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Upcoming Training Workshops:
Our training workshops equip engineers and operators with the skills they need to bring plant optimization to a new level.
So you don’t know which of your plant’s 100s of valves are racing toward failure? No worries –PlantESP does. With the expansive environment of a midstream processing plant it can seem impossible to know precisely which assets are at risk of premature failure. Luckily PlantESP continuously and proactively monitors performance on a plant-wide basis. It not only identifies issues that could affect the plant’s process performance, it also evaluates
the health of final control elements (FCEs) like valves and dampers.
In this webinar, we will cover the techniques that were used in this case study.
For any manufacturer a great deal depends on the ability to monitor and evaluate the performance of staff and facility alike. In terms of assessing the performance of production systems, the plurality of facilities rely on KPIs trended by supervisory control platforms. While the analysis is essential, the metrics focus more on maintaining safe, consistent output and less on optimizing the underlying regulatory control layer. As a result, the PID controller responsible for regulating each of a facility’s many, highly interactive control loops is routinely overlooked until its performance is linked to a slip in productivity. Indeed, the typical DCS doesn’t have metrics for Stiction, Oscillation, and Output Reversals. Measurements that proactively assess the PIDs effectiveness have only recently been viewed as a means of both improving overall performance and avoiding costly downtime.
A central question to ask is: What’s the true economic value of improving regulatory control?
Two of the most popular architectures for improving regulatory performance and increasing profitability are 1) cascade control and 2) feed forward with feedback trim. Both architectures trade off additional complexity in the form of instrumentation and engineering time for a controller better able to reject the impact of disturbances on the measured process variable. These architectures neither benefit nor detract from set point tracking performance. This paper compares and contrasts the two architectures and links the benefits of improved disturbance rejection with reducing energy costs in addition to improved product quality and reduced equipment wear. A comparative example is presented using data from a jacketed reactor process.