How Do I Tune a Temperature Loop?

Before Tuning that Temperature Loop…Understand the Basics of Continuous Temperature Control

Temperature is one of the more common types of self-regulating – also known as non-integrating – processes used in industry. Like other self-regulating loops, temperature loops tend to naturally settle at a new operating state when adjustments are made to the corresponding Controller Output. What’s more, temperature loops are nonlinear in their behavior and process dynamics can vary considerably from one range of operation to the next. The later attribute, in particular, the nonlinear one – can hamper effective control if the associated PID is tasked with controlling a wide assortment and volume of production inputs. Such a scenario can put even the most experienced practitioner’s skills to the test. In this post the focus is on temperature control of continuous processes.

When tuning temperature control loops, it’s usually best to start with a well-conceived plan for both testing the PID loop’s process dynamics and collecting good data. A good testing protocol aside, certain attributes of temperature control are worth noting:

• Dynamic Differences

Don’t be fooled – the dynamics associated with heating can be distinctly different from those associated with cooling. What’s more, insulation can amplify the difference between the two by retarding a system’s natural thermodynamics. Be sure to thoroughly test the Design Level of Operation of a temperature process by capturing dynamic process data both above and below Set Point (i.e. heating and cooling).

• Patience Required
  

Generally speaking, temperature loops are slow to respond. That’s especially true of arc furnaces, rotary kilns, ovens, and other processes that possess significant mass. Although it may take a while to complete a bump test, the positives of capturing good process data generally outweigh the negatives. Without good data, the process model’s accuracy will be questionable and any tuning parameters that result could be counterproductive.

• Alternate Application
  

Consider applying different tunings – aggressive vs. conservative – to the different zones of a multi-zone process. Large processes like ovens and extruders possess highly interactive characteristics. By staggering the tunings from one zone to the next, the tendency of adjacent zones to compete with one another is lessened. Another rule of thumb is to tune the first and last zones aggressively as that allows a process to speed its recovery from the disturbances of material entering and leaving.

• Derivative Advantage
  

For practitioners who want to put Derivative to work, here’s your chance. While the limitations of Derivative are widely documented, temperature processes involving significant inertia make the most out of Derivative’s strengths. Since noise is usually minimal in these applications, Derivative will not result in excessive wear and tear on the associated instrumentation. Rather, Derivative prevents overshoot and improves the overall performance of temperature loops.

Again, its important to apply an effective strategy that thoroughly tests a given process’ dynamics and provides quality data for both modeling and PID controller tuning. Controller tuning workshops are available from any number of vendors. If you’re new to controller tuning, then consider enrolling in a training workshop that focuses more on industry best-practices and less on the use of a particular PID tuning software. A little investment in training and skills development can result in significant gains in both PID performance and your facility’s overall control loop optimization effort.

In a future post, we’ll tackle temperature control of batch processes.