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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The operators were using less than $2,500/yr. of reagent; so our goal was to minimize project cost while still meeting the objectives of safe, responsible (i.e., no downstream effluent excursions outside 6 to 9 pH), and reliable operation (i.e., ability to operate automatically despite a single failure anywhere in the equipment or control system). We wanted a control system that would be operator friendly and not require operator intervention. We also realized that the future is continuous pH control — could it meet project objectives and, if so, what size of tanks would be required? According to the traditional guideline, three stages of neutralization are needed because the influent could be below 2 or above 12 pH. Could better reagent injection methods, high-resolution control valves and advanced control reduce the size and number of tanks?

Virtual plant
We opted for a virtual plant to help develop the control system, investigate design details, test whether a prototype would meet project objectives, demonstrate the results to the mechanical, process and automation engineers, enable operator review and input, check out the control system configuration, and train operators for better understanding and operability [4, 5, 6, 7, 8]. Test runs of the virtual plant were used to study the dynamics of the system to guide the decision process and detail the equipment, piping, valve, measurement and control system design.

The virtual plant included a dynamic model of the process with material and charge balances as well as mixing and injection delays, and a dynamic model of the control valves with deadband and resolution limitations. The models were configured and embedded in a distributed control system (DCS) along with the control strategies. The integrated nature of the virtual plant eliminated the need for separate programs, interfaces and emulations. We could develop and test the actual control modules and displays used in the plant.

titration curve
Figure 2. Titration curve -- It’s crucial to match
the slopes of the virtual plant and laboratory curves.
Click on illustration for larger image.

Titration curves. The first step was to get a representative titration curve for each part of the regeneration. It was critical to make sure the lab employed the same reagents used in the field, the sample bottles were kept closed, and the reagent concentration and drop size and the sample time and size were specified [1, 2]. A tabular printout was essential because a plot basically showed a straight vertical line between 3 and 11 pH. The next step was to match the titration curve of the process model with the laboratory titration curve. Here the slope of the curve, which sets the process gain and sensitivity, is most important. Figure 2 shows the same relative peaks in the slope in virtual plant curve near the control region evident in the lab curve. The slope at 7 pH was 18 compared to a slope of 0.016 at 12.2 pH. The maximum peak was moderated and a valley and second peak were created by the addition of small amounts of a weak base at 9 pH and weak acids at 4 and 6 pH. Once the curves were matched up, the demineralization unit batch sequence was run for different equipment, injection and automation system designs.

Speed. In a continuous pH control system, particularly for strong acids and bases, excursions in the effluent will occur because of upsets. It’s not commonly recognized that speed as well as size determines how disruptive a disturbance is to the effluent. Consequently, abrupt changes in flows from batch sequences, operator intervention, on/off operations and control valve stick-slip cause the biggest upsets.

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