Adding to the conversation of the Truth about controlling processes… post and response by Emerson’s Terry Blevins, is this response from Greg McMillan. Greg is the author of quite a few books on tuning and control which you can see in the DeltaV Bookstore and an author in the monthly Control Talk column in Control magazine.
The tuning techniques published by David St. Clair are essentially the ultimate oscillation and reaction curve methods developed by Ziegler-Nichols. These models each use two dynamic parameters. An ultimate gain and ultimate period is identified during closed loop tests (controller in automatic) for the ultimate oscillation method. A time delay and ramp rate is identified during open loop tests (controller in manual) for the reaction curve method. Time constants along with the dead time dominant, self-regulating, and integrating responses are also introduced in the publication to help discuss how the ultimate period varies from two to four times the time delay. Laplace transforms are added to explain dynamics and the PID algorithm. The techniques end up with five parameters and three types of processes, which is the same complexity as methods that more explicitly use a model. What seems to be the difference is that the user does not have to identify a process gain. Actually, this process gain is darn easy to find, in that for an integrating process, the integrating process gain is the ramp rate identified in the reaction curve method (same parameter with just an older name). Further, if you multiply this reaction curve ramp rate by the process time constant (largest time constant), you get the process gain for a self-regulating process. Alternately, in an open loop test self-regulating processes, the user could wait for the process to line out and divide the final change in the process variable in percent by the change in controller output in percent. The user just needs to remember to use percent instead of engineering units because the PID algorithm is based on a percent input and output.
The literal use of the reaction curve method assumes the process is lined out. This is rarely the case for tough loops and integrating processes. If a person understands the concept of an integrating process gain, he or she would realize to use the change in ramp rates rather than the lone ramp rate depicted in the reaction curve method.
In fact, all major tuning methods (e.g. Ziegler-Nichols ultimate oscillation and reaction curve, Lambda tuning, and Internal Model Control) for both self-regulating and integrating process reduce to the same form when the user wants maximum performance. Framing tuning techniques in terms of a simple model (process gain. time delay, and time constant) provides this insight. It also allows you to estimate the effect of plant dynamics on performance. If the user understands the sources of time delay and time constants (e.g. transportation delays, thermowell lags, mixing delays, deadband, sticktion, control communication and execution intervals), the ultimate performance of a control system can be improved. My November Control Talk column will explain the unification of tuning methods and the implications.
If the valve size or the calibration span of an instrument is changed, a user knows how to proportionally change the controller gain based on the change in process gain.
Nonlinearities are prevalent in process control but this is more of a reason to have a simple model so that nonlinearity can be identified and quantified. The user then has the option to schedule the controller tuning based on operating regions or use signal characterization. For example, signal characterization of a pH measurement based on the slope of the titration curve (process gain) has proven to be simple and extremely effective. For nonlinear valve characteristics, the combination of a digital positioner and low friction packing combined with signal characterization of the controller output significantly reduces the nonlinearity from deadband, sticktion, and valve trim. In both cases, the improvement in performance is particularly impressive for operation on the flat portions (low gain) portions of the curve.
The introduction of a fast secondary controller can remove most of the nonlinearity seen by the primary controller in a cascade control system. Also, reducing the time delay and improving the tuning enables a controller to stay closer to set point so that the loop sees less of operating point nonlinearities. This is an important technique for pH, reaction, and distillation column control.
Changes in model parameters provide insight as to what has changed in the plant whether it is an increase fouling of a sensor or heat transfer surface or sticktion in a valve. Models offer plant knowledge and allow you to take the blindfolds off. How you use the models are up to you but ignoring them increases war stories and myths and endless meetings where people go around in circle as to what is wrong and could be better. Some processes have been trying to solve the same old problem for decades because there are no models. A model is even more important if you consider process engineers are taught to think steady state, statisticians analyze snapshots of data, and operators want an instantaneous response. The ability to tune a controller from the same model is a plus.
We should not forget the great contributions from the past but we need to move on and seek greater knowledge and performance of our plants.
You can join the conversation here.