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 #85, and was written by Greg.

Control operators would say to me, “Can you do something to slow down the pH changes?” For a setpoint on a steep titration curve, the pH would often zip right past the setpoint on its way from one tail of the curve to the other tail. Titration curve plots from the lab for strong acids and bases have nearly horizontal lines, affectionately called “tails,” below 2 pH and above 12 pH. Between 2 and 12 pH is a vertical line, with slightly rounded corners at the tails. When the pH enters the vertical portion (the neutral region), the pH change accelerates as the pH approaches the neutral point (i.e., 7 pH at 25°C). To the PID controller, the accelerating response looks like a runaway response. Stopping the pH at a setpoint in the neutral region is like trying to stop on a dime in an accelerating car exceeding the speed limit with your foot still on the gas.

The process time constant predicted from the material balance (Tip #79) is for the response of the acid and base concentrations. The entire nice process time constant from having a well-mixed vessel essentially disappears as the process goes from the titration curve X axis, which is the ratio of reagent to influent, to the Y axis, which is pH. For example, a strong acid and strong base can cause a 20-minute time constant for the concentration response to become a 0.04 minute time constant for the pH response.

Fortunately, systems identified as strong acid or strong base have a slight concentration of a weak acid, carbonic acid, by virtue of the absorption of carbon dioxide. A little bit of a weak acid or a weak base goes a long way. The slight absorption of carbon dioxide from simple exposure to air causes a moderation of the titration curve slope near 6 pH that reduces the change in process gain from seven to three orders of magnitude in the neutral region.

In linear reagent control, the process variable for the controller becomes the X axis instead of the Y axis of the titration curve. The controller sees a concentration response that is more linear and slower. The process time constant achieved by the considerable investment in having a well-mixed vessel is restored.

Concept: The use of the X axis of the titration curve can reduce the nonlinearity by orders of magnitude. The fit of the control system curve to the lab curve is best measured in terms of how well the slopes (process gains) match near the setpoint. Even if the fit is not perfect, the improvement can be dramatic.

Details: The greatest benefits of this approach are seen for setpoints on the steep portion of the titration curve. A steep slope on the titration curve causes an acceleration of the pH change and amplifies valve limit cycles. The pH acceleration and the oscillations from an imperfect valve upset the controller and the operators. To slow down this pH excursion and reduce the amplitude of oscillations, translate the process variable (PV) from pH (Y axis) to percent reagent demand (X axis).

Use a signal characterizer in a Distributed Control System (DCS). The input to the characterizer is pH and the output is the PID PV in percent reagent demand. Add cascaded signal characterizers to provide enough resolution near the setpoint. Ask the plant lab to provide the titration curve data points in an Excel file. Make sure there are at least 20 data points in the operating range. Get the temperature of the sample and the concentration and type of reagent used. If there are not enough data points, ask the lab to repeat the titration in the operating range with a smaller reagent dose to create more points. Change the X axis scale from a ratio of reagent to sample volume to percent reagent demand.

Plot the curve and the slope of the curve created in the lab and created by the signal characterizer. Zoom in on the steep slope regions to compare the nonlinearity. Adjust the signal characterizer for a better match of the slopes near the setpoint. Provide the ability for the operator to enter a setpoint in pH and to read a PV in pH. Display and trend the linear reagent demand setpoint and PV. To facilitate operator understanding, add XY plots to the displays with the ability to zoom in on the control region. Train operators using a virtual plant so they become confident in the use of a linear reagent demand controller. Use solution pH temperature compensation besides the standard electrode temperature compensation of pH measurements. The reference temperature is the sample temperature when titration was done.

The use of linear reagent demand control does not eliminate the need for the precise, high rangeability control valves justified and obtained per Tips #65 and #80, #81, #82, #83, and #84. The need for linear reagent demand control is often greatest on the first stage of neutralization because this stage must cover the most ground and must deal with the majority of the disturbances. The last stage should be designed with a greater volume, better mixing, and more precise valves to keep the pH close enough to setpoint to reduce the nonlinearity seen by the controller. Use an adaptive tuner to compensate for changes in the titration curve.

Watch-outs: Temperature changes will change the water, acid, and base dissociation constants and consequently the solution pH and the titration curve slope. Chemists will sometimes use a weaker reagent to make the titration easier. For processes not exposed to air, the exposure of the sample to air and to humans exhaling carbon dioxide may cause enough carbon dioxide absorption to change the curve. The absorption of carbon dioxide and ions from the beaker and reference electrode becomes more problematic as water purity increases (e.g., boiler feed water, condensate, and deionized water).

Exceptions: For systems going from a steep part to a flat part of a titration curve or for titration curves that dramatically change in terms of slope, signal characterization is not beneficial.

Insight: Linear reagent demand control reduces pH nonlinearity and the amplification of limit cycles.

Rule of Thumb: Use linear reagent demand control when going from a flatter part to a steeper part of a titration curve for tighter control and less wear and tear on the valves and the operators.

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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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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