Missed Opportunities in Process Control - Part 1

Jan. 18, 2019
I had an awakening as to the much greater than realized disconnect between what is said in the literature and courses and what we need to know as practitioners as I was giving guest lectures and labs to chemical engineering students on PID control. We are increasingly messed up. The disparity between theory and practice is exponentially growing because of leaders in process control leaving the stage and users today not given the time to explore and innovate and the freedom to publish. Much of what is out there is a distraction at best.Ā  I decided to make a decisive pitch not holding back for sake of diplomacy. Here is the start of a point blank decisive comprehensive list in a six part series.

I had an awakening as to the much greater than realized disconnect between what is said in the literature and courses and what we need to know as practitioners as I was giving guest lectures and labs to chemical engineering students on PID control. We are increasingly messed up. The disparity between theory and practice is exponentially growing because of leaders in process control leaving the stage and users today not given the time to explore and innovate and the freedom to publish. Much of what is out there is a distraction at best.Ā  I decided to make a decisive pitch not holding back for sake of diplomacy. Here is the start of a point blank decisive comprehensive list in a six part series.

Please read, think and take to heart the opportunities to increase the performance and recognized value of our profession. The list is necessarily concise in detail. If you want more information on these opportunities, please join the ISA Mentor Program and ask the questions whose answers can be shared via Mentor Q&A Posts.

  1. Recognizing and addressing actual load disturbance location.Ā Most of the literature unfortunately shows disturbances entering theĀ process output when in reality disturbances enter mostly as process inputsĀ (e.g., feed flow, composition and temperature changes) passing through theĀ primary process time constant. Thinking of disturbances on the processĀ output leads to many wrong conclusions and mistakes, such as large primaryĀ  time constants are bad, tuning can be done primarily for setpoint changes,Ā  feedforward and ratio control is not important, and algorithms likeĀ Internal Model Control are good alternatives to PID control.
  2. Tuning and tests to first achieve good load disturbance rejectionĀ and then good setpoint response. While most of the literature focusesĀ on setpoint response tuning and testing, the first objective should beĀ good load disturbance rejection particularly in chemical processes. SuchĀ tuning generally requires more aggressive proportional action. Testing is simply done by momentarily putting the PID in manual, changing the PIDĀ output and putting the PID back in auto. Tuning should minimize peak andĀ integrated error from load disturbances taking into account needs toĀ minimize resonance. To prevent overshoot in the setpoint response, aĀ setpoint lead-lag can be used with lag time equal to reset time or a PIDĀ structure of proportional and derivative action on PV and integral actionĀ on error (PD on PV and I on E) can be used. If a faster setpoint response is needed, setpoint lead can be increased to Ā¼ lag time or a 2 Degrees of FreedomĀ (2DOF) PID structure used with setpoint weight factors for theĀ Ā proportional and derivative modes equal to 0.5 and 0.25, respectively. Rapid changes in signals to valves or secondary loops upsetting otherĀ loops from higher PID gain setting can be smoothed by setpoint rate limitsĀ on analog output blocks and secondary PIDs and turning on external-resetĀ feedback (ERF). We will note the many other advantages of ERF and itsĀ facilitation of directional move suppression to intelligently slow downĀ changes of manipulated flows in a disruptive direction in subsequentĀ months (hope you can wait). In Model Predictive Control move suppression plays a key role. Here we can enable it with additional intelligence ofĀ direction without retuning PID.
  3. Minimum possible peak error is proportional to dead time andĀ actual peak error is inversely proportional to PID gain. Peak error isĀ important to prevent relief, alarm and SIS activation and environmentalĀ violation. The ultimate limit to what you can achieve in minimizing peak errorĀ is proportional to the total loop dead time. The practical limit as toĀ what you actually achieve is inversely proportional to the product of theĀ PID gain and open loop process gain. The maximum PID gain is inverselyĀ proportional to the total loop dead time. These relationships hold bestĀ for near-integrating, true integrating and runaway processes.
  4. Minimum possible integrated error is proportional to dead timeĀ squared and actual peak error is proportional to reset time and inverselyĀ proportional to PID gain. The integrated absolute error is the mostĀ common criteria sited in literature. It does provide a measure of theĀ amount of process material that is off-spec. The ultimate limit to whatĀ you can achieve in minimizing integrated error is proportional to theĀ total loop dead time squared. The practical limit as to what you actuallyĀ achieve is proportional to reset time and inversely proportional to theĀ product of the PID gain and open loop process gain. The minimum reset timeĀ is proportional and the maximum PID gain is inversely proportional to theĀ total loop dead time.Ā  These Ā Ā Ā Ā  relationships hold best for near-integrating, true integrating and runawayĀ processes.
  5. Detuning a PID can be evaluated as an increase in implied deadĀ time. The relationships cited in items 3 and 4 above can be understoodĀ by realizing that a larger than actual total loop dead time is the effectĀ Ā on loop performance of a smaller PID gain and larger reset time settingĀ than needed to prevent oscillations. This implied dead time is basically Ā½Ā and Ā¼ the summation of Lambda plus the actual dead time, forĀ self-regulating and integrating processes, respectively.
  6. The effect of analyzer cycle time and wireless update rate dependsĀ on implied dead time and consequently tuning. You can prove almost anyĀ point you want to make about whether the effect of a discontinuous update is important or not by how you tune the PID. The dead time from anĀ analyzer cycle time is 1Ā½ times the cycle time. The dead time from aĀ wireless device update or PID execution rate or sample rate is Ā½ the time interval between updates assuming no latency. How important thisĀ Ā additional dead time is seen in how big it is relative to the implied deadĀ time. The conventional rule of thumb is that the dead time fromĀ discontinuous updates should be less than 10% of the total loop dead timeĀ (wireless update rates and PID execution rates less than 20% of dead time). This is only really true if you are pursing aggressive controlĀ where the implied dead time is near the actual dead time. A betterĀ recommendation would be a wireless update rate or PID execution rate less Ā Ā Ā Ā  than 20% of ā€œoriginalā€ implied dead time. I use the work ā€œoriginalā€ toĀ remind us not to spiral into slowing down update and execution rates byĀ increasing implied dead time and then further slowing down update andĀ execution rates.
  7. The product of the PID gain and reset time must be greater thanĀ the inverse of the integrating process gain. Violation of this ruleĀ cause very large and very slow oscillations that are slightly damped takingĀ hours to days to die out for vessels and columns, respectively. This is aĀ common problem because in control theory courses we learned that highĀ controller gain causes oscillations and the actual PID gain permitted forĀ near integrating, true integrating and runaway processes is quite largeĀ (e.g., > 100). Most donā€™t think such a high PID gain is possible andĀ donā€™t like sudden large movements in valves. Furthermore, integral actionĀ Ā provides the gradual action that will always be in a direction consistentĀ Ā with error sign and will seek to exactly match up PV and SP meeting commonĀ expectations. The result is a reset time frequently set that is orders ofĀ magnitude too small making the product of PID gain and reset time lessĀ than the inverse of the integrating process gain causing confusing slowĀ oscillations.
  8. The effective rate time should be less than Ā¼ the effective resetĀ time. While PID controllers with a Series Form effectively preventedĀ this due to interaction factors in the time domain, this is not the case for the other PID Forms. Not enforcing this limit is a common problem inĀ migration projects since older controllers had the Series Form and mostĀ modern controllers use the ISA Standard Form. The result is erratic fastĀ oscillations.
  9. Automation system dynamics affect the performance of most loops.Ā This should be good news for us since this is much more under the controlĀ of the automation engineer and easier and cheaper to fix than process orĀ equipment dynamics. Flow, pressure, inline temperature and compositionĀ (e.g., static mixer), and fluidized bed reactors are affected by sensorĀ response time and final control element (e.g., valve and VFD) responseĀ  time. Pressure and surge control loops are also affected by PID executionĀ rate.
  10. Reserve feedforward multiplier and ratio controller ratioĀ correction for sheet lines and plug flow systems.Ā  The conventional rule that on a plot ofĀ manipulated variable versus feedforward variable, a change in slopeĀ demands a feedforward multiplier and a change in intercept demands aĀ feedforward summer is not really relevant. A feedforward multiplierĀ introduces a change in controller gain that is counteracts the change inĀ process gain. However, this is only useful for sheet lines and plug flowĀ (e.g., static mixers and extruders) because for vessels and columns, theĀ effect of back mixing from agitation and reflux or recirculation creates aĀ process time constant that is proportional to the residence time. ForĀ decreases in feed flow the increase in process time constant from anĀ increase in residence time negates the increase in process gain. Also, theĀ most important error is often a bias error in the measurements. SpanĀ errors are smitten by a large span showing up mostly as a change inĀ process gain much less than the other sources of changes in processĀ Ā gain.Ā  Also, the scaling andĀ filtering of a feedforward summer signal and its correction is much easier.Ā  Ā  Ā 
About the Author

Greg McMillan | Columnist

Greg K. McMillan captures the wisdom of talented leaders in process control and adds his perspective based on more than 50 years of experience, cartoons by Ted Williams and Top 10 lists.

Sponsored Recommendations

Make Effortless HMI and PLC Modifications from Anywhere

The tiny EZminiWiFi is a godsend for the plant maintenance engineers who need to make a minor modification to the HMI program or, for that matter, the PLC program. It's very easy...

The Benefits of Using American-Made Automation Products

Discover the benefits of American-made automation products, including stable pricing, faster delivery, and innovative features tailored to real-world applications. With superior...

50 Years of Automation Innovation and What to Expect Next

Over the past 50 years, the automation technology landscape has changed dramatically, but many of the underlying industry needs remain unchanged. To learn more about whatā€™s changed...

Manufacturing Marvels Highlights Why EZAutomation Is a Force to Be Reckoned With

Watch EZAutomation's recent feature on the popular FOX Network series "Manufacturing Marvels" and discover what makes them a force to be reckoned with in industrial automation...