Modeling and Control

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.

When we first started Emerson’s advanced control program in the early 90’s, one of the initial objectives of the program was to develop an adaptive control capability that could be used in our control products. However, we realize that adaptive control is one of the most challenging advanced control areas to address from a technical standpoint. Thus, most of the programs resources were initially focused on other areas e.g. on-demand tuning, property estimation using neural networks, simulation, fuzzy logic control and model predictive control. Adaptive control was kept on the backburner for many years with work in this area restricted to technical evaluation of different technologies. Gradually, starting in the late 90’s, a more focused effort was put into addressing adaptive control. As a result of this work, the first release of our adaptive control technology was recently introduced as part of the DeltaV Insight product in the v9.3 release. The things that we learned in researching and developing this technology greatly influence the final design of DeltaV Insight.

In the early 90’s, one of the first adaptive control technique that we investigated was one developed by Professor Karl Astrom, Lund University. This technique allows the controller gain to be automatically adapted through on-line assessment of process gain. As part of this investigation, we worked with Professor W. K. Ho from the National University of Singapore in researching this technique. Even though the approach proposed by Astrom is technically very sound and is utilized in some commercial products, its application is limited to feedback control and adaptation of controller gain. Since our ultimate goal was to find a technique that could be used to adapt all components of PID feedback control (Gain, Reset, and Rate) and feedforward control (gain, Lead/Lag Time constant, and deadtime), we did not pursue this approach past this initial investigation.

At one point we were offered the rights to an adaptive control technique that had been developed by the engineering department of a major chemical company. To avoid polluting the Emerson development team, we hired an outside consultant to evaluate this technology. It turns out that the technique was based on pattern recognition and the application of rules to establish tuning. Even though this approach is used by some major process control companies, the feedback from customers who had tried this technology was not encouraging. There were reports of erroneous adjusted of controller tuning base on cyclic upstream disturbances that were interpreted as a sign of too much controller gain. Thus, we decided to avoid this approach.

In the late 1990’s, Willy Wojsznis came across a very interesting paper on model free adaptive control. This paper helped sparked work that lead to a unique design and implementation of model free adaptive control that we later patented. In the summer of 2000, we sponsored a graduate student under the guidance of Professor Dale Seborg, University of California at Santa Barbara, UCSC, to test and further investigate this technique using process simulations. The basic approach provided to be a reliable method for directly establishing feedback tuning. However, only through inference from the controller tuning was it possible to gain any insight into the process gain and dynamics. Also, the method could only be used for the adaptation of feedback tuning. Therefore, we continue to evaluate other approaches that better met our requirements and would give direct insight into the process gain and dynamics.

In the mid-90’s, a number of papers on the application of controller switching appeared in some of the major control conferences as a technique for evaluating best tuning. Also, a few papers were published on the use of model switching to identify process gain and dynamics. The concept as proposed was not practical to implement. However, these techniques offered the promise of allowing process models to be identified for both the feedback and feedforward path. After some consideration, Willy and I developed a new approach which we labeled model switching with interpolation and re-centering. This new approach to model switching required the evaluation of only a limited number of models at any given time. Testing of this technique by UCSB from 2001-2003 showed the method to converge very quickly for a variety of self-regulating and integrating processes.

An alpha version of adaptive control based on model switching with interpolation and re-centering was installed at two chemical plants in early 2004. The results from one of these sites, Solutia, were published in September 2004 issue of Chemical Processing. Based on the positive results of these installations, beta testing was conducted at four sites from 2005-2006 on approximately 1000 loops. As part of this beta testing, a special emphasis was place on quantifying the benefits of adaptive control for the batch industry. We created a video of the Lubrizol installation in which the customer discusses the benefits they realized from adaptive control on their batch process. The things we learned from these beta installations had a great impact on the final product design. In particular, the beta test proved the value of maintaining a record of the models that are identified over time from each loop. Also, the capability to automatically provide tuning recommendation using this technology was seen as a major benefit in improving plant operations independent of whether closed loop adaptive control was applied in the plant.

If you have an interest in learning more about the adaptive modeling technique used in Delta Insight, then the following presentation that Willy Wojsznis and I gave at Emerson Exchange provides information on the technical details on this technology.

Adaptive Technology

Also, additional detail can be found in the two patents that we have on the basic technology and its use with non-linear applications.

The control design and commissioning of a continuous process often focuses on the requirements associated with operating conditions found at normal plant throughput. Thus, during the startup of the process, many controls are left in manual and it is up to the operator to get the process to the point where the controls can be placed in automatic. From a practical standpoint, it makes sense that the design address normal operating conditions since this is where the process will operate most of the time. Thus, the problem of non-linear installed characteristics of final control elements, limited rangeability of measurement elements, etc are often ignored. The startup of a batch process has many of the same challenges associated with startup of a continuous process. However, since the startup phase of a batch process often represents a significant portion of the total batch cycle time, it is important to automate this phase of the operation. From this perspective, the design and commissioning of batch process controls can often be much more demanding than for a continuous process. Addressing the challenges associated with the wide range of operation during the startup phase of a batch process can pay benefits in terms of reduced cycle time. For processes that are not limited by market demand then any reduction in cycle time directly translates to increase production and associated profit. In some cases, manufactures have added features to the PID that may be used to address some of the requirements associated with the startup of a batch process.

It is common for one or two loops associated with a batch operation to exhibit a slow response. For example, the multiple lags associated with a critical temperature measurement may introduce the equivalent of a signification delay in the process response. Thus, this slow response may require that a long reset time be used in the temperature control. This slow tuning may be appropriate once the unit has reached the design processing conditions. However, during the batch startup this tuning may limit how quickly the unit may be brought up to design processing conditions. The time required for the startup phase of a batch may often be reduced using batch logic at the start of the batch to switch the control to manual and then to position the temperature controller output to a value required for normal operations. Once the impact of the change in the heating valve starts to be reflected in the batch temperature, then batch logic can be used to switch the control to Automatic when the temperature reaches a certain point. However, such a procedure is often complicated by the fact that a different valve position and point to switch the control to automatic may depend on the initial process conditions e.g. feed temperature or the product grade.

To assist a batch process in reaching normal operating condition in minimum time without the need for special batch logic, the DeltaV PID has two added parameters which were named anti-reset windup high and low limit, ARW_HI_LIM and ARW_LO_LIM. The PID is designed to address reset windup independent of these limits. Thus, the real purpose of these ARW parameters is to allow a batch process to quickly be brought on-line. For batch applications, these limits may be configured inside the controller output limits. When this is done, the controller reset time is reduced by a factor of 16X if the controller output is outside the ARW limit and the sign of the control action is driving the output toward the ARW limit. Thus, the control may be switch to Automatic mode at the start of a batch and the control output changes due to reset action will occur 16 times faster until the output reached the ARW limit and then the control will revert to normal reset action. This control action is illustrated in the following.

Response Using ARW limits

For batch applications that require this type of action during startup, the ARW limits should be set to values that are outside the range that will be required during normal batch operations. The ARW limit value may be adjusted to reach setpoint in minimum time without overshoot.

In some continuous applications, the process may be operating at saturation i.e. full open or closed valve under certain operating conditions. When the operating conditions abruptly change, then it may be necessary for the control to quickly position the valve of a slow action loop to avoid the associated control parameter from overshooting. In such applications, the ARW limits may be set to improve recovery from these process saturation conditions and get to setpoint without the use of special logic.

Where have all the instrument and process control engineers gone? Are they in Florida enjoying golf and the weather, are they filling in part time for a contract engineering design job oblivious to the ice or snow storm, or are they like me venting into the blog sphere?

It is easy for plants to forget about people responsible for the tuning and performance of the loops. The few instrument engineers and process control engineers left are focused on buying transmitters and configuring the DCS, respectively. They do not have the time or training to recognize and analyze the tuning and performance of the loops and more importantly it is probably not in their goals. The manager can readily understand that a production unit needs hardware and configuration to make the plant run but to date the opportunity for better tuning and dynamics in the plant is ambiguous at best, which means it is not going to survive corporate downsizing. Studies that show 30% of the loops are poorly tuned and 30% suffer from poor dynamics (e.g. principally valve stick-slip and process transportation delays) are easy to dismiss if there is no onsite data.

Even when loop tuning and performance is on the radar screen, the number of loops assigned to the instrument or process control engineer in a large continuous plant has increased dramatically to hundreds and even a thousand or more. Batch processes have an order of magnitude fewer loops but the ones they have are generally more difficult because there is no steady state (another story).

Astute process engineers who are looking at the loops try to fill in for the missing control people. However, improving loops is probably not in their job description and they usually haven’t had the opportunity to learn about tuning methods, valve resolution and deadband, and even simple process dynamics. These things are not normally taught in a practical manner in chemical or systems engineering, where the focus is on Laplace and Z-transforms to prepare 1-2% of the students to go on to graduate school to major in control theory and become professors. There are exceptions (see my Feb 4 blog on Washington University and the article by Tom Edgar from the University of Texas in InTech last Fall).

A significant part of the value of recent breakthroughs in thinking and online tools is the recognition of the importance and understanding of how the automation system (e.g. valve and sensor) and process (e.g. piping, mixing, and vessel) affects the process dynamics per Advanced Application Note 4, how the dynamics affect the tuning settings, and in turn how the tuning settings affect the performance of the loop.

For those who are tired of reading or have email to do, the takeaway is:

(1) Plant design sets the minimum and maximums of the process dynamics and how these change with operating point of the process and valve, which in turn determines how the tuning should be scheduled
(2) Process dynamics slowly change with aging, fouling, and frosting
(3) Process dynamics rapidly change with throughput and load (most noticeable during startup and turndown) and show up as a change in the valve’s operating point
(4) Valve, pump speed, and sensor resolution limits create a variable dead time
(5) Process dynamics determine the ultimate possible performance
(6) Tuning settings determine the actual achievable performance
(7) All tuning methods end up with about same controller gain for maximum rejection of process load disturbances if there are no extenuating circumstances
(8) The reduction in error for a load disturbance can be simply estimated from tuning
(9) Online tools can identify valve stick-slip, deadband, and the valve characteristic
(10) Online tools can identify the process dynamics and schedule tuning settings

An article in Chemical Processing provides information on an online tool for the identification and monitoring of process dynamics and control valve resolution and deadband, and a corresponding calculation and scheduling of tuning settings. Changes in the process dynamics provide considerable insight but you need "Insight" to appreciate this insight.

The following slides show how to estimate the improvement in integrated error from less sluggish tuning for a load disturbance at the process input. The equations assume the aggressive tuning does not cause the loop to oscillate more than what it already does from valve resolution/deadband or measurement noise. Equation 2-2b is derived from Equation 2-2a, which was derived in Appendix C of New Directions in Bioprocess Modeling and Control. The equations are useful in terms of simplicity and recognition of cause and effect.

Load Disturbance IAE

More aggressive tuning increases the rate of change of the controller output and hence decreases the dead time from valve resolution/deadband. While it does not affect the amplitude, it increases the frequency of the limit cycle from valve resolution/deadband. This may or may not be a good thing. A faster cycle is more effectively filtered out downstream by a process volume but a faster cycle may be more disruptive to associated loops on the input to the process volume (e.g. loop interaction). More aggressive tuning setting (e.g. high controller gains) may also amplify measurement noise. Thus, there is a need to monitor the variability of all loops, which is an important feature in online software today.

This is not to say that all loops are tuned sluggishly. We have seen several loops that are oscillating nearly full scale (essentially on-off control) and the users have actually gotten use to this. The process runs moderately well because the average of the oscillations is OK. The oscillations are tough on valves and equipment and tough on the process engineer because he/she cannot see a discernable pattern in the controller output important for diagnosing changes in the process and loads.

Getting back to the more common case of sluggishly tuned controllers, how far off the mark is the controller gain for maximum disturbance rejection in some important loops? A Lambda factor of 2 to 4 is commonly used because this is what is appropriate for the flow, liquid pressure, pipeline, and heat exchanger loops frequently encountered, particularly in pulp and paper. However, for loops on biological or chemical reactors, evaporators, crystallizers, neutralizers, and distillation columns (unit operations distinguished by a high degree of back mixing from bubble flow and/or agitation), a Lambda factor of about 0.2 provides the best disturbance rejection with acceptable robustness because the dead time to time constant ratio is less than 0.2. Note that Lambda is the Lambda factor multiplied by the process time constant so setting the Lambda factor equal to the dead time to time constant ratio corresponds to setting Lambda equal to the dead time. Thus, current tuning practice gives a gain that is ten times too low and thus an integrated error for load disturbances that is ten times larger than achievable for highly back mixed volumes.

Many of these loops behave like they have integrating processes (like level) and may be best modeled as integrating (e.g. “near integrating”) even if they are not perfectly integrating. The integrating process gain is inversely proportional to the back mixed volume.

People are starting to understand this problem and plants may have some how arrived at the more aggressive settings on critical unit operations. It is important to note that to avoid problems with more aggressive tuning during startup and a turndown (lower throughput rates), the controller gain should be identified and scheduled online since the dead time is inversely proportional to the throughput rate and the valve gain (curve slope) changes with operating point on the installed valve characteristic. Also, it may be advisable to institute set point rate limits on primary loops to prevent big steps in the controller output from a set point change.

A final point, if you don’t tune the temperature loop on a highly exothermic reactor aggressively, a runaway can occur due to positive feedback (higher temperature causes a higher reaction rate through Arrhenius equation). Customers have learned the hard way to use a more aggressive controller gain to keep the relief system from blowing. For these reactors there is a lower controller gain limit besides the normal upper limit for stability. There is also a window of allowable controller gains for integrating processes when the controller has integral action (PI or PID), but this is getting too deep.

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?

If you ever wondered if you are agitated enough, then this blog may help stir up some thoughts. Specifically, how does the relative type and degree of mixing in the plant design affect your job as an automation professional? If the process engineer tells you the project is installing a radial instead of an axial agitator, do you shudder with profound disappointment or just utter a sigh? What if the agitated vessel is replaced with a static mixer? Do you turn the project over to the intern and take early retirement?

I will first continue my role in life as a pH stalker but then move on to other processes and more general considerations.

I became sensitized to mixing because of the extreme sensitivity of pH loops to plant design. I have talked before about how pH processes are the best known indicators of valve stick-slip, particularly near the neutral point, A control valve resolution of 0.1% (exceptional by any standards) can cause a pH swing that is more than noticeable.

Similarly, pH processes are the best known indicators of the uniformity of mixing. Concentration fluctuations in hydrogen ion concentration as small as 0.0000001 normality can cause noise with a 1 pH amplitude at the neutral point. The only study I have seen on the mixing required for pH was a cop out because it was done at 4 pH where the sensitivity (slope of the titration curve) was 1000 times less.

Additionally, the consequences of mixing delays are most severely felt in pH processes. An increase in loop dead time increases the excursion in pH for a given load upset, which increases the nonlinearity seen by the control loop. The operating point nonlinearity for pH can be extreme. The process gain is proportional to the slope of the titration curve and inversely proportional to the total flow and can change by a factor of ten for each pH unit deviation from the neutral point in a strong acid and base system.

The game in a pH loop more than any other loop is to minimize noise and dead time.

For other processes, the required degree of mixing is a lot less, but whether you are talking about temperature or concentration control, poor mixing still shows up as more noise and more dead time. The percent nonuniformity from mixing multiplied by the conversion factors to get to percent of the measurement scale gives you the noise amplitude seen by the controller algorithm. The dead time from mixing in a well designed agitated vessel is roughly the turnover time, which in turn can be approximated as the liquid volume divided by the sum of the feed flow, recirculation flow, and agitator pumping rate. The average dead time is ideally more like ½ of the turnover time whereas the maximum dead time is the turnover time. This helps explain why you see ½ to 1 times the turnover time in the literature as the mixing delay. Since we are generally short on our dead time estimates because there are so many sources of process dead time, I don’t like to skimp on the mixing delay. See my Nov 20 2006 blog “Without Dead Time and Disturbances I Would Be Out of a Job” in the Plant Design category for a list of sources.

Unfortunately, the above assumes the liquid height is about the same as the vessel diameter (unless there a multiple levels of impellers), baffles every 90 degrees to prevent swirling, and an axial agitator to pull down liquid (not air) from the surface. If a camera shows the surface not being broken or swirling, or there is foaming, you can say “Houston we have a problem”, particularly if the vessel vendor or design firm is in Houston. In processes that cannot withstand high agitation because crystals or cells may be broken by the blades, there may be an opportunity to increase the recirculation flow and use a jet or eductor to amplify the effect of the flow (e.g. jet fermentors).

Bob Heider, adjunct Washington University professor, wisely pointed out that baffles cannot be used for biomass, crystals and particles when the baffles cause the solids to dam up or break up. Bob also provided the following memory dump on agitation.

Agitation Info

A bigger potential source of dead time is the injection delay from dip tubes for small manipulated flow (e.g. nutrient, reagent, reactant, or additive). The normal design practice is to have a robust sized dip tube go about halfway down the liquid to the impeller. Unfortunately, this creates a dead time when the manipulated flow is shutoff for a prolonged period of time that is the submerged dip tube volume divided by the flow. For example, just a gallon volume will cause a dead time of 1 hour for a 1 gph flow when the control valve reopens. There can be an even larger dead time because to see the final effect of stopping the flow, you have to wait till the concentration inside the dip tube drains and migrates into the mixture in the vessel. Various method of reducing injection and mixing delays are discussed in the ISA book Advanced pH Measurement and Control, 3rd edition, 2005.

This brings us to one grand generalization. For concentration changes, the residence time (volume divided by flow) becomes a process dead time for a pipe but becomes a process time constant for a well mixed vessel. Check out next week’s blog for the effect on tuning and loop performance. In the mean time, stay agitated.

Many control systems have traditionally provided some capability to limit the rate of change in a setpoint value used in control. When a setpoint change exceeds this rate of change limit, then the rate limited value is use in control. Thus, the full setpoint change is not immediately seen in control until the rate limited value catches up to the target setpoint. Setpoint rate limiting can be a very useful feature especially in batch control since it allows large step changes in setpoint to be made without causing an upset in the process operation. Thus, when the Fieldbus Foundation function block team reviewed the features that should be supports by the PID block, we decided to include setpoint rate limiting. Also, we included this feature in the Analog Output, AO, function block.

To make it easier to configure setpoint rate limiting, the limit value was defined to be in engineering units per second. One question that we had to address was whether the rate limit should apply to both increases and decreases in setpoint. In some control systems that support setpoint rate limiting, only one rate limit value is configured and this applies both to increases and decreases in setpoint. However, there are applications where setpoint rate limiting is only required in one direction. For example, the rate of increases in feed to a reactor may be limited by how fast the cooling system can respond but there may be no limits on how quickly the feed may be decreased. In some cases a physical limitation may place a limit on how quickly a setpoint change may be implemented. Thus, in the Foundation Fieldbus PID, the setpoint rate limits for a setpoint increase and decrease may be independently defined in the function block configuration. You may disable setpoint rate limiting in either direction by setting the associated rate limit value to zero(0).

One important consideration in the design of rate limiting was whether setpoint rate limiting should apply when the PID block is in Cascade mode i.e. when the setpoint is provided by another function block rather than the operator. For example, in a cascade control strategy, the PID associated with the secondary loop would normally operate in Cascade mode and its setpoint would be supplied by the PID in the primary loop. In cascade control strategies, the introduction of non-linearity behavior such as setpoint rate limiting in downstream blocks can complicate the commissioning of the upper level loop. Thus, the function block specification states that PID setpoint rate limiting should be disabled when the block is in Cascade mode. If an application requires that rate limiting be enforces on how quickly a valve may open or close, then this may be done using the setpoint rate limits provided in the Analog output , AO, block that is part of the loop. The setpoint rate limits of the AO block apply in both the Automatic (Auto) and Cascade (Cas) modes.

If the user defines setpoint rate limiting in the AO block , then when a change in setpoint becomes rate limited the AO block is required to reflect this high or low limit condition in the status provided with the BKCAL_OUT that is wired to the BKCAL_IN of the upstream PID. Also, the BKCAL_OUT value is required to be the rate limited setpoint value. Thus, the upstream PID of the cascade may take action to prevent the reset from winding up when the downstream block can not fully respond to the requested change in setpoint. For example, when the PID BKCAL_IN indicates a limit condition, then the manufacturer may design the PID to clamp the reset contribution in the direction of the limit condition. Alternatively, the manufacturer may design the PID to use external reset i.e. to use the BKCAL_IN value i.e. the limited setpoint valve in the calculation of the reset contribution.

In the Foundation Fieldbus specification, the PID and AO setpoint value after rate limiting is an internal “working setpoint” value. To make it easy to indicate to an operator when rate limiting is active, some manufactures have added the working setpoint value as a visible parameter of the PID and AO blocks. The application of setpoint rate limiting in the PID block and the AO block is illustrated in the following example.

PID and AO Setpoint Rate Limiting Examples

If you are interested in learning more about this capability, then a model and detailed description of setpoint limiting can be found in the Fieldbus Foundation function block specification – Parts 1 & 2.