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 #64, and was written by Greg.

The classic case for process control improvement uses a figure that shows the mean and standard deviation of a statistical distribution and the optimum of a process variable (PV). The optimum is often taken as the constraint on product quality. The case is made that if PV variability is reduced, the setpoint for the PV can be moved closer to the constraint. In my experience the bias to the setpoint due to variability is less than the bias by the operator because of measurement error, poor measurement and valve turndown, manual actions, on-off actions, abnormal operation, unknown changes in raw materials, equipment deterioration and modifications, process modifications, lack of process knowledge, operator “sweet spots” and war stories, and tactical tradeoffs between capacity and efficiency.

In this tip we will address the automation system considerations of improving setpoints, but there are bigger questions such as integration of the knowledge of the effects of equipment, piping, and process conditions and changes into the decision to move the setpoint. Simulations can provide the process knowledge. Unfortunately, maintenance and operational databases have not achieved the level of integration needed to understand how a setpoint should be changed, as noted in the Control Talk column Drowning in Data, Starving for Information – Part 4.

You may just need to move the setpoint. Better process knowledge or a simple trial run at a lower or higher setpoint might be sufficient. I have seen cases where running models provided the knowledge and confidence to try a better setpoint. In other cases, big improvements were directly made by engineers with practical process and control capability. Simply entering a better setpoint may get you a bonus, or at least a free meal.

A common example is temperature control. Distillation columns rely on temperature for an inferential measurement of composition. Temperature determines both production rate and quality in reactors. Reaction rate increases with temperature but just past the optimum, reverse reactions, side reactions, and product degradation may occur. Consequently, there is a peak in the plot of production rate versus temperature.

For bioreactors, the specific cell growth rate and product formation rate increase with temperature but cells start to shut down and die at temperatures to the right of the peak in the plot of specific growth rate versus temperature. The peak is different for cell growth and product formation, so temperature shifts are made generally after the exponential cell growth phase is fully established. However, the best size and timing of the shift are often not known. A similar situation exists in bioreactors for pH, with an even sharper peak. Measurement accuracy and control requirements of a few hundredths of a pH of setpoint are stated. The question is, do we really know the best setpoint to the same degree of precision?

Concept: Unless off-spec product is being downgraded, rejected, or recycled, the benefits of process control improvement are not realized until a setpoint is moved closer to the optimum. Greater plant knowledge offered by simulation and the integration of databases can lead to more intelligent setpoints. Better control strategies, measurements, control valves, and tuning and higher levels of control can enable operation closer to a constraint.

Details: Eliminate instrumentation and valves as the cause of a bias between the setpoint and the optimum. Coriolis meters, magnetic flowmeters, and precision throttling valves with sufficient pressure drop can eliminate limit cycles and errors in the ratios of flows and improve plant turndown. Resistance temperature detectors (RTD), integral mounted temperature transmitters, direct mounted pressure transmitters, and radar level indicators reduce measurement errors. The latest improvements in sensor, transmitter, and positioner technology can eliminate setpoints being shifted due to automation system limitations. Develop plant knowledge to find more optimum operating points. Process simulations, integrated maintenance and operation databases, and an asset management system (AMS) facilitate finding better setpoints.

Use a higher level of control to automatically find better optimums. Analyzers (Tip #63) offer a higher level of control. Feedback loops that fully exploit smart PID features (Tips #91-96 and #100) can eliminate operator actions (Tip #69), on-off actions, abnormal operation, and activation of safety instrumented systems (SIS). Valve position control (VPC) strategies described in Tip #97 and model predictive control (MPC) and a linear program (LP) can automatically optimize the setpoints of unit operations. Real-time optimization (RTO) can provide the ultimate in optimization of setpoints throughout a continuous plant if there is an accurate model and sufficient RTO expertise onsite.

Watch-outs: Operations personnel may be reluctant to believe that manual actions can be eliminated or that operator sweet spots and war stories are not valid reasons for operation further from a constraint. If there are no online process metrics (Tip #61), operators will naturally choose the setpoint that minimizes any perceived potential disruption, stress, and extra work. If the goal is business as usual rather than a more profitable business, Operations will be reluctant to make changes. If the operators are not fully trained in the higher levels of control (Tip #99) or control room support is not provided for all shifts for a sufficient duration, new control systems will be put in manual whenever something unusual happens.

Exceptions: If there are large blend tanks to attenuate product variability and if process capacity rather than process efficiency is the goal, flows may simply be set at a maximum. For conventional rather than fed-batch operations with only on-off valves (no control valves), the optimization involves batch sequences and totalized batch charge flows rather than setpoints.

Insight: Setpoints can be improved by achieving a higher level of control and knowledge.

Rule of Thumb: Use automation systems and plant knowledge to operate closer to constraints.

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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