Q-MAST™ is a powerful modeling and simulation tool that enables a control engineer, using historical plant data, to create process models and predict process behaviour.
With these models you can identify
- Process Gains
- Process Response
- Process Deadtimes
- Effect of disturbance variables on your process
Q-MAST uses the latest Windows NT™ technology to provide an easy point-and-click interface to Modeling. The advanced graphing capabilities of Q-MAST provide a powerful visual interface for process analysis, modeling and simulation.
Q-MAST provides graphical plots of
- Process Variables
- Model Goodness
- Step Response
The Plot interface includes zooming, panning, printing, exporting to bitmap and jpeg image formats, cursor values and markers.
The information that you gain from Q-MAST will enable you to tune your plant’s PID controls and use the QuickStudy – Adaptive Process Controller to achieve
- Tighter control / reduced variability
- Higher Quality
- Increased product throughput
- Lower Manufacturing costs / increased profits
Q-MAST walks you through the following steps to create models and analyze your process
Step 1: Define your data
On the Data Definition screen, you provide the location of the Data CSV file, the destination for the model files, the column numbers for your primary and disturbance variables. You can also view the variable plot from this screen as shown below.
Step 2: Run Modeling
On this screen you define the Model Parameters and “Run Modeling” to create the process models. You can also view the Variable Plot and the Goodness Plot.
Step 3: Review the models
On this Screen, you may review the models that were created. The Details Button, displays a new dialog with the Model Parameters, Transfer Functions and the Step Response as shown below.
Step 4: Run Simulation
On this Screen, you define the model to be used and the number of samples to process before switching to open-loop simualtion. You can then view the simulation plot as shown below.
The Simulation plot is a means to verify the validity of the model identified. If the phase, amplitude, process direction and length of simulation are sufficiently accurate, the model identified may be considered as a valid model.