Modeling and Control

Before I leave to enjoy a crawfish boil and Cajun music, here are some more myths.

(6) Loop oscillations can always be decreased by reducing the controller gain – too low of a controller gain can cause nearly sustained slow rolling oscillations in an integrating process (e.g. level or gas pressure) and instability in a runaway process (e.g. exothermic reactor temperature). Decreasing the gain makes the problem worse for these cases. There are also limit cycles that persist regardless of controller gain.

(7) Limit cycles can always be stopped by eliminating valve stick-slip – limit cycles can also be caused by output limits, IO card resolution limits, deadband in integrating processes and cascade loops, and extreme nonlinearities, such as the pH titration curve.

(8) The installed valve resolution and deadband meet catalog specifications – often tests by manufacturers are for hand tight packing at positions remote from the seating friction. Also, temperature and fouling can make the installed performance worse.

(9) The most accurate type of pH sensors are used most often – the most popular sensors are the ones that require the least amount of maintenance, such as references with solid electrolytes, even though these may require more time to equilibrate and have a more variable junction potential. The flowing liquid junction reference for the right materials of construction and electrolyte is generally the most accurate but the least used type of pH electrode in industry because of the need to pressurize and refill the reservoir.

(10) Thermocouples are faster than RTDs – while this would be true for a bare element, nearly all the installations in the chemical industry I have seen use thermowells. The fit, design, and materials of construction of the thermowell have a far greater effect than the sensor on the speed of response of the temperature measurement.

My head is spinning with myths. The examples seem endless.

(11) The process dynamics can be identified with the controller in auto without any perturbation – a loop startup or shutdown, change in set point, or an injection of a pulse or step in the controller output is needed to sort out the process dynamics from the controller dynamics. Bob Otto (Monsanto Fellow) alerted me to this dilemma 20 years ago. Cecil Smith published an article about 5 years ago making the same point. Now if you have inside knowledge of the process gain, it may be possible to find the process time constant from estimates of the dead time. However, even sophisticated process simulations have difficulty in providing an accurate process gain. So why not face up to the situation and benefit from perturbations? Batch processes often have plenty of perturbations because there is a loop startup for each batch and changes in the loop set point and output by the batch sequence. New closed loop process identification tools such as DeltaV Insight can do a good job of taking advantage of these batch opportunities. Continuous processes time often run at the same set point long periods of time and require a periodic injection of a pulse into the output. Fortunately, the size and duration of the pulse can be rather innocuous in most cases.

(12) Model predictive control is not suitable for batch operations – the reason frequently given is that the process is too nonlinear but that’s old news. The world of industrial process control is nonlinear. The real issue is more the one direction integrating response for bioreactors and some chemical reactors. If you make a translation of the controlled variable from composition to slope of the composition profile, an MPC can control a trajectory of a key component over a key portion of the batch. While the MPC applications in continuous processes dominate the literature, there have been successes in batch temperature and composition control with far greater benefits (e.g. 25% increase in capacity by 25% reduction in cycle time).

(13) Rate is more trouble than it is worth – don’t try controlling a severely exothermic reactor without rate unless you like exercising the plant relief system and alarm system. In general, for temperature control, rate should be set equal to 1/5 the process time constant or thermowell lag, whichever is largest.

(14) You need to start and end at a steady state to tune a loop in manual – there is no steady state for integrating and runaway processes. Even self-regulating processes may be moving with the controller in manual from frequent upsets. The short cut tuning method in my Good Tuning - Pocket Guide works well for these less than ideal conditions.

(15) Process dynamics are in the process – the most common loops are flow and pressure and for these loops most of the dynamics are in the automation system (valve, transmitter/sensor, and the PID execution and signal filtering). The process delay and nonlinearity is negligible compared to that of a control valve and the process lag is negligible compared to measurement filters. The term “process dynamics” is a misnomer. A better term would be “open loop dynamics” so people would better realize the dynamics are often in the automation system. The reality is I can’t get people despite 20 years of publications to think “open loop gain” instead of “process gain” and “open loop time constant” instead of “process time constant”, and “total loop dead time” instead of “process dead time.” Maybe I need some catchy phrases like “don’t blame it all on the process” or “the only loop with only process dynamics is in your college textbook.”