Source: Aspen TechnologyOne approach to prototyping is to use rigorous models to test MPC. This is done by using Aspen's OTS Framework with Hysys dynamic models to simulate the behavior of the unit under consideration. The resulting simulation provides the baseline process performance information. Once the baseline is established, the simulation is linked to Process Controller, enabling the developer to see how the controller would affect the unit (Figure 1). The application also calculates the difference between baseline performance and the resulting performance with the controller by using Aspen Watch performance monitor. This enables the user to gather and compute estimated benefits automatically. This prototyping environment also allows site staff to see how the controller would affect the operation of the unit, and how it would respond to disturbances. The availability of this information improves credibility, resulting in a more informed buy-in from operations staff and business managers. The major steps in this process include:
- Create a dynamic model of the unit using Hysys;
- Connect the Hysys model to OTS Framework;
- Connect the Aspen Control Platform (ACP) to OTS Framework;
- Make step tests from within Hysys model environment (manipulating the MV, passing MV and CV data to OTS Framework and on to APC;
- Use data collected by ACP during the step test process to create and tune the controller; and
- Connect the ACP controller back to Hysys to develop the business case benefits for using APC on the "virtual process."
"We added an Aspen DMCplus function block to Hysys software, which allows users to see the impact of APC on the unit as they run the simulation. They can also use AspenWatch's real-time monitoring to build calculations that capture the benefits," explains Golightly. "Within the simulation, the monitor is connected to the Aspen DMCplus function block. The simulation is used to generate baseline data and then the controller function block is enabled. The controller can modify the simulation's settings and check for differences, all of which is captured and compared using the monitoring tools. So, instead of taking months to justify an APC project by collecting and analyzing production data, we've been able to help the user create a baseline and compare that to the expected performance with APC in just a couple of days, which enables faster, less costly APC, buy-in from operators at the very start of projects, and much better business decisions.
Schnelle reports that DuPont began using the IP.21 data connections for rapid prototyping about four years ago. Consequently, while DuPont previously did one or two APC applications per year, its rapid prototyping method is now enabling it to perform 10-20 MPC implementations per year. "These are mostly focused on better control, pushing constraints and saving energy in smaller-scale applications, such as polymer, protein, wet chemical and other production applications," says Schnelle. "Production personnel are usually very conservative, and so our prototypes help convince them that MPC can benefit and improve their operations and can be maintained"
In fact, Schnelle adds the actual performance of many applications with MPC has been even better than their prototypes indicated, and has helped their MPC systems save labor, improve throughput and yield, save energy and minimize waste. "A good prototype can get everyone on the same page, and help drive consensus about which projects are worth going after," says Schnelle. "And, if operators can see their usual tags and data in a simulation, it enables better awareness, and makes it easier to decide if MPC is right for that certain situation."
In the future, Schnelle projects that prototyping may be applied in more of DuPont's batch and biological applications.