Process Control Trends--Part 3

In this portion of process control trends, let's discuss the need for gradually changing the very basis of our control mentality, which today is still based on the single PID loop. It is time for us to start thinking in terms of multivariable control. Right now, many still approach the control of processes by evaluating the dynamic response of one controlled variable to one manipulated variable (in terms of the dead time, time constant and gain of the response). We treat this single loop, as if it operated in vacuum, as if the opening or closing of a control valve affects only the variable we are controlling.

It is also high time we pay attention to the interaction among all the controlled loops of a process and to consider the relative gains of these interactions. Because the plants we work in do not produce flow, level or temperature; it stands to reason that the control of these variables should not be the ultimate goal. In short, the single loop mentality is wrong. It’s wrong because the ultimate goal is operating safety and production efficiency.

The time has come for the process control profession to start thinking in terms of Unit Operation Controllers. It’s time to develop multivariable systems that encompass and control the total unit’s operation (be it a boiler, a distillation column or a compressor). In such a unit-operations control scheme, controllers manage the interactions between the various controlled and manipulated variables that are built into the control strategy for that process.

Unit Operations Control

All expert systems take advantage of the large memory and the fast data manipulation capability of computers. There are hundreds of expert systems on the market and we should understand what these systems can and cannot do. One common feature expert systems share is that they all serve some form of optimization. What distinguishes these systems is the type of optimization they perform and the methods used to perform it. From this perspective, one can distinguish model-based from model-free methods.

Model-based control (MBC), model-predictive control (MPC) and internal-model control (IMC) are all suited for optimizing well-understood unit processes such as heat transfer or distillation. The performance of these model-based methods is superior to that of the model-free systems, because they are capable of anticipating and responding to new situations. In this sense performance is similar to that of feed-forward control systems, even though the model free systems behave in a feedback manner only.

Model-free expert systems can be compared to the behavior of a tennis player. The tennis player does not necessarily understand Newton’s laws of motion or the aerodynamic principles that determine the behavior of a tennis ball, but simply memorizes the results of a large number of past responses. This is also the basis of human learning. All the neural network software packages on the market mimic this learning method.

Neural networks, fuzzy logic and statistical process control are all such methods, which can be used without the need for knowing the mathematical model of the process. The major difference between fuzzy logic and neural networks is that the latter can only be trained by data, but not with reasoning. Fuzzy logic is superior from this perspective, because it can be modified both in terms of the gain (importance) and also in terms of the functions of its inputs.