Virtual Plant Provides Real Insights

Simulation points to a better strategy for controling pH

By Gregory K. McMillan, Emerson Process Management, and Roger D. Reedy and John P. Moulis, Monsanto

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 9. Control logic to periodically divert influent to the other tank and shut reagent valves to automatically read tank pH when the loop output was near the split range point; and

 10. Three electrodes with middle signal selection to inherently ignore a failure of any type and the slowest electrode, reduce measurement noise and facilitate online diagnostics and calibration.

The new control system offers significant flexibility. Each of the two tanks can be operated in a continuous or batch mode depending on level and feed valve positions. In the batch mode, the titration curve and slope can be generated online by ramping the reagent valve and historizing the pH and change in pH versus time.

 

Test results
The trend chart (Figure 4) shows that the biggest upsets occur at the start of step 2 (beginning of first regeneration cycle), seen as a low pH excursion, and at start of the step 4 (beginning of slow rinse cycle), seen as a high pH excursion. It indicates that reagent demand control outperforms conventional pH control. Reagent demand control linearizes the loop by translating the process variable (PV) and its set point (SP) from pH to % reagent demand (X-axis of the titration curve). The controller gains are 0.25 and 0.02 for reagent demand control and conventional pH control, respectively. The reset time is 8 sec. in both cases. The higher controller gain enables the reagent demand loop to catch up faster to a big change in the influent. The reagent demand controller recognizes the true distance of the PV from the SP, which also is important for startup of the system.

ph Trend Chart
Figure 4. Trend chart -- Reagent demand control
outperforms conventional pH control.
Click on illustration for a larger image.


We found the periodic spikes in the recirculation line pH originate from the stick-slip limit cycle of control valves due to the high pH sensitivity in the neutral region, which confirms the insight that steep titration curves require control valves with better than normal resolution. In fact, the typical valve resolution of 0.5% would have necessitated another stage of treatment with smaller reagent valves, conforming to the traditional guideline that two to three vessels in series would be required for this system. Besides increasing cost, adding another tank incurs the risk of simultaneous throttling of large (coarse) and small (trim) reagent valves in case of an upstream tank failure and self-inflicted disturbances from the limit cycle of the large valve that drive the pH out of specification for the last tank [9, 10].

Oneness
Each part of the system must be one with its role, goal and the whole to achieve the project objectives. The integrated nature of the virtual plant helps but, as usual, the details are important. The correct design and performance of a neutralization systems depends upon an integrated view of equipment, piping and control system dynamics. Once the simulated titration curve is matched to the lab curve, the problem becomes one of focusing on the design and installation of the right volumes, mixing, flows, valves, sensors, tuning and control strategies to meet the project goals. The virtual plant can serve in this role as well as provide a system for training and continuous improvement.

We should have field results later this year.

 


Gregory K. McMillan is a principal consultant for Emerson Process Management, Austin, Texas, Roger D. Reedy is a principal engineer for Monsanto in Luling, La., and John P. Moulis also is a principal engineer at Monsanto’s Luling plant. E-mail them at Greg.McMillan@Emerson.com, roger.luling.dale.reedy@monsanto.com

and john.p.moulis@monsanto.com.

 References
1. McMillan, G. and R. Cameron, “Advanced pH Measurement and Control,” 3rd ed., ISA, Research Triangle Park, N.C. (2005).
2. McMillan, G. K., A Funny Thing Happened on the Way to the Control Room, http://www.easydeltav.com/controlinsights/FunnyThing/default.asp.
3. McMillan, G. K., “Improve Process Loops,” Chemical Processing, p. 45 (October, 2007). www.ChemicalProcessing.com/articles/2007/200.html.
4. McMillan, G. K., Plant Design Category, http://ModelingandControl.com.
5. McMillan, G. K., Education Category, http://ModelingandControl.com.
6. McMillan, G. K., “Advances in pH Modeling and Control,” presented at ISA 54th Intl. Instrumentation Symposium, Pensacola, Fla. (May 2008).
7. McMillan, G. K. and M. S. Sowell, “Virtual Control of Real pH,” Control, p. 47 (Nov. 2007). www.ControlGlobal.com/articles/2007/385.html.
8. McMillan, G. K, “One Man’s Story — Back to the Future,” Control, p 91 (Oct. 2007). www.ControlGlobal.com/articles/2007/359.html.
9. McMillan, G. K., “What is your Flow Control Valve Telling You,” Control Design, , p. 43 (May 2004). www.ControlDesign.com/articles/2003/164.html.
10. McMillan, G. K., “A Fine Time to Break Away from Old Control Valve Problems,” Control, p. 57 (Nov. 2005). www.ControlGlobal.com/articles/2005/533.html.

 
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