Evaluating the overall performance of many individual control loops, with interactions among them, under a variety of operating conditions is key to process optimization. Analytical tools are making this easier.
Process engineers must look at all the loops regulating a process and determine which are working well, both individually and as a group. The problem is, there may be hundreds or even thousands of loops interacting in countless ways, so checking each one in-depth with only manual methods is often hopeless. The problem is compounded by different operating phases, multiple operating strategies, various products, and other running conditions. This may leave operators shifting parts of the process out of automatic and trying to run it by hand to compensate.
The answer for dealing with this level of complexity is a control loop performance monitoring (CLPM) program. Control Station makes one of these, and we believe our state-based analytics sets our PlantESP apart.
One of the main things that separate PlantESP from other less-capable platforms is its ability to evaluate what is happening under a multitude of different operating conditions, using condition lists to define unique states. It can deliver as accurate an evaluation of loop performance during an abnormal operational state as it does during stable operation, and that’s a big advantage.
PlantESP provides answers to these key questions and many more:
- Is the loop programming sophisticated enough to let it perform as well under various operational states—startup, shutdown, grade change, APC on/off, and more—as it does during stable operation?
- Can your CLPM program evaluate loop performance in sufficient depth and detail to capture that level of performance nuance?
- Are interactions among loops limiting performance?
The importance of this kind of state-based loop performance evaluation is the topic of our article in the June 2022, issue of Processing, titled State-Based Analytics Empower Plant-Wide Optimization. It explains how today’s technologies offer new capabilities able to handle increasingly complex process dynamics:
A new generation of CLPM solutions now incorporates state-based analytics capable of distinguishing a control loop’s many and unique operating states. Whether by developing a profile of tuning parameters which can be dynamically applied depending on the operating situation or by implementing an alternate control strategy that accommodates newly discovered process dynamics, users are now better informed and more capable of improving production performance.
The article gives a basic overview of the concept, plus, we’re happy to discuss how it can help your specific situation. Contact us today.
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