Next Generation Control System Technologies Promote Solutions

KEY WORDS

Process control and advanced process control

Communications

Embedded control

Multivariable sensors

Wireless communications

Sidebars: Complex problems require complex solutions

“Americans have done an outstanding job of inventing and documenting how their inventions work, but have tended to put those inventions on a shelf and go off to invent something new,” says Dick Morley, inventor of the programmable logic controller and entrepreneur.

Support for that proclamation is the change in philosophy and mission goals of the U.S. National Aeronautics and Space Administration (NASA, Houston, Tex.).

NASA is forming a tighter linkage with industry by adopting a “buy technology when it’s available and make it when necessary” philosophy. Using that philosophy as a springboard, NASA’s mission goals have changed from invent and extend technology to science based technology missions. NASA continuously asks, “How will each mission improve the lives of humanity?”

If the 20th century was about inventing gobs of technologies, the 21st century, at least the first ten or so years, needs to be about putting those technologies to work to improve our lives at home and at work.

“I believe the next 100 years will bring about new ways of looking at existing things, as opposed to finding new things to look at,” says Scott Adams, creator of Dilbert, in a brief moment of seriousness.

That’s pretty perceptive for a cartoonist, or anyone else for that matter. It will be our ability to find new ways of looking at existing things that will reshape the plant floor in the 21st century.

A new way of looking

Flexible, pull, agile, demand-driven, and faster is used to describe production requirements necessary for success in the 21st century. Being able to smoothly embrace-shifting paradigm is what excellent companies do, the rest hang on and hope.

In accessing how to infuse these new and ever-changing customer demands into existing production facilities, many companies are becoming painfully aware of just how “single-minded” their production lines and processes are. Facilities built to produce only one or two products just aren’t flexible enough to meet tomorrow’s customer demands.

Companies needing to push into new markets while retaining existing markets may need to take manufacturing nearer the customer, such as Flint Ink located at R.R. Donnelly & Son’s (printing), Delco Electronics at General Motors, and Air Liquide (oxygen) at GE Plastics.

Processes too will require rethinking and redeployment, possibly along the lines of what NACCO Materials Handling Group Inc. (NMHG, Danville, Ill.) has accomplished in the past few years.

NMHG had two assembly lines for its 7,000 to 13,500 pound capacity Hyster and Yale forklift trucks. With customer demand requiring improved quality, reduced lead-time, and ergonomic and performance improvements, it was time to rip out and start over.

Careful evaluation suggested NMHG adopt demand-based flow manufacturing principles and technologies to rethink how to assemble 22 models with over 1,500 options on a single production line.

Today a forklift truck drives off NMHG’s Danville, Ill., production line every 17 minutes. Work in process, raw materials inventory, and start to finish assembly time has been reduced; quality is up. NMHG rethought how and what they needed to do to become a flexible, agile, demand-driven forklift truck producer, and then did it.

Looking at NMHG-built forklift trucks, it’s the Hyster and Yale brand names that are prominent.

Often brand identification, especially in consumer products, is key to customer loyalty. Already there are branding companies and contract producer companies. In the future there will be more of each.

Branding companies will include Coke, Chevrolet, Proctor & Gamble, Mead-Johnson, Ford, Pepsi, and Colgate. Contract producer companies will be less well known, but are the ones most in need of flexible, agile, demand-based production facilities that deliver the best value to the customer—not necessarily the least cost.

Dick Morley says, “Customers are frequently wrong when they say they want low price. What they want is value. Low cost would have everyone driving a Yugo, but we aren’t. Best value includes cost of ownership, quality, and deliverability.”

Quality is key in the value proposition equation. By 2005, contract manufacturers will require six-sigma quality for second, third, and fourth tier suppliers, and that’s hard to achieve with unreliable or non-existent control and automation systems.

Conducting profitable business levels as a second, third, or fourth tier supplier is tough. Control and automation device suppliers looking to improve the value of their product offerings to this market must adopt NASA’s “buy technology rather than invent it” philosophy.

That means “borrowing” technologies from areas of medicine, telecommunications, finance, and business, and wisely integrating the technology to improve the control and automation device value.

Arenas ripe with technologies to change control and automation system designs include:

Communications;

Sensors;

Intelligent appliance (IA) silicon chips; and

Computing technologies.

Communication technologies

Communication technologies are experiencing huge advances in technology. Fifteen years ago the only people with wireless personal communicators were fictitious cartoon characters and motion picture spy heroes. Less than 10 years ago, wireless technologies meant microwave, satellite, radio, or television signals. Today nearly 20% of the world population is using some form of wireless communication on a regular basis, and by 2005 experts predict that number will double.

Communication technologies, so prominent in our personal lives, also are changing the plant- floor landscape. The trend to use short-haul wireless communications to/from sensors and control devices will increase to meet agile, flexible manufacturing demands.

For example, customer demands may require a production line rearrange its assembly stations and conveyors to a different configuration. Because each assembly station and conveyor section is designed modularly and instrumented with wireless communications, it’s practical to reconfigure the production line and meet the new demands without extensive rewiring over a weekend. Or, a food producer with a pipeless batch facility using automatic guided vehicles to move the processing vessel from process station to process station, requires wireless communications to continuously monitor content integrity regardless of vessel location.

Wireless localized positioning systems already exist. In the future, these systems will be embedded in credit card like employee badges that are activated when the employee “signs in” for work. If a hazardous incident occurs, emergency personnel will be able to account for every employee’s whereabouts before they arrive at their assigned muster station. Minutes saved in detecting where employees are located and direction of movement could be the difference between life and death. The same technology permits tracking location and movement of tanker trucks and/or portable vessels of raw and finished material within manufacturing sites.

Sensor technologies

Science fact in the 21st century may eventually follow science fiction, with “Star Trek”- like phasers, transporters, and holodecks, but not in the early years. The first few years will be less dramatic.

What we can expect is breakthrough improvements in wafer technology and the shrinking of sensors. These trends will accelerate online measurement accuracy previously reserved for laboratories and specialized analytical instrumentation. Additionally, we can expect manufacturing and process industries to adapt non-intrusive sensor technologies developed for medical diagnosis.

Sensor technologies capable of identifying specific airborne chemicals already exist and can be made a part of an employee’s identification badge. NASA has developed radar-based sensors to identify specific chemicals in airborne plumes and they have improved methods of measuring dissolved water/solute materials in the soil. When combined with industrial electronics and efficiently mass-produced, products as common as gas and fire detectors will emerge. The new class of technologies will improve online measurements to monitor stack emissions, what’s crossing the fenceline, and the soil contents under tank farms and loading/unloading stations.

Intelligent (smart) transmitters have been available for several years, but most still have a fairly low I.Q. (intelligent quotient); those with higher I.Q. find it difficult to locate equally intelligent collaborating peers. In the future, transmitter I.Q. will improve because the sensors will improve. For example, adaptive sensors will become more viable for online use. Adaptive sensors use multiple methods (i.e., color, smell, sound, patterns, etc.) to recognize a component or variable. “Smell” sensitive membrane technologies exist, and when smell technology is combined with neural network technology it becomes possible to duplicate the trained nose of a seasoned vintner, brewmaster, or perfume artist.

Another way transmitter I.Q. will improve will be when a slow moving measurement (i.e., temperature) is combined with predictive algorithms to hasten response times. For example, a batch temperature that normally takes several minutes to reach 95% of final value can be predicted in the first few seconds following a step change to the input. Knowing, with a high degree of accuracy, where a slow moving variable will level off will change the way closed-loop control is implemented, optimize energy usage, eliminate overshoot, and improve product quality and repeatability.

Sensors, traditionally treated as add-ons, are being enhanced, miniaturized, and embedded in manufacturing and/or processing objects (i.e., motors, pumps, valves, heat exchangers, compressors, agitators, conveyors, etc.). The embedded sensor trend will accelerate in the next few years and every object’s I.Q. will improve dramatically. For example, “off-the-shelf” compressors will provide unsolicited, online information about temperature, vibration, noise, starting and running torque, compression efficiency, ring and bearing wear, etc. Embedded sensors and transmitters with higher I.Q. will make implementation of predictive maintenance and asset management easy.

Intelligent appliances (IA)

Intelligent Appliance (IA) silicon chips—a technology being developed for commercial and residential use—will find its way into 21st century control and automation systems.

Companies like Philips, Sony, Microsoft, Intel, and Hewlett-Packard are wagering big money that IA chips will go in every commercial and residential appliance. (WebTV is first generation IA.)

IA chips as small as an aspirin and available for less than $10 can be used to turn any appliance into a self-contained web server that can communicate wirelessly or over existing copper wire to other IAs. Today we set the wakeup time on our clock radio, set the thermostat wakeup time and temperature, arm the security system, and set the turn on time on our coffeepot. An IA clock radio could be set once and collaborate with other residential IA devices to accomplish the same thing and more.

Imagine pulling into a convenience market where gas pumps, vending machines, car wash equipment, utility meters, security devices, and cash registers contains IA chips. Internet technologies enable business-to-business, supply chain transactions, such as price changes, restocking requests for vending machine refills, utility usage, and service company monitoring of refrigeration and car wash equipment’s internal health. It can and will be accomplished using value-priced silicon chips and existing communication technologies.

“Big deal!” shouts the skeptic.

It is a big deal and will be heavily applied to automation and controls. The concept behind IA allows focusing on the delivery of solutions instead of the technology. That was the beauty of PLCs (programmable logic controllers) and proprietary DCSs (distributed control systems), only the techies cared what the operating system or communication protocol was, it just needed to work. The introduction of “open” devices has shifted our focus to technology at the expense of solving business problems.

When systems are assembled using open devices the responsibility to make systems work shifts to the user. Systems assembled using open devices have caused many a user to learn more about operating systems, communication protocols, hardware and software compatibility (or not), and system upgrades than they really wanted to know. IA technology brings us closer to having the best of both worlds.

Remember the earlier reference to NASA’s change in philosophy that requires every mission must improve the lives of mankind? Ultimately—IA silicon could deliver enhanced health and medical diagnostics anywhere on or above the earth.

For example, recently a physician with a scientific team in Antarctica discovered a lump in her breast. Using wireless communication to send medical information, she conferred with colleagues, and they confirmed breast-cancer. Treatment was begun, and she later was evacuated.

Heart defibrillators with enough on-board I.Q. (intelligent quotient) to permit safe use by nearly anyone already exist for about $3,000.

Mass production of inexpensive IA silicon technology capable of connecting and allowing machine-to-machine collaboration, without concern about operating systems and communication stacks, can’t help but find its way to other industries including control and automation systems.

Computing technologies

Scalable control and automation systems began to appear about five years ago, but scalable means different things to different people. Process engineers equate scalable to variable size database and/or I/O subsystems. In computer technology, the roots of scalable systems can be traced to supercomputers created by companies like IBM and Cray. Scalable supercomputers first meant multiple-processors (parallel processing) where a supercomputer could host from one to hundreds of computer processing units (CPUs) in a single chassis. Today scalable can mean using CPUs distributed in multiple computer chassis and connected with high-speed communications-distributed parallel-processing.

The performance of parallel processing is phenomenal. For example, in 1996 Intel began work to develop a U.S. Department of Energy supercomputer using 9,000 microprocessors and 262 Gbytes of memory to deliver 1.8 teraflops of performance (one teraflop is one trillion floating point operations per second)—that’s more than 10,000 times faster than a Pentium processor.

Control and automation systems likely won’t need 1.8 teraflop performance for a few years, but operating a demand-driven, manufacturing process increases complexity and reduces available decision making time.

In his book “Business @ the Speed of Thought,” Bill Gates, ceo of Microsoft (Redmond, Wash.) describes why and how to create an enterprise-wide, technology-driven, “digital nervous system” that permits businesses to respond to unexpected “opportunities.”

Parallel processing and distributed parallel processing are key to achieving Mr. Gates’ vision of conducting business at the speed of thought.

A beneficial plant floor area for distributed parallel processing is real-time applications that compare design , planned, and actual production models to predict quality and throughput. Quality and throughput predictions share information with adaptive planning and scheduling (APS) models that collaborate with operators and expert systems to suggest actions to improve productivity and quality. Implemented changes force new model analysis, new predictions, and new action suggestions. The bravest of the brave will close the loop and achieve automated, high quality, demand-based production.

The complex, non-linear mathematical calculations necessary to evaluate a large number of seemingly unrelated variables in APS models and produce meaningful results already exist and are being used to predict the weather, military troop movements, investment markets, fingerprints, and cement truck delivery schedules (see Complex problems require complex solutions ).

Built-for-purpose solutions

A model for future control and automation systems is palm tops, cellular personal communicators, and Macintosh’s iMac. Each of these is an out-of-the-box, ready to use solution. Users are free from choices about operating system software, communication protocols, application software, etc.; by 2005 control and automation systems will be widely (some say they do now) assembled from built-for-purpose (BFP) devices incorporating IA and other technologies.

For example, building on the compressor example described earlier, the manufacturer develops a BFP that, in addition to collecting embedded sensor information, provides software logic to prestart, start, run, and shut-down the compressor. User interface to the compressor goes through a web browser. Or, a company with patented expertise in fermentation could provide specially designed vessels with sensors embedded in the vessel, valves, and motors— along with a BFP that gathers and manages sensor information and host the proprietary fermentation control strategy as a ready-to-run solution.

Every BFP solution will integrate into the businesses digital nervous system and support vendor-neutral data exchange among planning, scheduling, and production domains using eXtensible Markup Language (XML).

XML is a metalanguage-a language for describing other languages – that describes a class of XML documents and partially describes their behavior. XML is fully internationalized for European and Asian languages and is intended to meet media-independent publishing of documents for industry-specific Web content providers. For example, ISA’s SP-95 Enterprise/Control Integration committee is defining an XML library to facilitate passing information between plant floor control and automation systems and enterprise business systems. Though XML sounds like a database management system and similarities exist, XML experts insist XML does not posses database features and abilities.

Technology offers the potential to improve our business and personal lives and can help deliver on the promise of more leisure time, but only if we are willing to look at existing things in a new way.

Complex problems require complex solutions

Imagine your:

Business produces one product with a lot of variations;

Customers move every few days;

Deliveries can be halted or changed by government regulators, weather, lack of funds, and/or poor customer planning with little advance notice;

Customers expect delivery of one particular variety of your product within a one-hour window;

Volumes change from 20 trucks of product per day to 300 trucks per day;

Deliveries rely on efficiently navigating the streets of Mexico City; and

Your job is to schedule product deliveries.

Does your head hurt? This is what dispatchers at Cemex Concretos (Mexico City, Mexico) do every day.

Cemex, a +$3 billion cement and concrete company, has been using global positioning technology, strategically located reformulation sites, and complexity theory scheduling software for over four years to schedule customer deliveries. During the first four years of use, Cemex’s productivity of mobile equipment improved 30%, equipment maintenance costs and fuel consumption fell and, most importantly, customers receive exactly what they ordered, where they want it, within 30 minutes of the promised delivery time.

Complexity theory applies nonlinear mathematics to predict what seems unpredictable and has helped:

Citicorp (New York, N.Y.) manage currency- and stock-trading risk in turbulent markets;

John Deere (Moline, Ill.) schedule more than 1.5 million unique models through production; and

General Electric Aircraft (Evandale, O.) redesign jet engines.

Dick Morley, inventor of the programmable logic controller and advocate of applying complexity theory to manufacturing says, “Complexity theory flies in the face of what we’ve been taught, but when people are willing to open their minds and add complexity theory to their knowledge base, big dividends are possible.”

General Motors (GM, Fort Wayne, Ind.) truck manufacturing used Mr. Morley to apply complexity theory to paint booth scheduling and realized $1 million annual savings in paint costs. Three years later GM upgraded the painting robots but GM’s complexity theory champions were no longer around and the scheduling software built on complexity theory prediction calculations was not included in the upgrade; a disappointment for complexity theory proponents.

GM’s abandonment of the paint booth scheduling solution is disappointing to those who championed and made it work, but is indicative of what often happens when complex solutions are developed and implemented without long-term support commitments. Many are the software applications developed and hailed as improving productivity that cease to be used one or two years later because “no one understands how it works.”

Complexity theory and other predictive calculations could help many manufacturers improve productivity, reduce cost, and increase profits, but only if:

Executive management is willing to establish and continually encourage an environment of cross domain communication and cooperation;

Information systems and operations personnel are willing to learn, try, and support new approaches to solve old problems; and

Solution integrators are capable and willing to immerse the customer in the implementation of the solution and walk away only after the customer acknowledges the benefit gained is worth the effort to maintain.

For sources of information about complexity theory, visit

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