The PID enhancement for wireless (PIDPlus) offers an improvement wherever there is an update time in the loop. In the broadest sense, an update time can range from seconds (e.g. wireless updates and valve or measurement sensitivity limits) to hours (e.g. failures in communication, valve, or measurement). Some of the sources of update time are:

(1) Wireless measurement default update rate for periodic reporting (refresh time)
(2) Wireless measurement trigger level for exception reporting (sensitivity limit)
(3) Wireless communication failure
(4) Broken pH electrode glass or lead wires (failure point is about 7 pH)
(5) Large valve operating on upper part of installed characteristic (low sensitivity)
(6) Valve with backlash (deadband) and stick-slip (resolution and sensitivity limit)
(7) Valve with solids, high temperature, or sticky fluid that causes plugging or seizing
(8) Plugged impulse lines
(9) Analyzer sample processing delay and analysis or multiplex cycle time
(10) Analyzer resolution and sensitivity limit

The PIDPlus waits for an update in the measurement whereas a traditional PID continually ramps the output acting on old information. When there is an update, the PIDPlus considers the changes to have occurred over the elapsed time from the last update whereas the traditional PID thinks the entire change occurred in the PID module execution time. The result is a spike from derivative action by a traditional PID that is particularly large when a measurement recovers or a valve trim or solids break free.

The improvement in control by the PIDPlus is most noticeable as the update time becomes much larger than the 63% process response time (defined in the white paper as the sum of the process deadtime and time constant). When the update time becomes 4 times larger than the 63% process response time that roughly corresponds to the 98% response time frequently cited in the literature, the controller gain can be set equal to the inverse of the process gain. This controller gain can provide an exact correction for changes in the measurement and setpoint.

The PIDPlus execution is kept fast so that the PID immediately responds to changes in setpoint, feedforward, mode, tuning, detail display parameters, and remote output. We have the interesting result that when the update is much larger than the 63% process response time so we can set the controller gain equal to the inverse of the process gain, the controller output goes immediately to the value needed to achieve the setpoint. An increase in update time to prolong battery life can actually translate to a faster setpoint response. However, if the process gain changes with time or operating point, the PID will require several updates to home in on the proper correction. An increase in update time will increase the settling time for unrecognized changes in the process gain. The use of an adaptive tuner such as DeltaV Insight that automatically identifies the process gain and schedules the tuning setting accordingly can sustain a fast setpoint response despite nonlinearities and a large update time.

The Emerson White Paper DeltaV-v11-PID-Enhancements-for-Wireless.pdf discusses these opportunities in more detail. Later this month, an entry on this site will show and discuss the trend plots that compare the enhanced PIDPlus with the traditional PID for the applications tested including valves with stick-slip and backlash.

It is important to distinguish between an update time and process deadtime. The update time is the time interval between successive updates by the final control element (initiated changes to the process input) and successive updates by the measurement (reported changes in the process output). The process deadtime is a continuous train of values delayed by the deadtime. The most common source of a pure process deadtime is a transportation delay of temperature and composition changes in a conveyor, extruder, dip tube, heat exchanger, pipeline, sheet line, or any volume where there is plug flow (no back mixing). Small time constants such as thermal lags, sensor lags, signal filter times, transmitter damping settings, effectively become additional deadtime in terms of a first order plus deadtime approximation (single time constant plus deadtime). The PIDPlus algorithm does not correct for process deadtime. As the process deadtime increases and approaches the update time, the opportunity to increase the PIDPlus gain decreases. For compensation of deadtime, a standard deadtime block can be inserted between the BKCAL_OUT of the AO block and the BKCAL_IN of the PID block if the DCS uses the positive feedback method for the integral mode (external reset) as reported in Advanced Application Note 3 "Compensation of Deadtime in PID Controllers".

In a future Deminar we will look in greater detail at the effect of updates time of discontinuous measurements and process deadtimes on the ultimate period and ultimate gain and if there is an improvement in loop performance offered by a combination of PIDPlus and deadtime compensation.

It is hard to imagine that in this day and age we are relying on "after the fact" process performance analysis on a gross scale. I wonder if we still use spreadsheets and costs sheets to judge a process unit and operator performance. We can see differences in setpoints at a shift change but have little idea as which shift's operating points provide the most production and least cost of goods (COGs). If pet food and ethanol manufacturers have online process metrics to compare shift and plant performance, why don't we see more of it in chemical plants? As COGs, energy conservation, and environmental emissions become of paramount importance, the benefits of online process metrics should be obvious. Since "seeing is believing" the use of wireless measurements moved from unit operation to unit operation can help develop and justify installations.

For example, if the utility flow and the inlet and outlet temperatures or pressures to coils, heat exchanger, and jacket are measured for each reactor, evaporator, and crystallizer, the energy use rate can be put online as an indicator of reaction, evaporation, or crystallization rate. The energy use per unit feed used or product produced can be monitored online for energy conservation. The overall heat transfer coefficient can be computed online to provide a performance indicator of fouling.

Inlet and outlet pressure measurements provide an indication of fouling (a heat exchanger specialist in my days in Monsanto's Engineering Technology said the pressure drop through the tubes of a heat exchanger is the best indication of fouling). Pressure measurements also show when a key unit operation is being starved for cooling supply especially since cooling tower and refrigeration systems often lagged beyond production increases in the 1990s. For example, a sold out fed-batch reaction at the top of a structure was periodically starved for coolant water by less profitable batch operations. Override control cut back the feeds to prevent a temperature trip but wireless measurements could have led to a more profitable scheduling of unit operations and improvement in coolant system design. Piping system and pump problems also extend to the process side.

An example that had all of the above problems big time is the stressed out production unit in Figure 3-6 in Advanced Control Unleashed The multiple lines of reactors, evaporators, and crystallizers converging and diverging were going up and down like Yo-Yos due to frosting (formation of crystals on surfaces) and subsequent defrosting (heating to melt the crystals). The plant was operating at double its original design capacity. Controller outputs would go to their high limit due to pipelines and pumps that were too small. Bigger control valves did not help much because the available pressure drop was insufficient. De-bottlenecking projects actually reduced capacity due to lack of measurements and the process knowledge they provide.

Thermowells on columns can find the most sensitive tray location by making a simple manual change in the flow manipulated by the temperature control (distillate flow). The best tray is the one with the largest and most symmetrical change in temperature for changes in the distillate to feed ratio (greatest and most linear process sensitivity or gain) as shown in the Figure 7-6 of the ISA book Advanced Temperature Measurement and Control Figure 7-7 shows the consequence of selecting the best tray and not so good tray for closed loop temperature control (Advanced-Temperature-Distillation-Column-Control-Excerpt.pdf). Three dimensional (3-D) profiles can provide a dramatic visualization of the change in the process gain with time as feed composition changes.

3-D plots generated of cross direction and machine direction sheet thickness profiles from a meter traversing the sheet provided the knowledge for optimizing the mixer, extruder, and sheet line as studied in the ISA 2001 conference paper "Constrained Multivariable Control of Plastic Sheets"

3-D plots are a natural for batch operations to show composition (e.g. cell density), pH, and temperature batch profiles for a series of batches.

For more information do a search for "metrics" on this website and you will find a variety of opportunities discussed for online process metrics and plots to help operations, process technology, and process control improvement initiatives. Wireless measurements and metrics can fill in the missing information for process simulations and vice versa.

In the old days we installed thermowells, pressure connections, and orifice flanges so we could move temperature and pressure gauges around to provide coarse local indication of equipment and piping system performance. Today portable wireless measurements can provide accurate indication in the control room offering opportunities that could be subject of a book or two let alone the blogs on this website.

Since efficiencies and most of the process gains for temperature and pressure are function of flow ratios (reactant/product, fuel/product, fuel/steam, steam/product, reagent/product, and coolant/product) flow is a key measurement. While a favor coriolis mass meters for process stream since they provide an accurate mass flow and density useful for inferential measurements of composition, which ultimately what you want to know, averaging annubars and thermowells can enable the use of wireless DP and temperature measurements for computing mass flow. Just knowing the material balance would be a big step forward as discussed in the Jan-Feb 2010 InTech article "Advances in Flow and Level Measurements Enhance Process Knowledge, Control"

Wireless pH and conductivity 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"

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.

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.

As I look back over my experience with pH and conductivity measurements, the following opportunities stand out.

(1) Selecting the best sensor technology for a wide range of process conditions
(2) Eliminating measurement noise
(3) Predicting sensor demise
(4) Developing process temperature compensation
(5) Developing inferential measurements of process concentrations
(6) Finding the optimum sensor location

You really can't ship most chemicals to the electrode manufacturer and electrodes sent back after a problem often don't tell the whole story including handling, maintenance, and process conditions. The manufacturer's application support people are often at a loss as to what was really the problem. Then there are the insidious spikes that come and go with no sense of the source or the fix.

The biggest source of continual pH noise is fluctuations in acid and base concentration at the electrode. Operating points on the relatively steep portion of the titration curve require a degree of mixing that goes way beyond the norm. Electrodes are moved to a location that is the best compromise between noise and measurement delay and lag.

Users can install a test setup in the plant to compare the performance of various electrode technologies but this is time consuming and does not allow experimentation. Tests in the lab usually involve "dumb" lab meters with the data ending up in a spreadsheet oblivious to the historian and the tools in the DCS for neural networks and data analytics.

To see if the opportunities are more than a dream and if the problems can become just a bad memory, check out the InTech web exclusive article "Opportunities for Smart Wireless pH, Conductivity Measurements"

I will be doing the presentation McMillanISABostonExceptionalOpportunities.pdf next week at the Boston ISA section meeting. I will be giving out 10 free copies of my book The Funnier Side of Retirement for Engineers and People of the Technical Persuasion to balance out the serious stuff.

When?
Tuesday, October 20, 2009
6:00 - 7:00 Reception and registration
7:00 - 8:00 Dinner
8:00 - 9:00 Presentation

Where?
Best Western, Waltham, MA
380 Winter Street, Waltham, Massachusetts, 02451-8700, US
Phone: 781/890-7800 Fax: 781/890-4937