Argonne Visit notes

This is a quick highlights memo about the UFTO visit to Argonne, July 15, 16. A full report will be forthcoming early this Fall.

For the first time, a sizable contingent of UFTO member companies was present for the whole visit. I hope this can become our standard practice, with even a bigger attendance. Argonne made excellent presentations for us. We all agreed that it was a good *beginning* of what must become an ongoing dialogue.

If you want a headstart on some of Argonne’s work, here are a few things we heard about that really piqued the group’s interest:

Comprehensive GIS with massive data on gas system. See separate NOTE, or go to this webpage:
**User Access is available on request, on a collegial basis.** The limitation is server capacity, so ANL is not in a position to throw it wide open. They are also very open to any companies that want to provide better data on their own gas T&D systems–which can be kept confidential.
Contact Ron Fisher, 630-252-3508,

— Ice Slurry District Cooling
UFTO reported on this back in 93/94. It is now privately funded, and has advanced considerably. Ice slush dramatically increases the capacity of new or retrofitted central cooling distribution systems.
Contact Ken Kasza, 630-252-5224,

— On-Line Plant Transient Diagnostic
Uses thermal-hydraulic first principles, along with generic equipment data, in a two-level knowledge system. Neural net models of the system can rapidly indicate what’s causing a transient, e.g. water loss, heat added, etc., and identify where in the system the problem lies. The system wouldn’t need to be custom built for each plant, except to incorporate the plant’s schematics. It’s been run in blind tests at a nuclear plant. Next step is to hook it up to a full scale simulator, and then go for NRC approval. A fossil application would be much easier.
Contact Tom Wei, 630-252-4688,
or Jaques Reifman 630-252-4685,

— Advanced NOx Control with Gas Co-firing
Closed-loop controller adjusts furnace control variables to get optimal distribution of gas injection to yield greatest NOx reduction. Typical systems use gas at 20% of heat input, but this system gets same or better NOx levels with only 7%. Joint effort with ComEd, GRI, and Energy Systems Assoc.
Contact Jaques Reifman 630-252-4685,
or Tom Wei, 630-252-4688,

Sensor monitor and fault detection system knows if the system is misbehaving or the sensor is wrong. Can see slow drift, signal dropout, and noise, giving early indicators of sensor failure, and providing assurance that the process itself is operating normally, thus reducing unneeded shutdowns. It also can monitor the process itself, for wide ranging quality control applications. MSET stands for Multivariate State Estimation Technique. A model learns expected relationships among dozens or hundreds of sensor inputs, and makes predictions for what each sensor should say, and this is compared with the actual sensor signal. Argonne has patented a unique statistical test for residual error (the difference) which replaces the usual setting of fixed limit levels. There are also important innovations in the neural net modeling, which is completely non-parametric.

Applications range from the NASA shuttle engine, to several power plants, to the stock market.
ANL contacts are Ralph Singer, 630-252-4500,
Kenny Gross 630-252-6689,

A spin off company is doing applications in everything else but electric generation. (Think of the possibilities in T&D!!) They call the product ProSSense. Website is at http//
Contact Alan Wilks, Smart Signal Corp, Mt. Prospect IL 847-758-8418,


Here is the text of ANL’s overview “Topic Capability Sheet”. Many of you got hardcopies of the complete set in the mail. They’re still available from Tom Wolsko ( I’ve also posted them on the UFTO website, until Argonne puts a final verion up on their own website.

Argonne National Laboratory:
A Science and Technology Partner for the Energy Industry

Argonne is a multidisciplinary science and technology organization that
offers innovative and cost-effective solutions to the energy industry.

— Introduction
Argonne National Laboratory understands that energy companies must meet growing customer demand by creating, storing, and distributing energy and using the most efficient, cost-effective, environmentally benign technologies available to provide those services. We also understand that they must use increasingly more complex information for decision-making, comply with a multitude of environmental regulations, and adjust to a rapidly evolving marketplace.

Argonne has more than 50 years of experience in solving energy problems and addressing related issues, for both its customers and its own needs. Combining specialities such as materials science, advanced computing, power engineering, and environmental science, Argonne researchers apply cutting-edge science and advanced technologies to create innovative solutions to complex problems.

— Argonne Solutions
Recent applications of that expertise include
– A Spot Market Network model that simulates and evaluates short-term energy transactions.
– A “fuel reformer” that allows fuel cells to use a wide variety of hydrocarbon fuels to make electricity.
– Advisory systems for plant diagnostics and management based on sensors, neural networks, and expert systems.
– MSET, a real-time sensor validation system that provides early warning of sensor malfunction.
– Decontamination and decommissioning techniques developed for Argonne’s own facilities.
– Advanced materials for system components, batteries, ultracapacitors, flywheels, and hazardous waste encapsulation.

— Contacts
Argonne’s Working Group on Utilities:
– Dick Weeks, 630-252-9710,
– Tom Wolsko, 630-252-3733,

For technical information, contact the person listed under the category of interest.

Nuclear Technology
David Weber, 630/252-8175,
– Operations and Maintenance
– Materials
– Reactor Analysis
– Safety
– Spent-Fuel Disposition

Fossil Technology
David Schmalzer, 630/252-7723,
– Basic and Applied Research
– Technology Research and Development
– Market, Resource, and Policy Assessments

Transmission and Distribution
John Hull, 630/252-8580,
– System Components
– Energy Storage
– Distributed Generation
– Data Gathering and Analysis
– Biological Effects

Energy Systems and Components Research
Richard Valentin, 630/252-4483,
– Component Reliability
– Sensors
– Systems Analysis

Materials Science and Technology
Roger Poeppel, 630/252-5118,
– Materials Characterization
– Modeling and Performance
– Advanced and Environmental Materials
– Materials Properties
– Superconductivity

Fuel Cell Research and Development
Walter Podolski, 630/252-7558,
– Fuel Processing
– System Design, Modeling, and Analysis
– Testing
– Energy-Use Pattern Analysis

Advanced Concepts in Energy Storage
K. Michael Myles, 630/252-4329,
– Secondary Batteries
– Ultracapacitors and High-Power Energy Storage
– Flywheels
– Superconducting Magnets

Information Technology
Craig Swietlik, 630/252-8912,
– Computer Security and Protection
– Independent Verification and Validation
– Information Management
– Advanced Computing Technologies

Environmental Science and Technology
Don Johnson, 630/252-3392,
– Environmental Characterization
– Process Modifications
– Emissions Controls
– Waste Management
– Site Management

Environmental and Economic Analysis
Jerry Gillette, 630/252-7475,
– Electric System Modeling and Analysis
– Risk Assessment and Management
– Environmental Assessment
– Cost and Economic Analysis
– Legal and Regulatory Analysis

Decontamination and Decommissioning
Tom Yule, 630/252-6740,
– Operations
– Technology
– Technical Analysis

End-Use Technologies
William Schertz, 630/252-6230,
– Plasma Processes
– Ultrasonic Processing
– Electrodialysis Separation Processes
– Recycling Technologies
– Aluminum and Magnesium Production

Thermal Energy Utilization Technologies
Kenneth Kasza, 630/252-5224,
– Compact Heat Exchangers
– Ice Slurry District Cooling
– Advanced Thermal Fluids

For information on working with Argonne, contact Paul Eichamer, Industrial Technology Development Center, Argonne National Laboratory, Bldg. 201, 9700 South Cass Avenue, Argonne, Illinois 60439; phone: 800/627-2596; fax: 630/252-5230,


GASMAP — Analysis and Tracking Tool for the Natural Gas Industry
**User Access is available on request, on a collegial basis.** The limitation is server capacity, so ANL is not in a position to throw it wide open. They are also very open to any companies that want to provide better data on their own gas T&D systems–which can be kept confidential.

(lifted directly from ANL webpage:

GASMAP, a comprehensive geographic information system (GIS), contains information never before gathered in one place and organizes it for use by professionals in the industry. Data include:

–All the government data forms collected by DOE including FERC and EIA, integrated in a common format and linked together
–Spatial data on natural gas pipelines and their respective points
–Energy-related data about cogeneration units, electric utility plants, service territories of local distribution companies (LDCs), and natural gas storage fields

Users can locate map data on more than 100 interstate pipelines, information on more than 2,000 companies and more than 1,000 variables.

GASMAP contains many layers of graphic data. This map identifies and locates 100 interstate pipelines. GASMAP integrates three commercial applications and links them through a custom graphical interface. MapInfo serves as the GIS component, Microsoft FoxPro is the relational database engine, and Microsoft Visual Basic supplies the menu system and interface. The system is PC-based and uses Windows.

Professionals use GASMAP’s analytical and tracking capability to assess pipeline capacity and deal with routing and location issues. They can also extract sales, customer, system flow, and storage data on utility companies. With simple menu selections, users can:
–Produce maps displaying specific pipelines or pipelines layered with other energy data and supporting background information (e.g., roads, streams, railroads)
–Produce tables and graphs
–Review data by company, state, or topic and compare them with related data

A data dictionary links all the forms and information into index files. The user can view all data for a specific state or company without knowing the files or variables being used. The menus guide the user through the query, address it, and provide results in either tabular or graphical form.

Contact Ron Fisher, 630-252-3508,

You can email him directly to set up an account. Indicate which version of Windows you are running (i.e., 3.1, 95, or NT).

CURC Annual Technology Conf.

Here’s an advance notice just received about this years (2nd) annual CURC conference. I plan to attend on behalf of UFTO.

If you’re interested in receiving later announcements and information, I suggest you send an email to Mary Beth Salter at SCE (tel 626-815-7217) and ask her to add you to the email distribution list.
| ** UFTO ** Edward Beardsworth ** Consultant
| 951 Lincoln Ave. tel 650-328-5670
| Palo Alto CA 94301-3041 fax 650-328-5675

California Electric and Natural Gas Research
Annual Technology Exchange Conference

November 2-4, 1998

Doubletree Hotel near Orange County Airport (714-540-7000)

1. Exchange information on RD&D Projects
2. Identify opportunities for joint funding of RD&D Projects
3. Prevent duplicative RD&D
4. Promote consistency between RD&D programs and State energy policy

a. Identifying RD&D needs and current programs from Local, State, and National perspectives
b. Improving the RD&D Process and Achievement of Benefits
c. Special Keynote Address, Visit to National Fuel Cell Center, and Social at
Edison Field of Anaheim

Sponsor: California Utility Research Council (CURC)
– Members are CPUC, CEC, PG&E, SCE, SDG&E, SoCalGas
– In association with CIEE, CMUA, EPRI,GRI, LADWP, SMUD

– Early hotel registration available directly with Doubletree at $99/day.
Conference starts at 10:00 AM on 11/2/98.
– Conference registration details and forms are in-process
– Contact Mary Beth Salter at SCE 626- 815-7217,
or email for further details

For some fun scary summer reading:
Have a look at the current (August) issue of WIRED Magazine. In a big story on Y2K (page 122), it tells about computer experts who are convinced that civilization as we know it will stop, mostly due to the complete failure of the power grid, for many months or even longer. Some of them have turned into full fledged survivalists, setting up compounds in remote locations.

How serious do you think the threat is?

New Approach to Data Mining

Triada, a small company in Ann Arbor MI and Foster City CA, has developed a remarkable and entirely different approach to analyzing large amounts of data, producing knowledge and insights that can be acted on.

The company has already attracted interest of some huge manufacturing companies, and appears poised for a dramatic lift-off. They’re seeking demonstration opportunities with individual companies, and with partner/vendors who can take the product into various markets.

During a recent visit to their facility, I had the system explained and demonstrated. The implications truly do appear staggering, as you see how simple it is on one level, and how powerful on another.

The key is a unique “transform” process that looks at the data in an entirely different way. Engineers will appreciate an analogy with the Fourier Transform, used for more than a century to analyse continuous waveform data. Transforms can dramatically reduce the amount of data, and/or provide a different way of “seeing” it, e.g., making it possible to understand in ways that never would have been possible with the orginal data. (Imagine trying to make sense of frequency spectra without FT.)

The following material is taken directly from Triada’s company literature and website, at Note the white paper especially, under “Technology” for the complete rendition of this concept, along with graphics.

Contact: Bruce Borden, 650-378-7506,

— Triada’s NGram Transform.

The software uses a unique and patented method for identifying relationships between disparate data items and presenting those relationships in an extraordinarily compact and useful manner.

Triada’s revolutionary technology transforms information into associations. Modeled on human memory, the NGram Transform, learns raw data, remembering it in Associative Memory Structures (AMSs). These structures contain all associations that exist in the original data, presenting them in an interactive and intuitive form. Unlike data mining, which relies primarily on guesswork and brute force querying, the AMS reveals data knowledge quickly and visually.

Today’s enterprises are overwhelmed by a glut of data. The challenge is no longer how to gather and store information. Instead, it is how to turn very large amounts of data into useful knowledge.

The NGram Transform, mathematically transforms raw data from the data domain into powerful knowledge in the association domain. In the data domain, knowledge is difficult to identify. It is lost in the myriad of details collected in the database. By seeing all associations within data, NGramTM transforms data into the association domain, revealing knowledge and making it readily accessible to the analyst.

— Associative Memory Structures-the Power of NGram Transform

Because an AMS stores information as associations, redundant information shrinks as it is transformed. AMSs are typically much smaller than the size of the original data. And, as more information is tranformed into an AMS, its growth rate slows. This is because the AMS has already learned much of the information it is encountering.

In a corporate database, information is highly redundant. Think of the sales records for an automotive company. Each record contains fields such as date, model, options, color, price, warranty, dealership, and salesperson. A given model is sold thousands of times, with a few hundred combinations of options, color, and warranty. Prices vary from sale to sale, but, for one model, they will all fall within a rather narrow band, such as from the list price of $14,995 down to $11,999.

NGram Transform can associate all prices with all sales by all salespeople, etc. This transformation by association is knowledge. By simply looking at the AMSs, you can see many useful facts, such as how many trucks a particular dealership sold, how many red Fords were sold on a particular date, and which dealerships discounted more heavily than others. This means that you do not need to write and solve complex database queries to obtain the equivalent information.

Statistically Unusual Events Can be Significant

Things that happen infrequently can be just as interesting and potentially important as those that occur often. While low-association frequency events are often merely data entry errors, they can also be important events that data scrubbing applications almost always miss.

Some failures may occur very infrequently, but always happen in some combination of events: whenever a certain make of car is nine-months-old, the extra undercoat option was not applied, and if the car was purchased in northern cities, then the radiator always springs a leak. This association perhaps implies that the radiator is rusting from salt corrosion. NGram Transform captures the relationship as soon as it occurs. Summary or statistical analysis would most likely miss this association. Then, months later when it has become a sizable problem, the manufacturer’s engineers would begin searching for an explanation of why the defect occurs. Quite often, due to the enormous volume of data involved, the association that would be immediately evident with NGram Transform is never spotted by traditional methods, and the true root cause of a problem remains a mystery.

The Power of Association

Associations have many other powerful characteristics that simplify knowledge discovery:

• Associations act like memories of events. The human brain processes information in a non-linear way. NGram Transform makes similar associations, finding patterns in seemingly random fact sets. For example, NGram Transform can associate the number of warranty claims brought into a particular dealership, revealing that the most claims occur during January.

• Associations are facts. If a dealership sells 25 red pickups, NGram Transform can make that association.

• Associations act like intersections of events. If Sales Rep Jane sells 12 red Ford pickups on the same day Sales Rep Joe sells five minivans, those two events will be associated by NGram Transform.

• Associations act like Boolean operations. When we build up associations, we are doing Boolean operations (mostly ands) of multiple records. With NGram Transform the “and” of all of the pick-up sales records will be available in a node that combines color and make. The node counting pick-ups will be the “or” of all of the colors, models, sales reps, and so forth.

Associations are answers to questions yet to be asked. Because queries of a large database often require significant processor time and disk space, the user usually restricts the query so that it produces only a fraction of the information the user knows is available in the database. Using NGram Transform, the answer to the same query appears as an association in an AMS node. In fact, all the related associations are also revealed in the same node.

Vital Statistics

NGram Transform and Athena run under Windows NT on an Intel compatible PC. Depending on the amount of original data and its redundancy, the transformed AMS will be smaller than the original data by a sizable factor. In several recent cases, 50 GBytes of original data required only five GBytes of storage as an AMS. This is just the opposite of storage in an RDBMS, which typically adds 200% overhead, requiring roughly three times the original data size for storage. This compression allows AMSs to be backed up efficiently, transmitted, or replicated.

NGram Transform is very fast. It learns information at around 5 GBytes per hour per processor. Once built, you can copy an AMS from the system where it was built to each user’s system, or the AMS can be accessed through NT’s distributed file system. Multiple processors in one system can display the same AMS or independent AMSs. You can incrementally update an AMS. In general, an AMS never forgets, but you can remove records from it.


You will gain new business insights whenever you view an AMS transformation of your data. Every Triada customer is finding new and exciting ways to leverage the knowledge NGram Transform reveals. A financial credit company is using NGram Transform to find errors from their information sources. They plan to also use NGram Transform to identify target customers for mailing lists. An auto manufacturer is using Athena to pinpoint root causes of failures before they become bigger problems. They intend to also use Athena to identify option combination trends for target marketing.

** NGram Transform converts data from an information domain into an association domain. Once transformed, all associations in the data become directly viewable.

** NGram Transform is loss-less and reversible; the original records may be retrieved.

** NGram Transform converts raw data into Associative Memory Structures. AMSs typically require much smaller amounts of storage space then the original data size and they grow more slowly as more information is taught to them.

** The associative memory portions of an AMS are even smaller. These will fit in the main memory of most computer systems, requiring little or no I/O during the examination process.

Data mining applications require guesswork and extensive querying. Relying only on these tools is a risky proposition; you never know if you’ve discovered everything of interest. With NGram Transform, knowledge is instantly at your fingertips, revealed to you in an intuitive and visual form. Your analysts will save time and money.

Discover the power of NGram Transform and transform your data into valuable knowledge.

Fed. Restructuring Proposal

Forwarding note received this morning from DOE coordinators for the SEAB
Task Force on Electrical System Reliability.
| ** UFTO ** Edward Beardsworth ** Consultant
| 951 Lincoln Ave. tel 650-328-5670
| Palo Alto CA 94301-3041 fax 650-328-5675

Subject: Administration’s Electricity Legislation
Date: Wed, 1 Jul 1998 8:37:00 -0400

On June 26 the Administration forwarded its proposed electricity restructuring legislation to the U.S. Congress. The proposed Comprehensive Electricity Competition Act along with a section-by-section analysis can be found on the Internet at: http://www.DOE.GOV/ceca/ceca.htm.

The Act’s provisions on reliability are based on the recommendations of the Department of Energy Task Force on Electric System Reliability.