Modeling and Control Analysis Brings Big Capital Savings

Wow, what a fast and furious week… I’ll close the week by highlighting a recent article by ModelingAndControl.com‘s Greg McMillan. He coauthors the article, Virtual Plant Provides Real Insights, which appears in the January 2009 edition of Chemical Processing magazine.

Greg’s a published author on the topic of pH control and widely known his expertise. Working with Monsanto engineers, they sought a better way to control the pH of a wastewater pit. Maintaining pH between a permissible range of 6 and 9 was a labor-intensive activity. It also required veteran operators. Inexperienced operators, typically working night shifts, would call the plant engineers with a nearly full pit with an out-of-range pH and an imminent pumpout about to happen. Plant engineers typically don’t enjoy being awakened to hear this.

The initial solution was to replace the pit with two 40,000-gallon tanks. The issue with this solution was high capital costs and limited plant real estate for the tanks. Plant engineers worked with Greg to see if a better solution could be modeled and developed. The goals were to minimize capital costs, provide reliable operations, and be easy enough to operate, even for less experienced operators.

Using much of the wisdom he freely shares at ModelingAndControl.com, Greg developed a virtual plant running a DeltaV system with embedded simulation on his notebook computer. Virtual plant means it runs both the simulation of the plant and the control system logic. Greg describes the setup:

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.

Working with the lab data history, the team developed titration curve tabular data. They next matched the titration curve of the process model with the laboratory titration curve. They ran the demineralization unit batch sequence for different equipment, injection and automation system designs. The model showed where the biggest causes of upsets to pH level occurred.

They could also do what-if analysis to see if fast inline pH control could catch the disturbances and smooth them out. The result of the modeling and control analysis was that the pH could be controlled with 10,000-gallon tanks instead of the original 40,000-gallon tanks for project capital savings of more than 50%.

The article gives the design details of what process designs, process instrumentation, and control strategies were required to achieve the initial objectives sought.

If you’ve got a retrofit project ahead, you might consider a modeling and control analysis to see if large capital savings are possible. In today’s global economic environment, this could make you a hero.

I also wanted to pass along that Greg was conducting a pH survey for a revision to his pH book:

Help Greg McMillan fine-tune his focus on pH issues by answering a few questions online. Taking part will give you a chance to win a copy of “Advanced pH Measurement and Control” as well as other prizes. When taking the survey, if you don’t know the answer to a particular question, just select the 0-1% choice.

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