Hopefully your humor is wireless. Wired humor sounds dangerous.

The real reason for "wireless" in the title is that wireless is a hot subject and I am checking into ways to attract more people to my website.

My favorite quote is: "Life is far too important to be taken seriously" - Oscar Wilde

I have written a half dozen books with humorous intentions but lately I seem to have lost my sense of humor. If you find it, let me know.

Humor, music, and writing are my main sources of inspiration and pleasure. They seem to go hand-in-hand for me. I can't imagine writing without listening to music and without a smile on my face. Writing allows me to communicate and share everything I have learned and often leads to a deeper understanding on my part and hopefully the readers. Music expresses the depth of feelings better than any other media I know. Humor leads to creativity and problem solving capabilities. I think the barrier between people on difficult and highly opinionated issues can broken if you can get the people to laugh often by putting the problem into a bizarre context or taking the problem to a level of absurdity. Humor can provide a cognitive shift. Like music, humor is a gift from God and some theories propose it is an unexplainable mystery, very much like a mystical experience. A sense of humor requires empathy and the ability to put your self in the situation of others and see beyond your opinions and expertise and forgo the literal or analytical. Humor is a matter of taste and situation. I like the more cerebral humor of Woody Allen of Groucho Marx compared to the more physical (slap-stick) humor of the Three Stooges and Jerry Lewis. I have been blessed to have worked with many funny (and creative) people during my years at Monsanto. The ones that immediately come to mind are Jim Jordan, Steve Sanders (Dec 2009 Control Talk column "You Know You're a Geek if ..." , and of course my coauthor and cohort Stan Weiner. These guys were extremely successful and ended up being presidents or Fellows. Most recently I have had the fun of interviewing Randy Reiss, Mike Brown, and Hunter Vegas for Control Talk columns, all very funny besides being the best technically and able to tell the real story. Randy, Mike, and Hunter were able to rattle off "Top 10 Lists" for their respective interviews like it was second nature to them. Take for example Mike's Sept 2010 Control Talk "Top 10 Control Room KPIs That Will NOT Lead to Process Improvement"

For an outline of the many physical, cognitive, emotional, and social benefits of humor, check out the Benefits of Humor from the PBS series "This Emotional Life"

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
Use the information below to connect (if you're not using the available computer audio):
• Toll-free: +1 (877) 771-7176
• Toll: +1 (225) 383-1099
• Participant code: 264679

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.

When I first started teaching process control to junior and senior chemical engineers at Washington University in Saint Louis after retiring from Solutia, the students were less than receptive to my introduction of stuff they actually needed to know on the job. Except for the couple of students who were summer interns at Anheuser-Busch, my attempts of adding relevance were viewed as just being disruptive to the traditional task of learning frequency response and state space matrices. When I introduced the virtual plant for a weekly lab of hands-on learning, the attitude shifted from annoyance to enthusiasm. The skill and interest in using new computer tools and the fact the process simulations and graphics made the experience all seem real resulted in the labs becoming the highlight of the week. Several students decided to go on to careers in process control. One former student I met at Interphex became the manager of an automation group of a major pharmaceutical company. Even if the students didn't become process control engineers, the labs helped develop skills needed in industry. The distributed control system (DCS) is the window into the process and the ability to use and get the most out of the powerful tools and industrial standards in the DCS is important to anyone working in the process industry. This excitement and feeling that I was doing something significant to help students on "Day 1" of their prospective job, led me to think what can I do for bridging the gap between the leading edge research at universities and the opportunities for process control improvement in industry? The virtual plant to me seemed to be the way for universities and industry to get on the same page. This concept is summarized in the ACC 2009 paper ACC2009-BridgingtheGap.pdf.

The next step was to make labs as a self-learning experience available over the web with the idea that an employee could spend a few hours a month at a convenient time (e.g. lunch and learn) trying out the latest in PID control capability for various process and automation system designs and objectives. These labs provide a chance to find process control improvements by setting up scenarios that are of particular interest. Since the user interface employ operator graphics, knowledge of the particular DCS is not required. The capture of the last and best scores in terms of key performance variables (KPI) should help promote recognition and competitiveness for finding the best solutions.

I think we have barely scratched the surface of the true capability of today's PID controller with all of its features (e.g. structures, integral deadband, dynamic reset limit, and nonlinear gain). This spring and summer I will focus on generic control loops. This fall I will move on to the control of unit operations such as crystallizers, evaporators, extruders, neutralizers, and reactors. We hope users will twitter their results. The potential for learning and sharing is enormous and may be a way of getting the next generation of engineers to not only benefit from past expertise but take process control to a whole new level (see January 2010 Control article "The Future is Now")

I will conduct live seminars and demos twice a month to show how to use the labs. The connection and the topics and dates for the first 4 months are:

Recorded Live Seminar and Demo Series

To attend the event, go to http://bit.ly/JC-LiveMeeting
Use the information below to connect (if you're not using the available computer audio):
• Toll-free: +1 (877) 771-7176
• Toll: +1 (225) 383-1099
• Participant code: 264679

(1) PID Control of Sampled Measurements (How to Eliminate Oscillations from Analyzers and Wireless Measurements with a PID Enhancement) - April 7, Wed 1:00 pm CDT

(2) PID Control of Valve Sticktion and Backlash (How to Eliminate Continual Oscillations with the "Integral Deadband" PID option) - April 21, Wed 1:00 pm CDT

(3) PID Control of Slow Valves and Secondary Loops (How to Eliminate Bursts of Oscillations with the "Dynamic Reset Limit" PID option) - May 12, Wed 1:00 pm CDT

(4) Web Lab Access and Use Instructions (How to Use Free Online Process Control Labs for Fun and Profit and Become Famous by Friday or at Least Saturday) - May 27, Thurs* 1:00 pm CDT (* - Thursday date is to avoid conflict with the World Batch Forum)

(5) PID Tuning for Self-Regulating Processes (How to Compensate for Nonlinearities in Flow and Liquid Pressure Loops) - June 9, Wed 10:00 am CDT

(6) PID Tuning for Near-Integrating Processes (How to Reduce the Tuning Time for Column and Vessel Temperature and Pressure Loops by 90%) - June 23, 10:00 am CDT

(7) PID Control of True Integrating Processes (How to Reduce the Batch Cycle Time for Temperature and pH Loops by 25%) - July 14, 10:00 am CDT

(8) PID Control of Runaway Processes (How to Improve the Performance of Exothermic Reactor Temperature Loops) - July 21, Wed 10:00 am CDT

The tuning settings of many level loops aren't in the ball park. The result is persistent oscillations that spread throughout the process.

Level loops frequently manipulate feed flows to process operations. Variability in these feed flows causes variability in the temperature and composition in equipment whose process loops end up chasing continual changes in feed. Often the level loop creates slow rolling oscillations due to the product of level controller gain and reset time being too small. The solution of increasing the controller gain is counter intuitive and is rarely done correctly since the range of controller gains for level loops is exceptionally large and changes with the density of the fluid and the cross sectional area of the vessel.

Level loops make a good educational lab experiment in process control. To see how a DeltaV Insight adaptive controller automatically identified the tuning and compensated for nonlinearities for level control of a conical tank checkout the article "Adaptive Level Control". For more background on the dynamics and tuning of loops for integrating processes, see Appendix A referenced in this article and the September 2, 2009 entry on this website.

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.