There’s a wealth of information available in most every data historian. The data can be used to evaluate the performance of a plant’s regulatory control systems in general and to uncover PID controllers that require tuning in particular. Capitalizing on that resource can help manufacturers keep their processes within designated constraints and avoid out-of-spec production.Read More
PlantESP Loop Performance Monitoring Integration with PlantPAx System to Power Loop Analytics and Extend Process Optimization Capabilities October 20, 2015 – Control Station today announced the planned integration of its…Read More
Sometimes controller design feels like a choice between the lesser of not two but three evils. P-Only Control is simple and provides a fast response, but the resulting Offset hinders tight control. PI Control addresses Offset but it leaves room for improvement. And then there’s PID Control which enhances a loop’s Set Point Tracking but typically at the expense of the associated Final Control Element (FCE). If not for the ubiquitous nature of noise in industrial applications, then PID Control would be the clear choice. But since there’s no escaping noise, what’s a practitioner to do?
A previous post about the Derivative Term focused on its weaknesses. As noted, the primary challenge associated with the use of Derivative and PID Control is the volatility of the controller’s response when in the presence of noise. Noise is a major stumbling block for Derivative and PID Control as production data is routinely replete with process noise and other sources of variability. The use of PID Control in such an environment can drive frenetic changes in a loop’s Controller Output (CO) and unnecessarily wear out the associated Final Control Element (FCE). In summary: Little to gain; lots to lose.Read More
PlantESP and LOOP-PRO Selected as Finalists for 2016 Engineers’ Choice Awards October 20, 2015 – Control Station today announced that PlantESP™ and LOOP-PRO™ have been selected as finalists for the…Read More
Studies show that when individuals are given a set of three options they are instinctively biased to prefer the middle one. When this finding is applied to purchasing behavior a common outcome is that consumers pick the middle priced option with little-to-no rationale other than a desire to avoid being viewed as either too cheap or too lavish. It’s known as the Compromise Effect. While the PID controller offers three options – P-Only, PI and PID – the rationale for selecting the middle option is generally clear. But PI Control is not only the instinctive choice, on many occasions it is also the superior and simpler one.Read More
Effective Disturbance Rejection
“When your only tool is a hammer, every problem looks like a nail” – a concept attributed to Abraham Maslow. Such can be the situation with process control and the PID controller. For decades practitioners have applied the PID to tackle the majority of challenges related to process control. Fortunately for practitioners the PID is more like a Swiss Army Knife as it can take different forms. The focus of this post is on a pair of applications of P-Only Control – or Proportional-Only Control.
Names – like looks – can be deceiving. While jumbo shrimp are big relative to other shrimp there’s very little about them that could be considered gargantuan. So there should be no surprise to learn that in the realm of process control a Set Point Filter has nothing to do with filtering noise within a control loop’s data.Read More
Choices, choices. In the realm of process control practitioners are regularly forced to choose between competing options. Consider a PID control loop: Should it be tuned for faster disturbance rejection or tighter Set Point tracking? Should the Derivative Term be used or does the PI configuration provide a sufficiently fast Settling Time? And the choices go on and on. In that sense there are multiple choices for filtering noise too – options that provide very different benefits. Fortunately when it comes to filtering for Signal Noise the choice is typically clear.Read More
Noise is inevitable. To one degree or another it’s evident in the data of most every production process. Sure it can be absent in academic settings and similar lab environments where simulations often generate sanitized data. However, in the real world of industrial manufacturing noise is a factor that cannot be avoided. Failing to account for or manage noise can be a recipe for – well – failure.Read More