Manufacturing facilities across the process industries are increasing their adoption of plant-wide monitoring and diagnostic solutions. Essentially, they’re seeking to capitalize on existing investments in data as a means of improving production efficiency and throughput. One type of diagnostic solution in particular that is seeing a significant uptick in adoption is Control Loop Performance Monitoring (CLPM) technology.

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The Derivative Term is not only the last letter in PID (i.e. Proportional-Integral-Derivative) it’s also the most maligned of the three. With its big kick, the Proportional Term provides an immediate correction for changes in control and it is clearly the star of the PID controller. So too, the Integral Term is credited with its tenacious correction of Offset and for steadily pushing a loop back to Set Point. And then there’s Door #3: The lowly Derivative Term. Derivative works to counteract the rate of change of the process variable.

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Knowing how to tune a PID loop while satisfying its unique control objective is critical. Fortunately there is a group of descriptive statistics that characterizes a controller’s core performance attributes and that helps you to get the tunings right each time. Below are four (4) KPIs that process control experts frequently use when determining if their new PID tuning parameters are right for the job.

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Big Data and IIoT have captured the automation industry’s imagination. A key take-away from all that’s been written is that plant data holds the key to making many production processes safer, more efficient, and more profitable. While there’s growing evidence that Big Data and IIoT are delivering meaningful value, some scientists have begun to wonder if there is a limit to the amount of information that is actually helpful. They’ve posed the question: At what point does information become detrimental to the people who rely on it?

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