What`s Watt

Transcription

What`s Watt

Power Industry Division Newsletter
What’s Watt
In This Issue:
Director’s Message........................ 1
2013 Symposium Success............. 2
POWID Award Winners............... 10
2014 POWID Symposium............ 14
POWID 2013 Spouses View......... 16
Task Force for the Future............. 16
Resources Available.................... 17
Dr. Gooddata............................... 18
Equal Percentage Control Vavles....22
3rd Best Paper............................. 24
New Members............................. 45
Automation Week 2013.............. 52
Committee Updates.................... 53
Summer 2013
Director’s Message
By Denny Younie
ISA Power Industry Division (POWID)
Director
Case M&I, LLC
[email protected]
Welcome to the “End of Summer” edition of What’s Watt; the
Power Industry Division’s tri-annual newsletter. It has been a tradition for the Director’s message in this issue of the POWID newsletter to summarize our annual Symposium, generally held in early
June each year. I am quite unintentionally breaking with that tradition this year, and will summarize the 2013 Orlando Symposium in
the “Fall” newsletter in addition to reviewing Automation Week
that will be held in Nashville the week of November 4th, 2013.
What I can say about the 2013 Symposium today is that a number
of people worked an exorbitant amount of volunteer hours to
make the Symposium a success. Bill Sotos, Brandon Parker, Jason
Makansi, Tim Hurst, Terri Graham and a host of other POWID volunteers pulled off the Symposium in other than ideal circumstances, of which a last minute hotel change was thrown in the mix.
ISA staff provided their usual diligent support and pin-point focus
in support of the event. Working closely together is the only way
these groups can ever conduct a successful POWID Symposium.
I say “End of Summer” and “unintentionally” above because I
am so far behind, causing the summer newsletter to be more of
an early fall edition. Thank you to all that have been patient with
me while I adjust to several life changing events; one of which is a
self-declared and wife certified case of “Old-Timers Syndrome.”
I was unable to attend the Orlando Symposium; only the second
Symposium I have missed since 1998. During that 15 year span,
attendees had the opportunity to interface with nearly 3,000 colleagues at 12 different Symposium locations. The only repeat locations during that 15 year span have been Orlando and Scottsdale,
which is also the site of the 2014 Symposium. Each year held in
this location we have had at or near record attendance, and are
planning for great participation again in 2014. Look for upcoming details from the 2014 Symposium General Chairman Aaron
Hussey in the very near future. Like in 2008, the host Hotel will be
the Scottsdale Hilton.
In these days of cost reduction, right-sizing, and doing more with
less, ISA is no different. ISA as a whole and POWID in particular
are only as good as the “volunteers” that make up the membership. Everyone who is a POWID member ‘volunteers’ by virtue of
‘volunteering’ their dollars toward annual dues. Every ISA Division
and Section is run by volunteers, making it “Our ISA.” Countless
times we have all heard someone say “what’s in it for me?” Well,
if paying the annual dues and reading one newsletter a year is all
you put into ISA that may well be all you get out of ISA. But if you
participate in a Symposium, write and present a paper, attend an
ISA training session, become active in a local Section or Division,
you may find the additional benefits outweigh the effort invested
in your new level of participation. I urge you to get involved at a
higher level and make an investment—After all, it is “Our ISA”!
If you have any comments or suggestions on ways the Division can
improve, please feel free to contact me at [email protected].
Best Regards,
Denny Younie
POWID Director 2013–14
Upcoming ISA and POWID International
Conferences
ISA Automation Week 2013
Technology and Solutions Event
Nashville Convention Center, Nashville, Tennessee USA
5–7 November 2013
57th Annual ISA POWID Symposium
Hilton Scottsdale Resort, Scottsdale, Arizona USA
1–6 June 2014
Power Industry Division Officers
DIRECTOR
Denny Younie
Case M&I, LLC
(970) 443-4098
[email protected]
www.casemi.com
DIRECTOR-ELECT
Brandon Parker
Black & Veatch
Overland Park, Kansas
[email protected]
PAST DIRECTOR
Don Labbe
Invensys Operations
Management
33 Commercial St., C42-2A
Foxboro, MA 02035-2099
(508) 549-6554
[email protected]
NEWSLETTER EDITOR
Dale Evely
Southern Company
P.O. Box 2625 / Bin B463
Birmingham, AL 35202
(205) 992-6649
[email protected]
2014 Powid Symposium Committee
GENERAL CHAIR
Aaron Hussey
Expert Microsystems
35 Church Street South,
Suite 103
Concord, NC 28025
(980) 248-5841
[email protected]
NUCLEAR PROGRAM CHAIR
Bob Queenan
Curtiss Wright
[email protected]
HYDRO & RENEWABLES
PROGRAM CHAIR
Xinsheng Lou
Alstom Power
[email protected]
GENERATION PROGRAM
CHAIR
Neva Fox
Electric Power Research
Institute (EPRI)
[email protected]
EXHIBIT COORDINATOR
Brandon Parker
Black & Veatch
[email protected]
EXHIBIT REGISTRAR
Carol Schafer
ISA
[email protected]
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POWER MAGAZINE
CONTENT
Dr. Robert Peltier, Editor
in Chief
Power Magazine
[email protected]
EDITORIAL REVIEW
Terri S. Graham
Hurst Technologies, Inc
P.O. Box 1718
Angleton, TX 77516
(979) 849-5068
[email protected]
HONOR & AWARDS CHAIR
Don Andrasik
GenOn Energy
Morgantown Generating Station
[email protected]
PUBLICITY
Joe Vavrek
Sargent & Lundy
55 E. Monroe St. 25W53
Chicago, IL 60603
(312) 269-2270
[email protected]
ISA Professional Staff
ISA Senior Administrator,
Technical Divisions/
Symposia
Rodney Jones
ISA
P.O. Box 12277
Research Triangle Park,
NC 27709
(919) 990-9418
[email protected]
2013 ISA POWID Symposium a
Great Success
By Bill Sotos, Hurst Technologies
56th POWID Symposium General Chairman
The ISA’s 56th POWID (Power Industry Division) Symposium that
concluded in early June was a truly outstanding program, showcasing the latest developments in instrumentation, controls, software,
renewable energy technology, communications, government regulations, and cyber security. During this very full week, we offered
three tracks (Generation, Fossil, and Nuclear) that included 14 well
attended sessions. We also offered three formal training classes, as
well as EPRI industry meetings and standards committee meetings.
The exhibit hall was full of vendors who were showing off their
products and services. Attendees were able to come away with
valuable knowledge that they can apply to improve their technical
expertise, organizational efficiency and competitiveness. All of this
was contained in a wonderful meeting facility, the Rosen Shingle
Creek Hotel. It is not an exaggeration to say that this hotel, including its staff, was simply a fabulous place to hold our event.
Next year, the ISA POWID Symposium moves on to Scottsdale,
Arizona at the Hilton Scottsdale Resort and Villas. Aaron Hussey of
Expert Microsystems will be the General Chairman. Please support
Aaron and his team as they plan for ISA POWID 2014.
It was an honor and a privilege to be the General Chairman for
ISA POWID 2013. On behalf of the entire Symposium planning
team and the ISA POWID Executive Committee, we thank all of
the many attendees, exhibitors, speakers, volunteers, and ISA staff
for their efforts in making our event a success. I hope everyone
had a great experience at ISA’s 2013 Power Industry Division
Symposium and that they will continue to support the ISA POWID
Symposium in the future.
You can view the final program from this year’s Symposium at:
http://www.isa.org/~powid/powid_2013/2013_final_program.pdf
2013 ISA POWID Symposium
Supporters:
POWID 2013 Symposium leaders assembled an incredible technical program, clockwise from top left: Bill Sotos, General chair, Jason Makansi
and Brandon Parker, Program Chairs. Center: Rodney Jones of ISA served as the overall event coordinator. Bottom panorama: The POWID
Executive Committee was busy planning for the upcoming year: Bob Queenan, Alan Zadiraka, Dale Evely, Brandon Parker, Jim Batug, Aaron
Hussey, Seth Olson, Roger Hull, Cyrus Taft, Don Labbe, Mike Skoncey, Jason Makansi, Xinsheng Lou, Joe Vavrek (photographer) and guests.
Photographs in this edition of the newsletter were provided by Joe Vavrek, collages and captions were assembled by Don Labbe.
The editor would like to thank Joe and Don for their hard work in this regard.
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The site of POWID 2013 was the spectacular Rosen Shingle Creek with spacious accommodations and fine food supporting the traditional
friendly environment of the POWID Symposium.
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Kenneth B. Medlock III, Ph.D. provided one of the keynote addresses. He is the James A. Baker, III, and Susan G. Baker Fellow
in Energy and Resource Economics at the Rice University’s Baker
Institute and the senior director of the Center for Energy Studies,
as well as an adjunct professor and lecturer in the Department of
Economics at Rice University. He is a principal in the development
of the Rice World Natural Gas Trade Model, aimed at assessing the
future of international natural gas trade.
He has published numerous scholarly articles in his primary
areas of interest: natural gas markets, energy commodity price
relationships, gasoline markets, transportation, national oil
company behavior, economic development and energy demand,
and energy use and the environment. He also teaches courses in
energy economics and supervises Ph.D. students in the energy
economics field.
Dr. Medlock is currently the vice president for academic affairs for
the United States Association for Energy Economics (USAEE). In
2001, he won (joint with Ron Soligo) the International Association for Energy Economics Award for Best Paper of the Year in the
Energy Journal. In 2011, he was given the USAEE’s Senior Fellow
Award. He is also an active member of the American Economic
Association and the Association of Environmental and Resource
Economists, and is an academic member of the National Petroleum Council (NPC).
Medlock has served as an adviser to the U.S. Department of Energy and the California Energy Commission in their respective energy modeling efforts. He was the lead modeler of the Modeling
Subgroup of the 2003 NPC study of long-term natural gas markets
in North America, and was a contributing author to the recent
NPC study “North American Resource Development.” Medlock received his Ph.D. in economics from Rice in 2000, and held the MD
Anderson Fellowship at the Baker Institute from 2000 to 2001.
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Dr. Peggie Koon provided one of the keynote addresses. She is the 2013 International Society of Automation (ISA) President-Elect
Secretary and will be ISA President in 2014. Dr. Peggie Koon, director of strategy, partnership development and management at Morris
Communications Company, LLC has been named vice president of audience for TAC Media and The Augusta Chronicle, a new, senior-level
position created by each of Morris Publishing Group’s metro markets. This new role will focus on building a powerful community voice by
growing the news, audience and digital efforts. Dr. Koon has a BA in mathematics from Smith College in Northampton, Massachusetts. She
completed graduate studies in Industrial and Systems Engineering at the Georgia Institute of Technology in Atlanta, Georgia, and received
her doctorate degree in Management Information Systems from Kennedy Western University in Cheyenne, Wyoming.
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Scott Fowler is the Electrical & Controls Engineer at Lakeland Electric, a mid-sized, municipal-owned entity in Lakeland, Florida. His
responsibilities include oversight of Distributed Control Systems throughout the Power Production division and electrical generation
equipment throughout the plants. Prior to his employment at Lakeland Electric, Scott worked in the Chemical Manufacturing industry for
20 years in a variety of roles including: Instrumentation Technician, Software Developer, Database Manager, and Instrumentation & Electrical
Supervisor. Early in his career, Scott was employed as an Instrumentation & Control Specialist at two large nuclear plants, and prior to
this served six years in the US Navy as a nuclear Reactor Operator aboard a fast attack submarine. Scott holds a BS degree in Computer
Engineering and an MBA from the University of South Florida.
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The Power Industry Roundtable session chaired by Jason Makansi, President, Pearl Street Inc. provided an exciting view of the future of U.S.
power production. Panel members included: James Flowers, Southern Nuclear, Jim Colgary, Director, Government Affairs, Nuclear, Energy
Institute, Scott Fowler of Lakeland Electric, Dr. Peggie Koon, ISA President Elect, Dr. Kenneth Medlock, Senior Director, James A. Baker,
Institute for Public Policy’s Center for Energy Studies, Dr. Robert Peltier, Editor-in-Chief, Power Magazine.
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The sessions are the heart and soul of the POWID Symposium. With 16 sessions and over 65 presentations, the POWID Symposium is the
Showcase of Innovation for the Power Automation Industry.
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ISA POWID Award Winners Announced
By Don A. Andrasik
ISA POWID Honors & Awards Coordinator
Celebrating Excellence Award for
Standards Excellence
Congratulations to Cyrus Taft, who was
selected by the ISA Executive Board to
receive the Celebrating Excellence Award
for Standards Excellence. The award
presentation will be made at the 51st
Annual ISA Honors & Awards Gala which
will be held on Monday evening, November
4th, 2013 at the Renaissance Nashville Hotel
in downtown Nashville, Tennessee, USA.
POWID Service Award:
Again, congratulations to Cyrus W. Taft, recipient of the POWID
Service award at the 2013 Symposium.
As a committed and tireless leader, Cyrus Taft provides support
in the operation of POWID, ISA77 committee and ISA as a
whole. Cyrus has shown excellent service in fulfilling positions of:
Secretary, Director, Past Director, ISA Governance Structure Task
Force member, Program Chairperson, Session Chairperson, Paper
reviewer, Author, and ISA 77.43, 77.82, & 77.39 Chairperson.
Cyrus is now performing as the POWID Webmaster. ISA, and
especially POWID, have benefited from his dedicated service.
2013 Symposium recognition of the Best Three
Papers of 2012
Best Paper
Coordinated Feedwater Heater Energy Control to Achieve
Higher Peak Load Generation & Reduced NOx Emissions
By: Don Labbe, Invensys Operations Management
2nd Best Paper
Smart Firing Control System
By: Corey Houn, Wisconsin Public Service
Bernie Begley, Wisconsin Public Service
Alan Morrow, Invensys Operations Management
Don Labbe, Invensys Operations Management
Tom Kinney, Invensys Operations Management
Andy Speziale, Invensys Operations Management
3rd Best Paper
Robustness Enhancement of PID Cluster for a Nonlinear
Power Plant Model with Time Delay
By:Shu Zhang, Dept. of Mech. Sci. and Engr.,
Univ. of Illinois at Urbana-Champaign
Joseph Bentsman, Dept. of Mech. Sci. and Engr.,
Univ. of Illinois at Urbana-Champaign
Cyrus W. Taft, Taft Engineering
POWID Achievement Award
Congratulations to Dr. Robert Peltier,
recipient of the POWID Achievement
award at the 2013 Symposium.
As Editor-In-Chief of POWER magazine,
Dr. Peltier consistently promotes the
advancement of the power industry in
automation, and other technologies,
utilized in generation and distribution.
Dr. Peltier organizes the written word
of innovation in instrumentation, controls, automation and other
fields. His efforts provide an influential periodical that promotes
instrument and control as one of the full range of technologies
utilized in power. The industry has benefited from his editorials,
article selection, and guidance.
POWID Awards Nomination Request
to All POWID Members
By Don A. Andrasik, ISA POWID Honors & Awards
Coordinator
In meeting more and more of the members, I cannot help but be
impressed by the talent displayed in our POWID group. There are
many individuals that display their talents in “beyond the norm”
fashion. During your busy days, when such an individual is identified, recognize them by nominating that person for a POWID
award as listed below:
• POWID Achievement Award
• POWID Facilities Award
• POWID Services Award
• Robert N. Hubby Scholarship
Nomination forms for these POWID awards can be found at:
http://www.isa.org/~powid/awards/powidawardforms.zip
Do not forget there are also ISA “Celebrating Excellence” awards
of which POWID members are well deserving. Information on
those awards and how to submit nominations for them can be
found at: http://www.isa.org/Content/NavigationMenu/
General_Information/Honors_and_Awards1/Honors_and_
Awards.htm
10
Clockwise from left: Dr. Robert Peltier, recipient of the POWID Achievement award; Dr. Joseph Bentsman, Cyrus Taft and
Don Labbe, 2012 best paper award recipients; Mike Skoncey with Cyrus Taft, recipient of the POWID Service award.
Luncheon keynote speaker was Lieutenant Lee Cuthbertson, MSD Port Canaveral of the U.S Coast Guard. Lt. Cuthbertson
provided a fascinating discussion of the many duties of the U.S. Coast Guard covering a coastline stretching nearly 10,000
miles. Just one of the more notable remarks was that there are more NY city policeman than the entire U.S. Coast Guard.
11
The Symposium leaders were honored for their contributions to POWID 2013, clockwise from top left: Bill Sotos of Hurst
Technology, General Chair; Brandon Parker of Black & Veatch, Program Co-chair; Terri Graham and Tim Hurst of Hurst
Technology, Symposium coordinator and paper review chair; Mike Skoncey, past Honors & Awards Chair and Jason Makansi
of Pearl Street Inc., Program Co-chair. The technical success of the symposium is based on the hard work of the session
developers: Chad Kilger of AMS, Tim Hurst of Hurst Technology, Bob Queenen of Scientech, Xinsheng Lou of Alstom,
Michael Fox of ABB, Jim Batug of PP&L, Danny Crow of Invensys, Shizhong Yang of Alstom, James Flowers of Southern
Nuclear, Bruce Geddes of Southern Engineering Services, Ray Torok of EPRI and Brandon Parker of Black & Veatch.
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Top panel: audience for awards luncheon
Bottom panel clockwise from left: Gold Champions: Case M&I, POWER Magazine, Emerson, Siemens, Invensys and
Curtiss Wright; booth activity during conference; Silver Champions: Hurst Technology, Consolidated Controls, Honeywell,
Maverick Technologies, Doosan HF Controls and PAS.
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ISA 2014 POWID Symposium Is Looking for You
Aaron Hussey, Expert Microsystems, and Conference General Chairman, cordially invites you to…
Mark your calendar and submit an abstract:
57th Annual Power Industry
Symposium & Exhibits
June 1–6, 2014
Scottsdale, Arizona, Hilton Scottsdale Resort
If you and/or your company are involved in Instrumentation &
Control, automation, digital technology, wired and wireless communication, plant and performance software, asset and knowledge management, cybersecurity, and/or simulators and training
for power generation, plan to attend the industry’s leading forum
for sharing technology, application experiences, and best practices.
ISA’s 2014 Power Industry Symposium (POWID 2014) covers all
types of power stations—coal, nuclear, gas-fired gas turbine/combined cycle, and renewable energy (hydroelectric, solar, and wind,
and biomass), and smart grid, distributed generation, combined
heat and power, and micro-grids—all over the world.
POWID is large enough to provide a comprehensive program of
presentations and panel discussions necessary for professional
development yet small enough to induce intimate conversations around special topics critical to your company’s competitive growth and vitality. The exhibit hall typically attracts 30-40
companies giving you a chance to really get to know solution
providers without feeling overwhelmed by a hall requiring a GPS
to navigate. POWID has a long-standing relationship with Power
Magazine, which potentially can leverage your exposure from
several hundred attending a conference to an audience of tens of
thousands in print and on-line.
This year, the symposium’s theme is “Instrumentation & Control
Solutions for Today’s Industry Challenges.” Organizers seek papers
(peer-reviewed) and presentations (subject to review) on the following topics:
• cybersecurity
• environmental control systems
• combustion turbine and combined cycle plants
• advanced technologies and applications
• fleet management and performance/M&D centers
• sensors and wireless data communication
A full track with up to eight sessions on nuclear plant topics will
feature modernization strategies, post-Fukushima impacts, state
of cybersecurity requirements and solutions, regulatory challenges
and lessons learned, I&C strategies for small modular reactors
(SMR), digital equipment obsolescence, EMI testing requirements,
set points and uncertainties, operability determination experiences,
SRP Chapter 7 changes, and commercial grade dedication.
For more information, please visit www.isa.org/powid. The site
will include a link to an automatic paper abstract submission form.
However, if you have questions or wish to discuss your involvement in POWID (or you have problems with the automated on-line
forms), please contact one of the individuals below:
General Chair
Aaron Hussey, Expert Microsystems
[email protected].
Nuclear Program
Bob Queenan, Curtiss Wright
[email protected].
Hydro and Renewables Program
Xinsheng Lou, Alstom Power
[email protected].
Generation Program
Neva Fox, Electric Power Research Institute
[email protected].
Fossil Program
TBD—volunteer being identified
Exhibit Coordinator
Brandon Parker, Black & Veatch
[email protected].
Exhibit Registrar
Carol Schafer, ISA
[email protected].
Power magazine content
Robert Peltier, Editor in Chief, Power Magazine
[email protected].
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© tang90246 - Fotolia.com
POWID
Symposium2014
1–6 June 2014
Hilton Scottsdale Resort & Villas
Scottsdale, Arizona USA
“Instrumentation & Control Solutions for
Today’s Industry Challenges”
The 57th Annual ISA POWID Symposium will be held in Scottsdale, Arizona June 1–6,
2014 at the Hilton Scottsdale Resort. The POWID Symposium is the largest conference
dedicated to automation, control systems and instrumentation in the power
generation industry.
The Symposium Program Committee is soliciting abstracts for full papers and for presentations. All paper submissions will be peer reviewed
to ensure high quality and originality. Symposium Proceedings will be published on CD for distribution to attendees and also made available
on the ISA web site. Suggested topics for submissions include:
2014 ISA POWID Symposium Paper and Presentation Suggested Topics
Hydroelectric/Renewables
Innovations
• Steam Cycle Augmentation
• Energy Storage
Challenges
• Predictive Control
• Long-term reliability
Fossil
Environmental Control Systems
• Scrubbers
• SCR Controls
• Regulatory Challenges
Combustion Turbine and Combined Cycle Plants
• Operational Flexibility
• Start-up and Ramp Rates
• Load Range Extension
Nuclear
Plant Modernization
• SRP Chapter 7 and ISGs
• Digital Obsolescence
• Plant Modernization Experiences
• EMI Testing and Immunity
New Nuclear Plants
• Conventional Commercial Reactors
• Small Modular Reactors
Programmatic
• Setpoints, Uncertainties and TSTF-493 Implementation
• Commercial Grade Dedication
• Operability Determinations
Generation
Cybersecurity
• NERC CIP Requirements
• Implementation & Audits
• Testing & Intrusion Detection
Submissions
due
15 January
2013
Equipment Development
• New Sensors
• Wireless Sensor Applications & Standards
• Fieldbus
Smart Grid Outlook
• Impact on Generating Plants
• Communication Standards
New Generating Plants (non-nuclear)
• IGCC
• Renewables
Advanced Control Technology and Applications
• Simulation and Training
• Advanced Control
• Automation
Human Factors Engineering
• Alarm Management
• High Performance HMI
• Control Center Design
Fleet Management
• Remote Monitoring
• Inspection and Maintenance
• Condition Monitoring Systems
• Alarm management
• Training the Next Generation
Fukushima Accident Impact
• SFP Instrumentation
• FLEX Approach to Beyond-Design-Basis External Events
For more information on the 2014 ISA POWID Symposium and to submit an abstract, please go to
www.isa.org/powersymp or contact:
General Chair......................................................... Aaron Hussey, Expert Microsystems, [email protected]
Program Co-Chair, Generation................................ Neva Fox, [email protected]
Program Co-Chair, Hydro & Renewables................. Xinsheng Lou, [email protected]
Program Co-Chair, Nuclear..................................... Bob Queenan, [email protected]
Program Co-Chair, Fossil......................................... TBD—volunteer being identified
Exhibit Coordinator................................................. Brandon Parker, [email protected]
Exhibit Registrar...................................................... Carol Schafer, [email protected]
Power Magazine Content....................................... Robert Peltier, [email protected]
15
ISA 2013 POWID—
A Spouse’s Point of View
POWID Task Force for the Future
Initiative
By Tricia Logan
From Information provided By: Jason Makansi
President, Pearl Street, Inc.
ISA POWID Executive Committee (Excom) member
At the June 2012 Excom meeting in Austin, Texas, Jason Makansi
agreed to create a “Task Force for The Future” comprised of
professionals significantly younger than the typical POWID Excom
member. The objective was to solicit their feedback about the
next POWID conference, the co-located Excom meeting, and their
ideas and suggestions for how we might begin to adapt for more
appeal to the generation of engineers behind us. As a result, Jason
was able to interest three automation professionals in participating in those events this past June in Orlando, and Jason gratefully
received their feedback on behalf of POWID following the event.
All three of these individuals were enthusiastic about permanent
involvement with ISA POWID and proposed specific activities that
they’d be willing to undertake to help POWID grow and thrive, not
just survive the way we are now. However, they also illuminated
gaps that need to be addressed.
Orlando! Mickey Mouse, Disney, SeaWorld and so much more!
Once again ISA hosted its meeting in the beautiful Orlando area.
This year we had a tiny blip to contend with called “a weather system” bringing lots of clouds and rain, but it could not put a damper on our fun since most of the attractions were water related.
We missed Paula, Sherry, Jane, Teresa, and so many more this year
that have long been an instrumental part of the ISA “spouses
group.” We have always enjoyed each year’s meeting and this one
was no exception.
The Rosen Shingle Creek Resort was magnificent, as usual. Good
service and food sources all around. The pool areas and grounds
were beautifully designed with all the region’s amazing trees,
plants and flowers.
Thanks to Mike and Rodney a tiny glitch with the room access
keys to the spouses lounge was resolved expediently; and once
again we had a great meeting room to start our day. It’s always
fun to sit and visit with each other and catch up on the past year’s
developments. Each day was packed with shopping, dining, relaxing by the pool, site seeing, and much more!
Once again ISA made this an unforgettable week for all the
spouses, which was filled with laughter, fun, fellowship and great
memories.
I’m already looking forward to visiting with everyone in Scottsdale
next June so, until then, ya’ll have a great year.
16
Initial recommendations to the Excom, based on their
input, are as follows:
• Formally adopt an adjunct to the Executive Committee, the
Young Professionals Advisory Board (YPAB, or something like
that)
• Appoint the three individuals as founding members of the YPAB
• Give the YPAB as much latitude as possible to shape and direct
POWID towards their vision
• Appoint a mentor from the Excom to the YPAB who also can act
as a liaison to the Excom
• Consider putting into play the specific suggestions from the
group
Here are some of the things these three individuals
identified as potential benefits for them in POWID:
• Conversations with experienced professionals
• Tutorials on subjects that are essential to advancing their careers
and issues they face in real time in their jobs
• Networking opportunity in recruiting others for POWID
involvement, especially at the power stations
• Identifying and cultivating resources outside of their company
• Getting educated for an I&C position quickly and achieving a
competitive edge over others
• Getting educated about the latest vendor product/services
• Making connections by directing outreach to vendors and
POWID prospects
The Excom will be discussing all of this further at its next meeting
in November and hopefully will begin taking action on this shortly
after that.
Plant I&C/Wireless Technology
Guidebook Released by Power
Magazine
This guidebook exclusively
features plant I&C and wireless
articles, including full charts,
photographs, graphs and stepby-step instructions, previously
featured in POWER magazine.
The book is available in a PDF
format. 96 pages.
The Table of Contents for the guidebook is as follows:
• Innovative boiler master design improves system response
• Drum pressure the key to managing boiler stored energy
• Accurately measure the dynamic response of pressure
instruments
• Upgraded control system adds to merchant plant’s bottom line
• Digital networks prove reliable, reduce costs
• Pressure-sensing line problems and solutions
• Fully automating HRSG feedwater pumps
• Digital plant controls provide an essential edge
• How to avoid alarm overload with centralized alarm
management
• New tools for diagnosing and troubleshooting power plant
equipment faults
• Automated exhaust temperature control for simple cycle power
plant
• Increasing generation ramp rate at Morgantown Generating
station’s coal-fred units
• Concerns about temperature equalizing columns used for steam
drum level measurement
• Thermocouple response time study for steam temperature
control
• FBC control strategies for burning biomass
• Plantwide data networks leverage digital technology to the max
• Wireless technologies connect two LCRA plants
• Enhancing plant asset management with wireless retrofits
• Wireless technology unlock possibilities
• Low-cost wireless sensors can improve monitoring in fossilfueled power plants
• Artificial intelligence boosts plant IQ
• Distributed control technology: from progress to possibilities
Resources Available to ISA POWID
Members
This information provided by Bob Hubby
POWID Section/Division Liaison
The International Society of Automation (ISA) regularly provides
resource materials from the Divisions to District and Section leaders. As a POWID member, you have access to the Power Industry
Division (POWID) specific information, and if you are also a District
or Section leader, you have access to that type of information for
all Divisions. The following is the resource list that was recently
provided to those leaders by POWID’s Bob Hubby:
1. Automation Technical Papers—ISA’s comprehensive collection
of technical articles according to technical topic—a subscription service www.isa.org/techpapers
2. Division newsletters – all contain best technical papers—use
this as a section programming resource. All division newsletters can be found on the web at each division’s homepagebut access is restricted to division members. Main division
web page: www.isa.org/divisions
3. ISA Standards Catalog – a listing of current available standards. Presentation of a critical standard could be used as a
section programming resource. http://www.isa.org/
Template.cfm?Section=Standards2&Template=/
customsource/isa/Standards/AutomationStandards.cfm
4. ISA Publishing – a source for books, magazines, transactions,
directory of automation, technical papers and software.
http://www.isa.org/Content/NavigationMenu/
Products_and_Services/Publishing/Publishing.htm
5. ISA continuing education and training is available for
download at: www.isa.org/training
6. ISA Training Institute Regional Course Catalog—Regional
course catalogs are not available online, but available courses
can be seen by region at http://www.isa.org/Template.
cfm?Section=Find_Training&template=/TaggedPage/
LocationAlphaOrder.cfm&ICID=1
7. The Power Industry Division Website – This web site contains
power industry division papers for many years back to 1959
under the left click hot spot “Conference Proceedings”; these
are available only to division members. www.isa.org/~powid
8. All Division Websites – These websites can be reached
through the divisions list but many provide access to division
resources only to their division members. http://www.isa.
org/Template.cfm?Section=Division_List
9. ISA Web Seminars – Available when you are through the link
at www.isa.org/websem
10. Systems Integration Community page: www.isa.org/
systemsintegration
11. ISA Standards link for the member “view only” free standards
access benefit: www.isa.org/memberbenefits For more information or to download your own copy visit the
POWID online store at: http://www.powermag.com/
powerpress/511.html.
17
Dr. Gooddata (#6)
By Ronald H. Dieck
Ron Dieck Associates
[email protected]
Well, here we are again, ready to stride out into the fertile field of measurement uncertainty analysis. Good to see
you again! Last time we discussed the five types of systematic error (bias) and commented on how important it
was to estimate their potential magnitudes, or, systematic uncertainties (bias).
Note the ambiguity of the term “bias”. It has been used by many to refer to the systematic error for a single
measurement. They say “bias” meaning the actual difference between their measurement and the true value of the
test. Others at times will estimate the potential magnitude of this type of error and call that the “bias.” Here they
mean the +/- interval about the measurement that estimates the possible extent of the true systematic error.
Confused? So am I. Dr. Gooddata, therefore recommends we largely abandon the term “bias,” as it is used
ambiguously, and instead use the terms “systematic error” and “systematic uncertainty.” “Systematic error” is the
actual error that exists between a measurement and the measurand’s true value with zero random errors.
“Systematic uncertainty” is taken to mean the estimate of the limits to which we could expect the systematic error
to range with some confidence. Whoops, here come the statisticians again!
In the International Standards Organization’s (ISO) “Guide to the Expression of Uncertainty in Measurement,” it
is recommended that uncertainty analysts (that’s you) assign both a distribution and a confidence interval to each
systematic uncertainty estimated. The U.S. National Standard on test uncertainty, “ASME PTC19.1 - Test
Uncertainty,” has been rewritten and recommends that estimates of systematic uncertainties be assumed to
represent a Gaussian-Normal distribution and be estimated at 68% confidence. (That would make systematic
uncertainties estimates of one s X as the degrees of freedom are assumed to be infinite for each of these systematic
uncertainties.)
Remember also that the combined effect of several sources of systematic uncertainty is still determined by the
root-sum-square method and the result. This interval would contain the true value 68% of the time in the absence
of random errors (whose limits we now estimate with “random uncertainties.”)
The systematic uncertainty of the result would then be:
1
2

2
bR   bi  

 i
where each bi is a 68% confidence estimate of the systematic uncertainty for source i. This allows us to work with
equivalent s X values throughout this analysis. We will need that capability when we also deal with the random
uncertainties.
How about those random uncertainties! The latest U. S. National Standard recommends (as does the ISO Guide)
estimating their magnitude limits as one standard deviation for the average at a particular level in the
measurement hierarchy. That is, the random uncertainty for an uncertainty source is the standard deviation of the
average for that uncertainty source. It is noted as one s X . Here too, the random uncertainty for the test result is the
root-sum-square of the random uncertainties for each level in the measurement hierarchy.
The random uncertainty of the result is then:
 
1
2

2
s R    s X ,i 

 i
18
where each s X ,i is the standard deviation of the average for that level in the measurement hierarchy.
Note that with this approach, we are working with equivalent s X values for both systematic and random
uncertainties. Why is this important? How does this help us? Let’s see.
Now that we are all experts in the determination of the systematic and random uncertainties of a measurement, the
question we must approach with exceptional anticipation is this: “what good is it to calculate only systematic and
random uncertainties? Shouldn't we find a way to combine them in to a measurement uncertainty for the
measurement result?” (I know; that’s two questions.)
For a long time there were two primary approaches to this problem of calculating a single number to represent the
measurement result uncertainty. Those two uncertainty models (kind of like Ford and Chevy for your car buffs)
were the UADD and the URSS models. These were also known (that’s a.k.a. for your murder mystery buffs) as U99
and U95 respectively. That is the former provided approximately 99% coverage and the latter approximately 95%.
Well, what were these models and isn’t there something better after all these years?
The UADD model was:
U ADD  BR  t 95 S X 
The URSS model was:

2

1
2 2
U RSS  BR   t 95 S X 
Note that when it is said that UADD provides approximately 99% coverage (not confidence) and that URSS provides
approximately 95%, the key words are approximately and coverage .
We use approximately because these coverages were determined by simulation, not statistics. They are right, in
the long run, but not exact. How come we use the term coverage and not confidence? Also, what happened to that
new, better uncertainty model? Do any of you know the answers? Why coverage and what new model?
What is this coverage thing? Why not express these uncertainty intervals (hint, new word there) as confidence
intervals? The reasoning is this: The systematic uncertainty, BR, was an estimate of the limits of systematic error
to about 95 % coverage. BR was not a statistic but an estimate. S X was however, a true statistic. It was
appropriate to speak of confidence only with a true statistic.
Both of the above uncertainty equations combine a statistic, S X , with a non-statistic, BR. The result cannot be an
interval (that new word) with a true confidence but rather provides coverage as documented by simulation.
We first will handle one additional approach to estimating uncertainty. Until now our emphasis has been on
grouping uncertainty sources as either systematic or random. The ISO has published their “Guide to the
Expression of Uncertainty in Measurement”. This “Guide” does not recommend grouping uncertainty sources or
errors by systematic or random categories. It recommends grouping them as either “Type A” where there is data
to calculate a standard deviation, or, “Type B” where there is not. This approach seems in conflict with the
commonly applied terminology of “systematic” and “random” uncertainty sources.
19
However, there now is coming into vogue (popularity, not the magazine) a new uncertainty model that combines
the best features of both methods. It handles the ISO recommendations of using “Type A” and “Type B”
classifications and still allows the engineer to quote uncertainties in the more physically understandable venue of
“systematic” and “random”. How can this be? What compromises were reached?
Let’s address that super-secret, now revealed, new uncertainty model that combines the best features of the
US/ASME model and that of the ISO model. Let’s first review the basic principles of each model.
We’ll start with the ISO model. With this uncertainty model, sources of error and their uncertainties, the estimates
of the limits of those errors, are grouped by Type. Type A uncertainties have data associated with them for the
calculation of standard deviation. Type B uncertainties do not have such data and must be estimated by other
means (that’s methods not averages here). The total uncertainty, ISO calls it the “expanded uncertainty,” is then
calculated by root-sum-square of the two Types of uncertainties. But first, all the elemental Type A and Type B
uncertainties are combined by root-sum-square. That is, we first calculate:
1
2

2
U A   U A,i  


and then we calculate:
1
2

2
U B   U B ,i   .


Note here that the UB,i need an assumed distribution and degrees of freedom. The new U. S. National Standard
published by the ASME recommends that the UB,i be assumed to be representative of error sources that are
normally distributed and that the degrees of freedom are assumed to be infinite.
It is also important to recognize that all the UA,i and UB,i uncertainties are standard deviations of the average for
that uncertainty source. That is, they all represent one s X .
We then need to combine the UA and UB uncertainties into the total uncertainty (called the “expanded” uncertainty
by the ISO). That expanded uncertainty is:

U ISO   K U A   U B 
2
.
1
2 2
The constant out front, “K,” is used to provide the confidence desired. The most common choice for that constant
is Student’s t at 95% confidence. This would provide an uncertainty with 95% confidence. This ISO expanded
uncertainty would then be written:

U ISO  t 95 U A   U B 
2
.
1
2 2
Before the Student’s t95 can be determined, there is one more important step. Do you know what it is? Have you
any idea? The degrees of freedom for UISO are needed. How do we get that? Well, each standard deviation of the
average we’ve used in the two UA and UB equations above has its associated degrees of freedom. For the UA the
20
degrees of freedom come directly from the data that is used to calculate the standard deviations of the average,
that is,
 i  Ni 1
where  i is the symbol for degrees of freedom, sometimes abbreviated as d.f.
These degrees of freedom are for all the UA,i where Ni is the number of data points used to calculate the standard
deviations of the average.
For the UB,i, the degrees of freedom are assumed to be infinite.
The degrees of freedom, d.f. or the Greek letter , for the UISO is computed for the total uncertainty with the
Welch-Satterthwaite approximation. This formula is:
2

2
2
 U A,i    U B ,i  
i

d. f .     i
4
4
 U A,i 
U B,i  



 i 
i
 i  i
This formula is a real pain so put it in your computer program and use it as needed. Hand calculations are very
frustrating here. One simplifying aspect is that item in the last term in the denominator,
U 
B ,i
i
4
, is zero as the  i is infinity.
Now, with the degrees of freedom, d.f. or  , the Student’s t95 can be found in a table in any statistics text. Not a
problem.
Further, if 95% confidence is not desired but 99% or some other confidence is, just use the proper Student’s t.
Well, there you have it. Now, we need to consider the U. S. Uncertainty Standard and how to calculate that
uncertainty. What are its major components? Hint, they are not Type A and Type B which are associated with the
origin of the information used to estimate the values for the elemental uncertainties; they are associated with the
impact of uncertainties on the test result. Second hint, these groupings are familiar to engineers and they use them
the world over. Do you know what they are? Next time....
Until then, remember, “use numbers not adjectives.”
Ronald H. Dieck is the principal of Ron Dieck Associates, Inc., Palm Beach Gardens, FL. E-mail him at
[email protected]
21
Equal Percentage Control Valves and Applications
By Jacques F. Smuts, Ph.D., P.E.
OptiControls, Inc.
Houston, Texas
[email protected]
Far too often, equal percentage control valves are found in applications
where linear control valves should have been used. This article explains
equal percentage control valves and sets guidelines for their use.
What is an Equal Percentage Control Valve?
The relationship between valve stem position and the flow rate
through a control valve is described by a curve called the valve’s
flow characteristic curve, or simply the valve characteristic. An
equal percentage flow characteristic is a nonlinear curve of which
the slope increases as the valve opens, while a linear flow characteristic is a straight line (Figure 1).
However, up to now we have been talking about the inherent/design flow characteristic of control valves. This is the flow characteristic that a valve exhibits if the pressure difference across it remains
constant throughout its operating range. But in practice this is often
not the case. The pressure difference across a valve is often a function of flow, and it changes with valve position. Consequently, the
inherent flow characteristic is often distorted by the process and we
refer to the resulting curve as the installed valve characteristic.
So we have to refine our linearity requirement to reflect the
installed valve characteristic. Sometimes we need to use a control
valve with an equal percentage inherent characteristic to obtain
a linear installed characteristic. Two distinctly different scenarios
follow.
Scenario 1a
Consider a centrifugal pump for providing pressure, and a control
valve for controlling the flow (Figure 3). As the pump delivers more
flow, its capability for generating pressure decreases. Therefore
the pressure differential across the control valve is high at low flow
rates; and it is low at high flow rates. An equal percentage valve
can offset this change in differential pressure to exhibit a more
linear installed characteristic.
Figure 1. Equal percentage and linear flow characteristics.
Control valves manipulate the rate of liquid/gas flow through them
by altering the open area through which the liquid/gas passes.
Linear valves increase the open area linearly with valve travel, while
equal percentage valves open progressively more area with valve
travel (Figure 2).
Figure 3. Simple flow control loop with centrifugal pump.
Scenario 1b
Figure 2. Port shapes of linear and equal percentage valves.
Why do we need Equal Percentage Valves?
PID controllers are linear devices and, for optimal performance, the
process should behave linearly too. That is, if the controller output
changes from 10% to 20%, the process should respond just as
much as it would if the controller output changes from 80% to
90%. From this requirement, it seems that linear control valves
should be sufficient.
22
However, we can’t just assume that because we have a centrifugal
pump, we need an equal percentage valve. If the system pressure
(backpressure) downstream of the valve remains high, for example
when pumping into a pressurized system, the pump will likely stay
high on its curve, and the pressure across the control valve will not
change appreciably. In this case a linear valve might be a better
choice.
If we consider the pressure differential across the valve versus flow,
we can make the right choice in Scenarios 1a and 1b. If the pressure differential remains reasonably constant, a linear valve is required (but please read Scenario 2 below). If the pressure differential drops by more than 50%, equal percentage can provide better
linearity. To remove the guesswork, use valve-sizing software. The
software should allow you to specify a few pressure-differential
A Request from the Newsletter
Editor
By Dale Evely, P.E.
POWID Newsletter Editor
versus flow points and based on that, it will recommend the best
valve for the application.
Scenario 2
Let’s consider a steam-condensing heat exchanger (Figure 4). The
pressure upstream of the valve is kept constant by the boiler and
steam pressure controller. The pressure downstream of the valve
is determined by the condensate temperature, which is roughly
equal to the outlet temperature, which is controlled to a constant
setpoint.
The goal that POWID works toward is to publish three newsletters each calendar year; with the basic schedule being publication in March (Spring), August (Summer) and December (Fall).
All three of the newsletters are published electronically and the
Spring newsletter is also published in paper format and mailed
to those of you who live in the USA. Since the newsletter is only
as good as its content, I would like to encourage each of you to
submit technical articles as well as other articles of broad interest
for publication in future newsletters. Technical content that is
specific to the automation side of the power industry is what
provides the best benefit to our membership so please share
with your colleagues any tidbits that have been beneficial to you
in your job or in expanding your knowledge base. You can send
your articles to [email protected] (please limit any
attachments to 5MB or my mail server may not let them through
and I will never know that you tried to send them). If the article
was not authored by you, please provide us with a statement
that you have cleared publication of the material with the author.
I look forward to hearing from you.
Figure 4. Steam-condensing heat exchanger.
In other words, the pressure differential across the steam control
valve remains relatively constant, regardless of the flow. Should we
then use a linear valve. Well, we should actually use ratio control
in which we control the steam flow rate as a ratio of the process
flow rate and use a linear valve, but that is another story. Most
heat exchanger control designs are as simple as shown in Figure 4.
Even though the constant differential pressure across the valve calls
for a linear control valve, this process calls for an equal percentage valve. At low process flow rates, the outlet temperature is very
sensitive to changes in steam flow. At high process flow rates, the
steam flow must be changed much more to affect the heater outlet
temperature to the same degree. This can be accomplished by using an equal percentage control valve. At small valve openings, the
valve sensitivity is very low, which cancels the high sensitivity of the
process. The valve sensitivity increases as the valve opens more –
which is exactly what is required because the sensitivity of our heat
exchanger decreases with increased process flow rates.
Conclusion
An equal percentage control valve should be used when the
pressure differential across the valve decreases with increases in
flow rate. Valve sizing software should be able to find the right
valve characteristic for the job. Also, equal percentage control
valves should be used in control loops of which the process gain
decreases with increases in flow rate. If none of these conditions
apply, the loop is likely better off with a linear control valve.
Stay tuned!
Jacques Smuts
Principal consultant of OptiControls, and author of Process Control
for Practitioners.
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23
The Third Best Paper from Copyright
the 2012
ISA POWID Symposium
2012 ISA. All Rights Reserved.
During the Honors and Awards Luncheon in June 2013, the Third Best Paper Award for the 2012 POWID Conference in Austin, Texas was
2012
All Rights
Reserved.
presented to Joseph Bentsman, Cyrus W. Taft and Copyright
Shu Zhang
for ISA.
the paper
entitled
“Robustness Enhancement of PID Cluster for a Nonlinear
Power Plant Model with Time Delay.” This technical paper is provided in its entirety in this newsletter for your reading pleasure.
ROBUSTNESS ENHANCEMENT OF PID CLUSTER FOR A
ROBUSTNESS
ENHANCEMENT
OF PID
CLUSTER
FOR A
NONLINEAR
POWER
PLANT MODEL
WITH
TIME DELAY
NONLINEAR POWER PLANT MODEL WITH TIME DELAY
Shu Zhang
Dept. of Mech. Sci. and Engr., Univ. of Illinois at Urbana-Champaign,
1206 W Green St., Urbana IL 61801. Tel: (217)Shu Zhang
[email protected]
Dept. of Mech. Sci. and Engr., Univ. of244-0999,
Illinois at E-mail:
Urbana-Champaign,
1206 W Green St., Urbana IL 61801. Tel: (217)244-0999, E-mail: [email protected]
Joseph Bentsman (corresponding author)
(corresponding
author) 1206 W Green St., Urbana IL
Dept. of Mech. Sci. and Engr.,Joseph
Univ. Bentsman
of Illinois at
Urbana-Champaign,
Dept. of Mech.
Engr., Univ. ofFax:
Illinois
at Urbana-Champaign,
1206 W Green St., Urbana IL
61801.Sci.
Tel:and
(217)-244-1076,
(217)-244-6534,
E-mail: [email protected]
61801. Tel: (217)-244-1076, Fax: (217)-244-6534, E-mail: [email protected]
Cyrus W. Taft
Taft Engineering, Inc., Harriman,
TN.W.
E-mail:
Cyrus
Taft [email protected]
Taft Engineering, Inc., Harriman, TN. E-mail: [email protected]
ABSTRACT
ABSTRACT
Proportional-integral-derivative (PID) controllers have been widely utilized in power plant system as
Proportional-integral-derivative
controllers
have
power plant
systemfor
as
dominant control strategy for (PID)
the past
decades.
In been
the widely
previousutilized
work,inseveral
procedures
dominant control
thehave
pastbeen
decades.
previous work, several
procedures
for
simultaneous
tuningstrategy
of PID for
gains
appliedIntothe
an industry-standard
nonlinear
six PID-type
simultaneous
tuning
of
PID
gains
have
been
applied
to
an
industry-standard
nonlinear
six
PID-type
controllers cluster in the closed loop. The cluster was controlling 4-input-7-output nonlinear power
controllers
in the closed
loop. Thesufficiently
cluster waswell
controlling
nonlinearsystem.
power
plant
modelcluster
with time-delay
representing
a typical4-input-7-output
coal-fired boiler/turbine
plant
model
with
time-delay
representing
sufficiently
well
a
typical
coal-fired
boiler/turbine
system.
Although satisfactory time domain performance was achieved, it was discovered that through closedAlthough
satisfactory
time domainrobustness
performance
was achieved,
was discovered
thatthe
through
closedloop
linearization
the closed-loop
performance
of the itoriginal
model under
standard
PID
loop linearization
the closed-loop
performance
of the
modelaffected
under the
standard
PID
cluster
around operating
points isrobustness
rather poor.
This can be
onlyoriginal
marginally
through
tuning,
cluster around
is would
rather poor.
can beunder
onlynot
marginally
affectedchanges
through intuning,
implying
that aoperating
well-tunedpoints
cluster
requireThis
retuning
very significant
plant
implying that
well-tuned
cluster performance.
would requireInretuning
underAnot
very significant
changes
in plant
parameters
to amaintain
adequate
this paper,
consistent
robustness
enhancement
parameters
maintaintoadequate
performance.
In this
paper, Aofconsistent
robustness
procedure istoproposed
correct the
main structural
deficiencies
the existing
cluster byenhancement
introducing
procedure
is proposed
to correct
thefor
main
structural
deficienciesproportional
of the existing
cluster
byon
introducing
into
it additional
control
elements,
example
- off-diagonal
links,
based
full-rank
into
it
additional
control
elements,
for
example
off-diagonal
proportional
links,
based
on
controller design, to approximate the robustness performance of the latter. The full-rank
simplest
linear
controller
to approximate
the robustness
of redesign
the latter.could
The be
simplest
linear
nonlinear
PID
clusterdesign,
robustification
is presented.
This type performance
of PID cluster
easily
nonlinear
PID
cluster
robustification
is
presented.
This
type
of
PID
cluster
redesign
could
be
easily
implemented using the existing software/hardware control equipment. The closed-loop system
implemented
using
the existing
software/hardware
control
system
consisting
of the
original
and the robustified
PID clusters
with equipment.
the original The
plantclosed-loop
model is simulated
consisting
of
the
original
and
the
robustified
PID
clusters
with
the
original
plant
model
is
simulated
respectively. The simulation results have shown that the closed-loop performance of the original model
respectively.
results havedegrades
shown that
the closed-loop
performance
of the original
model
with
standardThe
PIDsimulation
cluster significantly
under
a typical plant
model perturbation,
while
the
with standard
cluster
significantly
degrades under
typicalloop
plantwith
model
latter
has muchPID
smaller
effect
on the performance
of thea closed
the perturbation,
original modelwhile
and the
latter has much
smaller effect on the performance of the closed loop with the original model and the
robustified
PID cluster.
robustified PID cluster.
1. Introduction and Background
1. Introduction and Background
Proportional-integral-derivative (PID) controllers have been widely utilized in power plant control
Proportional-integral-derivative
controllers
been
utilizedsystem
in power
plant control
system for the past fifty years. In(PID)
the previous
studyhave
[1] and
[2],widely
a closed-loop
consisting
of a 4system for the past
fifty years.
the previous
studytime-delay
[1] and [2],representing
a closed-loopsufficiently
system consisting
of a 4input-7-output
nonlinear
powerIn plant
model with
well a typical
input-7-output
nonlinear
power
plant
model
with
time-delay
representing
sufficiently
well
a
typical
coal-fired boiler/turbine system and a nonlinear cluster of six PID-type controllers was specified, with
coal-fired boiler/turbine
system and
a nonlinear
of six
PID-type
specified,
cross-coupling
of the variables
similar
to that cluster
in a real
power
plant. controllers
In order towas
capitalize
onwith
the
cross-coupling of the variables similar to that in a real power plant. In order to capitalize on the
24
Distributed with permission of author(s) by ISA [2012]
Distributed
permission 2012;
of author(s)
by ISA [2012]
Presented with
at ISA/POWID
http://www.isa.org
Presented at ISA/POWID 2012; http://www.isa.org
Copyright 2012 ISA. All Rights Reserved.
interactions among process variables and loops to attain better overall time-domain performance,
several procedures of simultaneous tuning of PID gains in multi-loop control system using local and
global optimizers have been utilized. The particular local technique selected - the IFT (iterative
feedback tuning) - used the linearized version of the PID cluster for signal conditioning, but the data
collection and tuning were carried out on the full nonlinear closed-loop system. The particular global
techniques (used in the local tuning) were particle swarm optimization (PSO), simulated annealing
(SA), and genetic algorithm (GA). They all provided the pre-specified time domain responses through
the appropriately chosen static and/or dynamic weighting of the individual terms in the performance
index. However, an additional outcome of simultaneous tuning has been the discovery that the
robustness performance of the standard PID cluster structure is rather poor and that this property can
be only marginally affected through tuning, implying that a well-tuned cluster would require retuning
under not very significant changes in plant parameters to maintain adequate performance. One possible
solution to improving the closed-loop robustness performance is to taking advantage of modern
multivariable control techniques such as robust optimization to maintain stability as well as desired
performance under the existence of system disturbances.
approach [14] [16] [17], and
Advanced multivariable control designs such as LQG [12] [13],
predictive control [15] have been successfully applied to the control of modern power plant system in
controller for a 4-input-7recent years. In the previous study [3], we designed a multivariable
output nonlinear boiler/turbine model with time-delay. The resulting robust control system was
demonstrated to display performance robustness superior to that of the fine-tuned nonlinear PID
controllers. In [14], the author has proposed a multivariable
controller for utility plant and then
controller to a multivariable PI controller to approximate its robustness. The
reduced the
approximated PI controllers are finally implemented to control the utility plant. Inspired by the
reduction methodology in [14], we propose a robustness enhancement solution to correct the main
structural deficiencies of the existing cluster by introducing into it additional control elements, such as
controller design, to approximate the
off-diagonal proportional links, based on full-rank linear
robustness performance of the latter. This type of PID cluster redesign could be easily implemented
using the existing control software/hardware equipment without introducing the complexity and
difficulty associated with the high order robust controller.
The paper is organized as follows. The nonlinear process model used in this paper and its linearization
controller design
around the operating condition are discussed in section 2. Section 3 presents the
for this boiler/turbine system in [3]. In section 4 the resulting
controller is reduced to a lower-order
PI controller which is then projected onto the existing PID cluster by introducing some off-diagonal
links. We compare the closed loop robust performance attained by the original PID cluster with that of
the proposed robustified PID cluster in section 5. Section 6 provides closed-loop simulation under
setpoint changes and model perturbation. Conclusions are given in section 7.
2. Plant Modeling and Linearization
The plant model used in this work is shown in Figure 1. The model is an incremental model which
describes dynamics of all deviation variables with respect to nominal operating condition and is
designed to represent a 250 MW plant dynamics around 80% operating point. The model dynamics
25
were selected based on the power plant model proposed in [4] and the authors’ experience with similar
plants. Additional details about the process can be found in [4]. The nominal operating point values are
specified as follows:
Megawatt Output = 200 MW
Throttle Pressure = 12.5 × 106 Pa
Steam Flow Rate = 80 %
Excess oxygen = 3 %
Air Flow Rate = 80 %
Drum Level = 0 m
Feedwater Flow Rate = 80 %
and all inputs are 80 %.
The process outputs in this model are: y1 - MW, Unit Load, (megawatts), y2 - TP, Throttle Pressure,
(Pa), y3 - SF, Steam Flow Rate, (%), y4 - O2, Excess oxygen, (%), y5 - AF, Air Flow Rate, (%),
y6 - DL, Drum Level, (m), y7 - FW, Feedwater Flow Rate, (%).
The control inputs to the process are: u1 - TV, Turbine Valve Position, u2 - FR, Firing Rate
Demand, u3 - FD, FD Fan Damper Demand, u4 - FWV, Feedwater Valve Position Demand, and k Controller parameter vector.
The model is nonlinear as shown in Figure 1. Deadtimes are included in the model, i.e. blocks “TV to
MW3”, “FR to PT2” and “FR to FF2” to represent the time delays inherent in the processes, such as
coal pulverizer dynamics. There are cross couplings in the model between several inputs and outputs.
The turbine valve position affects both the power output and the throttle pressure as does the firing rate
demand. The latter also affects the excess oxygen. The power output (steam flow rate) also affects the
drum level. The control system model structure used in the closed-loop simulation is that given in
Figure 2. Then nonlinearities of the control arise from the lookup table, bias and multiplication
components as shown in Figure 2. Thus, the controller is given by the six-PID cluster that includes one
lookup table and one multiplication operator and two biases, making the cluster nonlinear. This control
system model structure provides a simple but non-trivial testbed for the multi-loop tuning and
design.
26
Throttle Pressure (psi)
num(s)
1
Turbine Valve Position
2
Thr Pr
5s+1
TV to PT1
num(s)
s2 +0.0084s+4.9e-5
TP/MW
TV to PT2
900s
MW Output (MW)
1
230s+1
TV to MW1
1
MW
12s+1
TV to MW3
TV to MW2
(2 s)
.2
num(s)
2
Firing Rate Demand
3
Stm Flow
Gain1
s2 +.008s+3.3e-5
FR to PT1
FR to PT2
(30 s)
SF
0.24108
[SF]
FR to MW
144s2 +24s+1
FD to AF
s2 +7.994s+0.032604
FR to FF2
(30 s)
FW Flow (% )
FR to FF
1
9s2 +6s+1
Transfer Fcn2
Lookup Table
1
[SF]
15s+1
Transfer Fcn3
AF/O2
num(s)
25s2 +10s+1
u-1
Divide Bias3
15s+1
Transfer Fcn
1
Steam Flow1
Excess Oxygen (% )
u+80
Bias1
u+80
Bias2
num(s)
4
Feedwater Valve Position
Demand
5
Air Flow
Air Flow (% )
1
3
FD Fan Damper
Demand
Steam Flow (% )
4
Excess O2
A/F to O2
.05
1/s
Gain
7
FW Flow
Drum Level (inches)
FW/DL
Integrator
-.7
6
Drum Lev
7s+1
Transfer Fcn1
Figure 1 Simplified process model schematic diagram in Simulink
[MW]
PID
MW SP (MW)
Add5
PID1
MW
[MW]
Thr Pr
[TP]
Stm Flow
[SF]
Excess O2
[O2]
Air Flow
[AF]
Drum Lev
[DL]
Feedwater Valve Position Demand
FW Flow
[FW]
[TP]
PID
Thr Press SP (Pa)
Add
PID2
Add4
u+80 Bias1
[SF]
u-80
PID
Product
[O2]
Add6
PID
Add1
Firing Rate Demand
Bias2
FD Fan Damper Demand
PID3
PID4Lookup Table
O2 SP (% )
[AF]
Boiler & Turbine Model
[DL]
PID
Add2
PID5
Add3
PID
Add7
PID6
Drum Level SP (m)
[SF]
[FW]
Figure 2 SIMULINK representation of the nonlinear PID cluster
27
Although the model in the paper is nonlinear, the real process is almost always working in the vicinity
of its operating point around which the linearized model is a good approximation of the nonlinear
system. On the other hand, controllability and observability tests are easily applied to the linearized
model, providing indication for the controllability and observability of the nonlinear system around
operating condition. Therefore, linearization of the nonlinear model around its operating condition is
carried out in [3]. The linearized system is obtained in the form of a 7x4 transfer function matrix given
by:
0
0 
 MW   H11  s  H12  s 

 TP   H s H s
0
0 
22  

  21  
 TV 
 SF   H 31  s  H 32  s 
0
0 

  FR 

 
0
0
H
s
H
s

42  
43  
  FD 
 O2  


 AF 
0
0
0
H 53  s 


  FWV 

 
0
H
s
H
s
H
s
62  
64   
 DL   61  
 FW   0
0
0
H 74  s  

 
where
900s 5  9000s 4  4.05 104 s 3  9.45 104 s 2  9.45 10 4 s
H11  s  
,
2760s 6  2.784 104 s 5  1.266 105 s 4  3.007 105 s 3  3.153 105 s 2
2.551104 s  105
0.0001454s 4  9.691105 s3  2.907 105 s 2  4.523 106 s  3.015 10 7
,
s 6  0.6747 s5  0.2054s 4  0.03273s 3  0.00233s 2  1.762 10 5 s  6.844 10 8
3.677 s 2  0.0352s  0.001043
H 21  s   3
,
5s  1.042s 2  0.008645s  4.9 105
0.000603s 4  0.000402s3  0.0001206 s 2  1.876 10 5 s  1.25110 6
H 22  s   6
,
s  0.6747 s5  0.2054s 4  0.03273s 3  0.00233s 2  1.762 10 5 s  6.844 10 8
180s5  1800s 4  8100s 3  1.89 104 s 2  1.89 104 s
H 31  s  
,
2760s 6  2.784 104 s 5  1.266 105 s 4  3.007 105 s 3  3.153 105 s 2
H12  s  
2.551104 s  105
2.907 105 s 4  1.938 105 s3  5.815 106 s 2  9.045 107 s  6.03 10 8
,
s 6  0.6747 s5  0.2054s 4  0.03273s3  0.00233s 2  1.762 10 5 s  6.844 10 8
0.008151s 4  0.005434s3  0.00163s 2  0.0002536s  1.691 105
H 42  s  
,
25s8  226.5s 7  226.7s 6  105.6s 5  28.51s 4  4.664s 3  0.4349s 2
H 32  s  
0.01827 s  6.762 105
0.25
H 43  s  
,
4
3600s  2040s3  409s 2  34s  1
28
1
,
144s  24s  1
31.5s 6  317.7 s 5  1445s 4  3429 s 3  3591s 2  283.5s
H 61  s  
,
4.347 106 s10  4.505 107 s9  2.116 108 s8  5.297 108 s 7  6.32 108 s 6
H 53  s  
2
1.886 108 s5  2.323 107 s 4  1.305 106 s 3  2.94 104 s 2  105s
5.088 106 s 5  3.828 106 s 4  1.308 10 6 s3  2.455 10 7 s 2
H 62  s  
2.412 108 s  9.045 1010
1575s10  1498s 9  653.9s8  166.9 s 7  26.18s 6
,
2.458s 5  0.1267 s 4  0.003011s 3  2.015 10 5 s 2  6.844 10 8 s
0.0875s  0.0075
,
H 64  s  
5
945s  828s 4  246s3  28s 2  s
0.5
,
H 74  s   2
9s  6s  1
The staircase algorithm [5] is then used to determine the controllability and observability of the
linearized system after transforming the transfer function into a state-space representation
characterized by 52 state variables. From staircase algorithm, it has been shown that there are also 52
controllable states and 52 observable states. Therefore, by the definitions of the controllability and
observability [6], the linearized system is both controllable and observable although it essentially a
4  4 system since only four out of seven outputs need to track the setpoint changes.
3. Robust Controller Design
Following the notation and discussion in study [3], the standard representation of a closed loop plant
with uncertainties can be given by the feedback structure in Figure 3, where P( s) is the generalized
plant, K ( s) is the controller, and ( s) is a representation of the uncertainties in the model. The
diagram in Figure 3 contains the following signals: u and y are the vectors of control inputs to the
plant and measured outputs fed to the controller, respectively. The vectors wp and z p are specially
constructed quantities which may contain signals that have no direct representation at any point in
actual plant but explicitly relate to the design objectives. The vector wp is usually referred to as the
vector of ‘external’ or ‘performance’ inputs that cannot be controlled, such as actuator or measurement
noise. The vector z p is usually referred to as the vector of ‘performance’ outputs, which are signals to
be kept small, such as functions of errors or control signals (e.g. the ‘output’ of control signal activity).
The vectors wu and zu are referred to as the ‘uncertainty’ inputs and outputs respectively.
29
Figure 3 Standard representation of a closed-loop plant with uncertainties
By lumping wp with wu and z p with zu into vectors w and z , respectively, we can partition P( s) as
 P ( s) P12 ( s) 
P( s)   11

 P21 ( s) P22 ( s) 
where

z P11w  P12u

y P21w  P22u
or employing state-space representation and standard notation, as
 A B1 B2 
 D11 D12   C1 
1
P( s
) 
    sI  A  B1 B2    C1 D11 D12 

 D21 D22  C2 
C2 D21 D22 
Using an output feedback controller K ( s) in the control law u  K (s) y , the following transfer
function is obtained:
P11 P12 K ( I  P22 K )1 P21
z  Tw, z (s)w where Tw, z (s) 
The H  control problem ([10] and [11]) consists of finding a controller, K , that stabilizes P and
ensures that the infinity norm of the closed-loop transfer function is below some prespecified bound, 
, i.e. finding

K  K : K stabilizes P(s), Tw, z (s)



where
30
Tw, z ( s)

 sup
w
z
w
2

(1)
2
under the following assumptions
(1) ( A, B2 ) is stabilizable and ( A, C2 ) is detectable;
(2) D12 has full column rank, and D21 has full row rank;
 A  jwI
(3) 
 C1
 A  jwI
(3) 
 C2
B2 
has full column rank for all  ;
D12 
B1 
has full column rank for all 
D21 
Such controller, if found, stabilizes P( s) for all uncertainties ( s) satisfying
1
( s)   , ( s)  BIBO stable

while also satisfying (1), i.e. attenuating the effect of the performance inputs (noise and disturbances)
on the performance outputs (errors).
The development of the generalized plant model follows the procedures described in [8] and [9].
Measurement noise and plant disturbances are not considered and will be addressed elsewhere. The
uncertainty comes from the time delay and other approximations during the linearization. A diagram of
the complete generalized plant model used for the controller design is shown in Figure 4. In Figure 4,

w [r ] 

u [u] 
z 
z   c
 ze 
reference signals
control inputs
 control activity
 peformance error
 y   measured outputs
y   p
 ye   integrated output error
An additional state  , where  y  r , is added to this linear system. This state is introduced to ensure
tracking and is treated as an additional output, resulting in the following system
 x

X 

 
 y
Y 


 
A
C

C
0

0  x   0 B   r 

0    D  I  u 
0  x  0 D   r 

I     0 0  u 
31
Figure 4 Block diagram of the generalized plant model
For the boiler/turbine unit considered in this paper, the controller designed on the basis of the nominal
model given in Figure 1 should guarantee tracking of dispatched load demand and maintain the desired
throttle pressure, excess oxygen, and drum level under modeling uncertainty by manipulating the four
inputs.
To ensure the ability of the controller to force the plant output, y , to track a step reference signal, r , it
is necessary to introduce integrators as elements of the error weighting matrix We . In order to generate
stable control law under integral action and include this action into the optimization procedure, the
signal to be minimized is the ideal integrated error ze . This signal is calculated by subtracting the
reference input r from the plant output y p , and filtering the result through the integrator We and,
further, through the diagonal scaling matrix Ge . The signal ye is obtained by passing error signal
y p  r through the integrator We . While ze is the signal used in the design stage for optimization
purpose, signal ye is the actual quantity used in the feedback loop for control purpose.
The diagonal constant scaling matrices Ge and Gc in Figure 4 are used to tune the controller to yield
an acceptable close-loop transient response. Ge tunes the weighting of the integration error in the cost
function. If the weighting is increased, the controller will be more aggressive, and the system will have
faster transient response but bigger overshoot and higher peak values of the control signal. Gc is
designed to directly tune the control effort. If Gc increases, the resulting controller will generate more
conservative control signal with smaller peak values, with the overshoot of the closed-loop system
tracking response decreasing and the transient response time increasing.
For the specific chosen values of weighting and scaling matrices and the resulting
reader can refer to the previous study [3] for more details.
4. Robustness Enhancement of PID Cluster
32
controller, the
From section 4 in the previous study [3], we notice that the robustness of the standard PID cluster
structure in Figure 2 SIMULINK representation of the nonlinear PID cluster is rather poor. The
norm of the closed-loop system with linearized PID cluster is about 2700.5. It’s also been
demonstrated in study [3] that the
controller derived in section 3 did improve the closed-loop
system robustness, reducing the
norm to about 5.3047. Although the multivariable robust control
design successfully addressed the robustness deficiency existing within the PID cluster, a minimal
controller has order of 31. This high order controller will
state-space realization of the designed
cause practical implementation issues in real application and spur the need to investigate the
performance of simplified reduced-order controllers. Moreover, the PID control law is still the
dominant algorithm in the industry and the most familiar method to operational practitioners due to its
easy implementation and tuning properties. Therefore, in this section, we investigate the strategy and
performance of reduced order PID type approximated multivariable robust controller and project it
onto the original PID cluster to enhance the overall closed-loop robustness.
The robust controller can be represented by a state space realization of the form

x Ak x  Bk u

y Ck x  Dk u
where
,
is the number of states,
is the input
is equal to , assume
dimension and is the output dimension. If the rank of the matrix
( ), i.e. minimum singular value of , since we are most interested in the low-frequency band.
So by truncating the Taylor expansion of the controller with respect to the variable , we can derive the
following proportional type (P) controller:
K ( s) 
Ck ( sI  Ak )1 Bk  Dk
 Dk  Ck Ak1Bk
It is clear that based on the above procedure the resulting P controller achieves good approximation of
the robust controller at low frequencies.
In the present case, our goal is to approximate the 31st order
type controller. The P gains are given as:
[
controller at low frequencies with a P
]
Then the above P-controller is projected onto the PID cluster in Figure 2 by paralleling the P-controller
with the PID cluster, i.e. summing these two controllers together, to enhance the robustness
performance of the latter. This procedure is equivalent to introducing additional control elements, in
controller design, into the existing PID
this case some proportional links based on full-rank linear
control system to improve its robustness. For illustrative purposes, a simplified structure of the above
robustness enhanced PID control system is shown in Figure 5. The schematic of the robustified PID
cluster is shown in Figure 6. The four additional integrator blocks in Figure 6 are introduced because
the last four inputs of the full rank
controller as shown in Figure 4 are where ̇
.
33
Consequently the approximation P of the
controller also requires the inputs as instead of
That’s the reason why the integrators were used in Figure 6 to generate the desired inputs .
Figure 5 Structure of the robustified PID control system
34
.
1
s
-1
-0.0505
[MW]
0.0024
PID
MW SP (MW)
Add5
PID1
-1
PID
Thr Press SP (Pa)
PID2
Add6
PID
Add1
O2 SP (% )
PID4 Lookup Table
1
s
-1
[AF]
PID5
PID3 Add10
0.0012
Add11
PID
Add2
Stm Flow
[SF]
Excess O2
[O2]
Air Flow
[AF]
Drum Lev
[DL]
Feedwater Valve Position Demand
FW Flow
[FW]
FD Fan Damper Demand
-0.0451
-0.2492
[DL]
[TP]
Add9
PID
Product
[O2]
Thr Pr
Firing Rate Demand
Add4 u+80 Bias1 Bias2
u-80
[SF]
[MW]
Add8
0.0012
[TP]
Add
MW
Turbine Valve Demand
-0.0451
1
s
Add3
Boiler & Turbine Model
PID
Add7
PID6
Drum Level SP (m)
[SF]
[FW]
-1
1
s
-0.0795
0.0021
-0.0003
Figure 6 SIMULINK representation of the robustified nonlinear PID cluster
5. Robust Performance Comparison between PID Cluster and Robustness
Enhanced PID Cluster
To provide a meaningful comparison of the resulting robustified PID controller and the conventional
PID cluster presented in Figure 2, the
norm of the closed-loop system for the latter compatible with
that for the former should be defined. For this purpose, the block diagram in Figure 4 is reorganized to
duplicate the PID controlled closed-loop system in Figure 2. The new diagram is as shown in Figure 7.
35
Figure 7 Block diagram of the PID cluster controlled closed-loop system in Figure 2
Here the scaling matrices Gc and Ge are omitted to make the closed-loop system dependent only on
the chosen set of PID parameters.
Linearizing the nonlinear PID cluster in Figure 2 yields
0
0
0
0
0  PID1
0
0
0
0 0

0 0

 PID2
1
0
0
0
0
0
0
0


Kc ( s) 
0 0 PID3 0  PID3 0

 PID3  PID2 0.4  PID3  PID4
0
0
0


 PID6  PID5 
0
0  PID6 0
0
0
0 0 PID6 0
where
ki 2
1,
, 6, j 1, 2,3
 ki
3 s , ki j , i
s
Here PID1,3,4,5,6 are PI controllers while PID2 is PID controller. Two sets of PID controller parameters
are defined below. The first one is the set of the initial controller parameters in [1]:

k11 1,
k12 0.1,
k21 0.05,
k22 0.00001,
k23 2,
k31 4,
k32 0.1,
(2)

k41 4,
k42 0.4,
k51 1,
k52 0.0001,
k61 20,
k62 0.6
The second set is that for the IFT tuned controller parameters in [1]:

k11 0.7853,

k12 0.0987,

k21 0.3642,

k22 0.00001, k23  10.6147,

k31 1.5766,

k32 0.0739,
(3)

k41 1.4767,

k42 0.2126,

k51 0.6889,

k52 0.00009917, k61  17.4811, k62  0.3845
The PID cluster closed-loop system is still defined as z  Tw, z (s)w , where
PIDi ki1 

w [r ]  reference signals ,
 z   control activity,
z   c
 ze   peformance error,
36
and
Tw, z ( s)

 sup
w
z
w
2
 .
(4)
2
Figure 8 compares the Bode plots of the singular values of the closed-loop systems for the robustified
IFT-tuned PID cluster, the original IFT-tuned PID cluster control systems with PID gains of (3) and
full-order
controller. It can been seen from Figure 8 that the full-order
controller has the best
robustness performance among three controllers. The robustified PID controller rolls off to reject highfrequency noise signals sharply and responds to lower-frequency load disturbances and setpoints. The
closed-loop system with the robustified PID controller shows considerable improvements in overall
robustness when compared to that with the IFT tuned PID cluster, especially in the low-frequency
range. The
norms of the closed-loop systems controlled respectively by conventional PID cluster
using (2), IFT-tuned PID cluster using (3), robustified IFT tuned controller and full-order
controller are given in Table 1. Looking at the definition of norms in (4) and Figure 3, it is seen that
the robustness enhancement PID cluster provides better closed-loop performance (significantly lower
values of the performance error vector) under the same uncertainties than the PID clusters. Table 1
also shows that PID cluster tuning only marginally affects its performance robustness.
Twz

Initial
IFT
Robustified IFT
2700.5
4380.9
1000
71.6216
Table 1 Closed-loop uncertainty/performance
norm comparisons between the original PID
controller, the IFT-tuned PID controller, robustified IFT-tuned PID controller and full-order
controller.
37
Singular Values
100
IFT tuned PID cluster
Robustified IFT tuned PID cluster
Full-order H-inf controller
80
Singular Values (dB)
60
40
20
0
-20
-40
-16
10
-14
10
-12
10
-10
10
-8
10
-6
10
-4
10
-2
10
0
10
2
10
Frequency (rad/s)
Figure 8 Bode plots of the closed-loop singular values for IFT-tuned PID design and robustified IFTtuned PID design
6. Simulation Results
In this section, the performance of the robustified IFT PID controller was evaluated. It has been
demonstrated that the difference between the robust performance measure of the linearized closed loop
under PID cluster and that under the robustified PID cluster seen in Table 1 manifests itself in the
significant time domain difference in the closed-loop behavior with the original model under the same
plant dynamics change.
6.1. Simulation with the Original Model
38
0
1
500 1000 1500
Time(s)
Reference
0.5
0
-0.5
-1
0
0.5
500 1000 1500
Time(s)
Reference
0.3
0
-0.3
-0.5
0
500 1000 1500
Time(s)
10
0
-10
-20
0
1
500 1000 1500
Time(s)
IFT
0.5
0
-0.5
-1
0
0.5
500 1000 1500
Time(s)
IFT
0.3
0
-0.3
-0.5
0
500 1000 1500
Time(s)
0
0
10
500 1000 1500
Time(s)
Robustified IFT
0
-10
-20
0
1
500 1000 1500
Time(s)
Robustified IFT
0.5
0
-0.5
-1
0
0.5
500 1000 1500
Time(s)
Robustified IFT
0.3
0
-0.3
-0.5
y1(Megawatt Output)
y1(Megawatt Output)
500 1000 1500
Time(s)
IFT
5
y2(Throttle Pressure)
-20
0
10
y4(Excess Oxygen)
-10
0
Robustified IFT
15
y6(Drum Level)
0
5
10
500 1000 1500
Time(s)
Reference
10
y4(Excess Oxygen)
0
IFT
15
y6(Drum Level)
0
y1(Megawatt Output)
5
10
y4(Excess Oxygen)
Reference
15
y6(Drum Level)
r6(Drum Level)
r4(Excess Oxygen)
r2(Throttle Pressure)
r1(Megawatt Output)
First, the resulting robustified IFT-tuned PID controller is applied to the original nonlinear
boiler/turbine model to see the time domain performance of the closed-loop system under 2%/min
ramp changes in load demand setpoint. The control objective is to track the dispatched load demand
while maintaining throttle pressure, excess oxygen, and drum level under modeling uncertainty. Figure
9 compares the tracking performances of the original IFT-tuned PID cluster, the robustified IFT-tuned
PID cluster in Figure 2 SIMULINK representation of the nonlinear PID cluster and the full-order
control system under the same reference signals, showing good performance of all closed loops with
the nonlinear model, while the robustified PID cluster is performing comparably to that of the original
PID cluster. Figure 10 shows the corresponding control signals for the robustified PID cluster.
0
500 1000 1500
Time(s)
Robust
15
10
5
0
0
500 1000 1500
Time(s)
Robust
0
500 1000 1500
Time(s)
Robust
0
500 1000 1500
Time(s)
Robust
0
500 1000 1500
Time(s)
10
0
-10
-20
1
0.5
0
-0.5
-1
0.5
0.3
0
-0.3
-0.5
Figure 9 Comparison of the output responses generated by the closed loop with the original IFT-tuned
robust controller under
PID cluster, the robustified IFT-tuned PID cluster and the full-order
2%/min load ramping increase. The units are: megawatts for y1, psi for y2, % for y4, and inches for y6
39
Control Signal
u2(Firing Rate Demand)
3
2
1
0
0
4
2
0
-2
-4
0
500
1000
Time(s)
1500
6
4
2
0
1500
Control Signal
6
u3(FD Fan Damper Demand)
500
1000
Time(s)
Control Signal
8
u4(Feedwater Valve Position Demand)
u1(Turbine Valve Position)
4
0
500
1000
Time(s)
1500
Control Signal
5
4
3
2
1
0
0
500
1000
Time(s)
1500
Figure 10 Robustified PID cluster control signals behavior during ramp change in megawatt output
setpoint. The units are all %.
6.2. Simulation with the Perturbed Model
To assess the time domain performance robustness of both controllers, the plant model perturbation is
introduced in the form of two additional blocks as shown in Figure 11. One is increasing the signals in
the Turbine Valve Position to Throttle Pressure loop (block “Gain 2”). The other is decreasing the
signals in the Turbine Valve Position to Megawatt Output loop (block Gain 3”). In Figure 12, the
comparison was performed between the time responses of the closed-loop systems controlled by the
controller.
original IFT-tuned PID cluster, the robustified IFT-tuned PID cluster and the full-order
It is seen that the output responses of the PID controlled system are seriously affected by the change of
plant parameters – oscillations are present in all the system outputs and the settling time has been
significantly increased, whereas the robustified PID controller taking advantage of the best robustness
controller significantly reduces the outputs to their corresponding
performance of the full-order
setpoints with improved transient and steady state performance.
40
-3.6774
1
Throttle Pressure (psi)
2
5s+1
TV to PT1
2
Thr Pr
Gain2
-8.624e-4
s2 +0.0084s+4.9e-5
TP/MW
TV to PT2
900s
MW Output (MW)
1
0.5
12s+1
TV to MW3 Gain3
TV to MW2
(2 s)
230s+1
TV to MW1
1
MW
.2
2
Firing Rate Demand
6.03e-4
3
Stm Flow
Gain1
s2 +.008s+3.3e-5
FR to PT1
FR to PT2
(30 s)
SF
0.24108
[SF]
FR to MW
3
FD Fan Damper
Demand
144s2 +24s+1
FD to AF
FR to PT2
(30 s)1
FW Flow (% )
FR to FF
1
[SF]
Steam Flow1
Excess Oxygen (% )
u+80
Bias1
u+80
Bias
s2 +7.994s+0.032604
Lookup Table
5
Air Flow
Air Flow (% )
1
1/30.67
4
Feedwater Valve Position
Demand
Steam Flow (% )
9s2 +6s+1
Transfer Fcn2
1
15s+1
Transfer Fcn3
u-1
Divide Bias3
1
15s+1
Transfer Fcn
.05
Gain
20
25s2 +10s+1
A/F to O2
1/s
AF/O2
u+3
Bias2
7
FW Flow
Drum Level (inches)
Integrator
-.7
4
Excess O2
FW/DL
6
Drum Lev
7s+1
Transfer Fcn1
Figure 11 Process model with perturbation in the form of two gains: block Gain 2 in the Turbine Valve
Position to Throttle Pressure loop and block Gain 3 in the Turbine Valve Position to Megawatt Output
loop
41
0
4
500 1000 1500
Time(s)
Reference
2
0
-2
-4
0
y6(Drum Level)
1
500 1000 1500
Time(s)
Reference
0.5
0
-0.5
-1
0
500 1000 1500
Time(s)
20
0
-20
-40
0
4
500 1000 1500
Time(s)
IFT
2
0
-2
-4
0
1
500 1000 1500
Time(s)
IFT
0.5
0
-0.5
-1
0
500 1000 1500
Time(s)
0
40
500 1000 1500
Time(s)
Robustified IFT
20
0
-20
-40
0
4
500 1000 1500
Time(s)
Robustified IFT
2
0
-2
-4
0
1
500 1000 1500
Time(s)
Robustified IFT
0.5
0
-0.5
-1
y1(Megawatt Output)
y1(Megawatt Output)
0
-40
40
500 1000 1500
Time(s)
IFT
5
0
500 1000 1500
Time(s)
Robust
15
10
5
0
0
500 1000 1500
Time(s)
Robust
0
500 1000 1500
Time(s)
Robust
0
500 1000 1500
Time(s)
Robust
0
500 1000 1500
Time(s)
40
20
0
-20
-40
y4(Excess Oxygen)
-20
0
10
y6(Drum Level)
0
0
20
5
Robustified IFT
15
y4(Excess Oxygen)
40
500 1000 1500
Time(s)
Reference
10
y6(Drum Level)
0
y1(Megawatt Output)
0
5
IFT
15
y4(Excess Oxygen)
r1(Megawatt Output)
r2(Throttle Pressure)
10
r4(Excess Oxygen)
r6(Drum Level)
Reference
15
4
2
0
-2
-4
1
0.5
0
-0.5
-1
Figure 12 Comparison of the output responses generated by the original IFT-tuned PID cluster, the
robust controller under model uncertainty with
robustified IFT-tuned PID cluster and the full-order
2%/min load ramping increase. The units are: megawatts for y1, psi for y2, % for y4, and inches for y6
The above results demonstrate that the robustified PID cluster with additional static elements achieves
better tracking performance under perturbation in model dynamics than the original PID cluster. The
results show that although the robustified PID cluster does not attain the full performance robustness of
the full-rank
controller, it still provides substantially better performance robustness than the IFTtuned PID cluster of Figure 2.
6. Conclusion
In this paper, an effective methodology was proposed to enhance the overall robustness of the existing
well-tuned PID cluster with structural deficiencies on a nonlinear 4-input-7-output boiler/turbine
42
system. The method is introducing additional control elements based on full-rank linear
controller
design, to approximate the robustness performance of the latter. The detailed steps of the
controller
design procedure have been presented. The robust performance measures have been computed for
closed-loop systems under the original PID cluster and the robustifed PID cluster, and the former was
shown to be characterized by robustness significantly lower than that of the latter. The simulation
results have shown that under the presence of complicating factors such as coupling between variables,
time delay, and nonlinearities, the proposed robustification strategy provides time domain performance
comparable to that attained by well-tuned PID cluster. However, when the plant dynamics undergoes
changes, the PID cluster controlled closed loop exhibits a severe loss of performance, whereas the
closed loop under robustified controller exhibits substantially improved performance with reduced
controller,
oscillation and faster settling time. The performance robustness of the full-rank linear
however, is still higher and, hence, shows potential for further PID cluster robustification. The future
work will attempt to develop more comprehensive projections procedures to bring robust performance
of the PID cluster closer to that of the full-rank linear
controller.
References:
[1] S. Zhang, C. W. Taft, J. Bentsman, A. Hussey, B. Petrus, and V. Natarajan, “Simultaneous Tuning
of PID Gains in Complex Multi-Loop PID-Based Control Systems Using Iterative Feedback Tuning
Methodology”, Proceedings of the 19th Annual Joint ISA POWID/EPRI Controls and Instrumentation
Conference, ISA Vol. 477, Rosemont, IL, May 12-14, 2009.
[2] S. Zhang, Dong Ye, J. Bentsman, C. W. Taft, A. Hussey, “Assessment of Global Optimizers:
Particle Swarm Optimization, Simulated Annealing, and Genetic Algorithms in Local simultaneous
Multi-loop Tuning of PID Gains”, Proceedings of the 53rd Annual ISA POWID Symposium,
Summerlin, NV, Jun 7-9, 2010
control for a 4[3] S. Zhang, J. Bentsman, C. W. Taft, “Robust Performance Assessment of PID vs
input-7-output nonlinear power model with time delay”, Proceedings of the 54rd Annual ISA POWID
Symposium, Charlotte, NC, Jun 5-10, 2011
[4] Ollat, X. and Smoak, R., “Simultaneous control of throttle pressure and megawatts”,
Instrumentation, Control, and Automation in the Power Industry, 1991, 34.
[5] Rosenbrock, M. M., State-Space and Multivariable Theory, John Wiley, 1970
[6] Geir E. Dullerud, and Fernando Paganini, A Course in Robust Control Theory: a Convex Approach,
Springer, 2000.
[7] Matlab Control Toolbox Manual, The MathWorks, Inc., 2010.
[8] H. Zhao, W. Li, C. Taft, and J. Bentsman, “Robust Controller Design for Simultaneous Control of
Throttle Pressure and Megawatt Output in a Power Plant Unit,” International Journal of Robust and
Nonlinear Control, 9, 425-446, 1999.
[9] G. Pellegrinetti and J. Bentsman, “ H  Controller Design for Boilers”, Int. J. Robust Nonlinear
Control, 4, 645-671, 1994.
[10] J. C. Doyle, K. Glover, P. P. Khargonekar and B. A. Francis, “State-space Solutions to Standard
H 2 and H  Control Problems”, IEEE Trans. Automat. Control, AC-34, 831-846, 1987.
[11] K. Glover and J. C. Doyle, ’State-space Formulae for All Stabilizing Controllers that Satisfy an
H  - norm Bound and Relations to Risk Sensitivity’, Systems and Control Letters, 11, 167-172, 1988.
43
[12] R. Cori and C. Maffezzoni, “Practical Optimal Control of a Drum Boiler Power Plant,”
Automatica, vol. 20, pp. 163-173, 1984
[13] W. H. Kwon, S. W. Kim, and P. G. Park, “On the Multivariable Robust Control of a BoilerTurbine System,” in Proc. IFAC Symp. Power Syst. Power Plant Contr., Seoul, Korea, 1989, pp. 219223.
[14] Wen Tan, H. J. Marquez and T. Chen, “Multivariable Robust Controller Design for a Boiler
System”, IEEE Trans. On Control Systems Technology, Vol. 10, No. 5, 735-742, 2002
[15] B. M. Hogg and N. M. E. Rabaie, “Multivariable Generalized Predictive Control of a Boiler
System,” IEEE Trans. Energy Conversion, vol. 6, pp. 282-288, Jan. 1991.
[16] Bentsman, J., Zheng, K., and Taft, C. W., “Advance Boiler/Turbine Control and Its Benchmarking
in a Coal-Fired Power Plant,” Proceedings of the 14th Annual Joint ISA POWID/EPRI Controls and
Instrumentation Conference, Colorado Springs, CO, June 2004.
[17] Kai Zheng, J. Bentsma and Taft, C. W., “Full Operating Range Robust Hybrid Control of a CoalFired Boiler/Turbine Unit”, Journal of Dynamics Systems, Measurement, and Control, Vol. 130,
041011, 2008
44
POWID Membership Recognition
By: Dan Lee
POWID Membership Chair
February 2013 through June 2013
The Power Industry Division (POWID) of ISA continues to grow.
We would like to welcome all of our new and returning POWID
members and our new student POWID members. We hope you
will take advantage of everything POWID has to offer for your
work and your career including the opportunity to network with
power industry professional colleagues across the globe. Our
primary goal is to provide a means for information exchange
among engineers, scientists, technicians, and managers involved
in instrumentation, control and automation related to the
production of power. POWID is active in developing industry
safety and performance standards, working closely with two ISA
standards committees—ISA67, Nuclear Power Plant Standards,
and ISA77, Fossil Power Plant Standards. The Division also
conducts technical training and sponsors awards for power plants
and individuals advancing instrumentation and control within the
power industry. POWID welcomes your involvement in our division
activities. Opportunities are available to provide information for
our newsletter and web site, to develop papers for presentation
at our annual conference, and to participate in our division’s
management structure. It’s a great way to get to know other
industry professionals, to gain professional recognition, and to
keep informed!
Welcome New and Returning POWID Members
Mr. Curtis Benjamin
Senior Project Engineer
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Instrumentista
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Service Manager
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45
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46
Kyle Dittman
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National Grid
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ABB
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Sofcon
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Iris Power (Qualitrol)
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Scheck Industries
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Atkins
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Invista
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47
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Rico Lucy
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Universidad Distrital Francisco Jose
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49
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Duke Energy
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Perpetua Power
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Operation Technology Inc
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50
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Kenneth Oo
Cain Ortiz
Mr. Adan Ortiz
Christopher Osuala
Ms. Veenamol P V
Juhi Pandey
Tiago Pandolfi
Mr. Jimit Patel
Prashane Patel
Mr. Vaibhav Patil
Avadhut Patil
Mayue Patil
Vaibhav Patil
Pedro Henrique Paz
Mr. Jorge Penha
Aguinaldo Pereira
Mr. Felix Perez Zamora
Mccai Phelps
Mr. Kunal Phondekar
Milena Pinto
Ms. Leidy Poveda
Rahul Raj
Rojan Rajan
Shawn Ramsey
Prasanth Ravi
Chaitra Ravikumar
Mr. Gabriel Reisz
Mr. Jairo Rincon Ramos
Nolan Robertson
Gutierry Rocha
Maycon Rodrigues de Souza
Luis Rodriguez
Christopher Rowan
Mr. James Russell
Bhuvaneshwari Sabapathy
Ms. Saumya Sajib
Kunal Sakhardande
Alonso Sanabria
Elliot Santos
Mr. Alexson Santos Rocha
Sachin Saurav
Ketan Sawant
Sanjay Sawant
Thomas Schirnhofer
Ms. Pooja Sharma
Scott Shears
Ms. Gauri Shirstkar
Ms. Jennifer Shook
Carlos Sierra
Mr. Túlio Silva
Andres Silva Villalobos
Mr. DeWayne Simon
Ms. Neha Singh
Ms. Sneha Singh
Bjorn Skogguist
William Smit
Mr. Clarence Smith
Mr. Kevin Smith
Tate Smith
Mr. Thomas Smith
Pieter Smuts
Mr. Anthony Snarr
Shilpa Sonkamble
Douglas Souza
Shiva Subramanian
Josh Summerville
Mr. Devan Tank
Dhanashree Tembhare
Ms. Devashree Thakare
Sayli Thakur
Tushar Thakur
Seth Thomason
Ms. Smita Tiwari
Mayank Tiwari
Ing. Melissa Torres Salazar
Mr. Ronald Torres Vieira
Daniel Trout
Sangeeth TV
Maheshkumar Upadhyay
Ms. Riddhi Vaidya
Mr. Jamison Van’t Hul
Alvaro Varela
Mr. Puchparaj Varma
Vinicius Vasconcelos
Ryan Weber
Eric Wehmueller
Jacob Weninger
Cherril White
Mr. Adam White
Mr. Cameron Wingo
Hassanain Witwit
Semere Wondmagesn
Mr. Martin Wrobel
Bhagawantappa Yergal
Gederson Zilio
Indrajit Zope
Mr. Mohit Chhabra
Mr. Jose Mojica
51
safety
Safetypeoplebusinesstechnology
In the world of automation, they’re all connected.
In today's complex operating environments, decisions
made and actions taken in one area can have significant,
sometimes adverse, effects in others.
C
M
Y
CM
MY
CY
business
Organizations that take an integrated approach to
process management—one that aligns and incorporates
the requirements of safety, people, business, and
technology in unison—are those capable of fully leveraging the great power and potential of automation.
To get your plant in sync, and learn how to optimize the
value of your automation and control systems, attend
ISA Automation Week's unique technical conference.
CMY
K
people
www.isaautomationweek.org
technology
Register today.
ISA Corporate Partners
ISA Automation Week Partners
™
ISA Strategic Partner:
Systems Integration
Automation
Week
Technology and
Solutions Event
5–7 November 2013
Nashville Convention Center
Nashville, TN USA
ISA POWID Executive Committee
Update
ISA77 Fossil Power Plant Standards
Committee Update
The ISA Power Industry Division (also known as POWID) is organized
within the Industry and Sciences (I&S) Department of ISA to provide
a means for information exchange among engineers, scientists,
technicians, and management involved in the use of instrumentation
and control in the production of electrical power by any means
including but not limited to fossil and nuclear fuels. The POWID
Executive Committee (EXCOM) administers the activities of the
division. The Executive Committee normally meets three times
per year, traditionally in late winter or early spring, at the POWID
Annual Symposium in June, and at the annual Fall ISA Event
in the Fall. POWID Executive Committee meeting minutes and
attachments can be found at: http://www.isa.org/MSTemplate.
cfm?Section=POWID_Meeting_Minutes1&Site=Power_
Industry_Division&Template=/ContentManagement/
MSContentDisplay.cfm&ContentID=89063.
By ISA77 Committee Co-Chairs Bob Hubby and Daniel Lee
ISA67 Nuclear Power Plant
Standards Committee Update
Hello, Power Industry members! We are pleased to report that
the ISA77 committee has started the process of reaffirming
four documents: ISA77.13.01 Turbine Steam Bypass Systems,
ISA77.42.02 Feedwater Control—Drum level Measurement, ISA
77.43.01 Unit Plant Demand Development and ISA 77.60.04
HMI—Electronic Screen Displays. All four documents have been
balloted, comments have been returned, and the respective
sub-committees have reviewed the resolution to the comments.
Since the resolution will result in requirement changes within
these document, all four documents will enter a revision cycle
that requires both a future committee and public ballot. The
committees are working on preparing a final revision document
for balloting. The ISA77.20.01 Simulation subcommittee is
also starting a revision cycle for its document. The Simulation
sub-committee held a meeting in conjunction with the CS
PowerPlantSim conference in Tampa on January 28, 2013. In
addition, there are two subcommittees working on drafting
new documents. The ISA77.22.01 Power Plant Automation and
ISA77.30.01 Power Plant Controls System—Dynamic Performance
Test Methods and Procedures subcommittees are holding regular
meeting (live and physical meeting). The respective chairs are
looking for new committee members for these documents. If you
are interest in any of these topics and would like to contribute in
the development of these standards, please contact the respective
committee chair. Most committee meetings are held via web
meeting so no travel is required. Your technical input is greatly
appreciated. The ISA77 committee last met on Thursday June
5 at the Rosen Shingle Creek Hotel, Orlando Florida. The ISA77
committee meeting minutes, along with other information about
the committee, can be found on the ISA77 committee web site at
http://www.isa.org/MSTemplate.cfm?MicrositeID=248&Com
mitteeID=4710.
By ISA67 Committee Chair Bob Queenan
ISA67 is responsible for all ISA nuclear plant instrumentation and
control standards and last met on June 5th at the annual POWID
Symposium. There were not enough voting members present to
constitute a quorum, so no official business was conducted. No
changes in membership were proposed. SP67.01 on Transmitter
and Transducer Installation met at the symposium. Bill Barasa, the
chair, reported that the standard will need minor revisions to address
comments and will be submitted for ballot this year. SP67.02 on
Sensing Lines and Tubing also met at the symposium. Klemme Herman,
the chair, reported that the committee is working on a new draft and
intends a first ballot this year. SP67.03 on RCS Leak Detection did not
meet. Tim Hurst reported that the committee was staffing up, but
that there was no clear technical success path for reliably detecting a
one gpm leak in all circumstances. More work is needed before a draft
can be prepared. SP67.04 on Setpoints met as well; Pete Vandevisse
sat in for Jerry Voss, the chair. The recommended practice was issued
and no other significant items were raised. SP67.06 on Performance
Monitoring does have an active subcommittee that is addressing
comments to the current standard and intends to ballot a revision
this year. The meeting was adjourned with no new business being
brought forward. More information about the ISA67 Committee and
its activities can be found at the committee website at: http://www.
isa.org/MSTemplate.cfm?MicrositeID=212&CommitteeID=4674.
Please consider getting involved today!
53

What`s Watt

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