Advanced Controls: QuickStudy

QuickStudy – How it works

When you add QuickStudy to your existing process control system, you will see a rapid improvement. Your initial input is minimal—simply to identify the relevant process inputs and outputs, as well as the product parameters to be modeled and controlled. Modeling is done on-line using real-time process and product data. Tuning of the control strategy is automatic, and the response curve can be shaped by parameters you select.

One QuickStudy controller contains up to 16 Predictive Controller Set-up (PCS) blocks. Each PCS block is self-contained, including modeling, prediction, control and output generation. One of these blocks is required for each controlled variable and can accept 16 input variables. Each PCS block can execute at the frequency appropriate to its variable.

Model Quickly Takes On Actual Process Dynamics

QuickStudy’s modeling algorithm determines the coefficients of the process models using probability density functions. After a few sampling periods, the model begins to represent the actual process dynamics. As the sampling continues, the model becomes more accurate. Typically, 250 to 1,000 sampling periods are required for a new model to become accurate enough to assume control—far less than other systems which often require thousands of samples to build an accurate model. Adaptation of an existing model is normally accomplished in 50 to 100 samples with QuickStudy.

As inputs are scanned, the actual value of all process variables are compared to their predicted values. When a variable doesn’t match the predicted behavior, this information is added to the statistical database. The model’s parameters are able to adapt to the changes in process conditions and the controller acts, almost instantaneously, to assure that the process remains at setpoint.

The control function is accomplished in two steps. First, the quadratic optimization algorithm determines the required control action based on predicted values of the process variables. Second, the dynamic output generation algorithm computes the trajectory for optimal approach to setpoint. These calculations are updated every cycle.

Automically Triggers Proper Interaction of Variables

In many processes, action taken to control one variable will affect other variables. QuickStudy incorporates these interactions into its models so that a change in one control action automatically triggers the appropriate changes in others.

QuickStudy can handle the most complex process dynamics. Its modeling algorithm can handle high order models and can deal effectively with long dead-times, integrating characteristics and inverse responses. And the system’s configuration allows the user to design the response curve to meet the unique needs of each process. So response time is adjusted automatically to meet the process’s tolerance of overshoots in reaching the setpoint. In addition, QuickStudy is exceptionally stable.