Modeling and Control

What Have I Learned? - Bridging the Gap between Universities and Industry

Mon, 01 Jun 2009 13:12:04 -0600

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.

What Have I Learned? - Writing

Thu, 28 May 2009 12:21:36 -0600

I haven't had any special courses or training in writing and some may say it shows. I wouldn't advocate anyone following my style, especially if you trying to promote your products or ideas. I tend to lead the reader on path of discovery by laying out the situation and the problems and then some interesting ideas. Maybe it is the user in me (33 years at Monsanto and Solutia), the scientist in me (Physics), or my approach to writing that wants to leave it up to the reader to make the judgments and assessments.

However, people today want to know upfront the bottom line. They may not have time or the background to come to useful conclusions. Plus my emphasis on detailing problems can be formatted with a more positive approach of presenting opportunities and solutions. Next month I will offer a short introductory overview of my recent articles that emphasizes the scenario, essence, and value of the ideas developed.

The main point of this blog like all of my writing is to share what I have learned. My goal for next year is to help prevent significant expertise and knowledge in process automation from being lost forever. I would guess 100 or more automation professionals are retiring each year who have published at best an infinitesimally small portion of their expertise for posterity. Also, new engineers are facing special challenges. My sense is the new kid in the control room doesn't have the mentors or the internal technical training programs I took for granted. They may be thrown into the midst of a difficult problem with no guidance.

Beginning this Fall I will be making presentations at Local ISA sections and interviewing young and seasoned automation professionals recommended by the sections to get a better idea of new and lost process control expertise. The first stop may be the ISA Boston section. I lived in Cambridge when I was overseeing a project at Badger. I am looking forward to returning to Harvard Square and Legal Seafood Market. I can dream of returning to Fenway Park. The interviews will be published in my monthly Control Talk column in Control magazine. I enjoyed doing the 3-part series in Control Talk on "The Secret Life of pH Electrodes" based on interviews at Broadley-James and Rosemount Analytical in Irvine California (nice place to visit).

In the mean time here is what I have learned about writing in no particular order except what pops into my brain (kind of the way I do my first draft).

(1) An outline is a good idea but it is just a starting point. I don't truly know where the article or book will take me at the beginning. The value I get out of writing is the discovery process. I find concepts and ideas along the way. I gain knowledge besides sharing knowledge. In my next book, I realized after about 5 pages into the first chapter that an important message is the dramatic change in the performance of modern measurements. The installed accuracy of key measurements has improved by one to two orders of magnitude compared to my days in E&I design and construction. A smart closed coupled coplanar DP with static pressure and temperature compensation has 0.02% installed accuracy. A radar level gauge can detect changes as small as 0.04 inches in level. A Coriolis liquid flow meter can have an installed accuracy of 0.05% with a rangeability of 200:1. The bench top and installed accuracy of pressure, level, and flow measurements in 1970s and 1980s was typically 0.5% and 2%, respectively. I have an article from that era that quantifies the deterioration from uncompensated process and ambient operating conditions. Then there was the noise introduced by wiring problems (see May 19 entry). Smart wireless instrumentation offer a whole new ball game if you don't screw it up with a bad installation. The limit to control loop performance is not the measurement but more than ever is the control strategy, the final element, and how well you tune the controller.

(2) The most difficult thing is writing the first sentence. The second most difficult thing is writing the second sentence. The third most difficult thing is writing the first paragraph. The fourth most difficult thing is writing the first page. The lesson here is to just get started. Writing is an iterative process. My problem is that I get bored rehashing ideas I have unleashed and want to move on to the next page, article, column, or book. I don't iterate enough. Here is where a person who knows the subject can help by reading and commenting on your drafts. Beware of technical writers or copy editors who want to rewrite the whole thing because it often results in a loss or change in meaning and intent.

(3) Once you get going, don't stop. A flow is important. After about 10 pages, the thoughts start to flow fast and free. At this point, music (particularly the best of Concrete Blonde, The Goo Goo Dolls, Josh Groban, Don Henley, Meat Loaf, Matchbox 20, Bruce Springsteen, and U2) adds inspiration and makes writing more fun for me. I think this was the only way I was able to write a dozen serious technical books, a half dozen funny technical books, fifty articles and papers, and 8 years of Control Talk column. It also helps to have an understanding management and spouse. Can you envision getting approval of humorous books, columns, and "top ten lists" through the official channels of a big corporation? Can you imagine your spouse letting you spend 8 hours writing on a weekend? Lastly, I do the diagram and figures last. This is mind numbing work for me that would sap my creative energy and interrupt the flow.

(4) Use a lot of sub headings and bullet lists. This gets the reader interested and helps to cherry pick what is of greatest importance. My article "Maximizing PAT Benefits from Bioprocess Modeling and Control" is a good example of missing lists and sub headings. In my defense, the article was a last minute deal. I had just a couple of days and was just doing a core dump of what I thought was important.

(5) Go back and improve the introductory paragraph to each section and the introductory sentence to each paragraph. This is a good idea. I am going to try it when I have time.

(6) Develop the solution, include the assumptions, and detail the verification. I tend to do the first two parts of this suggestion and provide evidence of the last part. However, it would help the readers to make suggestions on how to prove out the idea for their application. No solution is universally true. There are always exceptions. I have benefited from the best minds in process control but I have noticed that the bigger the mind the bigger the ego. A blind spot is developed that makes experts unwilling to acknowledge when their solutions don't work in a particular application. Maybe it is my science background or an ego deficiency but I am always looking for exceptions and I never think any of my ideas as perfect. You learn more from things that don't work.

(7) Don't make statements that are to be accepted as fact. I am particularly sensitive to statements made to be accepted as true that are mostly untrue. For example, "Thermocouples (TCs) are faster than RTDs." This statement is generally accepted as true but is it useful and is it in fact misleading? What if someone chooses a TC instead of a more accurate RTD because he or she thinks the TC is faster? If you had a bare element there might be a difference of a couple of seconds in the response but the uncertainty in the time lags of most temperature systems are an order of magnitude larger. Once you put the element in a thermowell, the construction and fit and length of the thermowell determines a time lag for the assembly that is an order of magnitude larger as well (no pun intended). Also most controllers are tuned so slowly, you don't see the effect of small changes in time lags. The bottom line is that you will probably never see the difference in speed between a TC and an RTD. Having said that I can envision exceptions, such as the temperature control of inline mixing of streams with an aggressively tuned controller. I have just never seen this application in practice.

(8) Use short clear sentences and paragraphs that build on each other. Providing qualifications can result in long sentences. Also, one thought for me quickly leads to another and to another and to another, which leads to run-on sentences. I need to constantly go back and split my thoughts into separate sentences with a building block approach. I am currently trying in my first draft to break thoughts up. So far it doesn't seem to interrupt the flow, which was my original concern.

(9) Don't get hung up on perfect grammar or a perfect piece. If you don't give copy editors something to do, they will start some serious messing with the sentences. Everyone wants to feel like they are contributing and doing their job. Also, what copy editors are looking for in terms of commas and hyphens may change. Finally, obsession with sentence structure can lock up your mind. I talked to a copy editor who said he needs to find another job so he can write a book. As a copy editor, he thinks too much about the technical details of writing.

(10) Think of writing as if you were having a conversation with a good friend. This makes the whole writing process less intimidating and lets you be more frank and less formal.

(11) Use plenty of examples and illustrations. It takes time but sure drives the point home. I have seen a lot of misinterpretations of an idea or its intent. The fault is really my own and not the reader. For example, in my article "Is Wireless Process Control Ready for Prime Time" a reader thought there was a concern being expressed on noise from variable speed drives on wireless transmitters when what I meant was the opposite. Wireless transmitters should eliminate these and other noise problems associated with wiring. I no longer assume any concept is obvious.

(12) Use free association and both sides of your brain. This allows you to take creative leaps you probably didn't even realize before you started writing. In my case it also enables me to add humor such as "Top Ten Lists" and the cartoon descriptions for my illustrious friend Ted Williams.

What Have I Learned? - Wiring and VFD Problems

Tue, 19 May 2009 10:28:34 -0600

While wiring problems may not be pervasive, when they do exist, the upset is significant and tends to persistently reoccur for months to years. The intermittent transient occurrence is difficult to diagnose. The short term steps and spikes cause kicks in the controller output from gain and rate action. In my experience, the spikes in pH transmitter outputs often go unresolved. The article by Fred Sanders titled "Watch Out for Instrument Errors" (Chemical Engineering, July 1995) gives examples of the insidious and disruptive nature of wiring problems.

The problems seemed to get worse in the 1980s and 1990s when new low cost invertors were installed for variable frequency drives (VFD). The VFD is also known as a "variable speed drive" (VSD) because the speed of the motor is regulated to be proportional to the controller output. In a recent conversation with Owen Campney, I got an idea of what happened to make the VFD inverter noise a bigger problem.

The switching in the new invertors was faster creating sharper edges. This reduced the heat, size, and cost of inverters making them more abundant. Instead of being in dedicated inverter rooms in the motor control center, they started to appear in instrument rooms among the interface panels. The faster switching created higher frequencies with higher energy. These invertors had an output choke to prevent damage to motor insulation but the input choke was optional and was often missing or insufficient. Eventually, the noise in instrument signals became bad enough that chokes were offered to meet the International Electrochemical Commission (IEC) standards. Alternatively, isolation transformers were located close to the inverter with the power wiring between the inverter and transformer in hard pipe conduit to minimize the noise from this section of wiring. You hear VFD war stories to this day.

One such war story was related to me by Owen. A large intermediate plant installed smart HART input cards on some critical HART transmitters to take advantage of the diagnostic information digitally superimposed on the analog signal. Unfortunately, 3 to 6 times each day the digital signal would be momentarily lost. It was suspected but never confirmed to be triggered by a VSD on coolant in a temperature loop when demand changed. The problem persisted for several years until an electrical engineer patiently tracked down the problem to a second ground hidden from view in the wall.

Whether the steps, spikes, and noise in an instrument signal is due to wiring or not, the wiring is always suspect particularly if the user has been burned by VFD incidents. Consequently more hours are wasted than is generally recognized on trying to track down real or imagined wiring problems. The WirelessHART network should be immune to the VFD interference and eliminate the wiring questions. The potential savings from WirelessHART in maintenance cost may be currently underestimated.

For more information on WirelessHART checkout the article "Is Wireless Process Control Ready for Prime Time?" in the May 2009 issue of Control magazine.

What Have I Learned? - Cost and Source of Oscillations (Part 4)

Mon, 11 May 2009 17:19:42 -0600

I need to minimize the time delay to dinner so I will minimize this discussion of how to minimize the oscillation from analyzer sample time delay. So many minimums and so little time.

Composition measurements with sample systems and cycle times termed "at-line analyzers" offer incredible opportunities for understanding and controlling what affects what you ultimately want to know for a process output stream - the composition. The sample time delay from the cyclic results from an at-line analyzer is more problematic than the transportation delay for a continuous measurement via a probe in a sample line termed "in-line analyzers". The "at-line analyzer" has a stepped response and sometimes spikes from bad readings with no intermediate values. The result is a propensity for oscillations when used for feedback control.

One might think a deadtime compensator would help the traditional PID deal with the deadtime from a cyclic time delay. However, these deadtime compensators are notoriously sensitive to a mismatch between the actual process deadtime and the estimated deadtime used in the compensator. The loop deadtime from unsynchronized digital devices and at-line analyzers is extremely variable and can at best be estimated after the fact.

It is interesting that the solution for suppressing oscillations from at-line analyzers resulted from improvements to the PID developed for variable updates from wireless devices (see February 9, entry on "Unexpected Wireless Benefits"). The control solution for WirelessHART requires no estimate of deadtime and is more robust than a traditional PID. The PID execution is kept relatively fast (once per second). The contribution of the proportional mode is computed every execution. The proportional action every scan provides a good set point response for a PID structure with proportional action on error. The contribution of the integral and derivative mode is only computed when the measurement has changed per the resolution setting of wireless device. Furthermore, the time used in the integral and derivative mode calculations is not the scan time but the elapsed time from the last measurement update.

The use of the elapsed time in the integral calculation and a reset time the same as the process time constant provides an integral correction that is equal to and opposite to the process response in the elapsed time. Even if the process time constant changes, making an integral correction only when there is update eliminates the extraneous ramp of the integral mode in the traditional PID acting on old information. The suspension of integral action until there is new information also helps the PID deal with a valve that is momentarily stuck provided position read back is used for dynamic reset limiting.

The use of elapsed time instead of PID execution time in the derivative calculation spreads the change in the process over the elapsed time rather than taking it to all occur in the single execution time. This more intelligent rate action eliminates spikes in the controller output that would occur in a traditional PID when there is an update.

The wireless PID greatly stabilized the glucose control of a bioreactor which had at-line analyzer sample time delays that varied from 6 to 12 hours. The improvement is greatest for self-regulating processes and controllers tuned for maximum performance. The suppression of oscillations can be seen on slides 29 - 33 of the Interphex 2009 Presentation "Advances in Bioreactor Modeling and Control."
Interphex2009_Advances_In_Bioreactor_Modeling_and_Control.pdf

What Have I Learned? - Cost and Source of Oscillations (Part 3)

Mon, 04 May 2009 17:31:29 -0600

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

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

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

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

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

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

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

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

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

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

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

What Have I Learned? - Cost and Source of Oscillations (Part 2)

Mon, 27 Apr 2009 17:26:38 -0600

The loops with the most severe oscillations listed in order from biggest amplitude to smallest amplitude are pH loops, level loops, flow loops, pressure loops, batch temperature loops, heat exchanger temperature loops, and column temperature loops.

The following is a list of the sources of product quality oscillations in the approximate descending order of frequency of occurrence based on my experience. I have even offered my best guess in parentheses as to the percentage of applications that can be tracked to these root causes for chemical and biochemical products. You may wonder why pH loops didn't make the top of the list since it has the most severe oscillations. The main reason pH loops are down the list is that most pH loops are in waste treatment (WT). Also, the pH loops in reactors and bioreactors tend to have much lower process gains than WT pH loops and some process regulation from reagent consumption. Interacting temperature loops on furnaces, reformers, and reactors are severe problems but are near the bottom of the list for applications for specialty chemicals and biochemical products because multi-zone or profile temperature control are more prevalent in the petroleum, petrochemical, and bulk chemical industries. The following list is for normal operation of loops with good valves and does not consider oscillations that originate from the startup and shutdown and failure of equipment. Next week we will see the implications of "not so good" valves.

(1) Too much reset action in level loops on surge and feed tanks (40%)
(2) Discontinuities at split range point for pH, pressure, and temperature loops (20%)
(3) Interacting pressure and flow loops on headers (10%)
(4) Too much reset action in overhead pressure loops on columns and vessels (10%)
(5) Set point response of batch temperature loops (5%)
(6) Interacting temperature loops for 2 point composition control of columns (5%)
(7) Interacting temperature loops on furnaces and reactors (5%)
(8) Set point response of batch pH loops (5%)

What Have I Learned? - Cost and Source of Oscillations (Part 1)

Mon, 20 Apr 2009 15:24:15 -0600

All plants have oscillations. Process control improvement can reduce or eliminate these oscillations. In these days of tight budgets and resources, how do you justify cost and effort to fix the problem?

The "before" and "after" distribution and location of process variability depicted in slide 1 in ProcessControlBenefits.pdf is the classic presentation on how tighter control can result in benefits. If you can reduce the standard deviation (sigma), you can move the set point closer to the constraint without increasing the number of violations of the constraint. What I have added to the slide is the practical situation where operations give themselves a cushion or margin, particularly if there is no online monitoring system with data analytics that can provide the process knowledge and confidence needed to operate at edge of the product range to gain a competitive edge. In my experience the margin almost always exists, it is just a matter of how much. The margin is perhaps easiest to visualize in plastic sheet manufacturing. The greatest variability in sheet thickness and optical clarity occurs near the edge. An extra margin of sheet is trimmed off to make sure there are no off-spec sheets. Without doing anything to provide tighter thickness control, the trim width could be change if there was enough process knowledge and confidence. The benefit from less scrap can be taken as a decrease in raw material and utility cost to obtain the existing capacity or as an increase in capacity for the existing raw material and utility use as noted in the categorization of possible benefits on slide 2.

The key idea here is that most benefits are not achieved until we change a set point. We can find the existing margin by the intelligent use of an online data analytics system and we can create a new margin by tighter process control. Once we know the margin, we need to move the set point to eliminate the margin. A "good" process control engineer can draw straight lines. A "great" process control engineer can move the straight lines.

Often we are not so lucky to have an online measurement and closed loop control of the product quality or concentration that is the ultimate process output as implied by slide 1. What we have is lot of intermediate unit operations in a plant each with a multitude of process inputs and process outputs that can be oscillating. As a minimum many chemical and biochemical plants have a reaction unit operation followed by separation, purification, and formulation unit operations. For solid products, there is often additional equipment for crystallization, centrifuging, drying, and blending. Each of these unit operations has process inputs and outputs with a degree of variability.

So we have short term or long term oscillations at various points in the process and can reduce or eliminate these oscillations. How do we justify the cost and quantify the benefits of better process control?

In order to estimate what we can gain from process control improvement, we need to know process gains. A process gain is the change in a process output divided by the change in a process input. There is a steady state process gain which is the final change after all transients have dies out and the process has reached a new steady state. Steady state simulations can provide these process gains and through virtual experimentation quantify the changes in the product composition or quality for changes in an upstream process variable. For oscillations there is also a dynamic effect where the oscillations of a process variable are attenuated by downstream volumes. The attenuation is proportional to the period and inversely proportional to the residence time of volumes with back mixing from turbulence, recirculation, and agitation. The follow equation can be used to estimate the amplitude of oscillations in a process output (Ao) for oscillations in a process input (Ai), a steady state process gain (Kp) between the process output and input, a period of oscillation (Po), and for a residence time of a back mixed volume (Tm). The residence time is the volume divided by the total flow rate through the volume.

Ao = Ai * Kp * [Po / (6.28*Tm)]

We can compute the steady process gain from first principle equations as shown in Advanced Application Note 4 posted on March 25 or get it from a steady state simulation as long as we avoid a valve position as the process input. The installed characteristic of teh valve and hence the slope of this curve's contribution to the process gain is typically not simulated correctly. Dynamic simulations that have a flow-pressure solver should be able to predict the oscillation amplitude but in practice the results are poor because these simulations do not sufficiently model process and automation system dead times, valve backlash and sticktion, and control loop tuning that determines the period of the oscillations.

The best way to estimate the relationship is the find the process variable furthest upstream with the same dominant period of oscillations that are in the product. The ratio of the amplitudes (Ao/Ai) is the dynamic process gain. For a given reduction in the amplitude Ai, you can estimate the corresponding reduction in amplitude Ao. A power spectrum analysis of the process variables can be used to find the variables with the corresponding dominant frequencies. We then need to follow through and see how much of a margin we can create by a reduction in the product oscillation amplitude.

Once we have the margin, we need to work backwards (upstream) to get at what is the corresponding reduction in utility flow or feed flow. How do we do this? Again we need to use process gains. We divide the product margin by the process gain to get the change to be made in a key upstream loop set point once we have reduced the oscillations in the product. Consider the case where the key loop is a reaction or distillation temperature loop. We then divide the change in reactor or column temperature set point by the steady state process gain for the required change in coolant temperature and reflux flow, respectively. Next we divide this change in coolant temperature or reflux flow by the steady state process gain for the required change in coolant flow and steam flow, respectively. Finally, we multiply the required changes in utility flow by their cost per unit flow to get at the cost savings. Knowledge of the process and the gains are in the process gains and the periods of oscillation. Online data analytics can find the margins, power spectrum analyzers can find periods, and online controller tuning can find the process gains.

The leading cause of oscillations is a level loop with overly aggressive tuning and in some cases excessively sluggish tuning. Several very sophisticated process studies have come to down to this simple fix. Next week we will look in more detail at this culprit and explore the other causes of oscillations.

What Have I Learned? - Ratio Control (Part 2)

Tue, 07 Apr 2009 14:08:05 -0600

So the question on the minds of automation engineers for process control and even the members of congress for the banks and the economy is how do you fix your model? Will feedback correction be enough? Will the correction arrive too late? How do you deal with a response that is not self-regulating but is integrating or a possibly a runaway?

If you want the bottom line and don't have time for technical jibber-jabber: "The most universal but not well known solution for feedback correction of the flow feedforward model for ratio control uses a Ratio block in tandem with a Bias/Gain block as shown in slide 7 of RatioControl.pdf. The Ratio block operates in the AUTO mode and has its local setpoint adjusted by the operator. The Bias/Gain block runs in the CAS mode and has its CAS setpoint (bias) connected to the output of the process controller used for rapid feedback correction." Of course, you need to checkout and test this solution like any other.

Ratio control is basically a very simple flow feedforward model that involves a simple bias and gain applied to independent flow to compute the dependent flow. On a plot of dependent flow (Y-axis) versus independent flow (X-axis), the gain is the slope and the bias is the intercept. The feedforward multiplier and summer in a process controller for feedback correction of the ratio control would change the slope and bias, respectively. The slope is the ratio factor (delta dependent flow/ delta independent flow).

Nearly all PID blocks have internal feedforward functionality. Some PID blocks have feedforward multipliers besides feedforward summers but the internal structure is fixed and often difficult to understand and maintain. For ratio control, the feedback correction by multiplication or summation is best done outside of the PID block. The use of the Ratio block and Bias/Gain block provide the flexibility and visibility needed through its BKCAL and built-in features and options such as bumpless transfer to the existing ratio. In either case, the independent flow is the IN_1 input and the dependent flow is the IN input to the Ratio (RTO) block as shown in Slide 7 of RatioControl.pdf. The setpoint of the RTO block is the desired flow ratio and the PV is the actual flow ratio.

For a feedback correction by multiplication, the output of the process controller manipulates the ratio factor used in the multiplication of the independent flow. The RTO block is put in the CAS mode and the output of process feedback PID is connected to the CAS_IN of the RTO block. The output of the RTO block becomes the CAS_IN setpoint of the dependent flow loop.

For a feedback correction by summation, the output of the process controller directly manipulates a bias after the multiplication of the independent flow by an operator set ratio factor. The RTO block is put in the AUTO mode and the operator adjusts the local setpoint (SP). The output of the RTO block becomes the input (IN) and the process feedback controller becomes the setpoint (SP) of a Bias/Gain (BG) block. The output of the BG block becomes the CAS_IN setpoint of the dependent flow loop.

A straightforward feedforward explanation can be found on pages 73-83 of the E-book posted on this site on April 3 titled Continuous Control Techniques for Distributed Control Systems. Just ignore the antiquated Figures 5-1a and 5-1b that offered a solution to the missing adjustable filter and time delay blocks back in the early days of the DCS. For more on the nuances of feedforward, check out the May 2008 Control Talk Column "Feeding on Feedforward:" http://www.controlglobal.com/articles/2008/171.html

To visualize and quantify the correction you can use Excel to plot on the Y axis the dependent flow and on the X axis the independent flow for various operating conditions (e.g. compositions and temperatures) so you have a family of lines. If the lines all intercept close to zero, then the slope or ratio factor is mostly changing and a feedforward multiplier would be the apparent choice as shown in Figure 5-2a on page 77 for a ratio of reagent to feed flow. This relationship holds for most blend, composition, pH, % solids, and temperature control systems in continuous (self-regulating) processes. In other words, if the feed flow goes to zero, the reagent, reactant, blend, or coolant flow should go to zero.

On the other hand, if the intercept varies and the slope is relatively constant, then a feedforward summer is the first choice as shown in Figure 5-2b on page 78 for a ratio of feed water flow to steam flow where the blow down flow shifts the operating line.

The steady state process gain for continuous processes is best seen on a plot of the controlled variable (temperature, composition, % solids, blend, and pH) on the Y-axis versus the ratio of manipulated flow (coolant, reactant, dilution, blend, and reagent flow) to the feed flow. These plots can be generated from the first principle equations in the Advanced Application Note 3 posted April 3 on this website or by simulation programs that use first principle equations. The result is a steady state process gain that is inversely proportional to the feed flow. By using a feedforward multiplier, you are effectively multiplying the controller output by the feed flow which cancels out the steady state gain.

So why are feed forward summers mostly used in industrial applications? The short answer is that they work well enough and are easy to implement and understand. You can do an awful lot with a bias correction. The feedback correction of nearly all advanced control tools such as model predictive control, neural network estimators, and partial least squares estimators use a simple bias that is a fraction of the error between the predicted value and the measured value.

There are also good technical reasons to use a summer if you dig deeper. The bias corrects for offset and drift, which is the largest error in most flow measurements. You don't need to nail the ratio factor range for scaling the controller output. You can simply use a + and - % correction to the flow feedforward. In some older versions of the DCS you had to implement a bias of 50% so that we could get a "+ and - 50% correction. If the controller output was 50%, the flow feedforward was perfect. The deviation from 50% was a measure of the flow feedforward error. An integral only valve position controller (VPC) whose setpoint (SP) is 50% and whose process variable (PV) is the feedback controller output can then trim the ratio factor (RTO setpoint). If the VPC IDEADBAND option is employed so you get no integral action if the PV is within 10% of the SP, you get a gradual slope correction only if the fast bias correction is insufficient.

For well mixed vessels and distillation columns, the process time constant is inversely proportional to the feed flow. Since the maximum controller gain for load rejection is proportional to the process time constant divided by the process gain which itself is inversely proportional to flow, the net effect of feed flow on controller gain is cancelled out. The use of a feedforward multiplier now creates a nonlinearity where the controller tuned for low flow will tend to oscillate at a higher flow. This is often aggravated by an equal percentage flow characteristic whose slope (valve gain) is proportional to flow.

If you have an integrating process response, you need an overcorrection to get you back to set point. The correction is most readily visualized as a bias. The easiest to understand example of an integrating response is the level loop where the correct ratio of manipulated discharge flow to the feed flow is one. If the level is too high, keeping the discharge flow equal to the feed flow will not bring the level down. Batch temperature, pH, and composition control tend to have integrating responses. Continuous processes where the process output flow comes from vapor phase tend to have an integrating response in liquid phase. Conductivity (total dissolved solids) control of a boiler drum is an example because the only way to get solids out of the liquid is by blowdown. The ratio of blowdown flow to feedwater flow shifts based on the amount of unbalance in the integrated response. If the total dissolved solids is below the set point, the correct ratio of blowdown to feedwater flow is zero. Similarly, impurity concentration builds up in reactors with a vapor phase product or a significant recycle stream. Here the ratio of purge rate to fresh feed rate shifts due to the integrating response. The overcorrect requirements for a runaway response are even greater because the process is accelerating away from the setpoint. For some reactors, there is a point of no return where the best you can do is to implement the emergency and evacuation procedures. Let's hope that is not the case for the economy. Mars doesn't look terribly inviting and the Martians in the movies have bad attitudes

The main scope of applications where a feedforward multiplier provides a desirable compensation for a nonlinearity is when the feedback controller output goes to a linear installed characteristic or flow controller for blend, composition, % solids, and pH control at the outlet of a static mixer or for temperature control at the outlet of an exchanger because this process equipment has essentially plug flow (with very little backmixing) and hence a negligible process time constant.

This leaves us with the final question, why do oxygen controllers on a boiler stack correct the air flow rather than the ratio of air to fuel flow? Why go to the confusion of a calculated versus a real air flow? The main reason is to actively use the cross limits or lead-lag systems employed in a combustion control system to insure the air flow leads the fuel flow on an increase in firing demand and air lags fuel on a decrease in firing demand.

Regardless of whether a feedforward multiplier or summer is used, the desired ratio before feedback correction and the actual ratio after feedback correction should be displayed, historized, and trended along with the controller output and independent flow.

Books

Fri, 03 Apr 2009 10:52:37 -0600

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

Lectures

Fri, 03 Apr 2009 10:47:07 -0600

New Developments in Modeling and Control

Introduction Batch Control Story

Virtual Plant Concepts Dynamics

Measurement and Control

Batch Profile Analysis

Model Predictive Control

Process Optimization and Control

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

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

What Have I Learned? - Ratio Control (Part 1)

Mon, 30 Mar 2009 12:37:07 -0600

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

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

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

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

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

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

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

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

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

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

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

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

Application Notes

Wed, 25 Mar 2009 10:45:35 -0600

MPC Implementation Methods for The Optimization of a Slow MV with Good Load Rejection by a Fast MV, September 22, 2005 (970KB PDF)

MPC Implementation Methods for the Optimization of the Response of Control Valves to Reduce Variability, January 3, 2007 (463KB PDF)

Compensation of Dead Time in PID Controllers, December 6, 2006 (4,074KB PDF)

First Principle Process Gains, Dead Times, and Time Constants, December 28, 2006 (97KB PDF)

Effect of Sample Delay on Standard PID Tuning and Loop Performance, August 28, 2008 (1,919KB PDF)

What Have I Learned? - Manipulation of Multiple Flows (Part 3 - MPC)

Tue, 24 Mar 2009 13:45:11 -0600

In this final part of this series, we look at what model predictive control (MPC) can do for the following applications:

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

MPC is the more powerful solution for an optimization problem (applications 3 and 4). MPC also offers the simultaneous manipulation of multiple flows, objective oriented tuning knobs, and manipulated variable costs that make the optimization more a science than an art. An experienced regulatory control person can make a PID do almost anything but many plants don't have that experience base. The MPC offers a solution that a person with some basic knowledge of the process and dynamics (process gain, time constant, and dead time) can understand. In my experience in teaching process control to chemical engineers at Washington University in Saint Louis, it was easier for students to understand and use an MPC because it was process oriented. On the other hand, the PID had dozens of parameters with a hundred different opinions on how to set them. If you don't believe me, check out the 484 page documentary of setting 3 of the 20+ PID parameters in the Handbook of PI and PID Controller Tuning Rules, which doesn't get into structure, options, and windup. Just think about trying to teach the PID nuances and heuristic rules well enough to turn a new employee loose on an optimization problem. I think you have a much better chance of success if the neophyte is armed with an MPC. With the disappearance of mentors and in-house technical courses all but the basics of PID control may well be lost. The manager of a process control group at a large refinery told me that he starts up with a PID but quickly moves every loop to an MPC because he doesn't have a Shinskey in his group.

We will see that MPC can also be used for applications 1 and 2 and thus cover the range of opportunities we discussed last week for valve position control (VPC). The principle drawback of the VPC solution is the lack of tuning guidance, no embedded economics, and no move suppression in conventional PID controllers to address multiple objectives (tight control of the critical process variable and the minimization of costs and variability from unnecessary movement of the expensive and large flow).

I first explored an MPC solution for application 3 for the classic case of the manipulation of fast but high cost flow and a slow but low cost flow. The solution as outlined in AdvancedApplicationNote1.pdf involves setting up one of the flows as an optimization variable. Normally one would pick minimization of the high cost flow but this made the fast flow less available for tight control in my tests because the optimization routine had a tighter than expected grip on this flow even when the penalty on error (PE) for the optimization variable was greatly reduced. The high cost and fast flow tended to ride its low limit. I achieved better load rejection performance by setting up the MPC for maximization of the low cost but slow flow. For this setup, the maximization of the low cost flow took a back seat to the tight control of the critical process variable when I reduced the PE for this optimization variable.

If the MPC allows the user to write to the relative costs manipulated variables based on the priorities of each manipulated flow, MPC offers a solution for application 4 without the addition of an optimization variable.

I next explored an MPC solution for the manipulation of a big (coarse) valve and a small (fine) valve. The solution as outlined in AdvancedApplicationNote2.pdf involves setting up the small flow as a second controlled variable. I was able to get good load rejection and set point response while minimizing the use of the big valve. I reduced the PE of controlling the small valve at its optimum position. The stick-slip limit cycle from the big valve can be broken by writing a zero to the move size limit for the big valve when the small valve is within an acceptable throttle range. This MPC solution can be extended to applications 3 and 4 by writing a set point (target) for the small valve based on costs or priorities. If it is the simple of case of trying to minimize the small flow because it is expensive, the optimum set point corresponds to a minimum throttle position that doesn't have excessive seating or sealing friction and hence stick-slip. Is the 1st or 2nd MPC solution better? The more I think about it, I think the solution outlined in the second application note offers more flexibility and is easier to set up but maybe that is because I am an old VPC guy and this MPC is a smarter way of doing VPC.

An MPC could be set up for application 5 but I am not sure whether the advantage of the built-in knowledge of the dynamics of the valves outweighs the disadvantage of the MPC inherent approach for simultaneous movement of the manipulated variables. Application 5 really demands sequential manipulation of the flows so that you are not wasting energy or raw materials. To force sequential manipulation, it appears to me you would have to have extremely high penalties for both valves being open and be able to deal with the discontinuity of the split range point with an MPC that is expecting a linear model.

What Have I Learned? - Manipulation of Multiple Flows (Part 2 - Valve Position Control)

Mon, 16 Mar 2009 14:12:08 -0600

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

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

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

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

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

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

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

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

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

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

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