The concept of a control valve goes at least as far back as the bronze plug cocks used by the Romans in their aqueducts… The concept of a moving-stem (automatic) valve was introduced by James Watt in the late eighteenth century as a part of his fly-ball governor, which was developed to regulate the speed of his steam engine.
What makes them so critical to your process manufacturing is that they directly touch the flow of your process. If not operating correctly or incorrectly sized, they can introduce variability, quality issues, unplanned shutdowns, and other abnormal situations. The ModelingAndControl.com blog devotes a whole category to control valves and their importance in optimum control of your process.
Thirty to fifty percent of downtime is attributable to equipment problems, while the other 50-70% is due to operator error, process upsets, natural hazards and unknown causes.
While automation folks like to make sure their automation system has sufficient redundancy, this is the most reliable piece of the process loop. This loop includes measurement instrumentation, final control elements such as control valves, and the controller that runs control strategies such as proportional-integral-derivative (PID) control.
Much like our automobiles, which have incorporated more microprocessors for diagnosis, valves are being driven more by digital valve controllers. The diagnostic information communicates digitally with asset management software to provide a window into these diagnostics. In the article, Laura is quoted:
In general, about 70% of instruments and valves and 50% of mechanical equipment should be monitored online. By following these guidelines, plants can predict how long critical equipment may be operated before repair or replacement is necessary. Operating experience shows these types of predictive maintenance programs work–improving quality, throughput and availability–while reducing costs.
Priority in which control valves are monitored should be assigned based on their importance in the product quality, process variability, and process uptime.