In the blog post “Intermediate Variables in APC”, it was explained how Advanced Process Control (APC) applications can benefit from the use of intermediate variables. In this second blog, further potential for improvement is presented. If the concept of intermediate variables is new to you, check the previous blog first.
Advanced Process Control (APC) applications rely on models that accurately describe the process behaviour. The modelled response of one or more controlled variables (CVs) to a setpoint change in a manipulated variable (MV) is used to predict these CVs over the prediction horizon. The prediction horizon is usually several hours. Next, optimal setpoint moves are calculated for manipulated variables and a feedback mechanism ensures that the predictions stay in tune with reality.
Once installed, INCA MPC has proven to generate sustained benefits for operating companies. To configure INCA MPC, you need to take several steps: choose the targets and constraints, determine the controller structure, set up the communication between the controller and the process, and tune the controller… After the first version of the controller, updates will be needed later. These updates range from tweaking a tuning parameter to adding extra variables to the controller and updating the targets. All these steps must be executed carefully. The iterations involved in finding mistakes slow down the project’s progress.
Topics: Advanced Process Control
An earlier blog described how INCA Architect makes configuring an INCA MPC application easy by automating tasks, grouping related information and continuously monitoring the consistency and validity of the configuration.
But what if your situation is non-standard? Can you also use the INCA tools in that case? Can you verify the correct behavior of your configuration before going online? This blog discusses how the combination of INCA MPC and INCA Architect gives you all the tools to tackle these cases too!
Topics: Advanced Process Control
