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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They’re not really using Artificial Intelligence (AI) over at the St. Mary’s Paper plant in Sault Ste. Marie, Ontario, or are they? They are, but paper machine superintendent Dave Dube doesn’t look at it that way. “It’s part of a puzzle that controls the caliper, and the gloss and smoothness of our supercalendared paper,” he says. Sensors measure, in real time, the condition of the product going through manufacturing process, and automatically adjust the sheen in all directions.

This used to be very difficult. "We would spot check a few feet of sheet," Dube recalls, “adjusting the result through the use of steam boxes.” With real-time monitoring, Dube’s system can make adjustments using both caliper coils and steam boxes. "This is what all the engineers were telling us would be the real challenge on this project--not having caliper coils fight the steam boxes," Dube comments.

Such a procedure could only be done effectively in real-time, which meant not just scanning the output, but adjusting the manufacturing process as it happened.

No engineer can do this manually, but software can.

In this case, it’s a state-of-the-art, large-scale, model predictive controller for paper machines which strikes the balance. The software takes input from the sensors and passes instructions to the machines. Honeywell’s Performance CD Multivariable controller is, in essence, a neural network.

Honeywell’s technology, however, is 15 years old, senior consultant Lance Johnson notes. What gave it AI capabilities is Moore’s Law, computer power getting faster and cheaper. That means software can now be embedded “at the base level control layer for single loops,” resulting in “a model predictive controller at an execution interval of ¼ to ½ second,” says Johnson.

Then, by encapsulating multiple systems “in one cohesive horizon,” Johnson adds, you have a system for controlling a plant, not just a machine.

The result is a complete network of sensors, actuators, software and hardware that can manage a plant to its optimum without operator intervention. In other words, Artificial Intelligence.

From the Esoteric World of AI
Many techniques that came from the esoteric world of AI are now proving themselves on the plant floor. Besides neural networks, processors and manufacturers are installing software agents and expert systems. They are often retrofitting existing equipment with new software. But they don’t call it AI.

Instead they just call it improved process control. That’s the way Charles Cutler, president of Cutler Technology Inc. San Antonio, Texas describes what he’s offering.

Cutler, a CONTROL Process Automation Hall of Famer, first retired in 1996 after selling DMC Corp. and its Dynamic Matrix Control technology to AspenTech. "After I retired, I started thinking [about] this problem," he says, and began thinking of ways to improve the DMC algorithm.

According to Cutler it was a revelation. "You put constraints on the system, via a linear program, and it keeps the machine inside those boundaries. When it comes up against a boundary, it makes a decision based on software, rather than [on some] arbitrar[y factor]," he says. Engineers were turning his linear controller into an expert system.

Most major process control vendors are working on some form of expert system or neural network to enhance productivity. These companies are not just getting more production out of each worker, they’re improving quality, extending equipment lives and using all inputs more efficiently.

Although optimized control systems aren’t considered to incorporate “artificial intelligence” per se, the feedback loops, sensors and software management of the inputs paint another picture entirely. 
An OSCAR for Water
Michael Eaton lists himself as a neural network specialist at JEA (formerly Jacksonville Electric Authority) in Jacksonville, Fla. Eaton is using the G2 Reasoning Engine from Gensym Corp., which can view information in a web browser and supports major web standards such as CORBA and DCOM objects, Java and ActiveX controls. Eaton’s project is called Optimization System Control of Aquifer Resources (OSCAR). The idea is to react to changes in water demand, rather than just try to keep reservoirs full.

The utility pumps its water from aquifers. Simply building new pumps as demand rises is not only expensive, but could drain the aquifer too fast. “So I first built a pilot system to control four pumps in a single well field, forecasting consumption and then scheduling. all these are inputs to the model, along with water quality and plant quality," says Eaton. That first project reduced the stopping-and-starting of pumps by 50%, and allowed the company to defer drilling a new well, saving $1.4 million.

Single Expert Wanted
A single expert system can result in huge savings when it is applied throughout a company’s operations. Dow Chemical senior specialist Jim Sturnfield is using a G2 expert system to save money for Dow’s GulfCoast chemical plants.
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