Computer-based process simulations have been in routine use for more than 30 years for the design of new plant equipment and also for training. But they are rarely used for the design of control schemes or for the tuning and testing of controllers.
It is not always possible or useful to perform all the necessary studies and tests for tuning and testing on a live process. Therefore, simulations could be particularly handy in these cases.
Process designers were among the first intensive users of these tools. With them, process designers can carry out their work much faster and more easily, as well as study many more different situations and cases. However, these simulations in most cases still are static and not dynamic.
More recently, increasingly dynamic simulators have been developed, mainly for pre-startup training of operators when new equipment or even an entire new plant is installed. Some of them are quite complex and expensive, up to several million dollars. But they easily pay back the investment by enabling the smoother start up of new equipment or a plant, or to bring them to full capacity more quickly.
Here and there, engineers also tried to use such simulators for the development of new controls. In principle, they should offer the same benefits they do in design and training, namely the ability to look at more different scenarios and solutions or simply to save time. But so far they have not become a standard tool for control engineers.
One reason is that until a few years back there were basically only two types of dynamic simulation tools around. Some were large packages that allowed very realistic models but were much too complex, expensive, and difficult to be used in daily life. At the other end of the spectrum were numerous tools that were nearly academic in their simplicity. They were easy to use but they lacked the needed coverage of real-life situations, such as sticking valves, etc.
Recently, however, several more compact and relatively inexpensive packages have arrived on the market that show great promise. Let us take a look at three quite different but reasonably typical cases to see if we could use such tools today in the context of process control.
Distilling Trainer Knowledge
Some time back, I was called by an oil refinery to help develop several product quality controls for the distillation towers of its delayed coker unit. It quickly became apparent that because of the process dynamics the standard PID controller was not usable, and that model-based predictive control had to be applied. As the name implies, this technology is based on a mathematical description of the process behavior. It requires therefore quantitative knowledge of the static and dynamic effects as represented, for example, by the process parameters.
Normally, it is not too difficult to carry out the necessary tests on a distillation tower. But here the situation was different. The unit used several drums to store the coke. Whenever one particular drum was full, the coke was routed into the next one, and so on. These switches happened every few hours and caused a tremendous upset for the plant because the new drum had to be made oxygen-free with steam, heated up, etc.
The consequence of this situation was that the time span during which the plant was running smooth and steady was never long enough to conduct a meaningful test. This of course created a dilemma, because we definitely needed the process parameters.
In checking out the alternatives, we found the refinery still had in the training center a well-kept dynamic simulator, which had been used for pre-startup training of the operators. We hoped we could use it for our test work. Some checks proved that its behavior was realistic enough. Luckily, this simulation even gave us the possibility to disable the coke switching procedure so we could study the behavior of the distillation towers. In the end, all the necessary test were done on the simulator, all the needed information was obtained in a short time, and the controls could be developed and tuned. The simulator helped to overcome an unusual problem and allowed us to finally make a significant improvement in the operation of the unit.
Unfortunately, in other cases training simulators turned out to be not usable. The main reason was, after the end of the pre-startup operator training, there was no person assigned to keep them up to date. They were not a credible representation of the process any more. A real pity, because they were not only useless for control purposes but also for training of new operators. The lesson is: If such a simulator is built, then it should be properly maintained and also used actively for control development.
'Simple' Level Control
The second example has to do with a much less complicated situation. Level control is in principle quite easy and simple, but level problems can have significant negative effects on other, downstream parts of the plant.
In almost all cases, the standard PI controller is used for level control. Normally, the tuning is not calculated based on the process parameters with some suitable methods but simply found by trial and error.
This implies there are several tests with the controller necessary to find the proper settings. These tests are almost exclusively setpoint step-tests, because this is the only test that can be applied under normal conditions. It is certainly not acceptable to create a disturbance in the plant just to tune a single, simple PI controller.
But these setpoint tests do not give us the correct picture, because the setpoint of a level controller is practically never changed. The task of the controller is to deal with disturbances, a situation that typically cannot be tested during the tuning effort. We are thus tuning the controller for the wrong task.