There is a range of dynamic simulators available in today’s market. Generally, each
simulation platform is best suited to a specific production process. [20] Furthermore, modern plants tend to have several plant-wide operations which may fall into separate classifications. Similarly, there are a several MPC software packages to choose from. Despite this, many oil refining and petrochemical plants will adopt one and use it in a routine way for decades. This will see plant personnel become proficient with that particular technology but can lead to complications when companies merge or processes are combined. It can be beneficial to establish seamless and synchronised communication between different simulation platforms and MPC systems in order to exploit individual strengths and make use of existing software licences. This is known as co-simulation. [20]
2.2.1. ActiveX
Object Linking and Embedded (OLE) Automation (later renamed Automation) is a Microsoft Windows programming application. Automation makes it possible for different applications to manipulate objects within one another. An Automation server can reveal its functionality to an Automation client via its Component Object Model (COM) interface. This allows the client to utilise the services provided by any object exposed by the server. An ActiveX control is a type of Automation server. The application that hosts the ActiveX control is the
Automation client of that control. This client server relationship can be used to transfer data between software packages from different vendors. [21]
In [22], ActiveX was used to build a co-simulation between APD, MATLAB and Laboratory Virtual Instrument Engineering (LabVIEW) to develop an MPC strategy for a simulated distillation process. With an extensive database of chemical properties and applications, APD is one of the most widely used simulation packages in the chemical industry. However, while it is useful for analysing dynamic plant behaviour and PID control schemes, it does not support MPC. The DMCPlus software add-on can be purchased separately from AspenTech to extend the capabilities of APD. This includes an MPC algorithm known as Dynamic Matrix Control (DMC). To avoid the additional costs associated with DMCPlus, in [22], ActiveX servers were developed in MATLAB, LabVIEW and APD to extend the original APD software functionality. The MPC toolboxes in both LabVIEW and MATLAB were explored and MPC controller performance was compared to Proportional and Integral (PI). One of the initial aims was to develop an operator training platform in LabVIEW for students to experiment with MPC on the simulation in real time. This was not achieved due to a lack of documentation surrounding the object tree structure and was instead left as a future recommendation. [22]
2.2.2. OLE for Process Control
One disadvantage of using ActiveX controls is that they are intended to be embedded inside another application. This means that they are not capable of running standalone. On the other hand, OLE for Process Control (OPC) Servers can run standalone and are specifically
from different vendors to communicate seamlessly using a client server approach. An OPC server can communicate with a device using a vendor specific protocol and then make the information available to clients via its interface. Once configured, an OPC client can request the information from the server and use it freely. An application that consumes and supplies data can be both a client and a server. [24] As of 2009, the OPC market included over 2,500 vendors offering over 15,000 OPC-enabled products. [24]
In [20], a crude oil furnace was co-simulated using two rigorous dynamic simulation platforms - Apros 6 and NAPCON. Apros 6 can be used for a wide range of processes, however its primary application areas are power plants (nuclear, combustion and solar) and pulp and paper mills. NAPCON, on the other hand, has an extensive chemical component library with built in thermodynamics. To combine their strengths, the two simulators were interfaced at the heat transfer surface between the coils and crude oil in the furnace. OPC communication was used for scheduling and data exchange between the two simulation platforms and a NAPCON MPC controller. [20]
In [25], OPC was used to connect well known simulation and research tools MATLAB and LabVIEW in a co-simulated environment. This was done to support the study of Advanced APC and Network Control Systems. The major focus of this study was the development of an MPC-PID cascaded control system for a simulated non-linear boiler system as seen in Figure 1. The closed loop plant model was deployed as a periodic OPC server on LabVIEW. A series of step inputs were applied to the system and the data was collected via OPC connectivity for closed loop model identification. This was achieved using MATLAB’s Identification Toolbox. Finally, the identified model was used to create the MPC interface in MATLAB which acted as an OPC client. The MPC controller was first tested in MATLAB’s
simulation environment, Simulink, using functions of the OPC Toolbox. Once tested, it was connected to the closed loop simulation in LabVIEW. [25]
Figure 1:Co‐simulated MPC‐PI Cascaded Control System. Adapted from [25]
In [19], a real-time vacuum crystalliser process simulation was developed in Simulink. Variables were passed back and forward between the simulation and a Honeywell Profit Controller (see section 5.1) using MATLAB’s OPC Read and Write toolboxes, as seen in Figure 2. The simulation framework was designed as a proof of concept. It was proposed that a similar setup could be used to test and pre-tune advanced controllers in order to reduce the amount of onsite time required for commissioning. Further to this, it could be used to train engineers and operators in the use of APC software. It was suggested that this was the first successful integration of Profit Control and the MATLAB simulation environment. [19]