It’s hard to read anything that doesn’t mention artificial intelligence (AI). This is true even in our world of automation. Emerson’s Greg McMillan tackles this topic in a ControlGlobal.com article, Control Talk: Machine learning, deep learning and nonlinear controls. In the article, Greg interviews Ineos Group’s Vivek Dabholkar.
Greg asks Vivek about “recent developments in machine learning, deep learning and nonlinear controls”. Vivek shares:
The “sacred golden” principle of superposition is only valid for linear systems, no exceptions. In other words, one can’t superimpose the effect of past independent moves along with the calculated future independent moves to calculate their combined effect on controlled variables. This is a fundamental hurdle in practical applications of so-called “nonlinear control.”
On how linear controllers address process nonlinearities, he explains that advanced process control engineers have figured out how to manage nonlinearities in control valves, horizontal drums, differential pressure in high-reflux columns, and pH control through transformations.
Greg highlighted the use of signal characterization:
…to linearize process variables (PV) and manipulated variables (MV) are extensive. The dynamics from open-loop response tests aren’t size- and operating-point dependent and filtering of noise is more effective. Without signal characterization the open-loop, self-regulating process gain approaches the slope of the plot of PV versus MV for small steps but becomes quite different for larger steps spanning various changes in slope.
When asked about machine learning applications, Vivek explained that he sees:
…applications where a lot of manual steps are required with the lack of repeatability, for example, procedure-based decoking of furnaces or swapping of charge gas dryers. It would free operators from constantly watching the process over a long period of time and would lead to consistently safe transitions.
Read the article for more on addressing nonlinearities, fast front-end/slow back-end dynamics, and steady-state change in CV target (CVStep) applications.
