Using OPC technology to support the study of advanced process control

In this paper, OPC technology is the economical solution in introducing new APC technologies to a real process.
- OPC based setup is developed for the research of potential applications like distributed control.
- Demonstration of OPC Tools available from Matrikon is explored.
- Example of OPC based APC in MATLAB and Matrikon Simulation Server is shown.
- Matrikon and MATLAB OPC Toolbox can be used to study Advanced Process Control strategies for a distributed control environment.
- In example 1, OPC setup for control applications comprised of OPC Server and OPC Client on Ethernet network is implemented and demonstrated. OPC Server is simulating a nonlinear boiler model, while OPC Client is configured for remote supervisory monitoring and control. LabVIEW is used to develop the OPC Server and OPC Client applications.
- Example 2 demonstrates a co-simulation environment based on OPC technology. In this example, two regulatory loops of a nonlinear MIMO model of a boiler are simulated in MATLAB׳s SIMULINK environment. The MATLAB simulation of the process (Boiler) is connected to Matrikon׳s OPC Server. OPC Client is developed in LabVIEW as Shared Variable Engine. The set points for the two regulatory loops are sent from the OPC Client.

OPC, originally the Object Linking and Embedding (OLE) for Process Control, brings a broad communication opportunity between different kinds of control systems. This paper investigates the use of OPC technology for the study of distributed control systems (DCS) as a cost effective and flexible research tool for the development and testing of advanced process control (APC) techniques in university research centers. Co-Simulation environment based on Matlab, LabVIEW and TCP/IP network is presented here. Several implementation issues and OPC based client/server control application have been addressed for TCP/IP network. A nonlinear boiler model is simulated as OPC server and OPC client is used for closed loop model identification, and to design a Model Predictive Controller. The MPC is able to control the NOx emissions in addition to drum water level and steam pressure.