The following tip is from the ISA book by Greg McMillan and Hunter Vegas titled 101 Tips for a Successful Automation Career, inspired by the ISA Mentor Program. This is Tip #69, and was written by Hunter.

I taught a course in process control to junior and senior chemical engineering students as an adjunct professor. Students were generally in the class not because they thought process control was important, but because the course was required to get a degree. One student asked me why you needed a control loop when all you need to do is set the flow to match what was on the process flow diagram (PFD).

These students had almost exclusively been trained to understand and model steady-state processes. When I reviewed with a process engineer at a major pharmaceutical company the opportunities for process control, the process engineer said he wanted to fix the input flows. He would schedule the air, oxygen, reagent, glucose, glutamine, and nutrient flows to the bioreactor. He thought control loops were not desirable because they would vary these flows. While most other pharmaceutical companies use dissolved oxygen control to manipulate air and oxygen flow and pH control to manipulate reagent flow, the other flows are still scheduled even though analyzers, such as the NovaFlex BioProfile noted in Tip #63, are now available for closed loop control. Going from traditional batch where flows are charged sequentially to fed-batch where flows are manipulated by control loops is a big step for most process engineers.

Even tougher is for process engineers and operators to understand control loops can provide automated startups and abnormal situation protection by using key PID features. The basic principle missing in the education of process engineers and operators is benefit of the transfer of variability from key process variables (PV) to process input flows. The control loop can do this through feedback and feedforward control to compensate for unknowns, varying demands, and unmeasured disturbances. Process engineers are trying through intelligent guess to fix flows to achieve a desired PV. These engineers don’t realize that plant models have thousands of parameters with errors.

Processes are subjected routinely to disturbances from changes in raw materials and in the case of bioreactors, the whims of living cells. Batches inherently have changing demands. For example, the oxygen demand for a bioreactor can increase by two orders of magnitude from start to finish. Humans are inherently bad at anticipating the effect of lags and delays on responses. The natural consequence of this lack of patience is to overreact. The PID has knowledge of the process dynamics built-in to the tuning settings. Even if humans had better knowledge and patience, humans cannot possibly be as attentive as a control loop. A PID is constantly looking at results and making corrections.

If a process engineer or operator says that manual actions or the scheduled actions are too complex for control, the application is an excellent candidate for complete automation via a PID. A control loop can capture the best of these actions. The consistency of the automated response can lead to recognition of how better to improve these actions. Feedback control automatically takes care of unforeseen effects.

Concept: The best actions of process engineers and operators can be automated as the starting point for a control loop. The loop will subsequently compensate for unknowns and changes in desired operating points opening the door to improvement of actions and optimization of setpoints.

Details: Work with operations and process engineering to make sure you understand the process relationships and abnormal conditions. Set the initial manual actions for the start of a batch or unit operation and the best reactions for abnormal conditions via output tracking mode of the PID. Hold the output long enough for the PID to see the response to the actions. Release the PID for feedback control. Add ratio control to enforce and optimize ratios. Add feedforward control to give fast and smooth transitions to different product grades and production rates for flexible manufacturing per Tip #101. Tune the PID and use key features per Tip #71 for good disturbance rejection and setpoint response.

Use additional features to provide faster or slower responses based on direction and objective for coordination and optimization per Tip #72. See the Control Talk columns Show Me the Money Part 1 and Show Me the Money Part 2 or case histories of the elimination of operator actions. Use a virtual plant per Tip #99 for developing, prototyping, testing, demonstrating, and training. Keep the most experienced operator and process engineer involved from start to finish. Support the new automation by being in the control room at key times to adjust output tracking values, ratios, feedforward dynamic compensation, and PID tuning.

Watch-outs: Erratic measurements at low flows and excessive stiction near the seat may prevent the PID from controlling at low flows. Analyzers can give bizarre values or provide no new values. Use logic to screen out unreasonable analyzer values and to turn off integral action or use an enhanced PID per Tip #100 to avoid ramping of the PID output when an analyzer fails to update.

Exceptions: If the signal is too erratic or unreliable, closed loop control is not possible unless an inferential measurement can be developed and corrected online.

Insight: Close loop control can provide a response that accounts for unknowns, and is more consistent, timely, and amenable to improvement and optimization than the best process engineer or operator.

Rule of Thumb: Use a PID control to eliminate manual actions and scheduled actions whenever a reasonable PV measurement is available.

About the Author
Gregory K. McMillan, CAP, is a retired Senior Fellow from Solutia/Monsanto where he worked in engineering technology on process control improvement. Greg was also an affiliate professor for Washington University in Saint Louis. Greg is an ISA Fellow and received the ISA Kermit Fischer Environmental Award for pH control in 1991, the Control magazine Engineer of the Year award for the process industry in 1994, was inducted into the Control magazine Process Automation Hall of Fame in 2001, was honored by InTech magazine in 2003 as one of the most influential innovators in automation, and received the ISA Life Achievement Award in 2010. Greg is the author of numerous books on process control, including Advances in Reactor Measurement and Control and Essentials of Modern Measurements and Final Elements in the Process Industry. Greg has been the monthly "Control Talk" columnist for Control magazine since 2002. Presently, Greg is a part time modeling and control consultant in Technology for Process Simulation for Emerson Automation Solutions specializing in the use of the virtual plant for exploring new opportunities. He spends most of his time writing, teaching and leading the ISA Mentor Program he founded in 2011.

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About the Author
Hunter Vegas, P.E., has worked as an instrument engineer, production engineer, instrumentation group leader, principal automation engineer, and unit production manager. In 2001, he entered the systems integration industry and is currently working for Wunderlich-Malec as an engineering project manager in Kernersville, N.C. Hunter has executed thousands of instrumentation and control projects over his career, with budgets ranging from a few thousand to millions of dollars. He is proficient in field instrumentation sizing and selection, safety interlock design, electrical design, advanced control strategy, and numerous control system hardware and software platforms. Hunter earned a B.S.E.E. degree from Tulane University and an M.B.A. from Wake Forest University.

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