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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

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.

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

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.

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.

Before my talk at the Boston ISA section meeting on Oct 20, I had the opportunity to interview Sarah Tremblay and Ted Stillwell, automation engineers for a company that designs water and wastewater treatment systems. Sarah has a degree in mechanical engineering and has been on the job for one month. Ted has over 40 years of experience in the process industry. Like me, Ted started out in construction so he got a lot of first hand experiences on what worked in the field. The interview was an informal discussion for an upcoming Control Talk column on "Expertise Development" probably with a more catchy title such as "The Future is Now."

When I started as an E&I design and construction engineer after graduating with a degree in engineering physics, I went to a 12 week instrument school. One of the attendees at the ISA talk says he knows a company that had a 9 month training program. Such on-the-clock courses and programs are rare. Are we missing the boat? Sarah effectively said "not really" because such an intensive and extended training would not mean much to a new engineer who has not developed a real feel for the job. Sarah is learning by being responsible for small parts of a project. She asks a lot of questions. She visits job sites and goes on panel checkouts with Ted to see how designs translate to actual installations. This is the time honored tradition of how expertise is developed on the job. In 5 to 10 years, you have a proficient engineer. In my case, my development was accelerated by being sent after instrumentation school to E&I field construction for 2 years for the building or renovation and startup of 5 production units. Since sending new engineers to E&I construction is not a widely viable option, what can be done to improve this process?

There are no easy answers. Courses in chemical, electrical, mechanical, and systems engineering should have more emphasis on process measurement and control as practiced in industry. Practitioners (especially recent graduates) should be invited to give guest lectures on case histories of process control improvements and the type of jobs in the process industry. It should be emphasized that regardless of whether the job is in engineering, research, or production, all engineers rely on the automation system to see, analyze, and interact with the process. You need to know how to understand the system's interface and functionality to take full advantage of the systems capability. Process control labs with industrial control systems should be an essential part of this learning experience. Many of the leading universities have taken this approach as described in the June 1, 2009 entry on this web site "What I have Learned? - Bridging the Gap between Universities and Industry."

Sarah made a good point that course labs can be too controlled. The script is fixed and the student doesn't have the opportunity to explore different scenarios and ideas, implying the falsehood that on-the-job situations are typically as uneventful. To help address this issue, I think these labs should be offered as a stand-alone course rather than in addition to a "hands on" experience to demonstrate points in a lecture course. I think the lab should consist of both a physical and a virtual plant for the same unit operation. The virtual plant would allow the student to take the operation and control system to places not practical to achieve because of time and equipment limitations.

This education process needs to ongoing. It should not stop with the new job. Since extended training programs may be too much too soon besides being impractical from a standpoint of cost and time in today's work place, periodic seminars and demonstrations with a virtual plant would seem to be the most effective approach. Case histories and updates on technological advances are essential. The seminars and labs can be conducted via the web if interaction between the presenter and attendee is not sacrificed. Companies need to provide the time and encouragement for ongoing education. The ISA Certification of Automation Professionals (CAP) should be part of the career plan. Participation in ISA should be part of growth process for both the individual and ISA. There should be a company library of the best books on process measurement and control (see next week's entry here for my short list). Users should be encouraged to publish to help solidify their experience and share it with the profession. I always learned something about my application in the process of having to describe the problem, considerations, concept, and solution. See my May 28, 2009 entry "What have I Learned - Writing" on what worked for me. Sarah with a minor in English is ideally situated for this endeavor.

Given that the education process takes years of on-the-job experience it is critical that companies hire new automation engineers now to insure the existing expertise is transferred before the expertise is gone. See my Control Talk Column series "Going, Going, Gone" Part 1 (August), Part 2 (September), and Part 3 (October) for a discussion with some key people from what is probably the best process control group in the USA.

Most of the experienced engineers here in the USA are members of AARP.

My articles in 2009 are focused on pH and wireless measurement and control. Not listed below is an article planned for later this year on the use of wireless pH for inferential measurement of solvent concentration at the University Texas Research Campus pilot plant for carbon dioxide capture.

"Virtual Plant Provides Real Insights", Chemical Processing, Jan, 2009
"ImprovingpHSystemDesignandPerformance.pdf"

(1) Modeling and control in a virtual plant showed that the size of the neutralization vessels could be reduced from 40,000 to 10,000 gallons reducing the project capital costs by more than $500K for a strong acid and base system. The virtual plant was also able to detail mixing, reagent injection, and valve requirements

(2) Translation of the controlled variable from pH to percent reagent demand (X axis of the titration curve), provided faster recovery from upsets.

(3) It was expected that the resolution of the reagent valves needed to be exceptional. It was surprising how important resolution was for the feed valves. What would be normally considered a good resolution for the feed valves caused excessive deviations in the vessel pH. Stick-slip in the feed valves showed up as short term deviation rather than a limit cycle in the pH because of the feedback correction by the pH loop

(4) Innovative Methods of continuous and semi-batch mode offered maximum operational flexibility.

"Is Wireless Process Control Ready for Prime Time", Control, May, 2009

My time in spent building and starting up chemical plants, working in process labs, and dealing with pH measurement noise gave me a greater appreciation for the significance of being able to eliminate instrument wiring. This article offers my take on the value wireless and shows incredibly tight wireless bioreactor pH control. Some biopharmaceutical processes require control within 0.02 pH of set point for optimum operation. The pH control demonstrated in this wireless pH test on a bioreactor with a disposable liner (single-use-bioreactor) was an order of magnitude better than required, the tightest pH control I have ever seen. Most of the credit goes to new wireless PID algorithm and the exceptional capability of the pH electrode and wireless pH transmitter. Finally, the wireless measurement did not have the spikes exhibited by the wired pH transmitter from ground noise, showing that wireless can eliminate a significant source of noise.

"The Essentials of pH Measurement Design, Installation, Maintenance, and Improvement", ISA 55th International Instrumentation Symposium, League City, 2009

This paper is a chapter out of "The Essential Book" scheduled to be published in time for ISA Expo 2009 in Houston.

I am back from vacation. I am still feeling fine from a nice break from the heat of a book deadline and Austin's record temperatures. I was up north in Minnesota and Wisconsin where it was 25 degrees cooler. I happened across an exhibit of Cray computers in the Museum of Science and Technology in Chippewa Falls, the home of Cray Research, Inc. Samuel Cray attributed part of the company's success to a motto of "taking our jobs seriously but not taking ourselves seriously." Hopefully my Control Talk column is an example of this motto by combining a humorous look at ourselves with technical straight talk. A compilation of the column's comics was featured in the July issue of Control magazine in the online section "Out of Control Cartoons".

Then there are the outbursts of craziness designed to loosen us up such as The Funnier Side of Retirement for Engineers and People of the Technical Persuasion, which just won the ISA Raymond D Molloy Award as the best selling book in 2008. Since humor is derived from exaggeration of commonly recognizable traits, please don't buy this book if you want a detailed analytical realistic treatise. For this you can get any one of a dozen or more guides to retirement. If you like bizarre humor, this book may offer some laughs.

The following list of articles and associated papers in 2007 - 2008 are totally serious except for an occasional top ten list.

"Improve Control Loop Performance", Chemical Processing, Oct, 2007

(1) Nearly all control loops eventually affect the process by the manipulation of a flow via a control valve. Control loop performance depends upon valve performance.

(2) Valve specifications do not require a valve actually move in response to a change in signal. When valve performance has been considered, response time and rangeability are frequently the criteria. The real issues are valve resolution (sticktion) and deadband (backlash). If a properly selected and sized valve-actuator assembly has good resolution and sticktion, the valve will generally have good rangeability and response.

(3) Using a "state of the art" digital positioner can eliminate the positioner sensitivity problems prevalent in positioners for the last 50+ years but the positioner can be lying about valve performance if the feedback measurement is actuator shaft rather than ball or disk position in a rotary valve. Putting a digital positioner on a valve designed for on-off service and tight shutoff by a piping manufacturer is like putting makeup on a pig. On the other hand, putting a digital positioner on a valve designed by throttling service by a control valve manufacturer may be the best thing you can do for your loop.

(4) For pH control, the resolution of the control valve can determine the number of stages of neutralization needed.

"Virtual Control of Real pH", Control, Nov, 2007

"Advances in pH Modeling and Control", ISA 54th International Instrumentation Symposium, Pensacola, May, 2008

An online virtual plant can be adapted to match the actual plant by the simple innovative use of an integrated model predictive control (MPC). In this neutralization system, the influent acid concentration was quickly adapted to match the ratio of reagent to influent flow in the virtual plant to the actual plant. The virtual plant demonstrated of ability of model predictive control to replace fuzzy logic control for reagent optimization. An improvement in the kicker algorithm provided immediate savings of more than $100K per year in reagent cost.

"PAT Tools for Accelerated Process Development and Design", Bioprocess International, Process Design Supplement, Mar, 2008.

"Bioprocess Control: What the next 15 Years will Bring Part 2 - Process Modeling",
Pharmaceutical Manufacturing, June, 2008

Most process and control system improvements in bioreactors are set by biochemists and biochemical engineers in the research. A virtual plant running 500 times real time can complete a bioreactor batch in 15 minutes that would take several weeks in the lab or pilot plant. Virtual experimentation can accelerate process development and design. The integration of advanced control tools in the virtual plant can demonstrate the effectiveness of substrate and batch profile control. The results can justify additional online analytical measurements. The fast playback of virtual and actual plant batches in a minute or two offers incredible opportunities for online analysis via integrated data analytics and adaptive control tools. The potential benefits are faster commercialization, higher yields, and real time release.

"Unlocking the Secret Profiles of Batch Reactors", Control, July, 2008

The purpose of a batch reactor is to manufacture a product of a particular composition. The progression of the batch to the desired end point (the batch composition profile) is the most important indicator of batch performance. However, batch reactors rarely have any measurement of this profile. For chemical reactors, the main measurements indicative of the hidden profile of real interest are pressure, temperature, and feed flows. Multivariate statistical techniques such as Projection to Latent Structures (PLS) may be able to predict end points but the composition profile still remains a secret. If actual or inferential measurements of the profile are available, model predictive control can maximize the slope of the profile and hence the progression of the batch. The result is a faster batch for a given end point or a higher end point for a given cycle time. Also, the variability in batch profiles is transferred to feeds resulting in more repeatable batch profiles.

There is a misconception that biological processes are not as highly automated as chemical processes. Bioreactors generally have more control loops than a typical chemical reactor. Cell cultures have temperature, pressure, air flow, oxygen flow, inert flow, carbon dioxide flow, sodium bicarbonate flow, substrate flow, nutrient flow, pH, and dissolved oxygen control. Major advances in at-line composition measurements, such as the Nova Bioprofile Flex Analyzer combined with an auto sampler can provide measurements of substrates, nutrients, byproducts and cells every 4 to 12 hours depending upon the application. The Fogal Dielectric Spectroscopy probe can provide a measurement of the integrity of the cell membrane (cell viability). When combined with a turbidity measurement of cell density, the Fogale probe offers an online indication of live and dead cell concentrations.

One of the obstacles of online composition control is the time delay from the sample cycle time. The time in between samples for at-line analyzers can vary from an hour to a day. Fortunately, an unexpected side benefit of the enhanced wireless PID (developed to handle the larger and more variable time delays of wireless measurements) is exceptional control using measurements from at-line analyzers. The wireless enhanced PID has been shown to provide tight and stable control using at-line analyzers in specific studies for glucose control and in generic studies for continuous and batch processes. The results are documented in slides 29-34 of Interphex2009_Advances_In_Bioreactor_Modeling_and_Control.pdf. See the May 11, 2009 entry "What have I Learned - Cost and Source of Oscillations (Part 4)" for more details.

The new control algorithms (max slope MPC setting the enhanced wireless PID) coupled with new at-line and online analytical measurements will make bioreactor profile control common place leaving chemical reactor control even further behind. Are we going to let this happen?

Next week we conclude with the 2009 articles that include results of wireless control in a bioreactor with a disposable liner called a "Single Use Bioreactor" (SUB).

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.

Sometimes it seems universities and industry reside on planets that are light years apart. Too bad we don't have Star Ships with warp drive. Universities have leading edge research. Industry has "state of the art implementation."

Why are universities and industry "worlds apart?"

Engineers in industry don't seem to understand how to apply the research from universities. Professors don't appear to really know what is needed in industry. The tools are quite different. Engineers in chemical, pharmaceutical, and pulp & paper plants configure their control strategies in a distributed control system (DCS). Professors typically have their graduate students program their algorithms and test cases in Matlab.

One way to get industry and universities on the same page is to provide a DCS to the university with all the tools needed for research, such as a Matlab interface. In many cases the Matlab code can end up being configured in the DCS as part of the maturation of the innovation. The use of the DCS minimizes the reinvention of the wheel, such as the PID algorithm with all of its evolutionary enhancements. The setup facilitates the transfer of knowledge between the universities and industry. Being able to explore, prototype, and demo university innovations in a DCS makes it more real to industry and leads to rapid deployment and sharing of actual plant results.

If there is a unit operations lab, process control lab, or pilot plant, the DCS can be used to control the equipment used in the experiments. Students gain valuable experience in learning how to work with a toolset that is designed to meet industrial standards. Just learning the nomenclature and working with a DCS gives the student practical skills and confidence when as a new employee the student enters the control room. The window to see and affect the process is the DCS. Whether the student is going into automation or process design & technology, the student needs to be able to understand how to access and review modes, limits, options, and variables that determine how well a process runs. For example, the student gets to work in a university DCS on PID features commonly used in industry:

(1) PID limits (e.g. output, set point, and anti-reset windup limits)
(2) PID options (e.g. set point tracking of the process variable in manual, dynamic reset limiting, and nonlinear gain modification)
(3) PID form (series and standard)
(4) PID structure to determine whether each PID mode (proportional, integral and derivative) works on the process variable or the error (difference between the set point and the process variable)
.
The first semester I taught the Chemical Engineering course "Introduction to Process Dynamics and Control" at Washington University in Saint Louis as an adjunct professor, the students could not relate to my attempt to introduce practical plant applications and considerations in the normal course of Laplace transforms and bode plots. The second semester I added a virtual plant that consisted of a DeltaV DCS running in the Simulate mode integrated with HYSYS dynamic process simulations for each student. I later configured most of the process simulations directly in control studio. I was amazed how fast the students learned how to work in the graphical configuration environment and operator interface. All they needed was a few screen prints on navigation to get them started. Several of the students subsequently got intern or permanent positions doing configuration at the local DCS industry center. I had these students with experience in the automation industry come back to speak to the next class. The result was a dramatic turnaround in appreciation and understanding of what they would face in industry. The students decided on their own to go online to find and buy tee-shirts with Duncan, the DCS mascot, windsurfing. I ended up buying tee-shirts too and we all posed for a group photo by one of the students.

The main obstacle to the use of the DCS in the university is the initial installation and training. This is addressed by the support of industries with the same DCS who have a working relationship with the university and the local business partners of the DCS supplier. This method has enabled over 100 DeltaV DCS installations at educational institutions.

At the Automatic Control Conference in Saint Louis on June 11, I am co-chairing a session with Professor Tom Edgar from the University of Texas on "Bridging the Gap between Universities and Industry." The presentations are:

(1) "Bridging the Gap Between Universities and Industry"
(2) "Digital Process Control Lab at Washington University"
(3) "The Bioprocess Laboratory at Washington University"
(4) "Rose-Hulman Institute of Technology Unit Operations Laboratory"
(5) "Engineering Research Center for Structured Organic Particulate Synthesis (Rutgers, Purdue, New Jersey Institute of Technology, University of Puerto Rico at Mayaguez)"
(6) "Using a Distributed Control System (DCS) for Distillation Column Control in an Undergraduate Unit Operations Laboratory (University of Texas)"

My next blog will be June 22. In the mean time enjoy summertime.

• Adaptive Level Control - Control Magazine, February 2010 (1.1MB PDF)



• Advanced Control Smorgasbord - Control Magazine, May 2004 (153KB PDF)



• The Next Generation: Adaptive Control Takes a Leap Forward - Chemical Processing, September 2004 (466KB PDF)



• Full Throttle Batch and Startup Response - Control Magazine, May 2006 (138KB PDF)



• Maximizing PAT Benefits from Bioprocess Modeling and Control - Pharmaceutical Technology, November 2006 (808KB PDF)



• Improve Control Loop Performance - Chemical Processing, October 2007 (715KB PDF)



• Virtual Control of Real pH - Control, November 2007 (173KB PDF)



• Advances in pH Modeling and Control - ISA 54th International Instrumentation Symposium 2008 (332KB PDF)



• Bioprocess Control: What the Next 15 Years Will Bring Part 2 - Pharmaceutical Manufacturing, June 2008 (1,307KB PDF)



• PAT Tools for Accelerated Process Development and Improvement - BioProcess International, March 2008 (511KB PDF)



• Unlocking the Secret Profiles of Batch Reactors - Control, July 2008 (259KB PDF)



• Virtual Plant Provides Real Insights - Chemical Processing, January 2009 (1,906KB PDF)



• The Essentials of pH Measurement Design, Installation, Maintenance, and Improvement - ISA 55th International Instrumentation Symposium 2009 (480KB PDF)



• Is Wireless Process Control Ready for Prime Time? - Control, May 2009 (293KB PDF)

• A Funny Thing Happened On The Way To The Control Room E-BOOK



• Advanced Control Unleashed: Plant Performance Management for Optimum Benefit



• Advanced pH Measurement and Control, 3rd Edition



• Biochemical Measurement and Control E-BOOK



• Centrifugal and Axial Compressor Control Student Text and Instructor's Guide E-BOOK



• Continuous Control Techniques for Distributed Control Systems E-BOOK



• Good Tuning: A Pocket Guide, Second Edition



• Models Unleashed: Virtual Plant and Model Predictive Control Applications



• New Directions in Bioprocess Modeling and Control: Maximizing Process Analytical Technology Benefits



• Process/Industrial Instruments and Controls Handbook, 5th Edition



• The Funnier Side of Retirement for Engineers and People of the Technical Persuasion

Essentials of Modern Measurements and Final Elements

New Developments in Modeling and Control

• Introduction Batch Control Story


• Virtual Plant Concepts Dynamics


• Measurement and Control


• Batch Profile Analysis


• Model Predictive Control

• Control Loop Foundation for Batch and Continuous Control (1,036KB PDF)


• Opportunity Assessment and Advanced Control (1,679KB PDF)

My first experience with wet labs was about 35 years ago when I set up an acetic acid and water neutralizer and distillation column with conductivity, flow, level, pH, and temperature loops as part of a 6 week course to teach process control to new employees. I learned first hand how time consuming and expensive it is to set up and keep a lab running in top notch condition. Any compromises in hardware lead to headaches. I handed this off to someone willing to make the lab and the course a full time job.

About 10 years later, I managed to get a research technician to set up a lab to test pH electrodes from 9 different manufacturers. We got a lot of data on the effect of salt concentration and temperature on electrode performance but we had headaches with algae growth and dealing with undocumented features. The flat glass electrode developed large errors as the temperature and slat concentration was increase. Solid reference electrodes had acceptable performance but were not as accurate as some gel double junction electrodes. It would have been nice to study the effects of coating but the consequences in terms of cleaning out the equipment and lines were prohibitive. The lab was only used for one year but this still represented a cost of probably $50K in time and material. In retrospect, we should have studied the effects of velocity and mixing. I would love to have a lab today that is better than my 1980s lab to study the performance of new electrodes, diagnostics, and wireless reporting. It seems to me there is less information today than 20 years ago on the effect of process conditions on pH electrode performance. pHwetLab

While teaching a course on process dynamics and control at Washington University (WU) in Saint Louis, I was asked to teach a digital computer control lab for systems engineers. The lab was mostly a collage of instrumentation, valves, and controllers you would never see in industry. I arranged for the donation of the latest Fieldbus smart transmitters and smart control valves and a "state of the art" DCS system by Emerson. While teaching the lab would be neat, I knew from previous experiences it would an intensive effort that would take away from teaching, studying, and writing on modeling and control using the virtual plant I had set up at WU for my course for Chemical Engineers. Luckily I found the perfect choice in Bob Heider who enthusiastically has kept the lab in great shape and taught a course of great practical value for the last 5 years. The lab is unique in that engineers are taught how to calculate and evaluate process dynamics on a first principle basis.

My most recent experience is initiating and guiding a wet lab for bench top and single use bioreactors (SUB). Fortunately, Broadley-James Corporation (BJC) has committed the resources to make this happen. As with all process research labs, especially bio-reaction labs using new analyzers, cell lines, and media, strict schedules are an impossibility particularly considering this is an extracurricular effort for BJC. We have suffered from prototype sample system and analyzer failures, an NIR probe company going bankrupt, media problems, cell line discontinuation, infections, and delivery delays. Despite the slow progression, we have adjusted the virtual plant's model growth, death, and substrate kinetics to better match the lab runs and have tested closed loop glucose control, which is essential for an effective design of experiments (DOE) for parameter identification. We are also now testing wireless pH and temperature control on the SUB. PATLab

A successful wet lab requires patience, enthusiasm, and a focused "care taker." The practical experience and data gained for the development of sensors, control strategies, and dynamic models is well worth the effort. Experimentation in a wet lab can be more effective and is certainly much less expensive than experimentation in an actual plant even if permitted.

This is 100th anniversary of the glass pH electrode yet we still do not know much about what affects their life. Not much has been published about application problems and practices. There have been a few academic studies on the effect of process conditions on the glass electrode but these are over 20 years old. The best book on the theory of pH measurement The Determination of pH is over 35 years old. The glass electrode is more important than ever because of its extraordinary rangeability and sensitivity to hydrogen ion concentration. What other measurement can cover 14 orders of magnitude of concentration and detect changes as small as 0.00000000000001 (0 to 14 pH scale).

While the fundamentals of the glass pH electrode have not changed in a hundred years, there have been significant improvements in the glass formulation and construction so that it can handle high pH and high temperature fluids and repeated sterilizations. For example a new high temperature electrode increases the life expectance by 100% as seen in the attached test results. HighTemperatureGlassElectrodeLife The same company has developed a new electrode for the biopharmaceutical industry that can withstand 50 sterilizations. These new glass electrodes maintain a response time of seconds whereas other electrodes develop response times of several hours due to the premature aging of the outer glass surface.

As the number of pH applications increased dramatically due to the Clean Water Act and the growth of biopharmaceuticals and specialty chemicals, maintenance costs became a bigger issue. Filling and pressurizing reference electrodes and troubleshooting separate measurement electrodes became points of pain. In response, electrode suppliers developed the throwaway combination electrode that built a sealed reference, glass measurement, and temperature sensor into a single rugged probe body. Maintenance simplified to calibration and replacement. Some users unfortunately skipped the calibration part.

Reference electrodes maintain a low resistance path to the process for electrical continuity of the pH measurement by an internal electrolyte that is contact with the process fluid through a porous reference junction. The elimination of the flowing reference junction can lead to coating and contamination because there is no flushing action to prevent back flow (migration) of the process through the reference junction into the interior. To deal with these potential problems there have been major improvements in the construction of the reference electrode to reduce contamination (poisoning) by the use of multiple internal junctions and by the use of a porous solid with tortuous paths instead of a gel to slow down the migration of process ions and prolong the time it takes for the process to reach the internal silver-silver chloride element.

There have been attempts to develop alternatives to the glass electrode, such as the Iridium oxide, ISFET, and optical sensors. Yet we have not seen significant use.

The secret life of pH electrodes will be exposed in a series of Control Talk columns starting in the February issue of Control magazine. The series will include very candid views about the history and future of pH measurement from the most experienced people at major electrode manufacturers and the results from an online survey to find out what is really going on with pH applications. You can participate in the survey via the following link: http://www.zoomerang.com/Survey/?p=WEB228KQLJS4KT

If you want to learn more about pH measurement and control consider attending a short course. If you have a pH control problem, you can get it solved in the course and learn how to create a virtual plant of the pH system to demonstrate and prototype process control improvements. You can get info on my course via the following link:
http://www.emersonprocess.com/education/catalogrev/automationsystems/9060.asp

For more info on pH measurement check out previous entries in the pH and plant design categories on this web site. You can get more information on pH modeling and control from recent articles "Virtual Control of Real pH," Control, p. 47 (Nov. 2007) http://www.ControlGlobal.com/articles/2007/385.html
and "Virtual Plant Provides Real Insights," Chemical Processing, (Jan 2009).

In the future we can look forward to new electrode diagnostics, interrogation of the history of calibration adjustments for the electrode, and wireless transmission. CD Feng, receiver of the 2007 ISA Arnold O. Beckman Founder Award for his technical contribution and innovation has agreed to provide key chapters on pH sensors, diagnostics, and new technologies for the 4th edition of my book Advanced pH Measurement and Control. Maybe the life of the pH electrode won't be so secret.

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..."

I was at a "Guess Who" concert at the "One World Theatre" here in Austin the "Live Music Capital" last night thinking wow the lead singer who obviously wasn't born when the band had their greatest hits sounded the same but better than the original recordings. The tone and inflections were not only right-on but enhanced. While the singer I heard at the Austin "Bat Fest" who sounded more like Meatloaf than Meatloaf was impressive, this concert blew me away. I moved to the other side of my brain to the dynamic world of process control and pondered if "high fidelity" or in this case "hyper fidelity" is possible in dynamic models.

For the last 30 years I have been creating and using models of medium fidelity. When I got into bioreactor modeling, I moved into the realm of "high fidelity" as a result of necessity and opportunity.

For pharmaceutical processes, the process and control system design is set in the process research and development phase, often by the biochemist. By the process design and commercialization phase, the set points and control strategy or lack thereof is set in stone. If my modeling was going to be used for improving the product concentration and quality at the end of the batch or reduce the batch cycle time, I needed to move my model upstream from design into development. Also, standing with my bioreactor model demo next to the Broadley-James booth at Interphex 2007, Scott Broadley and I saw a synergist opportunity. Scott as a leading supplier of bench top systems completely automated with a full capability DCS and the latest technology in probes envisioned he could enhance the knowledge and system capability offered with a dynamic model of the process that could explore "what if" scenarios with a virtual batch running 1000x real time. I could see besides getting the needed process test data and characterization of the model, Broadley-James would be as interested as me in making the details public knowledge whereas pharmaceutical companies who expressed interested in participating in the model development would keep the results and conditions as closely guarded secrets. PATtools

Looking toward the future Emerson and Broadley-James (principally Trish Benton and Michael Boudreau) have ventured into the world of high fidelity by the parameterization of a bioreactor model in DeltaV Simulate Pro Control Studio based on cell culture runs to create a virtual plant.

So what if you are not into bioreactor or high fidelity modeling? There are plenty of uses and reasons for models of various levels of fidelity that can get you a virtual plant.

Top Ten Reasons for a Virtual Plant

10. You can't freeze, restore, and replay an actual plant batch
9. No software to learn, install, interface, and support
8. No waiting on lab analysis
7. No raw materials
6. No environmental waste
5. Virtual instead of actual problems
4. Batches are done in minutes instead of hours or days
3. Plant can be operated on a tropical beach
2. Last time I checked our wallet we didn't have $1,000K for a plant to test
1. Actual plant doesn't fit in my suitcase

For my own edification and possibly yours, I did the following core dump of uses and my assessment of the level of fidelity required on a scale of 1 to 10 where 1 is for tieback simulations where feedback by discrete values (e.g. valve limit switches and motor run contacts) go to the right status and analog values move in the right direction (e.g. loop process variables respond in the right direction to changes in controller output).

Typical Uses of Models and Levels of Fidelity Required

Process Development
Media or reactant optimization and identification of kinetics on the bench top - 10
Optimization of process conditions in pilot plant - 9
Agitation and mass transfer rates - 8*
Process scale-up - 8
* - assumes computational fluid dynamics (CFD) program provides necessary inputs

Process Design
Innovative reactor designs or single use bioreactors (SUB) - 7
Vessel, feed, and jacket system size and performance - 6

Automation Design
Real Time Optimization (RTO) - 7
Model Predictive Control (MPC) - 6
Controller tuning (PID) - 5
Control strategy development and prototyping - 4
Batch sequence (e.g. timing of feed schedules and set point shifts) - 3

Online Diagnostics
Root cause analysis - 5
Data analytics development and prototyping - 4

Operator Training Systems
Developing and maintaining troubleshooting skills - 4
Understanding process relationships - 3
Gaining familiarity with interface and functionality of automation system - 2

Configuration Checkout
Verifying configuration meets functional specification - 2
Verifying configuration has no incorrect or missing I/O, loops, or devices - 1

My world has been automation system design with some ventures in into process design for neutralization systems where pH controllability is so highly dependent on equipment and reagent injection dynamics but I am looking forward to the high fidelity experience.

Is this the time for process and automation system designs to minimize dead time and interactions? Is this the time for university graduates to understand controller modes and parameters and the power of the DCS environment? Is it possible for these graduates to know how to tune a controller with non perfect mixing, measurements, and valves? Is this the time for users to know how to improve control system performance? Is this the time for new engineers to have a virtual mentor? Is this the time for suppliers to be able to demo the value of better measurements, valves, tuning, and advanced control?

This could be the time if modeling is embedded in the DCS and becomes a common tool in universities and industry. It started to happen about 5 years ago. Terry Tolliver and Robert Heider at Washington University use an industrial DCS as a virtual plant and in a computer control lab to teach process modeling and control as part of their chemical and system engineering programs. Atanas Serbezov at Rose-Hulman Institute of Technology uses a DCS in a lab to teach process control to Chemical Engineers. A DCS system has just been installed at Purdue University as part of the Engineering Research Center with Rutgers and the New Jersey Institute of Technology. If you doubt the value, talk to the students. In industry, Broadley-James, Lilly, Lubrizol, Monsanto, and Solutia have started using embedded modeling in a DCS for process design and control.

Modeling was such an integral part of my career, it is difficult for me to imagine how I would have learned and accomplished anywhere near as much without it. Modeling was a key part of my job even in the old days when you had to key punch cards for the IBM Continuous Simulation Modeling Program (CSMP) and submit them for an overnight run in a room full of main frame computers. When I got terminal server access to a computer with the Advanced Continuous Simulation Language Program (ACSL), I thought I was in heaven even though ACSL was designed for the aerospace industry. When graphical flow sheet simulators on a PC came along that we could interface to a DCS, I was blown away even though the interface was slow and cumbersome and the model speedup was rather minimal and inconsistent. I gave this all up to retire in 2002 and set up the virtual plant at Washington University. This wasn't quite enough so I ended up in Austin in the fall of 2004 to see if I could help the future of modeling by making it more accessible. While only 50% of my 75% part time venture is actually doing modeling, I am at a juncture to see how well it can be used.

Eventually the automation world will evolve to where modeling is an integral tool for learning and 4D processes (development, design, deployment, and diagnostics). While my physics and process modeling background leads me to focus on models based on first principles, data driven models such as neural networks, linear dynamic estimators (e.g. MPC models), and multivariate statistical process control such as projection to latent structures (PLS) have great relevance because they have make no assumptions and detect the inevitable unknowns. I envision a future of hybrid models embedded in the DCS that use the best that each of these modeling technologies offers. Is this the time?

What if the bench top system used in the research lab that is the basis of core process knowledge had a DCS with all of the major control technologies embedded? What if a dynamic model was embedded in the DCS to form a virtual plant that enabled exploring, discovering, and prototyping optimum operating conditions and advanced controls? What if a process trial run that normally takes 10 days could be completed in 10 minutes? What if the benefits demonstrated could help develop and justify better online analytical measurements, advanced controls, and data analytics? What if the model and control system could be scaled up to the pilot plant and ultimately the commercial plant? What if the same model and control system could be deployed to the industrial facility and be used for operator training and the development of process diagnostics?

A pioneer out west is making this future a reality. Scott Broadley, the president of Broadley-James, asked himself what if the pH and dissolved oxygen electrodes he was selling were packaged with a bench top bioreactor and a DCS optimized to make it more flexible and easier to use by the biochemist in the lab environment. He had the vision and conviction to put a DeltaV DCS with all of its innate capability on bench top bioreactors when others were going for the cheapest lab controller they could find. With determination and insight, Scott grew the business into a state of the art operation. But this was just the beginning. After seeing my demo of a virtual plant at Interphex 2007 in the Emerson booth next door, Scott thought what if a virtual bioreactor could be part of a concept to accelerate process development through dynamic modeling and advanced control embedded in the DCS. Even though it was a major expense and a 2 year commitment to run dozens of 10 to 20 day bioreactor batches, he saw the potential. In the process of the beta test, he found new probes that could measure media components, amino acids, and cell volume, size, and viability online. Even the possibility of measuring product and precursors to cell death online now appears to be within reach opening the door to incredible opportunities to increase product concentration and quality through media and amino acid concentration and batch profile control. Scott is convinced that the results will determine how bioreactors will be run for the next 15 years.

The beta test team of Trish Benton, Broadley-James cell consultant, and Michael Boudreau, Emerson principal consultant, has the right combination of skills and attitude to make it happen. The mammalian model prototype I developed in the fall of 2007 is moving forward in their fully capable hands. Michael scaled down my industrial size bioreactor (15,000 Liters) model to the lab scale (5 Liters) and incorporated the Broadley-James lab optimized control system to form a versatile virtual plant. The team is now in the throes of identifying the process conditions and parameters for an innovative new cell line and a scale up to 100 Liter Single Use Bioreactors (SUB). For more information on the beta test, check out the article "PAT Tools for Accelerated Process Development and Improvement" at http://www.easydeltav.com/news/viewpoint/BioProcess0308.pdf

None of this would have happened without the understanding, support, and encouragement provided by Grant Wilson, vice president of DeltaV Technology, who has the PAT and technical background to see the opportunity. Ever since my first PAT presentation with Grant on bioreactor modeling and control at the 2005 Emerson Exchange, Grant has been the best advocate for advancing PAT through modeling and control.

My core dump of myths went on longer than intended because there were so many stuck in my brain, it was so freeing, and it was so easy to unload them when pressed for time.

My recent spike in work load has put me in danger of being kicked out of various retirement associations including the Retired Automation Professionals (RAP). Apparently, my excursion into 40+ hour weeks means I not retired. Personally I think this is a bad rap. I still take 12 weeks off a year to visit friends, relatives, national parks, and beaches. I am having too much fun being part of the future to give it all up. Plus there are the benefits of active membership in the Adorable Automators Association as noted in my April 2008 Control Talk. http://www.controlglobal.com/articles/2008/118.html

The myths provided a reality check and a basis for looking forward to new tools that address many of the issues raised. Friendlier and more proficient versions of all of the process control technologies are being embedded as standard tools in a DCS. Now you can explore, discover, prototype, justify, and deploy process control improvements in the same configuration environment used for the basic control system. Adaptive tuning, a rich spectrum of PID enhancements, fuzzy logic control, loop performance monitoring, model predictive controllers, neural networks, on-demand tuning (auto tuners), and process dynamics identification are presently embedded. Soon online data analytics (multivariate statistical process control) and process modeling capability will be added. Terry Blevins (principal technologist) and Mark Nixon (chief architect) had this vision at Emerson Process Management and made it happen in DeltaV.

This comes at a turning point in industry where most of expertise in the application and understanding of the value of control opportunities are becoming full time members of RAP. Also, most of the opportunities are now overseas. The change in demographics is obvious when you look at the weekly questions on process automation submitted to Liptak where 95% of the questions are from overseas and cover a wide range of practical and essential application issues.

When I was helping the Instrumentation Systems and Automation (ISA) society in the early stages of the development of the Certification of Automation Professionals (CAP) program, I realized that it was difficult to find a book that addressed the day to day needs. I realized that publications in our field including my own were at too high a level and assumed too much for the new workplace where mentors and company training programs are scarce. Also books on process control were too mathematical and theoretical and the books on instrumentation were often a rote description of the principles of operation. Not much was offered on selection, application, installation, performance, and maintenance for the extensive range of process types and conditions needed by relatively inexperienced professionals to do their job. The Automation Book of Knowledge (ABoK) developed as part of CAP and various handbooks by Liptak, Boyes, and myself help but much remains to be done. Toward this goal, I am looking forward to working with Terry Blevins and Mark Nixon to provide a hands-on learning source/guide employing a full suite of embedded technologies in a virtual plant. This book focuses on the opportunity of doing a better job of process control for both batch and continuous processes.

We conclude with the following myths from Appendix D in my guide Models Unleashed - Virtual Plant and Model Predictive Control Applications published by ISA, 2004.

(21) You need an advanced degree to do advanced control. Not so anymore. New software packages used to form a virtual plant automate much of the expertise needed and eliminate the need for special interfaces. The user can now focus mostly on the application and the goal.

(22) Dynamic simulations and model based control are only applicable to continuous processes. Since most of the applications are in the continuous industry, this is a common misconception. While it is true that a steady state simulation is not valid since there is by definition no steady state in batch, dynamic simulation can follow a batch as long as the software can handle zero flows and empty vessels. Model predictive control (MPC), which looks at trajectories, is suitable for the optimization of fed batch processes during particularly important points in the batch cycle. The opportunities to improve a process's efficiency by MPC are about 25% for batch compared to 5% for continuous operations.

(23) You need consultants to maintain models and advanced control systems. No longer necessarily true. The ease of use of new software allows the user to get much more involved, which is critical to make sure the plant gets the most value out of the models. Previously, the benefits started to drop as soon as the consultant left the job site. Now the user should be able to tune, troubleshoot, and update the models.

(24) You don't need good operator displays and training for well designed advanced control systems. The operators are the biggest constraint in most plants. Even if the models used for real time optimization and model based control are perfect, operators will take these systems offline if they don't understand them. The new guy in town is always suspect, so the first time there is an operational problem and there is no one around to answer questions, MPC systems are turned off even if they are doing the right thing. Training sessions and displays should show the individual contribution of the trajectories of each controlled, disturbance, and constraint variable to the observed changes in the manipulated and optimization variables.

(25) You need to know your process before you start a model based control system application. This would be nice, but often the benefits from a model stems from the knowledge discovery during the systematic building and identification procedures. Frequently, the understanding gained from developing models leads to immediate benefits in terms of better set points and instruments. The commissioning of the MPC is the icing on the cake and locks in benefits for varying plant conditions.

The virtual plant offers a break through in training and knowledge discovery but its potential depends upon the ability to develop dynamic simulations that capture the process relationships and response important for process understanding and control.

The best practice of practical real time simulation could easily fill a book but I need to wind this up and move on to other opportunities so here are a few ideas on how to make a process model more flexible in terms of cost and performance and maintainability. It is important to realize the art of simulation is simplification to what is essential.

A significant portion of the time is spent trying to decipher the intricacies of a plant's DCS configuration and displays. If there is an accurate P&D with the relative location of every pump, fan, valve, and measurement noted along with the complete DCS tag name, and there is browser access to each tag name to assign DCS outputs as process inputs and process outputs as DCS inputs in the model, the need to dig into the configuration is vastly reduced. Note that special DCS I/O such as pulse counts must still be identified and separately addressed.

The computational requirements, numerical hazards, and data requirements on the piping system and fluid flow of a pressure-flow solver are considerable. If there are flow loops for every throttle valve, then the complexity and cost of a pressure-flow solver may be avoided. Of course, this simplification will not identify improperly sized pumps, valves, and pipes. I propose it would be better to add imbedded flow loops in the process simulation rather then venturing into a pressure-flow solver. This simplified approach uses a combination of flow loops and a pathway methodology where the 1 or 0 status of on-off valves and pumps determine an open piping path. The total flow coming out of a piping tee can be written back as the flow going into the tee. The use of flow loops reduces but does not eliminate the need to simulate valve backlash and stick-slip. If a pressure-flow solver is deemed valuable, than I suggest a sequential modular method to avoid ill conditioned matrices and numerical problems during batch operations and the startup and shutdown of equipment.

If the model starts out with initialized but settable molecular weights, densities, and heat capacities, then levels, temperatures, blending, and temperature can be simulated. If the dissociation constants for bases and pH are added, then pH can be added. For the modeling of vaporizers and evaporators, it may be sufficient to add vapor pressures and boiling points of selected components as a function of composition. For reactors, the standard form of Arrhenius and Michaelis-Menten kinetics may be sufficient. Neural networks may be able identify kinetic rates to provide a simpler and higher fidelity hybrid model. The complexity of a full blown physical property package could be reserved for more complex vapor equilibrium problems such as distillation.

Finally, it is most important to get the dynamics right. The process models from on-demand and on-line tuning packages such as DeltaV Insight and model predictive controllers such as DeltaV Predict can be used to supplement or replace first principle models for specific parts of the process.

For my virtual plant experience and top ten list check out
http://www.controlglobal.com/articles/2007/385.html
http://www.controlglobal.com/articles/2007/359.html
and the "Education" and "Process Simulation" categories on this website.

Around 1984, there was a breakthrough in use of simulation for checkout and training. Software packages, such as MIMIC and its predecessor SIMVOX, automatically generated tieback simulations from the configuration and the input and output (I/O) cards and emulated the serial communication between the simulation and the DCS. These packages enabled the simulation to read all of the DCS outputs and send back all of the corresponding DCS inputs. Besides inherently providing a test of the I/O channel assignments, the simulation was separated from the DCS and expanded to cover the entire plant. The tieback simulation sent back the proper motor run contacts for the valve limit switch positions for discrete I/O that was particularly critical for batch operations. For control loops, the process variables was the PID controller output multiplied by a process gain and possibly delayed and filtered to simulate process dynamics. For indicators, fixed values were entered. A method was developed to switch these fixed values and to zero out loop process variables based on whether a flow path was established. A 1 or 0 status of each pump and valve in the piping path were multiplied together to determined the status. Ramps triggered by path status were added to simulate batch and startup responses. Batch operations could be run 100 times faster than real time, and be reset. Failures could be introduced. In Monsanto, these customized tieback simulations were credited with reducing the time to checkout and startup a DCS by 60% or more. By 1986 all Monsanto projects used the software package and its associated methodology and by 1994 nearly all of Monsanto's 100 operating units were controlled by a DCS. The rapid deployment of the DCS had immediate benefits in terms of safer and more efficient operation plus provided a basis for a program of process control improvement over the next 6 years that lead to 4% further reduction in the cost of goods.

The tieback simulations with pathway logic and custom ramps achieved rapid education of the operators on how to effectively use displays and configuration. To develop better process understanding, the tieback simulations were in some cases enhanced by first principle process models. While the lack of a standard methodology resulted in custom process models of limited scope that were difficult to keep updated, the concept of a process model being connected to an actual DCS forever changed the landscape of process simulation. Up until this time process simulations for operator training used very expensive emulations of the control system at a cost of 200 thousand to 2 million dollars. Most nuclear power plants and some chemical plants and refineries went this route. However, it was not practical to include the detailed features of the control loops (e.g. structure, form, modes, and feedforward), sequences, batch executives, and the operator interface (e.g. displays and historian). Attempts to match and maintain were costly and prone to over simplification. The use of the actual DCS allowed the dynamic simulation to focus on the modeling of the process. The development of packages such as DeltaV Simulate Pro provided the ability to download the actual configuration and displays to a personal computer creating a virtual plant eliminating the need for the DCS console and controllers without any emulation or translation of the control system for training.

Plants are losing experienced operators and engineers so there is an even great potential benefit from periodic operator training. How can we provide training systems that wow decision makers when there may be no one left in the plant to support or even appreciate process simulations for operator training? I don't have all the answers but here are some key aspects based on my experience:

(1) Live demos of virtual plants for key processes
(2) Online process metrics
(3) Expansion of audience beyond operations to process, control, and maintenance
(4) Modular and generic framework
(5) Ability to run slow processes much faster than real time
(6) Focus on process dynamics and interactions
(7) Readily increasing levels of fidelity for flexible cost and performance

For a perspective of the importance of the operator and some possibilities of online process metrics, check out the Dec 28 entry in the "Tuning and Control System Performance" Category. Next week we will look at some approaches to make the first principle process model more flexible in terms of cost and performance.

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?

I just completed today a World Batch Forum (WBF) Webcast on Process Analytical Technology (PAT). The Webcast was fun and I can see where it opens up a whole new avenue of education even though the technology is not quite as far along as I expected. The Webcast went well thanks to the help of Deb Franke and Ed Guinn at Emerson Process Management and Mike McEnery (committee chairman for the WBF Webcast & Education Committee). I would encourage anyone doing this for the first time to do a pretest and trial run with the same equipment and in the same room used for the Webcast with two PCs and the help of audio and Web people. One PC is in the normal view to see the Q&A pane and provide faster navigation between slides. The other PC is in the full screen presentation mode. It is also important to realize that custom animation is not yet consistently feasible for a webcast due to variations in internet connection speeds and there are compatibility issues between Internet Explorer 7.0 and "Live Meeting."

This WBF PAT Webcast was based on the book New Directions in Bioprocess Modeling and Control. You can check out a review of the book by Control magazine editor Walt Boyes in his March 13 blog http://waltboyes.livejournal.com/207809.html

The following questions and answers may be instructive:

What are some examples of "near" and true integrators for batch operation?

The classic true integrator is level where the rate of liquid mass accumulation in a batch (level ramp rate) is proportional to the feed rates.

Gas pressure in the head space is a "near" integrator when the change in drop across the vent valve from a change in head space pressure is small compared to the normal pressure drop. Also, since the process constant is much larger than the process dead time, the open loop response looks like a ramp in the control region. If the drop across the vent valve becomes critical, the gas pressure becomes a true integrator because a change in pressure does not cause a change in vent flow.

For liquid temperature and composition, there is no discharge flow during the part of the batch of interest. Consequently, there is a loss of self-regulation inherent in continuous processes. For a change in temperature, there is change in temperature drop across the heat transfer surfaces (e.g. jacket), but this is small compared to the normal drop. Like the gas pressure loop, the process constant is much larger than the process dead time for the temperature loop. Finally, the magnitude besides the relative size of the process time constant is very large making any steady state beyond the time frame of interest.

For pH and substrate control, as the reagent or substrate is consumed (e.g. ammonia and glucose), the response is a "near" integrator from its large process time constant although the nonlinearity of the titration curve may cause the response to accelerate or decelerate for increases and decreases in pH, respectively.

Normally there is a peak in the plot of biomass growth rate or product formation rate versus pH, substrate, or temperature. Deviations from the optimum operating conditions can alter the metabolic rates enough to change the reagent demand and cause a delayed and very slow secondary effect in the same or opposite direction of the initial change.

How much wireless communication delay can I have before I see degradation in fermenter control?

Let's assume there are no aliasing or jitter issues communication delay so we can focus on the effect of an increase in lop dead time on loop performance.

Dead time dominant loops do not have as much leeway as loops where the process time constant is greater than the dead time but a communication delay that is less than 20% of the existing dead time is normally within the variation already seen from the many sources dead time so this is a reasonable rule of thumb. This allowable additional dead time is quite small for secondary flow and speed loops and depends heavily upon the module execution time and final element resolution.

For "near' or true integrators you can introduce an interval up to 50% of the existing dead time for a controller that has a Lambda factor of one (Lambda equal to the process time constant). This permissible additional delay is quite large for the slow primary fermenter loops.

When should a batch MPC for production rate optimization be turned on?

The MPC should be turned on when the concentration and rate of change of the concentration becomes significant. In the example given, the MPC was turned at about the peak in the product formation rate so the set point track PV feature could capture the best rate for the batch and try to hold it until the end point was reached. There could be a separate MPC to first maximize biomass growth and then to maximize product formation rate.

What are some other examples of MPC used for bioreactors?

Amgen at the 2004 Emerson Exchange and Rutgers in the Control magazine August 2004 issue showed the use of MPC for pH and DO. In this blog site we discussed the setup of an MPC to eliminate split ranged controller outputs and the associated limit cycling around the split range point. The MPC is documented in the Advanced Application Note 002 titled "MPC Implementation Methods for the Optimization of the Responses of Control Valves" http://www.modelingandcontrol.com/repository/AdvancedApplicationNote002.pdf

How can I get enough batch data for batch analytics?

Best bet is to run bench top trials that have an industrial DCS and data historian with automated lab entry. Match up the virtual plant to these profiles and then use the virtual plant to generate more data.

How can I predict batch end points?

You could run a virtual plant faster than real time out to completion of the batch. If you have MPC helping to maintain the slope of the batch profile, after the peak in the product formation rate you can multiply the slope by the remaining batch time and add it to the product concentration from the last sample. You can keep updating this prediction after each lab sample. If the slope is variable, you could do the prediction piece wise based on a reference profile. If none of this is possible, you could simply bias the predicted batch profile by the difference between it and the current profile much like the simple feedback correction of the future process trajectory for MPC. A prediction is generally not viable until the concentration and rate of change of the concentration are both significant.

My favorite "Far Side" cartoon has Einstein at a chalk board full of derived equations ending up with the ultimate equation "time=money." In my mind, the negative free time of the process control engineer places some doubt as to whether this endangered species still exists. There have been sightings but the uncertainty principal says we can only ascertain their location or function but not both.

Experimental models do a good job of minimizing the time and expertise required of process control engineers by not relying upon process knowledge. Since these models are identified from test data, they are consistent with the ultimate goal of matching reality even if process understanding lags behind. Each technique excels at addressing a particular aspect. For example, Neural Networks (NN), Projections to Latent Structure (PLS), and Model Predictive Control (MPC), excel at identifying the nonlinear, interdependent, and dynamic, respectively, nature of process inputs. The strong point of one method is often the weak point of others and in the end somebody with some sort of process understanding should check to make sure the models make physical sense. There are several watch outs. For example, avoid extrapolation by a NN outside of its training data range because nonlinear relationships can take off exponentially. Since PLS and MPC assume linearity, you have to be careful about deviating too far from an operating point to the point where turndown and startup may require the identification and switching of different models. NN and PLS don't try to model the process time constant or integrating process gain, so there is a model mismatch for well mixed volumes where the residence time translates to a process time constant or a "near" or "real" integrating process gain. Also, NN and PLS are often sold based on just throwing existing historical data at them ignoring the transfer of variability by closed control loops and not perturbing process inputs. The richness of the dynamics, the rangeability, and the identification of cause and effect suffers. What has been so important to the success of MPC, seems to have been lost

What about all the other types of models?

Tiebacks are very attractive because they initially require hardly any effort. They can be automatically generated from the configuration. These are great for control system familiarization and interface improvements (e.g. operator training and critiquing of graphics) and I/O checkout. They can be used to mimic the process response by the heuristic customization of ramp rates triggered by piping path logic to test out the configuration, particularly important for complex continuous and batch control systems.

Finally, there are the models based on chemistry and physics (not necessarily popular subjects). Very sophisticated software has been developed to provide a graphical flow sheet simulation of processes. Unfortunately, these generally require a sophisticated budget and user. Most of the big players focus on continuous steady state operation, the traditional realm of chemical engineering programs. Separate special purpose packages are typically required for batch. My experience with "state of the art " process modeling software is that they do a good job of process design but are not as good as you might expect in showing the process dynamics especially considering they carry the label "high fidelity". The process gain is off because the installed characteristic of the control valve and measurement scale are not included, the process dead time is too small because transportation and mixing delays are missing, and the process time constant is too small because thermal lags and jackets/coils are missing. To top it off, the trends are way too smooth because there is no mixing or sensor noise and no limit cycles from control valve stick-slip or backlash. For more enlightenment on the issues with dynamic process simulators see the Control magazine August 2005 article titled "The Light at the End of the Tunnel is a Train (Virtual Plant Reality)".

When you sit back (something I am getting better at being partly retired) and look at the whole picture, it seems fractured.

Why aren't there basic generic first principal models that focus on the process dynamics without getting bogged down in the complexity needed for process design? Why aren't there hybrid models that take advantage of the best of what each method has to offer? What would we call these models that provide the type of fidelity needed for process control? Are we stuck in a rut because each expert thinks their particular method is best? Are there people with broad enough skills and attitude to pull it off?

The students at Washington University in Saint Louis are ahead of the curve by virtue of the efforts of affiliate professors Terry Tolliver and Robert Heider who have a combined total of more than 65 years of industrial experience at Monsanto and Solutia.

Terry Tolliver teaches a process control course for junior and senior chemical engineers. The students have access on their desk to a virtual plant with embedded high fidelity process simulations and industrial control modules, trends, and operator graphics. The following file shows the university, the virtual plant class room, and text book.

WU Virtual Plant

Robert Heider teaches a computer control lab for chemical and systems engineers that uses an industrial automation system for the control of actual process equipment, such as vessels, heat exchangers and dryers for blending, level, temperature, and moisture control. The equipment, piping, instrumentation and valves are assembled on a cart with quick connects for utilities and Fieldbus signals to make each lab portable. The following file shows one of the lab experiments.

WU Hardware Lab

After some concise instructions with screen prints, the students have had no difficulty in accessing and using the DeltaV DCS system. The only people who seem to have trouble are the other professors who are not accustomed to seeing an industrial control system, which is probably more of a justification than a prohibition for taking this approach.

Modern DCS systems use Fieldbus standards for control module configuration and parameters. Also, most operator graphics and industrial historians have a similar feel that is distinctly different or entirely missing from academic software. Statements that industrial systems are specific are valid if it is meant specific to industry and not a particular manufacturer. Even the 2% of the students who are going on to an advanced degree in control and a future life in academia are better equipped for working with industrial consortia by understanding industrial systems and terminology.

Washington University graduates understand standard Fieldbus terminology (CAS, RCAS, and ROUT modes) and even such far out stuff as the units of reset time (e.g. sec/repeat). They can act more intelligently when they first venture into the control room. Even if they don't pursue a career in process control, since the DCS is the window into the process and method of affecting the process, WU students are better able to hit the ground running on their first job. After a few labs, a light comes on with chemical engineers. They understand the significance of this approach. With systems engineers it may not happen because they are hoping to end up at an aerospace firm rather than in a chemical plant.

I would ask any skeptics of the validity in using an industrial system in university labs to first speak to some of Terry's students before passing judgment.

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.

Professor Tom Edgar of the University of Texas and Joseph Alford from Eli Lilly and Co. have been collecting data and opinions regarding the current syllabus of the typical undergraduate Chemical Engineering Process Control Course and its relevance to the skills and knowledge needed in today's industrial process control environment. An upcoming issue of Intech magazine will have an article by Edgar on how process control education in the universities should be changed. There will be replies from a key group of professors and practitioners. My contribution is the first 250 words of the following:

Terry Tolliver and I have taught a course on dynamic modeling and control at Washington University (WU) in Saint Louis since 2002 that is a requirement for a degree in chemical engineering. The course uses an industrial virtual plant and the ISA book Advanced Control Unleashed. The students are very computer literate and pick up on the use of industrial software from just a few screen prints put into the laboratory exercises. The knowledge gained is generally applicable since the function blocks are based on Foundation Fieldbus used in millions of devices and by over a hundred manufacturers. The configuration environment is also consistent with the international standard IEC 61804. The students learn how to intelligently discuss and use an industrial process simulation, DCS, and data historian that form a virtual plant on their desk. There is companion course taught by Bob Heider where an actual hardware version of the same DCS is used to control the temperature, pressure, and level of vessels in a hardware lab. The three professors have a total of more than 100 years experience in industry.

Most of the chapters in Advanced Control Unleashed start with an introductory section on "Practice," continues with sections on "Opportunity Assessment" and "Application" and concludes with "Theory". The strategy is to provide the relevance and practical considerations before getting into the theory that offers a deeper understanding. For example, in Chapter 2 - "Setting the Foundation", the student gets an overview and perspective, list of opportunities, examples, application detail, and rules of thumb before getting into the theory where the focus turns to the set up of the differential equations for the material and energy balances to enable the student to learn the source of process time constants and gains in terms of process parameters. The students are not asked to solve or integrate these equations. Instead, the students graphically create a dynamic simulation of processes for unit operations commonly encountered on the job. Blocks for filters, dead times, noise, periodic disturbances, and backlash and sticktion are added to make the challenge of process control more realistic. Additionally the students configure an actual control system that can be downloaded into a real DCS. The students apply industrial embedded tools for auto tuning, statistical analysis, and model predictive control (MPC). The course centers on time response because this is what they see on the trend charts in the control room but there is a session to show how to go from the time domain to the frequency domain.

When I recently went back to WU and gave a guest lecture on the use of PID and MPC for fed-batch control of a fermenter, a student asked "what is a batch?" I knew that students were taught to think in terms of a steady state and the material and energy balances on a Process Flow Diagram (PFD) for continuous operations but I didn't fully realize the implications until the question.

I have had chemical engineers in industry ask, why do you need a PID or MPC when you can just set the flow shown on the PFD? In fact, the batch sequences defined by process engineers today often try to set a predetermined step sequence of flows instead of using feedback control to sort it out. I have also have had experienced instrument engineers ask why do you need a Coriolis density measurement when the composition is constant as shown on the PFD? I also see ads for pressure and temperature compensated differential pressure orifice meters that claim to offer an unqualified mass flow measurement. If only the composition in all the pipelines were constant. This would sure make life easy. Product quality would be a non issue. Obviously the importance of dynamics and disturbances for process measurement and control is often missing in action.

In a batch process, the product concentration follows a profile. In some case there is also a temperature profile and in almost every case where a PID or MPC is used, the transfer of variability for a constant set point means there is a profile in the controller output. This understanding is lacking when chemical engineers are taught to think steady state. The lessons from batch would also be useful for the automated startups and grade transitions in continuous operations.

To add a bit of levity, I offer the following Top Ten List:

Top Ten Reasons Why an ISA Book on Control is not a University Text

10. Costs less than $100
9. The authors spent too much time in industry
8. Contains top ten lists and cartoons
7. Shows flow sensors upstream instead of downstream of the control valve
6. Discusses stick-slip and backlash
5. Shows unmeasured load upsets as inputs to the process
4. Includes field implementation considerations
3. Estimates tuning settings to just two significant digits
2. Doesn't use tensor analysis for flow loops
1. Depicts signal lines as electronic instead of the pneumatic