Process Control Trends--Part 3

The main limitations of all model free expert systems is their long learning period, (which can be compared to the growing up of a child) and the fact that their knowledge is based solely on past events. Consequently, they are not prepared to handle new situations and therefore, if the process changes, they require retraining, because they are not well suited to anticipation.

Artificial Neural Networks

One of the tools used in building internal models is the Artificial Neural Network (ANN), which can usually be applied under human supervision or integrated with expert and/or fuzzy logic systems. The figure shows a three-layer ANN, which serves to predict the distillate boiling point and the Reid vapor pressure of the bottoms product of a column. Such predictive ANN models can be valuable, because they overcome the limitations of analyzers, which include both availability and dead time.

Model Prediction Tool

Artificial Neural Networks (ANNs) are well-suited

to build internal models. The three-layer ANN shown here

predicts the distillate boiling point and Reid vapor pressure.

The process model’s knowledge is stored in the ANN by the way the processing elements (nodes) are connected and the importance that is assigned to each node (weight). The ANN is "trained" by example and therefore it contains the adaptive mechanism for learning from examples and to adjust its parameters based on the knowledge that is gained through this process of adaptation. During the "training" of these networks, the weights are adjusted until the output of the ANN matches that of the real process. Naturally, these networks do need "maintenance", because process conditions change and when that occurs the network requires retraining. The hidden layers help the network to generalize and even to memorize.

The ANN is capable to learn input/output relationships and inverse relationships and hence it is useful in building IMC based on the ANN constructed plant models and their inverses. In a neural controller the ANN is used in calculating the control signal.

Consider applying model free herding control when a large number of variables are involved. This approach to control can be compared to the herding of sheep, where the shepherd’s dog goes after one animal at a time and does that on the basis of pursuing some overall goal. For example, if the herd is moving too slow, it will urge on the slowest animal to move faster, or, if a change in direction is needed, it will change the direction of the sheep that is farthest away. Because this control strategy was successful in returning herds of Hungarian sheep to the church of each village at exactly the same time every day, I named this strategy the "Hungarian Puli algorithm."

Such herding algorithms were successfully applied in designing the controls of the new headquarters building of IBM ("Lessons Learned" Nov. 2003, p24), which was made self-heating by herding the warm air from the offices which became the heat generators in winter (interior offices) to the perimeter, where heat is needed (offices with windows). This was done by looking at the thousands of offices and changing the destination of the return air from one "hot" office at a time (the one which is the warmest in the building) and simultaneously opening the supply damper of the "coldest office" to that same header.

I will not be surprised, to find that, if by the end of this century, we will all be using self-teaching computers. These devices would be watching the operation of a refinery or the landing and take-off of airplanes and eventually would possess as much knowledge as an experienced operator or pilot.

Some might argue that this would be a step forward, because machines do not forget, do not get tired, angry or sleepy. While this scenario might come true, I would still prefer to land in a human-piloted airplane because the knowledge of the past, which is the knowledge of the computer, is not enough to bring it safely to earth.

Bla Liptk, PE, process control consultant can be reached at Liptakbela@aol.com.