Using OPC technology to support the study of advanced process control

Area of Science:

• Automation and Control Engineering
• Process Systems Engineering
• Computer Science

Background:

• Object Linking and Embedding (OLE) for Process Control (OPC) enables communication between diverse control systems.
• University research centers require cost-effective and flexible tools for advanced process control (APC) development.
• Distributed control systems (DCS) present complex challenges for control strategy implementation and testing.

Purpose of the Study:

• To investigate OPC technology as a research tool for studying distributed control systems (DCS).
• To develop and test advanced process control (APC) techniques within a co-simulation environment.
• To demonstrate the application of OPC in model identification and controller design for a nonlinear boiler system.

Main Methods:

• A co-simulation environment was established using Matlab, LabVIEW, and TCP/IP networking.
• A nonlinear boiler model was implemented as an OPC server.
• An OPC client was utilized for closed-loop model identification and Model Predictive Controller (MPC) design.

Main Results:

• The OPC-based co-simulation environment proved effective for APC research in DCS.
• Model predictive control (MPC) was successfully designed and implemented using the OPC framework.
• The developed MPC effectively controlled NOx emissions, drum water level, and steam pressure.

Conclusions:

• OPC technology offers a viable and economical solution for advanced process control research in academic settings.
• Co-simulation using OPC facilitates the development and validation of sophisticated control strategies.
• The study successfully applied OPC for integrated control of multiple process variables in a simulated boiler system.