On the other hand, it is simple to simulate the dynamic effects of level in a drum or basin. In the case of a vertical drum, only the diameter and the distance between the measurement taps are needed in order to describe the situation sufficiently. If there is a suitable simulation tool, such as Matlab, MatrixX, etc., available, then the controller can be very quickly tested under the correct situation. If tools such as TOPAS are available, different disturbances can be simulated.
Figure 1: Find Level Best
With such aids, it is also possible to calculate the tuning swiftly and specifically for the prevailing situation. For example, in a case where smooth action on the manipulated flow is needed, we can quickly test if a so-called error-squared PID controller could deliver better results (Figure 1). For new designs, within a few minutes it could be checked to see if the dimensions of the vessel are sufficient for proper control without negative influences elsewhere.
The third case involves a commonly found situation in which we have to make a decision regarding the control scheme structure and are looking for extra information to support that decision. Let us take a simple example: a furnace.
The key control objective is to keep the product temperature as close as possible to the setpoint. If this furnace is subject to frequent changes in the product flow rate, then it is certainly difficult if not impossible to avoid large fluctuations in the temperature just by use of feedback control. We can use a disturbance compensator, a feedforward, to reduce the effect of disturbance and consequently these variations. To do so, we need to be able to recognize the change in the disturbance variable and to react in time.
In the perfect case, all process parameters used in the feedforward are 100% correct and there is sufficient time to react--i.e., the deadtime of the disturbance is longer than the deadtime of the manipulated variable. In this perfect scenario, we could even achieve total compensation of the effect of the disturbance.
But what usually happens is that the deadtime of the disturbance, the product flow in our example, is shorter than the deadtime of the manipulated variable"for example, the fuel gas flow. The only thing we know is that even in the academic case, where all other parameters are 100% exact, we could never compensate the disturbance in full. The feedforward will always act too late.
Figure 2: Feedforward or Not?
This leads to a simple but crucial question: In such a situation, does a feedforward make sense at all? Can it still improve the performace of the temperature or not? After all, it takes extra effort to develop and to maintain it. The answer can be found by calculations, but this is quite a tedious task. Much faster and more convincing is to simulate the situationÃ¢â‚¬“once with feedback control alone and once with the imperfect feedforward (Figure 2).
Such a comparison delivers the answer with ease and in very short time. We have to invest some effort though. We have to get at least a reasonable estimate of the process parameters in order to conduct a meaningful study. But once we have them, the simulation is done quite easily and quickly.
In addition, the effort to get the process parametersÃ¢â‚¬“at least those for the manipulated variable-- is not wasted, even if we decide against the feedforward. They can be used to calculate the best suited tuning of the feedback controller.
So computer simulations are indeed valuable tools. They're not just for the design of new equipment and for training and practicing, but they can give very valuable support in dealing with common control problems as well as special situations.
However, they are not used as widely as they could and should be. One reason is the persistent misconception that such simulations must be of extremely high fidelity in order to deliver meaningful results. That's usually taken to mean complexity, which in turn often means unacceptably high cost and difficulty in use.
At least the last two examples show this is not necessarily the case; that even relatively simple simulation tools can make valuable contributions toward operational improvement. They can help produce better control solutions, often with less disturbance of the process, and especially can help save time.
Simulation tools are sufficiently realistic. They allow us to describe and study many real-life situations, especially disturbances and problems, in an easy-to-handle way. They can provide all the needed control functionality, and allow also the easy transfer of the results into the DCS.
Hans H. Eder is president of ACT. E-mail him at firstname.lastname@example.org.
Simulation of a furnace shows that when a disturbance such as an increased product inlet temperature is introduced (yellow line), adding feedforward (right) can reduce product temperature excursions (blue line) significantly compared to using feedback alone (left).
In a case where smooth action on the manipulated flow is needed, simulation shows the output of an error-squared PID controller (red line) is much smoother than a typical standard level controller.