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ARTIFICIAL INTELLIGENCE

Artificial Intelligence: Smart Thinking for Complex Control

AI technologies add new connections, making them more useful enterprise-wide tools in the battle of the bottom line.

by Frank J. Bartos

Artificial intelligence (AI) techniques are alive and well. No, it's not another suggestion of machines taking over the world, but of advanced controls better able to complement classical techniques for the new industrial enterprise that continually seeks lower costs and shorter product development cycles.

  AI technologies include artificial neural networks (ANNs), expert systems (ESs), fuzzy logic (FL), genetic algorithms (GAs), and others still to come. Their underlying methods differ from conventional computing and include concepts that simulate the way humans solve problems or how processes work in nature. For example, ANNs learn by training, ESs reason based on rules and experiences of experts, and FL works with uncertainty and partial truth. They often work in concert, adding terms like neurofuzzy and neurogenetic to our vocabulary.

Fuzzy developments

These techniques are intended to tackle highly complex, nonlinear problems beyond the capability of conventional methods, or too costly or time consuming for them. AI has had many successes, though not always in the role of an exclusive solution. AI methods are also controversial due to their nature and have not been fully accepted by some proponents of conventional approaches. Today, however, AI methods are closer to the mainstream as an extra tool for the control engineer.  Separate sidebars provide some insight on the various AI techiques. Fuzzy developments Fuzzy logic is progressing on several different fronts: in software tools, in controllers, and embedded in chips. Controllers with FL capability include models from Fuji Electric, Klckner-Moeller, Rockwell-Automation/Allen-Bradley, Siemens, and Yokogawa, among others. Controller manufacturers teaming up with software suppliers is another route for expansion.  

As applications widen, “the challenge becomes to make FL widely available on many hardware and software platforms,” says Constantin von Altrock, director of Fuzzy Logic Technologies Div., Inform GmbH of Aachen, Germany (Inform Sofware Corp., is the firm’s U.S. entity in a suburb of Chicago).  

In some ways fuzzy logic is already an “accepted” technology. The International Electrotechnical Commission standard IEC 1131, Programmable Controllers, has a proposed extension on Fuzzy Logic Programming. Now at voting stage, IEC 61131-7 CD1 (committee draft 1.0, Dec. 6, 1996) is intended to be Part 7 of IEC 1131. Mr. von Altrock is confident that it will be in effect before 1998. 

 Inform’s fuzzyTech FL design and user interface software is already based on IEC 1131-7. Object-oriented fuzzyTech integrates fuzzy logic functions with standard PLCs (A-B, Modicon, and Siemens) and with popular process control software, such as Citect (Ci Technologies), the FIX (Intellution), and InTouch (Wonderware). The latest version of fuzzyTech (5.0) is optimized for Windows NT. It was shown at the recent Hannover Fair.  

Wonderware and Inform have also developed an ActiveX extension for InControl, based on the fuzzyTech package. Shown in preliminary version at ISA ’96 in Chicago, this FL ActiveX product is due to ship this fall.  

As chipmakers increase integration of FL at the silicon level (e.g., Motorola’s 68HC12 microcontroller, see CE, July 1996, pp. 47-50), design of FL systems will benefit. “Fuzzy computation speeds can become as efficient as in traditional logic,” says Mr. von Altrock, “making possible control loops as fast as fractions of a millisecond.”

Neural networks in control

Among trends noted by NeuralWare Inc. (Pittsburgh, Pa.) is the move toward hybrid systems that combine neural network technology with other methods (not necessarily of the AI variety). Casey Klimasauskas, cto and director of NeuralWare’s Advanced Technology Group, mentions partial least squares (PLS, see first sidebar) and principal component analysis (PCA) as valuable add-ons to neural networks. The resulting hybrid ANNs are effective in system modeling and batch monitoring.  

Batch processes, with strong nonlinearities and complex control, are especially attractive to these methods. “A variety of approaches looks at how statistical techniques, such as PLS and PCA, can be combined with neural networks to do run-to-run control of batch processes,” says Mr. Klimasauskas.  

An evolving application for ANNs is to enhance plantwide first-principle models (FPMs). It includes two areas:

  • Modeling specific nonlinear processes for which FPMs don’t exist or are not very accurate, or would be very costly to develop
  • Providing a correction term for the FPM

  NeuralWare attributes improved plantwide optimization and other incremental benefits to FPM enhancement through neural nets.  

Sensor and data validation represent another new solution area for ANN technology. This is important especially for model-predictive control. How does it work? The idea is to use redundancies in measured data to identify correlations among key variables. “When the structure of these correlations changes, it can indicate a potential problem,” explains Mr. Klimasauskas.

Expert system views

Gensym Corp. (Cambridge, Mass.) is active in several AI sectors, among them expert systems. These methods are incorporated in its main product, called G2, a graphical object-oriented package “for creating intelligent systems.” Newly introduced version 5.0 of G2 makes major upgrades in user interfaces and connectivity to host systems. It’s good news especially for expanding expert system capabilities. For example, new “rule triggers” that speed up response to events in real time are part of the G2 enhancement.  

Robert Moore, president of Gensym, stresses the vital need to properly present knowledge of the process and plant to an operator from the mass of information available. “An expert system must bring time-critical decision support to an operator clearly and rapidly. Enhanced graphical functions make the management of faults and abnormal situations more intuitive,” he remarks.  

Enhancements to G2 include Internet-related products (ActiveX, Java, etc.) and are scheduled for second-half 1997 introduction.  

Indicating real growth for industrial uses of AI, an April ’97 meeting of the Gensym Users’ Society in Paris drew over 150 worldwide attendees and presented 55 papers. Applications ranged from gas production and pipeline safety to batch management and scheduling, with a sprinkling of topics in industrial machinery, power plants, and pharmaceutical production.  

Comdale Technologies (Canada) Inc. (Toronto, Ontario) looks at intelligent system development, including ESs, in a streamlined way. Wayne Thompson, vp of marketing, thinks a lower cost “component expert system” can be easily embedded into any system, such as a SCADA system or DCS. “Through object-oriented technology, OLE, and ActiveX, it can be deployed without substantial AI experience, in a fraction of the time [compared to older ESs],” he says. Embedding is the key element; it allows use of existing control software and host system.  

A product example in this category is the Comdale Expert Optimizer. It operates in either advisory or control mode, and contains a FL element for determining the magnitude of control actions needed based on the user’s confidence about the process variables.  

“Data glut” affecting system operators and users is another concern at Comdale. One solution ap-plies alarm detection and management within an expert system. Such functions are part of Comdale Smart Alarm, where an embedded inference en-gine filters, changes priority, or suppresses repetitive and nuisance alarms by looking at current and historical data. A still higher level of alarm management called “meta-alarms” (or alarms about alarms) promises to further reduce unnecessary information flow. It provides advice to the operator about root causes of alarms and recommends actions to take even in a process upset. Now in beta test, a demo of this unnamed product is slated for the October Anaheim, Calif., ISA show, says Mr. Thompson.

From the field

User experiences with AI remain quite closely guarded. A recent success for fuzzy logic comes from the Rheinbraun Corp.’s plant at Huerth, Germany. The complex process to stabilize coal gasification—with coal of varying quality—uses a fuzzy logic supervisory control system for setpoints of eight underlying PID controllers. FL control strategy is defined by 115 fuzzy rules and uses 24 input variables. The plant is automated with equipment from Foxboro/ Eckardt; the FL controller is implemented in Inform’s fuzzyTech software and uses USDATA’s FactoryLink FL Runtime Module. Besides rapid implementation time (6 months), “the fuzzy controller adapted the plant’s operation point to changing coal quality more than twice as fast as previous human operators,” states Inform.  

NeuralWare cites an application of its system integrator in Poland, Transition Technologies, to predict NOx emissions and also optimize performance of pulverized-coal boilers at the Ostroleka Power Plant. The three-unit plant (600 MW) has Westinghouse WDPF control systems. NeuralWare’s new NeuCOP II controller (on a Solaris 2.5 platform) is completely integrated with the WDPF system. A complex control problem is compounded by special burners and nozzles that distribute the combustion process over a larger volume to reduce NOx emissions. However, the process raises the amount of unburned carbon, thereby lowering boiler efficiency.  

ANNs in NeuCOP II model the system, predict NOx emission values, and change secondary air flow to compensate for burning efficiency. Preliminary results of modeling indicate that NOx reduction of 15% or more is possible with increased boiler efficiency, as well. Start up of ANN control in an advisory mode is complete; full on-line operation is expected by mid-July. “NeuCOP II was chosen as it is one of the most advanced and reliable neural-network controllers, and for its applicability to large scale systems,” comments Konrad Swirski, director of Trans-Tech.  

Inferential sensing in NeuCOP II can also predict NOx emissions off-line based on boiler control parameters. “Such ‘soft monitoring’ methods have been allowed by the U.S. EPA to replace or defer the purchase of costly, high maintenance monitoring equipment,” says NeuralWare’s Mr. Klimasauskas.  

At Amoco’s Texas City oil refinery, time delays from on-stream analyzers can take over 30 minutes. To rein in this quality control problem, Gensym’s G2 Diagnostic Assistant and NeurOn-Line software packages predict real-time “soft sensor” values for the composition of five petroleum products being refined. “The result is more than 95% closed-loop [plant] utilization, and estimated savings of over $500,000 per year in recovered product,” according to Amoco.  

Many large automation and process control companies develop an internal AI element for their plant control systems. At Fisher-Rosemount Systems (Austin, Tex.), the approach has an added intent: Make AI methods available to the routine user in the plant by full integration with overall controls. “The perception by many users that AI applications are just too difficult to use and require consultants to implement is no longer the case,” says promotions manager, David Holmes. Glen Mertz, a manufacturing technologist at Monsanto Co. (St. Louis, Mo.) uses F-R Systems’ ANN software. “The neural network software is almost intuitive. I can sit down and in less than two hours be building a model with plant data.” remarks Mr. Mertz.  

Ease of use and installation was found to be helpful in another F-R Systems application—FL controllers for process water temperature control. Ken Wildman, senior engineer at Eastman Kodak (Rochester, N.Y.) states, “As a result of greater startup stability with fuzzy logic control, time-consuming manual startups are avoided and demand is quickly met for process water at a precise temperature throughout a vast production area.”  

Motor and motion control continues to be a growing area for AI techniques. As an example, at the 1st IEEE International Electric Machines and Drives Conference, May 18-21 in Milwaukee, Wis., some 20 papers included fuzzy logic, neural nets, and genetic algorithms (or their combination) as problem solving tools.  

Two major upcoming seminars in Europe will further highlight FL, NN, and GA technologies. The 5th European Congress on Intelligent Techniques & Soft Computing (Sept. 8-12) and the 1st European Symposium on Applications of Intelligent Technologies (Sept. 9-10) will run in parallel in Aachen, Germany.

From chaos to order

Above the various AI technologies are more advanced adaptive systems that resist definitions of state. They’re loosely termed here as chaos systems (CSs), but some people prefer to call them complex systems or emergent systems. “Complex system behaviors are emergent in the chaos regime, rather than explicit; they’re deterministic, not random,” explains Dick Morley, president of Flavors Technology Inc. (Manchester, N.H.).  

In a CS, behaviors of individual elements (agents) work together to form system behavior that cannot occur in the elements. Examples include the swarming of bees and wolfpack hunting tactics. Applications closer to CE’s audience are predictive scheduling, raw material allocation, and transportation needs. “Neural nets, fuzzy sets, logic statements, and expert systems are, in truth, subsets of behavioral identities we call agents,” says Mr. Morley.  

He sees growing use of the chaos approach in several areas of manufacturing and process industries. The goal is to dramatically cut the software effort for solving highly complex problems. And results look very promising: reduction factors of 10 to 100 in software development effort, according to Mr. Morley. How is all this possible? Because the CS works with behavioral rather than data aspects of objects, only the various types of entities (but relatively few) need to be described to yield a system solution—not the behavior of the system or individual entities. “In effect, it’s a bottom-up approach that starts with objects and lets the system evolve.”  

He thinks engineers can make do with classical control methods for the near future, but in just a few years “the average control person had better know how to use this powerful system for solving problems such as MES, ERP, MRP, and predictive scheduling and diagnostics.” Tool sets for chaos system control are still limited. Paracell, from Flavors Technology, is one of them.  

There is a downside, of course. A change in mind-set will be needed for people to accept chaos methods. “The technique is seemingly unbelievable, both as to benefits and the efficiency of solutions,” adds Mr. Morley. Some danger of hype always exists, but over the next decade, chaos system design might become the basic advancement in the control engineer’s tool box that it’s meant to be.  

Looking ahead, the combination of AI methods can build strengths and offset weaknesses. Summarizes Inform’s Mr. von Altrock, “In the future, these technologies will merge together. This trend, dubbed ‘soft computing,’ exploits the fact that each AI technology has its strengths and weaknesses. Thus, a clever combination of AI methods can be good synergy.”

Artificial Intelligence Suppliers

Company   Technology
  ANN FL ES GA CS
AI Ware        
Adaptive Logic        
Bios Group L.P.        
BYKOM        
Comdale Technologies    
Exsys •  •       
Fisher-Rosemount    
Flavors Technology        
Gensym  
Inform Software        
The MathWorks      
Motorola AMCU        
NeuralWare    
Pavilion Technologies        
ANN - Neural nets FL - Fuzzy logic ES - Expert sytems GA - Genetic algorithms CS - Chaos systems



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