The decision to implement an advanced control application using model predictive control (MPC), advanced regulatory control (ARC) or some combination in a continuous process plant is more than a technology decision. Both tool sets can perform well, but users must take support for the implemented application into consideration.
Before MPC, if the process required advanced control techniques, the process control practitioners had to assemble the correct control functions to achieve the required results. If, for example, the process control group could measure a feed stream composition change before the stream entered a distillation column, they could apply feedforward control to allow the draw stream controller to anticipate the change and take corrective action in advance, minimizing the impact on product quality.
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Process control engineers have developed much more complex ARC techniques to address a wide variety of process conditions, control loop interactions and overall process constraints. But with MPC so widely accepted and used, some may not be taking advantage of the ARC capabilities built into most DCSs.
MPC has earned widespread respect and acceptance due to its often-spectacular payback. Despite the considerable implementation cost often involved, users have cited MPC projects that have delivered return on investment in 18 months or less. As a result, the "culture of MPC," might unduly influence some companies to implement MPC, when in some cases, advanced regulatory control implemented right in the DCS control blocks might actually provide the best solution.
Not highlighted as much is the fact that MPC requires a continued investment. The models are built based on the set of process conditions, feedstocks, ambient conditions, variable interactions and business objectives that exist at that point in time. Over time, however, any or all of these may change, requiring the model to be modified or rebuilt. As long as the company has invested in training, or has access to MPC specialists to maintain the models, the results are typically satisfactory. If not, the value of the MPC will be reduced (sometimes to the degree that operators will simply turn it off).
Balancing ARC and MPC
So here’s a good question: If all the process needs to perform better is feed-forward, do you really need to build (and maintain) a model to accomplish this? In many instances, advanced control could be accomplished by configuring function blocks and tuning each to remove loop interaction, provide feedforward action, and other advanced regulatory control techniques. If so, could this possibly be a better approach than MPC? More to the point, how's a plant to decide?
The ARC Advisory Group has been working with process control end users in world-class plants for many years. Since 2005, many of these end users have participated in the Benchmarking Consortium, which focuses largely on automation and control. Consortium participants have participated in efforts to benchmark MPC performance, along with such indicators as the number of support personnel, the number of alarms per control operator, and other performance indicators that could be compared across companies. Recently, consortium members added metrics associated with advanced regulatory control to the list.
For example, the consortium’s APC saturation metric is designed to indicate the use of MPC in a plant relative to where it could possibly be applied. If, for example, the APC saturation is 25 percent, then it raises the question of what is happening in the other 75 percent of the plant where MPC could be applied.
The advanced regulatory control metrics provide a good indication of non-MPC advanced control deployed in the plant. These metrics are represented as percentages of the total PID loop count and include: non-base layer controllers, feedforward controllers and cascade controllers.
Factors such as the skills of control personnel, technology comfort levels, and degree of management acceptance determine whether individual companies (or plants) will use advanced regulatory control or MPC technology to improve process control further.
If the staff at the site is not trained in maintaining MPC models, then ARC could be a better choice. If uniformity across the enterprise is important, then it is important that the company consider how it will support remote locations, regardless of whether they choose MPC or ARC.
In the end, both MPC and ARC may be the right decision. Benchmarking can help
>> Dick Hill is Vice President and General Manager for ARC Advisory Group (www.arcweb.com). For more information on the ARC Benchmarking Consortium, visit www.arcweb.com/Benchmarking.
