A controller with just the right tuning is a rare bird but knowing when tuning is important is the real game. While it is nice to say that all loops should be tuned better, what are the benefits and issues? New software can identify the process dynamics but the user is still left with the question – how fast or slow should I tune the controller? This choice comes up as a relative specification (fast or slow) as a menu choice or slider bar or a numerical specification, such as Lambda (closed loop time constant), or a Lambda factor (ratio of the closed loop time constant to the process time constant).
Controller tuning that is too slow (controller gain too low and/or too reset time high) may transfer insufficient variability from the controlled variable (primary process variable) to the manipulated variable (e.g. final element or secondary loop set point). Slower tuning results in larger standard deviations of the controlled variable during upsetting times, startups, grade changes, and batch operation. During quiet operation of continuous self-regulating processes with good control valves (minimal stick-slip) the standard deviation may be negligible so slower tuning does not always mean a problem. Furthermore, the value of reducing the standard deviation depends upon the economic importance of the process variable, blending, and control in downstream equipment. Column, crystallizer, evaporator, extruder, dryer, kiln, and reactor temperatures are indicators of composition and thus generally important. However, if the oscillation period is much faster than the downstream blend time of surge or storage tanks or is much slower than the Lambda of concentration control loops downstream, the effect may be negligible. For example, an opportunity assessment of continuous polymerization line with plug flow reactors showed there was an opportunity for better polymer temperature and pressure control both of which was important for product quality. However, the process engineers placed no economic value on a reduction of the standard deviation because fluctuations from an individual polymer production line were averaged out by the huge storage tank downstream. A similar state of affairs occurred for the temperature of a purification column train. A fair question is why not take out the storage or run at a lower level? Well in this case many lines or trains dumped into the same tank so the tank had to be large enough to accommodate a dynamic unbalance between supply and demand. Less inventory translates to more changes in production rate to match changes in customer and distribution requirements, which means transferring more variability from sales and transportation to production.
My general experience is that temperature loops on large agitated or boiling volumes (e.g. columns, evaporators, fermenters, and stirred reactors) are tuned too slow because the appropriate Lamdda factor is in the range of 0.05 to 0.5 whereas users are comfortable with Lambda factors of 1 to 10 that are suitable for volumes without much back mixing (e.g. extruders, heat exchangers, kilns, pipelines, plug flow reactors, sheet lines, and static mixers) and for flow loops. Similarly, gas pressure control requires lambda factors an order of magnitude lower than liquid pressure control loops. Thus, reactor gas pressure controllers are often tuned too slow. An important point to remember is that variability in a manipulated variable, such as steam, coolant, or vent flow, is usually not as important as decreasing a variability in the controlled variable that is an inference of product composition.
Stay tuned for Part 2 on the signs and consequences of a loop tuned too fast, Part 3 on the quantitative assessment of slow tuning, and finally Part 4 on suggested tools.