially no process dead time.

Why am I obsessed with dead time? The ultimate performance (IAE) achievable for unmeasured disturbances with the fastest tuning is proportional to the dead time squared and the ultimate period for this dead time dominant loop is twice the dead time.

The second application was incinerator pressure controlled by the manipulation of an induced daft (ID) fan speed. The loop behaved like an integrator. If the controller was put in manual, the pressure could ramp and hit the interlock trip point in less than a second. Since open loop testing for exact quantification was not reasonable, a dynamic simulation was used to show it could occur in 0.25 seconds. While the residence time was on the order of 0.1 minute, the process gain was incredibly large because the measurement scale span was just a few inches of water column. The simulation also showed the decoupler between the forced draft (FD) fan and ID fan speed (air flow feedforward) was doing more harm than good because of the inverse response associated with the cold air flow. After elimination of the decoupler and retuning, the frequency of furnace trips was reduced but trips still occurred every couple of days. This process was controllable only because the process dead time associated with the furnace volume was essentially zero. For gas pressure systems the process dead time originates from gas volumes in series separated by flow resistances. The pressure sensor was seeing and the ID fan was acting on the same gas volume. The dead time in the loop was all due to the automation system.

Summarizing, a digital controller with a 0.1 second execution time was tried on startup but the furnace trips were excessive despite the best tuning and strategy. In 0.1 second, the pressure was almost half way to the trip point. When the digital controller was replaced with an analog electronic controller the pressure trips were eliminated.

This application and a phosphorous furnace application are discussed in chapter 3 titled "Pressure Control - Without Dead Time I would be out of a Job" in the aforementioned book A Funny Thing happened on the Way the Control Room.

Next week I will share my experiences with making control valves respond faster. With this under our belts, I will offer how fast digital devices and communication and data historians need to be once you get these valves to "turn on a dime or at least a quarter".

Note that the equations for computing process dead time and time constants for these systems is in Tuning and Control Loop Performance - 3rd edition, but is out of print.

In my December 18 blog "Linear in a Nonlinear World" we discussed the use of signal characterization to compensate for the installed characteristic of the control valve where the valve gain depends upon the operating point on the control valve characteristic. In part II we are looking at the use of signal characterization to compensate for a nonlinear process gain by translation of the original nonlinear process variable to a new linear one to enable adaptive controllers to better focus on other nonlinearities such as feed flow. Here in part II the process gain depends upon the operating point of the process variable. Examples of this translation to a new controlled variable are:

(1) Conductivity to % acid, base, or salt concentration
(2) Column top temperature to % reflux demand
(3) pH to % reagent demand

For conductivity, there is a peak in a plot of conductivity versus the acid, base, or salt concentration. The new process variable scale must be on one side or other of the peak. There is uncertainty in the exact location of the peak. If the operating point were to cross the peak, the process gain would go to zero and then change sign, which is disastrous to a control loop. The operating point must steer well clear of the peak.

For all of these examples, concentrations of other components in the feed can shift or change the shape of the curve but often the translation is better than no compensation at all for the process nonlinearity. For conductivity and pH, the effect of process temperature based on lab samples should be part of the calculation. For temperature, the effect of column pressure should be included (e.g. shift in boiling point with pressure).

The implementation involves first plotting the original versus the new process variable. For the examples noted this would be conductivity versus ion concentration for various temperatures, column temperature versus % reflux to feed ratio for various pressures, and pH versus % reagent to feed ratio for various temperatures. Since you are getting the X axis from the Y axis (the opposite of what is being done by the process), the data points for signal characterization are entered as Y,X pairs with a nonlinear bias to Y from a fit to the shift in the family of curves. Since the Fieldbus signal characterizer allows variable space of data points, closer points are used in the area of greatest curvature near the set point. This translation must be done for both the set point and the process variable. The original and new set points and process variables must be displayed and historized.

The benefits are most noticeable in pH loops because of their extreme sensitivity nonlinearity, and rangeability where changes in process gain of 100:1 and of reagent demand of 1000:1 are routine. Signal characterization has been shown to make dramatic reductions in startup time by the loop's recognition that the acid or base reagent flow is really decades away from set point. It also prevents pH from zipping right through the neutral point (e.g. 7 pH) and banging between the flat portions of the titration curve, offering a settling time where there was none. The characterization restores the process time constant by slowing down the excursion rate and helps a continuous pH loop look more self-regulating by removing the acceleration from movement to steeper slopes on the titration curve. Thus, you see and realize the benefit from an investment in a well mixed vessel where the residence time is a process time constant that slows down concentration disturbances as discussed in blogs from the past few weeks.

There are some issues besides inaccurate curves and confusion in the operator interface. If your set point is always on a flat portion of the curve and the control system can keep the operating point close to the set point for the largest disturbance, the benefit from linearization is minimal. Additionally, if an excursion to the steep portion of the curve represents an extremely undesirable situation for equipment or environmental protection, then the elimination of the overreaction of the loop by removal of the acceleration through linearization may be the wrong thing to do even though it reduces overshoot and wasted reagent when returning the pH to its set point.

While you increase the dead time from valve dead band and resolution limits when the set point is on the steep part of the curve because you are slowing down the rate of change of the process variable and the overreaction of the controller output, this normally is much less important then the suppression of oscillations. The increase in dead band for operating points on the steep portions of the valve characteristic can be a concern for control valves because there is usually no stability issue from the much less severe nonlinearity of a valve.

These and other considerations and an application for pH control are shown in the attached file on "Linear Reagent Demand Control" which is an excerpt from my ISA pH Web Seminar at 2:00 EDT on May 16.

Linear Reagent Demand Control

I conclude with a top ten list that will appear in a future "Control Talk" column.

Top Ten Reasons Not to Use Linear Reagent Demand Control

(10) How do you know it is a pH loop if it is not oscillating
(9) You can better see if the pH sensor is still alive
(8) You can better tell if the operator is still alive
(7) You like bang-bang control
(6) Gives you chance to try out the manual mode
(5) The titration curve from the lab shows a straight line through the set point
(4) You like seeing the full effect of valve stick-slip
(3) Retuning loops is job security
(2) You can eat more doughnuts while waiting for a loop to startup and settle out
(1) Linear loops are for wimps

If you ever wondered if you are agitated enough, then this blog may help stir up some thoughts. Specifically, how does the relative type and degree of mixing in the plant design affect your job as an automation professional? If the process engineer tells you the project is installing a radial instead of an axial agitator, do you shudder with profound disappointment or just utter a sigh? What if the agitated vessel is replaced with a static mixer? Do you turn the project over to the intern and take early retirement?

I will first continue my role in life as a pH stalker but then move on to other processes and more general considerations.

I became sensitized to mixing because of the extreme sensitivity of pH loops to plant design. I have talked before about how pH processes are the best known indicators of valve stick-slip, particularly near the neutral point, A control valve resolution of 0.1% (exceptional by any standards) can cause a pH swing that is more than noticeable.

Similarly, pH processes are the best known indicators of the uniformity of mixing. Concentration fluctuations in hydrogen ion concentration as small as 0.0000001 normality can cause noise with a 1 pH amplitude at the neutral point. The only study I have seen on the mixing required for pH was a cop out because it was done at 4 pH where the sensitivity (slope of the titration curve) was 1000 times less.

Additionally, the consequences of mixing delays are most severely felt in pH processes. An increase in loop dead time increases the excursion in pH for a given load upset, which increases the nonlinearity seen by the control loop. The operating point nonlinearity for pH can be extreme. The process gain is proportional to the slope of the titration curve and inversely proportional to the total flow and can change by a factor of ten for each pH unit deviation from the neutral point in a strong acid and base system.

The game in a pH loop more than any other loop is to minimize noise and dead time.

For other processes, the required degree of mixing is a lot less, but whether you are talking about temperature or concentration control, poor mixing still shows up as more noise and more dead time. The percent nonuniformity from mixing multiplied by the conversion factors to get to percent of the measurement scale gives you the noise amplitude seen by the controller algorithm. The dead time from mixing in a well designed agitated vessel is roughly the turnover time, which in turn can be approximated as the liquid volume divided by the sum of the feed flow, recirculation flow, and agitator pumping rate. The average dead time is ideally more like ½ of the turnover time whereas the maximum dead time is the turnover time. This helps explain why you see ½ to 1 times the turnover time in the literature as the mixing delay. Since we are generally short on our dead time estimates because there are so many sources of process dead time, I don't like to skimp on the mixing delay. See my Nov 20 2006 blog "Without Dead Time and Disturbances I Would Be Out of a Job" in the Plant Design category for a list of sources.

Unfortunately, the above assumes the liquid height is about the same as the vessel diameter (unless there a multiple levels of impellers), baffles every 90 degrees to prevent swirling, and an axial agitator to pull down liquid (not air) from the surface. If a camera shows the surface not being broken or swirling, or there is foaming, you can say "Houston we have a problem", particularly if the vessel vendor or design firm is in Houston. In processes that cannot withstand high agitation because crystals or cells may be broken by the blades, there may be an opportunity to increase the recirculation flow and use a jet or eductor to amplify the effect of the flow (e.g. jet fermentors).

Bob Heider, adjunct Washington University professor, wisely pointed out that baffles cannot be used for biomass, crystals and particles when the baffles cause the solids to dam up or break up. Bob also provided the following memory dump on agitation.

Agitation Info

A bigger potential source of dead time is the injection delay from dip tubes for small manipulated flow (e.g. nutrient, reagent, reactant, or additive). The normal design practice is to have a robust sized dip tube go about halfway down the liquid to the impeller. Unfortunately, this creates a dead time when the manipulated flow is shutoff for a prolonged period of time that is the submerged dip tube volume divided by the flow. For example, just a gallon volume will cause a dead time of 1 hour for a 1 gph flow when the control valve reopens. There can be an even larger dead time because to see the final effect of stopping the flow, you have to wait till the concentration inside the dip tube drains and migrates into the mixture in the vessel. Various method of reducing injection and mixing delays are discussed in the ISA book Advanced pH Measurement and Control, 3rd edition, 2005.

This brings us to one grand generalization. For concentration changes, the residence time (volume divided by flow) becomes a process dead time for a pipe but becomes a process time constant for a well mixed vessel. Check out next week's blog for the effect on tuning and loop performance. In the mean time, stay agitated.

Where have all the instrument and process control engineers gone? Are they in Florida enjoying golf and the weather, are they filling in part time for a contract engineering design job oblivious to the ice or snow storm, or are they like me venting into the blog sphere?

It is easy for plants to forget about people responsible for the tuning and performance of the loops. The few instrument engineers and process control engineers left are focused on buying transmitters and configuring the DCS, respectively. They do not have the time or training to recognize and analyze the tuning and performance of the loops and more importantly it is probably not in their goals. The manager can readily understand that a production unit needs hardware and configuration to make the plant run but to date the opportunity for better tuning and dynamics in the plant is ambiguous at best, which means it is not going to survive corporate downsizing. Studies that show 30% of the loops are poorly tuned and 30% suffer from poor dynamics (e.g. principally valve stick-slip and process transportation delays) are easy to dismiss if there is no onsite data.

Even when loop tuning and performance is on the radar screen, the number of loops assigned to the instrument or process control engineer in a large continuous plant has increased dramatically to hundreds and even a thousand or more. Batch processes have an order of magnitude fewer loops but the ones they have are generally more difficult because there is no steady state (another story).

Astute process engineers who are looking at the loops try to fill in for the missing control people. However, improving loops is probably not in their job description and they usually haven't had the opportunity to learn about tuning methods, valve resolution and deadband, and even simple process dynamics. These things are not normally taught in a practical manner in chemical or systems engineering, where the focus is on Laplace and Z-transforms to prepare 1-2% of the students to go on to graduate school to major in control theory and become professors. There are exceptions (see my Feb 4 blog on Washington University and the article by Tom Edgar from the University of Texas in InTech last Fall).

A significant part of the value of recent breakthroughs in thinking and online tools is the recognition of the importance and understanding of how the automation system (e.g. valve and sensor) and process (e.g. piping, mixing, and vessel) affects the process dynamics per Advanced Application Note 4, how the dynamics affect the tuning settings, and in turn how the tuning settings affect the performance of the loop.

For those who are tired of reading or have email to do, the takeaway is:

(1) Plant design sets the minimum and maximums of the process dynamics and how these change with operating point of the process and valve, which in turn determines how the tuning should be scheduled
(2) Process dynamics slowly change with aging, fouling, and frosting
(3) Process dynamics rapidly change with throughput and load (most noticeable during startup and turndown) and show up as a change in the valve's operating point
(4) Valve, pump speed, and sensor resolution limits create a variable dead time
(5) Process dynamics determine the ultimate possible performance
(6) Tuning settings determine the actual achievable performance
(7) All tuning methods end up with about same controller gain for maximum rejection of process load disturbances if there are no extenuating circumstances
(8) The reduction in error for a load disturbance can be simply estimated from tuning
(9) Online tools can identify valve stick-slip, deadband, and the valve characteristic
(10) Online tools can identify the process dynamics and schedule tuning settings

An article in Chemical Processing provides information on an online tool for the identification and monitoring of process dynamics and control valve resolution and deadband, and a corresponding calculation and scheduling of tuning settings. Changes in the process dynamics provide considerable insight but you need "Insight" to appreciate this insight.

The following slides show how to estimate the improvement in integrated error from less sluggish tuning for a load disturbance at the process input. The equations assume the aggressive tuning does not cause the loop to oscillate more than what it already does from valve resolution/deadband or measurement noise. Equation 2-2b is derived from Equation 2-2a, which was derived in Appendix C of New Directions in Bioprocess Modeling and Control. The equations are useful in terms of simplicity and recognition of cause and effect.

Load Disturbance IAE

More aggressive tuning increases the rate of change of the controller output and hence decreases the dead time from valve resolution/deadband. While it does not affect the amplitude, it increases the frequency of the limit cycle from valve resolution/deadband. This may or may not be a good thing. A faster cycle is more effectively filtered out downstream by a process volume but a faster cycle may be more disruptive to associated loops on the input to the process volume (e.g. loop interaction). More aggressive tuning setting (e.g. high controller gains) may also amplify measurement noise. Thus, there is a need to monitor the variability of all loops, which is an important feature in online software today.

This is not to say that all loops are tuned sluggishly. We have seen several loops that are oscillating nearly full scale (essentially on-off control) and the users have actually gotten use to this. The process runs moderately well because the average of the oscillations is OK. The oscillations are tough on valves and equipment and tough on the process engineer because he/she cannot see a discernable pattern in the controller output important for diagnosing changes in the process and loads.

Getting back to the more common case of sluggishly tuned controllers, how far off the mark is the controller gain for maximum disturbance rejection in some important loops? A Lambda factor of 2 to 4 is commonly used because this is what is appropriate for the flow, liquid pressure, pipeline, and heat exchanger loops frequently encountered, particularly in pulp and paper. However, for loops on biological or chemical reactors, evaporators, crystallizers, neutralizers, and distillation columns (unit operations distinguished by a high degree of back mixing from bubble flow and/or agitation), a Lambda factor of about 0.2 provides the best disturbance rejection with acceptable robustness because the dead time to time constant ratio is less than 0.2. Note that Lambda is the Lambda factor multiplied by the process time constant so setting the Lambda factor equal to the dead time to time constant ratio corresponds to setting Lambda equal to the dead time. Thus, current tuning practice gives a gain that is ten times too low and thus an integrated error for load disturbances that is ten times larger than achievable for highly back mixed volumes.

Many of these loops behave like they have integrating processes (like level) and may be best modeled as integrating (e.g. "near integrating") even if they are not perfectly integrating. The integrating process gain is inversely proportional to the back mixed volume.

People are starting to understand this problem and plants may have some how arrived at the more aggressive settings on critical unit operations. It is important to note that to avoid problems with more aggressive tuning during startup and a turndown (lower throughput rates), the controller gain should be identified and scheduled online since the dead time is inversely proportional to the throughput rate and the valve gain (curve slope) changes with operating point on the installed valve characteristic. Also, it may be advisable to institute set point rate limits on primary loops to prevent big steps in the controller output from a set point change.

A final point, if you don't tune the temperature loop on a highly exothermic reactor aggressively, a runaway can occur due to positive feedback (higher temperature causes a higher reaction rate through Arrhenius equation). Customers have learned the hard way to use a more aggressive controller gain to keep the relief system from blowing. For these reactors there is a lower controller gain limit besides the normal upper limit for stability. There is also a window of allowable controller gains for integrating processes when the controller has integral action (PI or PID), but this is getting too deep.

When we first started Emerson's advanced control program in the early 90's, one of the initial objectives of the program was to develop an adaptive control capability that could be used in our control products. However, we realize that adaptive control is one of the most challenging advanced control areas to address from a technical standpoint. Thus, most of the programs resources were initially focused on other areas e.g. on-demand tuning, property estimation using neural networks, simulation, fuzzy logic control and model predictive control. Adaptive control was kept on the backburner for many years with work in this area restricted to technical evaluation of different technologies. Gradually, starting in the late 90's, a more focused effort was put into addressing adaptive control. As a result of this work, the first release of our adaptive control technology was recently introduced as part of the DeltaV Insight product in the v9.3 release. The things that we learned in researching and developing this technology greatly influence the final design of DeltaV Insight.

In the early 90's, one of the first adaptive control technique that we investigated was one developed by Professor Karl Astrom, Lund University. This technique allows the controller gain to be automatically adapted through on-line assessment of process gain. As part of this investigation, we worked with Professor W. K. Ho from the National University of Singapore in researching this technique. Even though the approach proposed by Astrom is technically very sound and is utilized in some commercial products, its application is limited to feedback control and adaptation of controller gain. Since our ultimate goal was to find a technique that could be used to adapt all components of PID feedback control (Gain, Reset, and Rate) and feedforward control (gain, Lead/Lag Time constant, and deadtime), we did not pursue this approach past this initial investigation.

At one point we were offered the rights to an adaptive control technique that had been developed by the engineering department of a major chemical company. To avoid polluting the Emerson development team, we hired an outside consultant to evaluate this technology. It turns out that the technique was based on pattern recognition and the application of rules to establish tuning. Even though this approach is used by some major process control companies, the feedback from customers who had tried this technology was not encouraging. There were reports of erroneous adjusted of controller tuning base on cyclic upstream disturbances that were interpreted as a sign of too much controller gain. Thus, we decided to avoid this approach.

In the late 1990's, Willy Wojsznis came across a very interesting paper on model free adaptive control. This paper helped sparked work that lead to a unique design and implementation of model free adaptive control that we later patented. In the summer of 2000, we sponsored a graduate student under the guidance of Professor Dale Seborg, University of California at Santa Barbara, UCSC, to test and further investigate this technique using process simulations. The basic approach provided to be a reliable method for directly establishing feedback tuning. However, only through inference from the controller tuning was it possible to gain any insight into the process gain and dynamics. Also, the method could only be used for the adaptation of feedback tuning. Therefore, we continue to evaluate other approaches that better met our requirements and would give direct insight into the process gain and dynamics.

In the mid-90's, a number of papers on the application of controller switching appeared in some of the major control conferences as a technique for evaluating best tuning. Also, a few papers were published on the use of model switching to identify process gain and dynamics. The concept as proposed was not practical to implement. However, these techniques offered the promise of allowing process models to be identified for both the feedback and feedforward path. After some consideration, Willy and I developed a new approach which we labeled model switching with interpolation and re-centering. This new approach to model switching required the evaluation of only a limited number of models at any given time. Testing of this technique by UCSB from 2001-2003 showed the method to converge very quickly for a variety of self-regulating and integrating processes.

An alpha version of adaptive control based on model switching with interpolation and re-centering was installed at two chemical plants in early 2004. The results from one of these sites, Solutia, were published in September 2004 issue of Chemical Processing. Based on the positive results of these installations, beta testing was conducted at four sites from 2005-2006 on approximately 1000 loops. As part of this beta testing, a special emphasis was place on quantifying the benefits of adaptive control for the batch industry. We created a video of the Lubrizol installation in which the customer discusses the benefits they realized from adaptive control on their batch process. The things we learned from these beta installations had a great impact on the final product design. In particular, the beta test proved the value of maintaining a record of the models that are identified over time from each loop. Also, the capability to automatically provide tuning recommendation using this technology was seen as a major benefit in improving plant operations independent of whether closed loop adaptive control was applied in the plant.

If you have an interest in learning more about the adaptive modeling technique used in Delta Insight, then the following presentation that Willy Wojsznis and I gave at Emerson Exchange provides information on the technical details on this technology.

Adaptive Technology

Also, additional detail can be found in the two patents that we have on the basic technology and its use with non-linear applications.

While approaching an optimum something can sneak up that catches the loop off guard. Because of the deadly foe dead time, by the time the loop sees and reacts, it may be too late, particularly if it was blind sided.

The classic example is compressor anti-surge control. When moving to a lower discharge pressure or recycle flow (lower energy use), an inaccurate surge curve or untimely dip in feed can cause a precipitous drop to zero or negative flow in 0.03 seconds followed by huge reversals in flow from surge. Just a few of these surge cycles can damage the seals enough to reduce the efficiency of an axial compressor.

Another impressive case can occur for exothermic reactor control. During the approach to a higher reaction temperature and higher reaction rate (lower batch time) a higher than expected raw material concentration or catalyst activity can initiate a runaway acceleration of temperature and reaction rate.

Not quite as dramatic but still important in terms of environmental scrutiny occurs for an approach to a lower pH set point in a static mixer (lower base reagent use). A strong acid upset from a batch operation or level switch controlled sump can cause a low RCRA pH violation within seconds. Even if it lasts a few seconds and therefore has no measurable affect on any decent downstream volume, it can be a recordable environmental violation. In one particularly large application, an interlock diverted the feed from the plant waste treatment system if the control system could not do its job and a violation was eminent.The open loop backup successfully eliminated nearly all of these diversions.

A much slower but still important situation can occur for a bioreactor. During the approach to a lower substrate (glucose) concentration with less substrate inhibition (greater yield), non-ideal mixing and a drop in substrate feed can trigger starved biomass to eat their own product (ugh).

In each of these cases, there is a significant undesirable event that requires a slow approach to an optimum and a fast recovery from an inadvertent excursion into an extremely undesirable operating region. This is particularly true for the first three cases, which involve environmental and property protection. The last thing you want is to test the adequacy of your interlock system or have a recordable incident.

(1) Compressor Anti-Surge Control
(2) Exothermic Reactor Temperature Control
(3) Static Mixer RCRA pH Control
(4) Bioreactor Substrate Concentration Control

An open loop back up has been applied in the above applications to assist but not interfere with the PID controller trying to do its job. The calculation simply consists of incrementing the controller output from its last value via the ROUT mode every module execution when the process variable has exceeded a limit. The increment is stopped when the process variable has recovered beyond the trigger point plus some differential (e.g. noise band). It is normally only activated only when the controller is not in manual. There is a bumpless transition to PID action when the open loop backup is cleared.

For surge control, the clearing of the open loop back up has a time delay to insure the compressor is out of surge and the control system is not fooled by a flow reversal.

In each case, the need to get out of trouble as quick as possible overshadows any temporary loss in efficiency.

Another strategy is to use a fast opening but slow closing of the control valves for compressor vent or recycle flow, reactor coolant flow, pH reagent flow, and bioreactor substrate feed. This can be implemented by putting a rate (velocity) limit on a decreasing signal to the control valve. This can be implemented in the analog output block via the SP_RATE_DN parameter, which in this block is active on the set point even when the block is in the CAS mode. To insure the reset action in the PID block is not faster than the rate limiting in the AO block, the "Dynamic Reset Limit" option must be enabled in the PID and the "Use PV for BKCAL_OUT" option enabled in the AO block to use the working set point for the BKCAL_OUT. Any rate limit will affect tuning and must be implemented before running any tests to identify dynamics or tuning settings. The strategy also works on variable speed drives for reagent and substrate feeds to allow a fast increase but insure a slow decrease in speed.

The attached screen prints show a simple example of an open loop calculation and enabling of the above options. As with any new technique, the configuration should be thoroughly tested by a realistic simulation before used in an actual application.

Open Loop Backup and Slow Closing Valve Option

Another option is to schedule the controller reset action to be much faster (reset time much smaller) when the process variable approaches a risky region to promote a fast recovery. There may be some overshoot of the set point but a slow approach back should prevent a second crossing to the more eventful side of the set point. Scheduling a drastically higher controller gain may not be a good idea because it can cause a bounce back toward the undesirable region from proportional action before the process variable even gets near the set point. Some new DCS software, such as DeltaV Insight, can automatically identify process dynamics and schedule the corresponding tuning settings.

Sometimes the open loop back up is called a kicker. The following is an excerpt from the January 2005 Control Talk column in Control magazine that describes a kicker used by Terry Chmelyk to reduce the number of feed diversions required to prevent the violation of an environmental constraint. It is similar conceptually to the previously described RCRA limit application, but here the measurement was conductivity instead of pH.

Terry: In a multi-effect evaporator system, we used built-in and integrated model predictive control (MPC) and optimization to reduce variability in the product density from 2.8% to 0.3% and increase throughput by 6 to 8%. We also used innovative environmental constraint handling to increase the interval between diversions by an order of magnitude.

Greg: Environmental limits can come on suddenly and unexpectedly. My experience is that these involve unmeasured disturbances and scenarios you can not initiate to develop a model. There is nothing sadder than an advanced control engineer without a model. What did you do?

Terry: We added an external "kicker" algorithm around the MPC because of the highly non-linear characteristics of the constraint variables (in this case it was condensate conductivities). The environmental impact required us to take immediate and "substantial" action to eliminate the contamination in the condensates. In essence, we built a basic fuzzy algorithm that "kicked" the weak black liquor (WBL) feed to the evaporators during a significant upset.

The first slide in the attached file summarizes the achievements of the MPC/kicker application. The second slide shows how the "kicker" backed out the WBL flow on high condensate conductivity to prevent a diversion yet allowed the MPC to recover quite well from the disturbance.

Conductivity Kicker

Last year we had disturbing thoughts on how fast upsets were particularly disruptive and anything you can do to slow them down makes the job of a loop much easier. In real processes, step disturbances are quite rare. However, there are some noticeable cases (e.g. on-off level control, interlocked and sequenced valves, and compressor surge control) where stuff comes at the loop too fast.

If level switches are replaced with a Hart or Fieldbus properly applied level transmitter, correctly tuned level controller, and throttling control valve with a digital positioner, you will make everything smoother downstream. The cost of the better automation system will pay for itself in terms of better reliability and visibility and reduced variability.

On-off valves must in many cases be sequenced and interlocked. The effect of these valves may be underestimated. Even for large valves with slow stroking times, most of the time is spent on the flat portion of the installed characteristic. For example a reactor air feed isolation valve had been deliberately slowed down by a restrictor on the actuator to take 145 seconds to stroke to allow the air compressor surge control system time to open its vent valve. An analysis of the installed characteristic revealed that there was actually only 1.7 seconds between when the flow dropped below the anti-surge controller set point and the flow hit the compressor surge line. The total time on the steep portion of interest in the installed characteristic was less than 3 seconds. The speed of the upset could have been regulated much better by a programmed partial reduction in air feed flow set point followed by a fast closing of the on-off valve to prevent reverse flow. I am must make it very clear at this point that a control valve should not be considered as a replacement for an on-off valve, or vice-versa. They serve distinctly different purposes. A control valve needs to have minimal seating friction for throttling and an isolation valve tight shutoff for isolation, which may be conflicting objectives.

Once compressor surge starts, not much can be done by a flow controller because it is like going over a cliff. The precipitous drop in flow occurs in less than 0.03 seconds. This was mistakenly interpreted as requiring a special microprocessor with a scan time of less than 0.05 seconds when really the control valve on big compressors often didn't do much of anything for a second or more because you physically couldn't move enough air out of the big actuator for the fail open vent valve. Also, the feedback controller needed to do something before it hit the surge curve. Once a compressor is in surge, an open loop back up is used to get out of surge because a flow reversal occurs every second or so totally confusing the controller.

For more details on compressor surge control see the books Centrifugal and Axial Compressor Control and A Funny Thing Happened on the Way to the Control Room (reprints available through UMI). Next week we will talk about the use of a simple open loop backup configured in a DCS to assist a PI loop for those applications were you need fast recovery for property and environmental protection.

Compressor anti-surge control is an extreme case but there are many applications particularly for parallel trains of equipment and batch to continuous transitions where it is advantageous to slow down disturbances by a coordinated startup and shutdown of flow set points.

Pressure waves travel at the speed of sound in the fluid (e.g. 1100 fps) whereas composition changes travel at the pipeline fluid velocity (e.g. 5 fps). The pressure waves can also reflect back and forth (e.g. water hammer), which like surge can be totally disruptive. Whether you are talking about pressure or composition changes it is wise here as well to slow them down by ramping the flow controller set point in the DCS rather than restricting the air flow to the actuators of on-off valves in the field. It is also beneficial to use pressure transmitters instead of pressure gages. Operator typically cannot outrun a pressure wave to get to the right one. In some cases we don't know even whether a pressure upset is originating upstream or downstream let alone where it specifically starts. If you think about it, some field pressure regulators are also best replaced by pressure loops in a DCS to provide more visibility and control over the propagation of pressure waves and the allocation of pressure drops to prevent interaction and cavitation.

Momentum balances, normally not a part of dynamic process models, are required to simulate pressure waves and surge.

Top Ten Stuff That Comes at You Too Fast

(10) On-off level controlled flows
(9) Sequenced and interlocked flows
(8) Strong acids and bases
(7) Pressure waves
(6) Compressor surge
(5) Dunk shots
(4) Ice pucks
(3) Late night car commercials
(2) Corporate Restructuring
(1) Retirement

Time is precious so here is your chance to learn in five minutes what took me five weeks of investigation. While most of these thoughts were banging around in my mind for last couple of decades, they might never have congealed if not for some triggering thoughts from my colleagues Terry Blevins and Willy Wojsznis and some knowledge discovery in my favorite laboratory, the virtual plant. All of this stuff has been discussed to some degree in last year's blogs with more detail available in my Control magazine articles and Control Talk columns, the book New Developments in Bioprocess Modeling and Control, and Advanced Application Notes 1-4. The notes and presentations based on my ISA books as they become available are free for the downloading at http://www.modelingandcontrol.com/2009/03/application_notes.html

(1) All the most popular tuning rules reduce to the same equation for the controller gain for maximum load rejection.

(2) While the ultimate performance of a loop is proportional to the dead time squared, the actual performance is set by the tuning (reset time and controller gain).

(3) Nearly all studies on the beneficial effect of improving loop dynamics retune the controller for better performance. If the controller was not retuned, there would be no immediate recognizable benefit in most cases.

(4) You can estimate the amount of dead time you can add before the loop performance deteriorates for unmeasured disturbances by comparing the present controller gain to the maximum controller gain for maximum load rejection.

(5) I would be out of a job if there was no dead time or disturbances, because barring any extenuating circumstances the controller gain could be set higher than you have ever seen or the control valve just sequenced to predetermined positions.

(6) Continuous temperature, concentration, and pH control loops on large well mixed volumes are best treated as "near integrators" for tuning.

(7) The use of dynamic reset limiting and a delayed external reset can provide dead time compensation that is easier to implement and more robust than a Smith Predictor. If the valve position PV for single loops and the secondary loop PV for primary loops is used for external reset, it prevents the controller from outrunning the valve or secondary loop and the dead time compensation is more accurate.

(8) If the model dead time used for the Smith Predictor is 100% larger than actual, the Smith predictor can break out into rapidly growing oscillations. A model dead time that is too large besides too small can cause instability in this predictor.

(9) The controller gain setting must be significantly increased beyond the normal maximum controller gain to realize the benefit from dead time compensation.

(10) A zero discharge flow causes the mass to increase as a batch progresses, which causes concentration and pH control to have an integrating response. The integrating process gain here is inversely proportional to level. For vessel pressure control where the vent valve pressure drop is large or critical, the pressure response's integrating process gain is proportional level because the vapor space volume is decreasing. However, for temperature control where there is significant heat release and cooling capability, vessel level has little effect on the controller gain except when it is above or below the heat transfer surfaces (e.g. coils) because the effect of more mass is cancelled out by more heat transfer area covered by liquid.

Control systems assume linearity. Unfortunately the world is basically nonlinear. For the next few weeks we are going to explore how gains, process time constant, and dead times change with plant design and operating condition. This week we start out looking at valve gains.

A plot of the flow versus valve position (installed characteristic) of most control valves is nonlinear. Here the slope is the valve gain. If we were to plot a process variable versus this flow, such as temperature or composition, it would also be nonlinear. Here the slope is the process gain. These are called operating point nonlinearities. If the process variable stays close to its set point, the slope doesn't change much. Thus, for a constant set point, minimal dead time, and good tuning, the process nonlinearity is not much of an issue. On the other hand, the control valve may have to move a lot to achieve tight control. The loop is more likely to see the nonlinearity of the control valve. Generally the slope of the installed characteristic gets too flat at low and high positions. Entech published a gain specification that the % flow divided by % signal should be between 0.5 and 2.0 (a gain change of 4:1). The following examples of installed characteristics show that the throttle range is shortest for a butterfly valve and longest for a sliding stem valve. For a detailed discussion of these figures see Chapter 2 of Advanced Control Unleashed.

Valve Gains

The rangeability statements by valve manufacturers are defined in terms the uniformity of the inherent characteristic. These statements do not take into account a gain specification, an installed characteristic, or the increased stick-slip at low valve positions from friction of the seating and sealing surfaces, particularly for tight shutoff valves.

A signal characterizer block can be inserted between the controller output and analog output block to compensate for the nonlinearity of the control valve gain. The characterizer is set up to calculate the % flow from % position (the Y axis from the X axis of the installed characteristic). The input signal to the control valve is now % desired flow rather than % desired position. This can confuse operations and maintenance if not adequately documented and displayed. The accuracy of this gain compensation depends upon the knowledge of the system pressures and friction losses that affect the pressures at the inlet and outlet of the control valve. Software can predict the installed characteristic but this is done typically offline with manual entry of data. There is an opportunity for pressure measurements upstream and downstream to provide better compensation of the valve nonlinearity besides facilitate the monitoring and trouble shooting of disturbances. Many times I wished more pressure transmitters were installed to figure out why a loop just got clobbered, but this is another story.

Another practical issue relates to valve stick-slip and backlash, whose effect and compensation we alerted readers to in our Dec 4 and 11 blogs. For operation on the steeper portion of the installed characteristic, the characterizer makes the change in signal to the control valve smaller. Thus it takes longer for the signal to work its way through the resolution limit and dead band. However, for operation on the flatter portion of the installed characteristic, the change in the control valve signal is larger reducing the dead time from the resolution limit and dead band. If you ever waited for the controller output to work its way along the upper flat portion of a butterfly valve characteristic for a process unit operating at or beyond its design limit, you can appreciate the acceleration offered by the signal characterizer. Of course, at some point you just run out of valve and need to take a look at the pump and piping system design besides the valve size.

Over the last few years the process industry has expressed a growing interest in the application of wireless technology for field measurements. The ISA-SP100 Committee was established in early 2005 to set standards and recommended practices for implementing wireless systems in the automation and control environment with a focus on the field level. Also, various industry consortiums have been established to promote the use of wireless technology. For example, the Hart Communication Foundation has adopted the use of IEEE 802.15.4 physical layer for the implementation of wireless HART. At the ISA2006 conference the HART Communication Foundation sponsored a booth in which wireless transmitters from multiple vendors were demonstrated. However, one of the technical challenges that manufacturers face in applying wireless technology to process measurements is how to reduce the power consumption to a level that can be supported for many years without the need for external power.

If the information from a wireless transmitter is only used to monitor slowly changing measurement values e.g. levels in a tank farm then the transmitter power requirements may be minimized by simply slowing down how often a measurement is made and communicated. However, if the measurement is used in control applications that respond in seconds rather than minutes, then simply slowing down how often a measurement is made and communicated will negatively impact control response. To provide best control, it is necessary to reduce the latency in control response to setpoint or load disturbances. In a traditional control system it is possible to minimize latency by over-sampling the control measurement used in control. However, such an approach is not an option if your objective is to minimize wireless transmitter power consumption.

One means of reducing the need for over-sample control measurements is to synchronize the measurement sample with control execution as is done in Foundation Fieldbus device. Using some of the proposed wireless protocols, such as Time Synchronized Mesh Protocol (TSMP), it is possible to synchronize a measurement sample and its associated communication with control execution done in another node. However, the traditional approach of executing control 4-10X faster than the process time constant still will create communication loads that are a barrier in applying wireless devices in faster process applications.

A few years ago we started looking at techniques that could be used to reduce wireless communication load without sacrificing control performance. It turns out that for many applications a 10X reduction in communications load can be achieved by following simple rules in communication and by restructuring the PID control to use non-periodic sample values. Much of this work is documented in a paper that we presented at ISA2005, Similarity-Based Traffic Reduction to Increase Battery Life in Wireless Process Control Network. An overview of this work is provided in the following:

Control Using Wireless Transmitters

If you would like to learn more about the wireless technology, then a good starting point is Protocols and Architectures for Wireless Sensor Networks (Hardcover) by Holzer Karl and Andreas Willig.

A control valve isn't doing much to help a control loop deal with the minute by minute onslaught of disturbances if it does not respond to the controller's output. Yet there is normally nothing in a control valve's specification form to insure the control valve actually moves. A step forward has been the ANSI/ISA standard 75.25.01 for a control valve step response testing procedure but I wonder if any where near as much effort is put on making sure the valve movement is smooth and sensitive as is spent on the valve size and leakage spec?

I was sensitized to the sensitivity of the control valve because my first area of expertise was pH when I moved from E & I Construction to Engineering Technology. The high process gain for strong acids and bases makes pH loops ideal for identifying valve response limitations. A jump in valve position of just 0.1% can cause a several pH swing. Putting a pH loop in automatic may initiate large amplitude oscillations even though there are no load upsets. In the end I realized great control valve sensitivity could reduce the number of stages of neutralization and save big bucks in process equipment required (see the 3rd edition of the ISA book titled Advanced pH Measurement and Control).

There is a growing awareness that a resolution limit from stick-slip in a control valve can cause a limit cycle in a control loop because the valve position is never exactly were it needs to be. Even if there are no disturbances, integral action in the controller drives the output until it moves, but then it steps right past the right valve position. Besides the limit cycle, there is also a dead time that is the resolution limit divided by the rate of change of the valve signal (controller output). To make things worse a slower rate of change of the controller output increases the resolution limit in some positioner designs. Consequently as the controller tuning is slowed down (Lambda is increased), the dead time and possibly the resolution limit is increased.

Deadband can be just as deadly. Whenever the controller output has to reverse direction, the change has to be greater than the deadband before the valve moves. The result is a dead time that is proportional to the deadband divided by the rate of change of the valve signal (controller output). If the are two integrators in the loop, deadband also creates a limit cycle. The two integrators can be the result of a controller with the integral action on an integrating process (e.g. level) or a cascade loop where the secondary and primary loops both have the integral mode (e.g. PI or PID controller) as discussed in the article "Life is a Batch" in the June 2005 issue of Control magazine.

Stick-slip normally originates from friction in stem packing or from sealing surfaces on the trim. Excessive tightening of the packing, high temperature packing (e.g. graphoil), older types of environmental packing, tight shutoff ball and disc seals, and low gain or spool positioner designs create more stick-slip. The friction is generally worse near the closure position, so most tests results are cited at higher valve positions (e.g. > 20%).

Ever since I started my career almost 40 years ago, inexpensive actuators and positioners have been added to tight shutoff rotary valves original designed for on-off or isolation service. The package is attractively priced and pitched as a control valve that meets or more unfortunately exceeds the valve's capacity and leakage spec. If the process, mechanical, and instrument design engineer each add extra capacity in the piping, pump, and valve, the result is the extreme sport of a control valve riding the seat. If engineers attempt to make the control valve serve the additional purpose of isolation besides throttling, the problem of popping on and off the seat is magnified. In general, an isolation valve does not make a good throttling valve and vice versa.

In rotary valves, shaft windup can occur, where the actuator shaft twists but the ball or disc does not move because of high friction of the sealing surfaces. Eventually, the ball or disc breaks free and jumps to a new position. If the positioner, no matter how smart it think it is, measures actuator shaft position rather than ball or disc travel, it may report everything is relatively OK. I have seen a whole series of fancy plots from a smart digital positioner with vertical travel actuator shaft position feedback consistently show the stick-slip was less than 0.5% for a butterfly valve designed for tight shutoff (not too bad for the particular application). A travel gage added to the disc in the shop test setup gave the reality check that the stick-slip was actually 9% (lousy for any application).

Deadband is also known as backlash and is often larger in rotary valves because of rotary actuator and shaft coupling design or the need to translate from vertical to rotary motion. Be careful about the use of the term deadband. Purists will argue that deadband is the offset in the plot between an increasing and decreasing valve position for a full scale change in valve signal. In practical terms we think of deadband as the reversal in valve signal necessary to reverse valve position anywhere in the signal range. In the following plot of actual ball travel versus controller output, the stick-slip is evident for changes in the same direction and the deadband shows up for a change in direction of the valve signal. This plot is for the controller in automatic and shows that with a bit of understanding and practice, the dead band and resolution limit can be identified from trend charts. For rotary valves, this presumes there is a measurement of the actual ball or disc position or flow thorough the valve. For sliding stem valves, actuator shaft position read back is normally sufficient because there is a more direct connection of the shaft to the trim stem and no translation of motion.

Valve Deadband and Resolution

For the use of a model predictive control to achieve better valve sensitivity and rangeability see the article "A Fine Time to Break Away from Old Valve Problems" in the October 2005 issue of Control magazine. For equations on how to estimate the amplitude and period of limit cycles from a resolution limit or deadband see the article "What is Your Flow Control Valve Telling?" in the May 2004 issue of Control Design magazine.

To end on a lighter note, here is list to identify with:

Top Ten Exceptional Valves

(10) A measurement with 0.1% repeatability
(9 A control valve with 0.1% dead band
(8) A control valve with 0.1% resolution
(7) A controller that is tuned
(6) A process that is simulated
(5) Any computer picked out by your son
(4) Any canceled all week team building exercise
(3) Any afternoon meeting at the Oasis in Austin
(2) Any conference in Park City
(1) Any writing expedition in Naples

Next week's blog discusses the merits of a block added to the PID controller output to compensate for valve resolution and deadband.

How can we get rid of dead time in our loops so we can be rich and famous by Friday? PID controllers with dead time compensation are reported to eliminate dead time in terms of a controller seeing the effect of changes in its controller output. For set point changes where all the controller needs to be concerned with is how its output responds to a new set point, the results are impressive for an exact knowledge of the process dead time. However, for unmeasured load disturbances at the process input, the only way to eliminate dead time other than an improvement in the plant or control system design is to accelerate the control system to the speed of light. So unless you have Scotty and Warp Drive on the Starship Enterprise, you are stuck with the dead time from the process equipment, piping, control valves, instrumentation, and digital devices. A dead time compensator can offer some improvement in load rejection by facilitating more aggressive tuning of the PID but with a considerable risk of oscillations from an inaccurate dead time.

If you don't have time for the details or just want to cut to the chase, here are the recommendations

(1) First improve the PID controller tuning before even considering dead time compensation. Setting Lambda equal to the maximum dead time (Lambda factor equal to the maximum dead time to time constant ratio) is effective for load disturbances at the process input if there are no extenuating circumstances.

(2) Add feedforward control whenever it is possible to measure or infer load disturbances at the process input.

(3) If there is economic justification for further improvement and the dead time can be updated within 25% accuracy for varying operating conditions, trial test and closely monitor a PID with delayed external reset for low dead time to time constant ratios.

(4) For loops with high dead time to time constant ratios, multiple manipulated variables, interactions, or constraints, consider model predictive control.

The ultimate performance achievable in terms of load disturbance rejection depends upon the dead time. In the "Theory" section of Chapter 2 of Advanced Control Unleashed equations are developed that show the minimum peak error is proportional to the dead time and the minimum integrated error is proportional to the dead time squared for unmeasured load upsets. How close the actual performance of a control loop comes to this ultimate performance depends upon PID structure, tuning, and enhancements. This blog focuses on the effect of variations in dead time on the performance and robustness of dead time compensation as an enhancement and Lambda as a tuning rule for disturbance rejection. The two predominant methods of dead time compensation studied here are the Smith Predictor PID and the PID with a delayed external reset.

The Smith Predictor was extensively documented in the 1970s. It provides a new controlled variable that is the response of the process variable to its controller output without dead time. It requires entry of three parameters commonly known as process gain, dead time, and time constant. The Smith Predictor uses these parameters to create models of the process from the controller output. In its most documented form, the Smith predictor subtracts a model of the process with dead time from a model of the process without dead time and adds the net result to the measured process variable to create a new controlled variable. If the model is perfect, the new controlled variable has zero dead time in terms of the controller seeing the effect of its own controller output. Since the maximum allowable controller gain is inversely proportional to dead time, the controller gain can theoretically be increased without limit for a perfect model provided you ignore extenuating circumstances, such as loop interaction, measurement noise, and final element dead band and resolution. One of the practical issues with the Smith Predictor is that the new controlled variable of the PID is no longer the actual process variable. The original process variable must be restored for the operator interface to the PID. Also, performance monitoring or trending must look at the original process variable rather than the new controlled variable used by the PID. Terry Blevins proposed in the 1979 ISA paper "Modifying the Smith Predictor for an Application Software Package" a multiplicative and additive correction of the process variable to deal with changes in the slope (gain) and intercept (bias), respectively in the process model.

The PID with a delayed external reset was informally presented in the 1980s and published in the early 1990s. It simply consists of putting a dead time (DT) block in the external reset. This method only requires that a single parameter commonly known as process dead time be entered as the dead time in the DT block. Terry Blevins documented in the early 1990s how the Smith Predictor for a particular Lambda tuning reduces to this PID with a delayed external reset.

The results presented here show that for a perfect model and the same controller tuning the PID with a delayed external reset performed better for processes with a small dead time to time constant ratio (time constant dominant), whereas the Smith Predictor performed better for processes with a large dead time to time constant ratio (dead time dominant). The Smith Predictor did not do as well for small dead time to time constant ratios because the control error seen in the controlled variable by the PID is much smaller than the actual control error in the process variable. In both cases, the improvement was not as impressive as the improvement gained from setting Lambda equal to the dead time rather than the time constant. Surprisingly the improvement in load disturbance rejection from dead time compensation was greater for processes with small dead time to time constant ratios. This goes against the conventional wisdom that the best opportunity for dead time compensation is for dead time dominant loops. The results can be explained in terms of the ultimate limit for performance of dead time dominant loops being lower. The reduction in the peak excursion from more aggressive tuning settings is negligible for dead time dominant processes because the peak error is essentially the open loop error.

Another startling result was how quickly a Smith Predictor erupted into rapidly growing oscillations in the controller output when the model dead time was more than twice the actual process dead time. The fast full scale oscillations in the controller output resembled on-off control. While it is relatively well known that dead time compensators are sensitive to model mismatch, the effect was expected to be gradual and thought to be more in terms of a model dead time being too small. The concern for rapid deterioration for a model dead time being too large was raised in Good Tuning - a Pocket Guide and was documented for model predictive control in Models Unleashed. While a PID with delayed external reset is also adversely affected by a dead time mismatch in both directions, this PID develops a small amplitude high frequency dither rather than a full scale oscillation in controller output for an excessively high model dead time. The consequence is less severe and may be adequately handled by the addition of a small dither filter inserted in the PID controller output, but this was not tested.

PID controller tuning for self-regulating processes without extenuating circumstances can develop oscillations when the identified (model) process dead time is too small. PID controllers with dead time compensation and model predictive controllers can develop oscillations when the identified (model) dead time is too large as well as too small.

In order to get the performance benefit from dead time compensation, the PID must be tuned more aggressively. In other words, a PID with dead time compensation will perform the same as a PID without dead time compensation if they are tuned the same. While the improvement in integrated absolute error (IAE) for load upsets from more aggressive tuning (higher controller gain and lower reset time) can be accurately estimated for a regular PID, the equation does not work well for a dead time compensator. Furthermore, a dead time compensator soon reaches a point of diminishing returns. For example, the improvement in load rejection of a Smith Predictor from a controller gain that is quadrupled may not be noticeable whereas for a regular PID, it normally results in a four fold reduction in IAE. It is important to remember there is a tradeoff between performance and robustness for any feedback controller in that as you make controller tuning more aggressive to improve load rejection you make the controller more sensitive to changes in the process gain, dead time, or time constant.

A nonlinear gain from the installed characteristic of a control valve has been widely discussed. However, the nonlinearity of the process gain of the temperature or composition response is the inverse and consequently the combined effect is less than documented when these loops directly manipulate a control valve. The variability of dead time is often larger than the variability of the process gain or time constant because the dead time is inversely proportional to a rate (e.g. flow rate or pumping rate or rate of change of a signal) and has many different sources (e.g. valve deadband or resolution, piping transportation delay, mixing delay, process lags in series, sensor lags, signal filters, and discrete communication or scan intervals). Thus, it is problematic to compute the dead time accurately enough to get the benefit of a dead time compensator.

In all of the following test results AC1 is always an uncompensated PID with Lambda equal to the process time constant (lag), which is equivalent to a Lambda factor of one.

The first set of test results illustrates the effect of different tuning. Here AC2 is an uncompensated PID with Lambda equal to the process dead time (delay), which is equivalent to a Lambda factor set equal to the dead time to time constant ratio.

Tuning Rule Test 1

The second set of test results shows how well a Smith Predictor can do. Here AC2 is a Smith Predictor PID with the gain doubled and the reset time halved after Lambda has again been set equal to the process dead time. In other words, this AC2 has twice the proportional and integral action of the uncompensated AC2 in the first set of test results.

Smith Predictor Test 2

The third set of test results shows how well a PID with a delayed external reset can do. Here AC2 is a PID with delayed external reset with the gain doubled and the reset time halved after Lambda has again been set equal to the process dead time. In other words, this AC2 has twice the proportional and integral action of the uncompensated AC2 in the first set of test results.

Delay Comp Test 3

For discussion of the test results and configuration, request from me a copy of the Advanced Application Note 003 titled "Compensation of Dead Time in PID Controllers."

If the total loop dead time was zero, you could set the controller gain as large and the reset time as small as desired. If there were no disturbances, you could simply sequence the controller outputs for startup, transitions, and shutdown. Process dynamics, controller tuning, and loop performance would be a non issue.

I once had a loop with zero dead time. I was studying the performance of my new algorithm for adaptive pH control in an Advanced Control Simulation Language (ACSL) program for my Master's Thesis. The larger I set the controller gain, the tighter the control I got. I was ecstatic. I was going to become "way famous". Then the let down - I had inadvertently turned off the dead time function. All I had left for process dynamics was a single time constant. The operating point nonlinearity of pH had no effect because I could stay incredibly close to set point. Since then I have seen tuning studies for a single time constant that beat to death a scenario where all the normal concerns are non existent. I decided to become sensitive to dead time especially since I could reduce my time on a pH startup by reducing dead time.

Control textbooks and studies tend to focus on set point responses ignoring unmeasured disturbances at the process input (e.g. load upsets). Special algorithms can be designed and tuned to prove a point. This may work well in simulations, aerospace, and hydraulic systems where dead time is either negligible or predicted/compensated and the servo response rules, but the real world of industrial process control isn't so kind.

The variety and variability of the sources of dead time and disturbances in process control is quite impressive. The following lists are just some major sources that come to mind.

Sources of Disturbances

1) Limit cycles (split ranged point discontinuity, resolution, and cascade dead band)
2) Interaction between loops
3) Slow secondary loops (cascade control)
4) Design limits (equipment operating limits)
5) Low residence times (e.g. undersized feed, recycle, surge, and waste tanks)
6) Manual procedures and manual valves
7) Field switches (e.g. on-off level control)
8) Activity (catalytic and metabolic)
9) Ambient conditions
10) Interlocks and sequences
11) Raw materials
12) Recycle streams
13) Startups, shutdowns, and product transitions
14) Fouling (e.g. process coatings) and frosting (e.g. crystal accumulations)
15) Parallel trains
16) Undersized cooling towers
17) Bored board operators
18) Shift change
19) Initiatives
20) Goal reviews

My worst experiences have been with undersized recycle, surge, and waste tanks. The residence time (volume divided by throughput rate), which is the process time constant, is so low there is not enough filtering of the changes in stream composition. Also, the level control on these tanks is forced to jockey the feeds to downstream operations to keep the tank from overflowing or running dry. Plants tend to avoid putting in the bigger tank to save money and reduce inventories when they need to debottleneck or push a process.

Sources of Dead Time

1) Discrete execution and communication interval
2) Analyzer cycle time (e.g. chromatograph)
3) Transportation delay (e.g. sample line)
4) Mixing delay (e.g. agitator, eductor, and sparger)
5) Injection delay (e.g. back filled dip tube)
6) Resolution limit (e.g. VSD, control valve)
7) Dead band (e.g. VSD, control valve)
8) Instrument time constants in series (e.g. sensor and signal filter lag)
9) Process time constants in series (e.g. thermal lags and residence times)
10) Lab samples (e.g. sample hold, processing, and analysis time)

Dead time is often inversely proportional to a rate and therefore a function of test conditions. The dead time from transportation delays, sample lines, sensor lags, and residence times in series is inversely proportional to flow rate. Mixing dead time is inversely proportional to agitator pumping rate or eductor flow rate. The dead time from dead band and resolution limits is inversely proportional to the rate of change of the signal (e.g. rate of change of process variable for measurement resolution limits and rate of change of controller output for valve dead band and stick-slip). The time it takes a measurement to get out of its resolution limit or noise band can be significant for level or temperature and depends upon how fast the process is driven to change and hence the step size in the controller output or set point. The dead time for control valves becomes just the summation of the pre-stroke dead time, discrete processing, and communication interval (all usually small) if the step in controller output is larger than the valve dead band or resolution limit. The dead time effect of dead band and resolution limits unfortunately does show up for unmeasured load upsets at the process input.

My intention is now to avoid any further dead time or disturbances to an evaluation of dead time compensators and model predictive control so check here next week for more fun than control engineers should be allowed to have with advanced control.

Last week we discussed the effect of disturbance timing on performance. This week we turn our attention to the location and speed of the upset and the Delay/Lag (dead time to time constant) ratio of the process.

Most control text books and papers show a step disturbance on the process output, which is the process measurement. This is the worst case scenario in that the disturbance fully hits the controller before the controller can take any corrective action. The abrupt change in the process measurement can cause a large step and bump in the controller output from gain and rate action, respectively. In some respects, this disturbance location is similar to noise. Conventional Lambda factors (>1.0) do well in helping a controller to not overreact to this disturbance.

Most control literature also tends to focus on a process where the delay (dead time) is comparable in size or larger than the lag (time constant). In these cases, conventional Lambda factors again give good performance and robustness.

I have often heard professors and operators say that a loop is terrible because it has a huge lag (process time constant). This is true for disturbances downstream of the process entering directly into the measurement. For a load upset (e.g. feed, utility, or ambient upset) into the process, the large process time constant (Delay/Lag < < 1.0) can provide incredibly tight control if a much smaller Lambda factor is used (<<1.0).

Most of the important loops I have worked on in the chemical industry (column or vessel composition, pressure, and temperature control), have disturbances on the process input and a Delay/Lag ratio much less than one. The book New Directions in Bioprocess Modeling and Control discusses how the interactive process temperature time constants cause the Delay/Lag ratio to be about 0.2 and how batch composition responses have a Delay/Lag ratio so small they look like they have an integrating process response.

Static mixers used for neutralization have a Delay/Lag ratio about one but the addition of the electrode time constant or signal filter makes the Delay/Lag ratio less than one. Poor reagent piping, injection, and mixing design and a large control valve dead band or resolution limit, can cause the delay to sky rocket. Large Delay/Lag ratios are often a symptom of poor plant/system design for chemical processes. On the other hand, there are processes, such as sheet or web thickness, and analyzers with large cycle times and transportation delays that make the loop very dead time dominant (Delay/Lag >> 1.0).

Feed composition, catalyst activity, metabolic pathway, and ambient temperature disturbances are generally very slow (upset lag of hours). Cooling water and steam disturbances can be faster depending upon system design (upset lag of minutes). Feed flow disturbances are much faster and generally reflect the response from reset action (upset lag of seconds). Step flow changes occur when pumps are turned-on and on-off (isolation valves) are opened.

As the upset slows down (upset lag increases), the peak error (maximum deviation) and integrated absolute error (total error) decreases but the fractional improvement in IAE from more aggressive tuning stays the same for loops with a large process time constant (Delay/Lag < 1.0) or increases for dead time dominant loops (Delay/Lag > 1.0). In a way, the upset lag performs a similar task to the process time constant in terms of slowing down the excursion rate of the process variable.

If there were no upsets, you wouldn't need a controller. You could just set the control valve to a predetermined position.

The following screen prints and excel file compares the performance of different types of tuning for various Delay/Lag ratios for load upsets that enter as process inputs. Lambda tuning does well for dead time dominant processes and can made to do as well as the Simplified Internal Model Control (SIMC) for lag dominated processes by the use of a Lambda equal to the dead time (Lambda factor equal to the Delay/Lag ratio). See our first blog on the Unification of Tuning Methods for more info.

Delay/Lag Ratio Test

Tuning Rules Results

Not discussed here is interaction and noise and how it reduces the desired degree of transfer of variability from the controlled variable (controller PV) to the manipulated variable (controller output). Also, not addressed is what change in the loop gain, delay, and lag (nonlinearity) can occur and does this change in dynamics make the loop too oscillatory. In general there is a trade off between performance and robustness whenever you are tuning a controller. Larger Lambda factors reduce the transfer of variability and improve the robustness of the controller. In summary, to evaluate a control strategy, algorithm, or tuning one should consider:

(1) Desired degree of transfer of variability from controller PV to controller output
(2) Amount of nonlinearity and its affect on variability
(3) Timing of disturbance
(4) Location of disturbance
(5) Speed of disturbance
(6) Delay/Lag ratio

How upsetting is this to dead compensators and model predictive controllers? For answers to this and more, stay tuned.

We could talk about how important communication is for our society and even more importantly our marriage but let's stick to something we are more interested in as automation engineers particularly since we essentially have no control over politicians and spouses. So let's talk about communication intervals, control execution intervals, analyzer cycle times, and input scan times.

We tend to think that faster is better but this is not always the case. For example, a bioprocess control engineer recently s