Process control is so detailed, fragmented, and experience dependent, it is difficult to see the commonality of process control solutions. In Deminar #9 at 10:00 am CDT Wednesday Sept 8, I will detail 10 key concepts in a unified approach that will be useful for process control improvement in 90% or more of the applications. Demos will be offered of the more dynamic consequences. The deeper understanding gained should be useful in developing process control improvements, most of which can be demonstrated by free use of virtual plants on the process control lab website http://www.processcontrollab.com/ .

To attend the event, go to http://bit.ly/JC-LiveMeeting
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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.

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.

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 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.