Call it what you will, it's still artificial intelligence

Artificial Intelligence is proving it's worth in process and in practice in plants nationwide. Meanwhile the costs, in time and money, of adding AI capability to a plant keep falling.

By Dana Blankenhorn

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Sturnfield first applied G2 to a plant in Victoria, Texas, saving the company $1.25 million per year. Since then he has worked on implementing the same techniques for a plant in Texas City, Texas and one in St. Charles, La, near New Orleans. "These [plants] make a wide range of chemicals," he explains, “and we’re using cogeneration to generate power and steam for the production units.”

Sturnfield describes some of the pitfalls. "The problem you have with a chemical plant, as opposed to a refinery, is [that] you have a very wide variation in the system. You must react to changes caused by one production unit or the other, and you don’t always have an easy way to identify the change, but you must respond to it." That’s the process Sturnfield looked to optimize, transforming an open loop optimizer into a closed loop.

"If you’re not in closed loop, operators are busy trying to make things run and don’t want to run close to the boundaries--the limits of your turbines, even though economically that’s the best way to go,” Sturnfield notes. “If an operator is only going to look at things once a shift, they don’t want to have to be constantly change things. In closed-loop optimization you’re changing values every five minutes, based on numbers and software."

The application Sturnfield helped write runs in the central control for each plant’s energy system and operates the equipment, making fine adjustments to the balance among outputs of a gas turbine, supplemental burning and condensing.

"Before, we didn’t worry about the economics,” he says, “We worried about running the plant. You ran equipment as efficiently as you could, and tried to keep things in a range where you could respond to changes. A lot of things were run by header pressure controls–if it started dropping you’d increase supplemental firing, or decrease condensing turbine flows. That was the main control scheme.”

According to Sturnfield, the company has more flexibility, in terms of how much power it buys and sells to the grid. Sturnfield has been working with what amounts to an expert system since 1996, and says the units generating plants can now react dynamically, not just to changes within processes, but to the market as well.

"When someone does a steady-state model you assume everything has settled down. You look where you want to go and get there. You don’t worry about the dynamics,” he points out. “But the dynamics may affect sensor readings. If I’m not careful about those readings I may fool myself into thinking my boilers are inefficient when they’re just starting to move up. You need intelligence to know what you need in the dynamics," says Sturnfield.
And that’s what his system delivers. "You can optimize a system easily. But the advantage here is being able to implement the results of the optimization. The expert system figures out what information to feed into the model and how to put that into action."

Applied Neural Networks
Over at IMC Agrico in Mulberry, Fla., Tony Lazanowski has the title of chief technology development engineer. He applies neural networks to the production of phosphates.

"We mine phosphate ore here," he explains. "We bring it into a washer to separate the pebble fraction, and the remaining feed goes through a feed tank, an a hydro-sizer. Then we do particle flotation to sink the sand. After that, we recondition again and do an aiming flotation where we sink the phosphate.” The result, says Lazanowski, is concentrated phosphate ore which is then reacted with sulphuric acid to produce fertilizer in other plants.

Lazanowski applied G2 to the problem of optimizing these various processes. He started in the control room of one of IMC’s smallest mines, then "as time went on, we added other features and started getting better results," he says, gradually expanding the program to other locations.

"We had an independent review done by two Ph.Ds who came to the conclusion that overall, we improved recovery 2% and reduced reagent cost by 50 cents per ton of concentrate product, which is pretty significant."

The program is ongoing, so improvements are constantly being made--some slowly and others suddenly--as the virtual analyzer Lazanowski built, based on the program’s neural net technology, came to set reagent flows and controls to desired targets in more areas of the operation.

Meanwhile the costs, in time and money of adding AI capability to a plant keeps falling. Just don’t call it AI.

 

 


Dana Blankenhorn is a freelance writer based in Atlanta and a frequent contributor to CONTROL.
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