Building the Foundation for Batch and Continuous Control

Last week I mentioned uploading two of blog’s Greg McMillan‘s recent presentations. Like I did with his first presentation, here’s a short recap of the second one, Control Loop Foundation for Batch and Continuous Control:

What are great about Greg’s presentations are the specific application examples. Visit the slides 19-21 to see ways of improving neutralizer control using Feed forward control, signal characterization and proper piping to provide proper spacing for measurement devices. Similarly, slides 22-24 show ways to improve distillation column control using Feed forward control and signal characterization. You mostly don’t realize the benefits of improved control until you reduce variability and move the setpoint closer to the operating limit.

Greg is really good at boiling things down. Here are his words summing up basic opportunities in process control (from slides 27 and 28):

  • Decrease stick-slip and improve the sensitivity of the final element (Standard Deviation is the product of stick-slip, valve gain, and process gain)
    • Use properly tuned smart positioners, short shafts with tight connections, and low friction packing and seating surfaces to decrease valve slip-stick and dead band (do not use isolation valves for throttling valves)
    • If high friction packing must be used, aggressively tune the smart positioner
    • Improve valve type and sizing and add signal characterization to increase valve sensitivity
    • Use variable speed drives where appropriate for the best sensitivity
  • Improve the short and long term reproducibility and reduce the interference and noise in the measurement (Standard Deviation is proportional to reproducibility and noise)
    • Use magnetic and Coriolis mass flow meters to eliminate sensing lines, improve rangeability, and reduce effect of Reynolds Number and piping
    • Use smart transmitters to reduce process and ambient effects
    • Use RTDs and digital transmitters to decrease temperature noise and drift
  • Reduce loop dead time (Minimum Integrated Error is proportional to the dead time squared)
    • Decrease valve dead time (stick and dead band)
    • Decrease transport (plug flow volume) and mixing delay (turnover time)
    • Decrease measurement lags (sensor lag, dampening, and filter time)
    • Decrease discrete device delays (scan or update time)
    • Decrease analyzer sample transport and cycle time
  • Tune the controllers (Integrated Error is inversely proportional to the controller gain and directly proportional to the controller integral time)
  • Add cascade control (Standard Deviation is proportional to the ratio of the period of the secondary to the process time constant of the primary loop)
  • Add feed forward control (Standard Deviation is proportional to the root mean square of the measurement, feed forward gain, and timing errors)
  • Eliminate or slow down disturbances (track down source and speed)
  • Add inline analyzers (probes) and at-line analyzers with automated sampling since ultimately what you want to control is a composition
  • Optimize set points (based on process knowledge and variability)
  • To realize the benefit of reduced variability, often need to change a set point

He sums up the presentation with these key points:

  • Tune the loops
  • Use digital positioners and throttle valves to get resolution better than 0.5%
  • Use Coriolis and Magmeters to get accuracy better than 0.5% of rate
  • Add cascade and feed forward control for disturbances
  • Model the process to dispel myths and build on process knowledge
  • Improve the set points
  • Add composition control
  • Reduce the size and speed of disturbances
  • Transfer variability from most important process outputs
  • Add online data analytics (multivariate statistical process control)
  • Add online metrics to spur competition, and to adjust, verify, and justify controls

View or download the presentation if you think some of this guidance might benefit you.

GreenPodcast.gif MP3 | iTunes

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