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TOPICAL REPORT NUMBER 25 SEPTEMBER 20

Power Plant Optimization

Demonstration Projects

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Cover Photos:

Top let: Coal Creek Station

Top right: Big Bend Power Station

Bottom let: Baldwin Energy Complex

Bottom right: Limestone Power Plant

A report on our projects conducted under separate cooperative

agreements between the U.S. Department o Energy and:

Great River Energy

Tampa Electric Company

Pegasus Technologies

NeuCo. , Inc.

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Power Plant Optimization

Demonstration Projects

Executive Summary .......................................................................................4

Background: Power Plant Optimization ......................................................5

Lignite Fuel Enhancement Project ...............................................................8

Introduction ......................................................................................................... 8

Project Objectives ................................................................................................. 9

Project Description ............................................................................................. 10

Benefts ............................................................................................................... 11

Neural Network-Intelligent Sootblower Optimization Project ..................11

Introduction ........................................................................................................ 11Project Objectives ............................................................................................... 12


Results................................................................................................................. 14

Conclusions ........................................................................................................ 14

Mercury Specie and Multi-Pollutant Control Project ...............................15

Introduction ........................................................................................................ 15

Project Objectives ............................................................................................... 16


Anticipated Benefts ........................................................................................... 19

Demonstration o Integrated Optimization Sotware at the Baldwin

Energy Complex Project ..............................................................................19

Introduction ........................................................................................................ 19Project Objectives ............................................................................................... 20


Anticipated Benefts ........................................................................................... 22

Conclusions ................................................................................................. 23

Bibliography .................................................................................................24

Acronyms and Abbreviations .......................................................................26

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Executive Summary

The Clean Coal Technology Demon-

stration Program (CCTDP) and the two

ollowing programsthe Power Plant

Improvement Initiative (PPII) and the

Clean Coal Power Initiative (CCPI)

are government and industry co-unded

programs. The goal o these programs is to

demonstrate a new generation o innovative

coal-utilization technologies in a series

o projects carried out across the country.

These demonstrations are conducted ona commercial scale to prove the technical

easibility o the technologies and to

provide technical and nancial inormation

or uture applications.

A urther goal o these programs is to

urnish the marketplace with a number

o advanced, more eicient coal-based

technologies that meet increasingly strict

environmental standards. These technologies

will help mitigate the economic and

environmental barriers that limit the ull

utilization o coal.

To achieve these goals, beginning in 1985

a multi-phased eort has been administered

by the U.S. Department o Energy (DOE)

National Energy Technology Laboratory

(NETL). The CCTDP, the earliest program,

initiated ve separate solicitations. The next

program, the PPII, sent out one solicitation,

and the CCPI has had two solicitations to

date. The projects selected through these

solicitations have demonstrated technology

options with the potential to meet the needs

o the energy markets while satisying

relevant environmental requirements.

This report describes our projects

aimed at improving or optimizing the

perormance o coal-red power plants. All

our projects are being conducted under the

CCPI and PPII programs. The rst project

deals with upgrading high moisture lignite

by partial drying to enhance its quality

and improve overall plant perormance.

The remaining three projects involve the

development o sotware that optimizes

overall power plant perormance or some

aspect o perormance by incorporating

eatures o articial intelligence (AI), a

decision-making capability that simulates

the human brain.

The Lignite Fuel Enhancement proj

is demonstrating improved plant per

mance by using waste heat to partia

dry lignite, which is normally high

moisture.

The Neural Network-Intelligent So

blowing (NN-ISB) project with t

Tampa Electric Company (TECO) B

Bend Power Station was intended

demonstrate improved eciency a

lower emissions o nitrogen oxid

(NOX) by using a computer-based neu

network to determine when sootblowi

is needed.

The Mercury Specie and Multi-Pollut

Control project with Pegasus Techno

gies is demonstrating the capability

optimize mercury speciation and cont

o emissions rom an existing power pl

using state-o-the art sensors and neu

network-based optimization sotware

NRG Texass Limestone Station.

The Demonstration o Integrated Optim

zation Sotware project at Dynegy Midw

Generations Baldwin Energy Compl

where NeuCo, Inc., is demonstrating t

integration o ve separate optimizati

computer programs to optimize over

power plant operation.

Great River Energys Coal Creek Station

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Background: Power

Plant Optimization

Overall optimization o a coal-red

power plant is a highly complex process.

One must rst decide what constitutes

optimal perormance. Obvious answers

include maximum thermal eciency,

lowest possible emissions, lowest possible

cost, readily marketable by-products, and

maximum system availability or power

generation. In reality, these goalsand

othersare interrelated. In some cases,however, these optimization goals are at

odds with each other. For example, high

excess air will result in better carbon

burnout and less carbon monoxide but will

also result in higher emissions o nitrogen

oxides (NOX). These interactions must

be kept in mind and addressed with any

optimization program.

There are a number o relatively xed

items that aect overall plant operation.

These include boiler design, cooling water

conditions, burner type, design steam

conditions, and environmental control

systems that capture and remove particulate

matter, sulur dioxide (SO2), NOX, and

mercury. Coal quality is also a major

actor that aects plant perormance. High

moisture and/or ash content decreases

eciency and increases wear and power

requirements on the pulverizers. High

sulur content results in more reagent

consumption and increased by-product

generation.

The benets o optimizing the overall

process o generating power rom coal are

signicant. Eciency is increased, total

maintenance costs are reduced, emissions

are decreased, and reliability is improved.

While the greatest benet can be achieved by

optimizing the overall operation, important

benets can also be achieved by optimizing

one or more o the actors that contribute to

the overall eciency o the plant.

Many optimizations can yield substantial

positive results. For instance, use o lignite

and sub-bituminous coals, which are high

in moisture, lowers the boiler eciency,

increases the load on the pulverizers, and

increases fue gas volume. Drying the coal

beore it is ed to the preparation system is

generally not practical due to the energy

required. Switching to a higher quality

coal, even i available, is oten not practical

either due to cost or to the act that a boiler

designed or a specic coal may not unction

as well with other coals. I such a switch

is made, the unit may need to be de-rated.So, i drying the coal can be economically

integrated into the overall power plant

process, potential benets are substantial.

There are several obvious systems that

can be optimized independently and result

in better perormance. Some involve simply

upgrading a specic piece o equipment.

Power Plant OptimizationDemonstration ProjectsTopical Report Number 25

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For example, a reurbished steam turbine

will improve heat rate and result in less uel

consumption per megawatt (MW). The

cost o electricity is then reduced, as are the

emission rates o some pollutants.

In some cases, optimizing one aspect o

boiler operation can have several benets.

For example, during boiler operation, ash

slowly builds up on boiler tubes. This causes

reduced heat transer to the boiler eed water

and steam which results in lower eciency

and higher NOX emissions. The buildup is

removed by blowing it o the tubes with

high-pressure steam. But when sootblowing

occurs, the electrostatic precipitator (ESP),

or baghouse, is temporarily overwhelmed

by the high particulate load at the inlet.

Sootblowing is traditionally done on a

set schedule rather than as needed. Thisresults in some boiler sections accumulating

excessive ash on the tubes while others,

having little ash buildup, are serviced when

it is not required. Optimized sootblowing

can solve these problems by using sensors

and articial intelligence (AI) sotware

to determine when a particular section o

the boiler needs to have the ash removed

rom the tubes, thus minimizing steam

consumption (and improving heat rate),

reducing the requency o a high particulate

load in the fue gas, and reducing NOX

ormation.

As one would expect, optimizing the

operation o multiple components normally

gives better results than optimizing one

aspect o the operation. Maximizing the

overall perormance o multiple pieces

o equipment does not normally have an

adverse eect on the other power plant

components. However, when using AI/

neural network systems to optimize multiple

aspects o power plant operation, care must

be taken to consider the possible negative

impact on other parameters. This can bestbe accomplished by designing the sotware

packages to communicate with each other

through a management sotware package.

This document describes our opti-

mization projects within the PPII and

CCPI programs. The ollowing are brie

descriptions o the our projects:

In the Lignite Fuel Enhanceme

project, Great River Energy has installed

ull-scale prototype dryer module to supp

one-sixth o the coal required or a 546 M

unit. Results to date have shown improv

perormance in overall operation o t

unit. In the next phase o this project, GrRiver Energy will design, construct, a

perorm ull-scale, long-term operation

testing on a complete set o dryer modu

to supply all the coal needed or the

operation o this unit.

The Neural Network-Intellige

Sootblower (NN-ISB) project with

Tampa Electric Company (TECO) Big Be

Power Station is complete. This proj

showed that the concept o using a neu

network system to optimize the sootblow

process is sound but that additiondevelopment and better equipment

needed. Mechanical problems with sens

and water cannons were encountered a

overall results were aected by these issu

However, some benet was obtained w

respect to stack opacity and nitrogenoxid

reduction.

In the Mercury Specie and Mul

Pollutant Control project, Pegas

Technologies will utilize state-o-th

art sensors and neural-network-bas

optimization and control technologies maximize the proportion o mercury spec

that are easy to remove rom the boi

fue. This project will demonstrate h

integrating sensors, controls, and advanc

analysis techniques into multiple ac

o plant operation can lead to improv

economics and environmental complianc

With the Demonstration of Integrat

Optimization Software project

Dynegy Midwest Generations Baldw

Energy Complex, NeuCo, Inc.,

integrating and optimizing their sotwaSCR-Opt, CombustionOpt, SootOpt

PerormanceOpt, and MaintenanceOpt

ProcessLink is the integration sotw

that coordinates these programs to achie

overall plant goals. The project is ongoi

as o this time and results to date appe

promising.

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Artifcial Intelligence

Articial intelligence (AI) is commonly dened as the science and engineering o makingintelligent machines, especially intelligent computer programs. Relative to applications with coal-red power plants, AI consists o aspects or considerations that deal with the ollowing:

Neural networks, which mimic the capacity o the human brain to handle complex nonlinearrelationships and learn new relationships in the plant environment

Advanced algorithms or expert systems that ollow a set o pre-established rules written in

codes or computer language

Fuzzy logic, which involves evaluation o process variables in accordance with approximate

relationships that have been determined to be suciently accurate to meet the needs o plantcontrol systems

Neural networks (NNs) are a class o algorithms that simulate the operation o biologicalneurons. The NN learns the relationships between operating conditions, emissions, andperormance parameters by processing the test data. In the training process, the NN developsa complex nonlinear unction that maps the system inputs to the corresponding outputs. Thisunction is passed on to a mathematical minimization algorithm that nds optimum operating

conditions.

NNs are composed o a large number o highly interconnected processing elements that workin parallel to solve a specic problem. These networks, with their extensive ability to derivemeaning rom complicated or imprecise data, can be used to extract patterns and detect trendsthat are too complex to be detected by either humans or other computer techniques. NNsare trainable systems that can learn to solve complex problems and generalize the acquiredknowledge to solve unoreseen problems. A trained NN can be thought o as an expert in thecategory o inormation it has been given to analyze. NNs are considered by some to be bestsuited as advisors, i.e., advanced systems that make recommendations based on various types odata input. These recommendations, which will change as power plant operations change, suggestways in which plant equipment or technologies can be optimized.

Advanced algorithms, on the other hand, are programmed to incorporate establishedrelationships between input and output inormation based on detailed knowledge o a specic

process. They are used by computers to process complex inormation or data using a step-by-step,problem-solving procedure. In particular, genetic algorithms provide a search technique to nd trueor approximate solutions to optimization problems. These algorithms must be rigorously denedor any computational process since an established procedure is required or solving a problem ina nite number o steps. Algorithms must tell the computer what specic steps to perorm andin what specic order so that a specied task can be accomplished. Advanced algorithms are nowpart o the sophisticated computational techniques being successully applied to power plants toincrease plant eciency and reduce unwanted emissions.

Fuzzy logic (FL), the least specic type o AI sotware, is equipped with a set o approximate rulesused whenever close enough is good enough. Fuzzy logic is a problem-solving control-systemmethodology that has been used successully with large, networked, multi-channel computers orworkstation-based data-acquisition and control systems. FL can be implemented via hardware,sotware, or a combination o both. Elevators and camera auto-ocusing systems are primaryexamples o uzzy logic systems. Fuzzy logic stops an elevator at a foor when it is within a certainrange, not at a specic point.

FL has proven to be an excellent choice or many control system applications since it mimicshuman control logic. By using an imprecise but very descriptive language, FL deals with input datamuch like a human operator. FL is very robust and provides a simple way to arrive at a deniteconclusion based upon vague, ambiguous, imprecise, or missing input inormation. However, whilethe FL approach to solving control problems mimics human decision-making, FL is much aster. TheFL model is empirically based, relying on operator experience rather than technical understandingo the system.

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Lignite Fuel

Enhancement Project

Introduction

The use o low-rank coals (lignite and

subbituminous) has seen a signiicant

increase in recent years. Because o the

low sulur content o such coals, many units

have adopted uel switching to meet sulur

emissions specications, and other units

have been built specically to burn low-

rank coals. However, a major disadvantage

o low-rank coals is their high moistu

content, typically 25 to 40 percent. Wh

such coal is burned, considerable ener

is required to vaporize the moisture

contains, thus raising the heat rate o

power plant and lowering its eciency.

Fuel moisture has many eects on u

operation, perormance, and emissions.

uel moisture decreases, the uels heati

value increases so that less coal needs to

red to produce the same electric pow

thus reducing the burden on the co

handling system. Drier coal is easier

convey as well, which reduces maintenan

costs and increases availability o the co

to the handling system. When the crush

coal is gravity-ed into bunkers, the dr

coal fows more readily than the wet co

causing ewer eed hopper bridging aplugging problems. Drier coal is easier

pulverize as well so that less mill power

needed to achieve the same coal nene

Finally, with less moisture in the uel mo

complete drying o coal can be achiev

in the mill, which results in an increas

mill exit temperature, better conveying

coal in the coal pipes, and ewer coal pi

plugging problems.

The mixture o pulverized coal a

air rom the pulverizers is combusted

the burners. With drier coal, the fatemperature is higher since there is le

moisture to evaporate. At the same tim

heat transer processes in the urnace

modied. The higher fame temperatu

results in a larger radiation heat fux to t

urnace walls. Also, drier coal results

less moisture in the fue gas, which chang

the radiation properties o the fame. T

change in the fame emissivity also ae

the radiation fux to the wall. With a high

fame temperature, the temperature o c

ash particles is correspondingly high

which could increase urnace ouling aslagging, reducing heat transer and resulti

in a higher fue gas temperature at the urna

exit. However, the reduction in coal fow r

as uel moisture is reduced also reduces t

amount o ash entering the boiler, which lea

to less solid-particle erosion in the boiler a

decreased boiler maintenance cost.

The Clean Coal Technology Program

The DOE commitment to clean coal technology development hasprogressed through three phases. The rst phase was the Clean CoalTechnology Demonstration Program (CCTDP) , a model o government andindustry cooperation that advanced the DOE mission to oster a secure andreliable energy system. With projects completed, the CCTDP has yieldedtechnologies that provide a oundation or meeting uture energy demandsthat utilize the vast U.S. reserves o coal in an environmentally sound manner.Begun in 195, the CCTDP represents a total investment value o over$.25 billion. The DOE share o the total cost is about $1.0 billion, orapproximately 0 percent. The project industrial participants (non-DOE)

have provided the remainder, nearly $2 billion.

Two programs have ollowed that have built on the successes o theCCTDP. The rst is the Power Plant Improvement Initiative (PPII), a cost-shared program patterned ater the CCTDP and directed toward improvedreliability and environmental perormance o the nations coal-burning powerplants. Authorized by the U.S. Congress in 2001, the PPII involves veprojects that ocus on technologies enabling coal-red power plants to meetincreasingly stringent environmental regulations at the lowest possible cost.Four projects have been completed and one is still active. The total value othese projects is $71.5 million, with DOE contributing $1.5 million or .percent.

The second program is the Clean Coal Power Initiative (CCPI), alsopatterned ater the CCTDP. Authorized in 2002, the CCPI is a 10-year

program having a goal o accelerating commercial deployment o advancedtechnologies to ensure that the nation has clean, reliable, and aordableelectricity. Total Federal unding will be up to $2 billion, with a matchingcost share by industrial participants o at least 50 percent. To date, twosolicitations have been completed and nine projects have been awarded orare in negotiation. These projects have a total value o approximately $2.billion. The DOE share is $5 million or 19.9 percent .

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The fue-gas fow rate rom a urnace

ring dry coal is lower than one ring wet

uel, and the specic heat o the fue gas

is lower due to its lower moisture content.

A lower fue gas fow rate also results in

a lower rate o convective heat transer.

Thereore, despite an increase in initial fuegas temperature with drier uel, less heat

will be transerred to the water or steam in

the boiler convective pass.

Drier coal is expected to lower the

temperature o lue gas leaving the

economizer and air preheater (APH).

APH perormance will also be aected by

changes in the ratio o air and fue gas fows

through the APH and changes in specic

heat. Improved overall process eciency

will result rom drier coal as the auxiliary

power decreases due to decrease in orceddrat, induced drat, and primary air an

power as well as decrease in mill power.

Previously, a number o proposals have

been advanced to dry low-rank coals prior

to combustion, but none o these eorts

has resulted in a successul commercial

operation. The two major problems with

drying schemes beore this have been the

cost o the energy required and the act that

low-rank coals become pyrophoric when

dried beyond a certain point. The Great

River Energy Lignite Fuel EnhancementProject overcomes these problems by using

waste heat to dry the coal and removing only

about 25 percent o the moisture, enough to

appreciably improve plant perormance but

not enough to cause handling problems.

Project Objectives

The objective o this project is to

demonstrate an economic process o

moisture reduction o lignite, thereby

increasing its value as a uel in powerplants. The project is being conducted

at the Great River Energys Coal Creek

Station in Underwood, North Dakota. The

demonstration activities ocus on using low

grade condenser waste heat and fue gas

in the plant to lower the moisture content

o the coal by about 10 percentage points

Aerial view o Great River Energys Coal Creek Station lignite power plant

(e.g., reduce the lignite moisture rom 40

to 30 percent). A phased implementation

is planned: In the rst phase, a ull-scale

prototype dryer module was designed or

operation o one o the pulverizers on one

o the two 546 MW units at the Coal Creek

Station.

The objectives o prototype testing were

to gain operating experience, conrm pilot

results, and determine the eect o air fow

rate, bed coils, bed depth, and coal eed

rate on dryer operation in order to optimize

perormance. The lessons learned rom the

prototype were incorporated into the design

o the dryers being installed in the second

phase. A total o our dryers will be built

or Unit 2. Although operating with wet

lignite requires seven pulverizers, six will

provide all the dried lignite required by theboiler.

Following successul demonstration

in the rst phase, Great River Energy is

designing and constructing a ull-scale,

long-term operational test on a complete

set o dryer modules needed or ull power

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10

operation o one 546 MW unit (our

dryers). The coal will be dried to a number

o dierent moisture levels. The eect

o coal drying on plant perormance will

be measured with respect to increase in

plant eciency and availability, reduction

in emissions, and improvements in plant

economics. The dryer design and operating

conditions will be determined or optimal

plant perormance.

Project Description

In response to the rst round o the Clean

Coal Power Initiative, Great River Energy

(GRE) submitted a proposal or a ull-scale

test o a lignite-drying technology that

they had been developing since the 1990s.

The previous work included bench-scale

research and development, eld trials, andpreliminary drying studies. These studies

convinced GRE o the technical easibility

and economic benets o lignite drying and

prompted the submittal o their proposal.

The Department o Energy evaluated and

selected their proposal, and a cooperative

agreement was awarded on July 9, 2004.

The project team or the Lignite Fu

Enhancement Project consists o GR

participant and site provider; the Elect

Power Research Institute, collaborat

Lehigh University, collaborator; B

Engineering, lignite handling; and Falk

Mining and Couteau Properties, lignsupplier. The project is sited at GREs C

Creek Station in Underwood, North Dako

Coal Creek Station is a mine-mouth pla

burning approximately seven million to

o lignite per year and consisting o t

546 MW, tangentially red Combusti

Engineering boilers. Steam is produc

at 2,400 psig and 1,000 oF with a 1,000

reheat temperature. The Coal Creek stati

has eight pulverizers per unit (seven acti

and one spare). The station has two sin

reheat General Electric G-2 turbines.

The gure at let provides a simpli

fow diagram o the lignite drying proce

Warm cooling water rom the turbi

exhaust condenser goes to an air hea

where ambient air is heated beore being se

to the fuidized bed-coal dryer. The cooli

water leaving the air heater is returned

the cooling tower. A separate water strea

is passed through coils in the fuidiz

bed-coal dryer (a two-stage dryer is us

to enhance heat transer). The purpose

these coils is to provide additional heat

the fuidized bed to reduce the amount air required. The dried coal leaving t

fuidized bed is sent to a pulverizer a

then to the boiler. Air leaving the fuidiz

bed is ltered beore being vented to t

atmosphere.

The technical aspects o the proj

are being implemented in two phas

The rst phase involved the constructi

and operation o a prototype dryer, a u

sized dryer with a maximum capacity

112.5 tons/hour (225,000 lb/hour). It w

designed to reduce the moisture conte

o lignite rom 38 percent to 29.5 perce

and improve the higher heating value ro

6,200 Btu/lb to 7,045 Btu/lb. The prototy

unit was ully automated and integrat

into the plant control system. The rst c

was introduced into the prototype dryer

January 30, 2006, and perormance testi

was carried out in March and April 2006

Schematic o Lignite Coal Drying Using Waste Heat From Condenser Water and Flue Gas

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Benefts

Firing drier coal results in improved

boiler eiciency and unit heat rate,

primarily due to lower stack loss and lower

auxiliary power (lower an, pulverizer,

cooling tower, and coal handling power).This perormance improvement will allow

greater electrical output with existing

equipment. Perormance o back-end

environmental control systems (scrubbers

and electrostatic precipitators) will also

improve with drier coal due to the lower

fue gas fow rate and longer residence time.

The reduction in required coal-fow rate and

modied temperature prole will directly

translate into lower emissions o NOX,

carbon dioxide (CO2), SO2, and particulates.

For units equipped with wet scrubbers,

mercury emissions resulting rom ringdrier coal would also be reduced. This is

due to reduced APH gas outlet temperature,

which avors the ormation o mercuric

oxide and mercuric chloride at the expense

o elemental mercury. These oxidized

orms o mercury are water-soluble and can

thereore be removed in a scrubber.

During testing o the prototype coal

dryer in 2006, at a eed rate o 75 tons/

hour (14 percent o total uel rate to the 546

MW unit), there were no major operating

problems. The moisture o the total coalwas reduced by only about 1.1 percentage

points. Yet there were signicant benets

in the prototype dryer operation or the

546 MW unit.

Perormance measures showed that

the lignite fow rate was reduced by 2

percent, pulverizer power was reduced by

3.3 percent, boiler eciency improved

0.5 percent (absolute), net unit heat rate

improved 0.5 percent, NOX emissions

decreased 7.5 percent, and SO2 emissions

decreased 1.9 percent. These results indicatethat there will be signicant improvements

in operations once the project is ully

implemented.

The potential market or GREs coal-

drying technology is quite sizeable.

There are 29 units with a total capacity

o 15.3 gigawatts (GW) that are burning

lignite directly, and another 250 units

with a total capacity o over 100 GW

burning Powder River Basin coal. I all

these units were to adopt coal drying,

the economic and environmental benets

would be quite large.

Neural Network

Intelligent-Sootblower

Optimization Project

Introduction

A neural-network-driven computer

system oers the potential to optimize

sootblowing in coal plant boilers, reduce

NOX emissions, improve heat rate and unit

eciency, and reduce particulate matter

emissions. Installed at the Tampa Electric

Company (TECO) Big Bend Power Station

in Hillsborough County, Florida, the

Pegasus Technologies neural network-

intelligent sootblowing (NN-ISB) system

was designed to be used in conjunction

with advanced instrumentation and water

cannons to prevent soot rom building up ina boiler. O the our coal-red units at the

power station, Unit No. 2 was selected or

installation o the NN-ISB control system.

Fired with bituminous coal, this wet bottom

pressurized Riley Stoker single-drum

radiant boiler has a total o 48 coal nozzles

on a single elevation, 24 on each side, ring

toward the center line o the urnace. The

nal project cost or the NN-ISB control

system (equipment/instrumentation, sotware,

testing, and reporting included) was $3.4

million, including a 27 percent Department

o Energy (DOE) cost share. Sotware costso a ew hundred thousand dollars were a

small part o the total cost. Project testing

o the NN-ISB was completed in December

2004, and the nal report on the system was

issued in September 2005.

Tampa Electric Big Bend Power Station

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12

Project Objectives

The project objective at the Big Bend

plant was to develop a neural-network-

driven system that could initiate, control,

and optimize sootblowing in response to

real-time events or conditions within the

coal-plant boiler rather than relying on

general rule-based protocols. The project

demonstrated and assessed a range o

technical and economic issues associated

with the sensing, management, display,

and human interace o sootblowing goals

as they relate to emissions and eciency

o a coal-red utility boiler. Specically,

this optimization process targeted reducing

baseline NOX emissions by up to 30 percent,increasing unit eciency by 2 percent, and

reducing particulate matter (stack opacity)

by 5 percent.

Project Description

This neural network project was

implemented under the Power PlantImprovement Initiative (PPII), a DOE

program designed to demonstrate plant

improvement technologies and processes

in commercial settings. At the time o the

award, this installation was the rst domestic

project to use neural network technology to

optimize the sootblowing process within

a boiler. Started in 2001 ater a series

o brownouts and blackouts had plagued

major regions o the country, the initiative

targeted new technologies that could help

coal plants boost their output and improve

their environmental perormance. The BigBend project was designed to be a ull-

scale demonstration o the neural-network-

driven technology on a large commercial

boiler, using state-o-the-art controls and

instruments to optimize boiler operation

and systematically control boiler slagging

and ouling.

Communications Architecture at Big Bend Power Station

In a coal-red boiler, the continui

buildup o ash and soot on the boiler tub

leads to reduced boiler eciency. I perio

ash and soot removal (sootblowing) is nperormed, this leads in turn to higher f

gas temperatures and ultimately to high

NOX ormation and reduced ecien

Thereore, cleaning the heat-absorbi

suraces is one o the most importa

boiler auxiliary operations. Typical

sootblowing uses mechanical devices

online cleaning o reside boiler ash and s

deposits on a periodic basis. Sootblow

clean by directing steam or water throu

nozzles against the accumulated soot a

ash on the heat-transer suraces in ord

to remove the deposits and maintain hetranser eciency. Basically, sootblow

consist o our components: a tube or lan

that is inserted into the boiler and carries t

cleaning medium, nozzles in the tip o t

lance to accelerate and direct the cleani

medium, a mechanical system to insert

rotate the lance, and a control system.

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Because it either uses steam that would

otherwise be used to generate electric

power or it requires energy or pumps or

compressors, sootblowing has a direct

impact on plant eciency. Thus, optimizing

sootblower operation is important in

maximizing unit eiciency. Typically,sootblowers operate on a specied timed

cycle or, alternatively, operation is initiated

by an operator who believes sootblowing

is needed. The purpose o an intelligent

sootblowing system is to decide when to

sootblow based on inormation rom boiler

instruments. The overall objective is to

sootblow when, and only when, necessary.

The Pegasus Technologies NN-ISB

control system uses a neural network to

model the characteristics o the boiler.

Designed to recognize patterns in inputdata, this network must be trained using

historical data beore it can associate a

particular pattern with a corresponding

plant state. Once this training has been

completed, the system can respond rapidly

to new inputs. An advantage o a neural

network is that i any inputs are aulty

the prediction capability degrades only

gradually compared to most other modeling

techniques.

The project installed at Big Bend Unit

No. 2 includes 16 heat fux sensors, 8 slagsensors, a heat transer advisor, acoustic

pyrometers, a sootblower control system,

an online perormance monitor (OPM),

and an advanced calibration monitor

(ACM). For the communications layout,

the combustion optimizing system and

intelligent sootblowing (ISB) sotware

were loaded into one computer. For this

application, the models were partitioned

so they could unction separately or work

interactively. This approach was important

since it permits upgrades to existing power

plants as well as applications to new boilers.Although the demonstration was carried

out on the hardware and sotware systems

developed or this project, the equipment

(including the distributed control system)

could be obtained rom any manuacturer.

Ater verication that the core elements

o the NN-ISB system were satisactorily

installed and operational, detailed model

tuning was completed. During this task,the unit was operated under a variety

o conditions, including some non-ideal

variations. This helped to dene acceptable

operating limits and constraints used by the

neural network while optimizing the system.

During system optimization, appropriate

adjustments were made to allow the system

to learn and to make recommendations

on Unit 2 operation, including both manual

or advisory (open-loop) and automatic

(closed-loop) operation. The advisory mode

provided recommendations to the operatorsand engineers, who used those results to

urther tune the system. This activity

also proved very valuable in assessing and

recording the perormance and status o the

new sensors and systems.

Pegasus Technologies NN- ISB Control System

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1

NRG Texass Limestone Power Plant in Jewett, Texas

Results

The automated closed-loop activation

o the sootblowers during this projectconrmed that neural-network, adaptive

sootblowing can benet eciency. There

was a clear improvement at low loads, with

the benet decreasing as the load increased.

During closed-loop operation o the NN-

ISB, Pegasus reported that eciency gains

were in the range o 0.1 to 0.4 percentage

points compared to baseline. Results with

open-loop operation were slightly lower.

With more operating experience, gains at

the high end o the load range should be

achievable.

NN-ISB closed-loop (automatic)

operation was shown to be better than

open-loop (non-neural network baseline)

operation. Other Pegasus results indicated

an improvement o 1.0 to 1.5 percent in

opacity or closed-loop compared to open-

loop operation during certain tests.

While it is reasonable to expect th

optimizing sootblowing would be benec

or NOX reduction (due to an improv

temperature prole in the urnace),

Big Bend project was unable to clea

demonstrate this. Supporting equipm

and material issues (e.g., unavailabilo the water cannons during the N

ISB tests, underperormance o much

the instrumentation) greatly limited

optimization sotware rom perorming

expected.

Prior to this project, sensors and contr

related to sootblowing were usually trea

as isolated systems. In contrast, the Big Be

NN-ISB system had the ability to understan

evaluate, and optimize the process as an ent

system with multiple, real-time objectiv

Integration o the sensors went well acommunication was established to the neu

network system with all sensors and eleme

o the project. The project demonstrated t

such systems can be linked together desp

the use o proprietary networks. Further

conrmed that the sensors can provide d

that can be correlated to achieve a set

objectives. Generally, the NN-ISB syst

appears to have merit and can improve boi

perormance.

Conclusions

The major conclusion rom th

project is that the Pegasus Technolog

NN-ISB control system is a sound id

with signicant potential. The Big Be

project successully demonstrated a neu

network, closed-loop operation on a u

scale boiler without causing unit ups

or violating any constraintsand it a

achieved operator acceptance. The NN-I

appears to provide generating compan

with an integrated solution that will ass

in optimal economic and environmenreal-time, online operation o a unit.

The NN-ISB is modular in design a

can be readily applied to a variety o pow

generating units. The solution architectu

and inrastructure are designed to all

ull or staged deployment, depending

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the needs o the generating company. The

technology applied throughout allows unit

fexibility (i.e., existing systems can be

integrated within the overall solution) and

is extendible (new modules/new equipment

can be readily modeled and incorporated).

In general, the project provided a testing

ground or several innovative measurement

devices and eedback on their operation

that may also lead to improved instruments.

Since some equipment and instrumentation

(e.g., water cannons, heat fux sensors,

slag sensors, and acoustic pyrometers) did

not ully operate as expected during this

testing, an additional project with improved

equipment and instrumentation may be

needed in order to ully quantiy all the

benets. Other project goals were also

achieved:

Promoted the use o coal by making

coal more uel-ecient automatically,

reducing all pollutants on a per megawatt-

hour basis. In addition, reducing NOXemissions should lower the resistance to

coal use or electrical generation.

Enabled rapid deployment into the

market. All coal-ired boilers employ

sootblowers which, in turn, require

control systems. Since current systems

cannot achieve the desired results

in sootblowing operations, a neural

network control system appears to oer

signicant advantages. Further, no new

hardware needs to be developed since the

hardware is o the shel and readily

available.

Expanded U.S. revenues through world-

wide market acceptance. The same rapid

deployment capability and acceptance by

domestic plants should apply to oshore

coal-ired boilers. Since the United

States is presently the world leader in

AI (o which the neural network system

is a subset) there should be minimal

competition rom oshore suppliers.

In summary, the project provided

valuable inormation on neural networks

and the positive results should encourage

other power plants to install these systems

to control sootblowing, improve boiler

eciency, reduce NOX emissions, and

improve other aspects o their operations.Although equipment and instrumentation

issues may have precluded the NN-ISB

project rom achieving all o its goals, the

project clearly demonstrated the validity o

using AI to control a major aspect o boiler

operation.

Mercury Specie and

Multi-Pollutant

Control Project

Introduction

Implemented under the CCPI, the proj

at the NRG Texas (ormerly Texas Genc

Limestone Power Plant in Jewett, Texas

designed to demonstrate the capability

optimize mercury speciation and cont

emissions rom an existing power pla

NRG Texas, with a generating capacity

more than 14,000 MW, has plants primar

based on ossil uels and is an importa

producer o electricity in Texas.

Perormed by Pegasus Technologi

Inc., a division o NeuCo, Inc., this demo

stration is occurring on an 890 MW util

boiler that uses 14,500 tons o coal p

day. The Pegasus technology provid

plant operators with the ability to asse

detailed plant operating parameters th

aect mercury capture eciency, over

heat rate, particulate removal, and fue g

desulurization (FGD) eciencies. The

data are also provided to a neural netwo

optimization system that controls pla

subsystems to provide the lowest possipollutant emissions, highest heat rate, a

least risk o environmental non-complian

all with minimal capital expenditure.

NRGs Limestone Power Plant

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1

Once demonstrated, this technology

is anticipated to have broad application to

existing coal-red boilers and a positive

impact on the quality o saleable by-

products such as ly ash. The project

began in April 2006, with perormance

testing targeted or December 2008. Thisestimated $15.6 million project will be

38 months in duration, with a DOE cost

share o 39 percent.

Project Objectives

On a large utility coal-ired boiler,

Pegasus Technologies is demonstrating

the ability to aect and optimize mercury

speciation and multi-pollutant control

using non-intrusive advanced sensor and

optim ization technologies. Plant-wideadvanced control and optimization systems

are being integrated into a coal-red, ste

electric power plant in order to minim

emissions while maximizing the ecien

and by-products o the plant. Advanc

solutions utilizing state-o-the-art sens

and neural-network-based optimizati

and control technologies are being usto maximize the portion o the mercu

vapor in the boiler fue gas that is oxidiz

or captured in particle and chemical bon

resulting in lower uncontrolled releases

mercury.

This neural-network-based control a

optimization system gathers data rom c

composition, combustion gas compositio

mercury species, eed rates, etc., and u

this inormation to optimize power pl

operations. The greatest advantage o neu

networks in power plants is their abilitygeneralize rom previous inormation a

develop possible similar patterns or utu

use. Such intelligent control is expected

improve mercury capture by over 40 perce

reduce NOX emissions by 10 percent, redu

uel consumption by 0.5 to 2.0 percent, a

improve operating fexibility.

Project Description

The estimated 48 tons o mercu

emitted annually by domestic coal-rpower plants is about one-third o t

total amount o mercury released annua

rom all human activities in the Uni

States. Mercury emissions take a num

o chemical ormsor speciesincludi

the pure element, as part o a gaseo

compound, or bound to particulates

fue gas. Certain mercury species, su

as mercury that is adsorbed onto fy-a

particles or bound in the FGD, are relativ

easy to remove rom fue gas. Adjust

certain parameters during combusti

can optimize the speciation process amaximize the mercury captured in parti

bonds. This results in greater capture

mercury and lower uncontrolled releases

Control System Schematic or NRG Texas Limestone Power Plant

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The NRG Texas demonstration power

plant is equipped with a tangentially-red

boiler that uses a blend o 70 percent Texas

lignite and 30 percent Powder River Basin

subbituminous coal, which are known to

emit relatively high levels o elemental

mercury under routine combustionconditions. Pegasus Technologies will

apply sensors at key locations to evaluate

the mercury species (elemental and

oxidized mercury), develop optimization

sotware that will result in the best plant

conditions to promote mercury oxidation

and minimize emissions in general, and

use neural networks to determine the

optimization conditions.

The unit is equipped with a cold-side

ESP rated at approximately 99.8 percent

particulate removal eciency and a wetlimestone FGD system rated at approximately

90 percent SO2 removal eciency. Both

devices are capable o high mercury-capture

eciency, especially when the mercury is in

an oxidized state rather than an elemental

vapor state.

Using a neural network to aect and

optimize mercury speciation and multi-

pollutant control, the non-intrusive advanced

sensor and optimization technologies will

act as a highly trained operator, making

decisions on inputs to the process bymeasuring and learning the outputs. By

using AI and simulation technologies,

Pegasus will minimize the use o raw

material resources and pollutant emissions

while simultaneously optimizing the

operating capabilities o the plant.

This project involves the installation and

demonstration o sensors and optimization

sotware in six separate technology

packages. While the modular design is

transparent to this project, it is important to

the uture marketing o this system becauseo the fexibility needed with utilities to

include or exclude a particular module

based on either the existing equipment or

budget or a specic plant. Many o the

sensors and optimizer technologies that will

be installed are utilized across the modules;

thereore, they have been included under

the module in which they are most used.

The technology packages or this project

include the ollowing:

The intelligent uel management system

(FMS): The FMS is composed o the

Pegasus Combustion Optimizer system,

the Ready Engineering CoalFusion

system, and a Sabia elemental analyzer.

The mercury specie control system:

This system includes the boiler areaoptimization, Pegasus virtual online

analyzers, and various sensors. Mercury

emissions will be measured through

continuous emission monitors.

The advanced ESP optimization

system: The ESP optimization system

is composed o a carbon-in-ash virtual

online analyzer, a carbon-in-ash sensor,

and Pegasus ESP optimization sotware.

The advanced ISB system: The ISB

system is made up o Pegasus ISBsotware. This module has been

previously demonstrated.

The advanced FGD optimization system:

The FGD System is composed o Pegasus

FGD optimization sotware.

Key locations where sensors will be applied to evaluate mercury specie s

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1

The intelligent plant (unit optimization):

This is the Pegasus i-Plant Optimization

System that will contain a simulator and

will arbitrate among the solutions or the

above systems. This system will interace

with users through a commercially

available computer.

Each technology package includes

non-intrusive sensors and the appropriate

sotware needed or data acquisition,

optimization, and integration with the

overall neural network. In using this

approach, all acets o coal-red power

plant operation will be optimized by

balancing the inputs and outputs o the plant

within a realm o multiple constraints. The

intended result is to improve the eciency

o plant operations while operating within

regulatory and commercial constraints.

During the rst o three perormance

phases, sensor installation, sotware system

design, and baseline operating metric testing

will be completed. Instruments or instrument

technology packages to be installed include

a coal elemental analyzer (part o the uel

management system), mercury sensors,

coal fow sensors, laser-based urnace gas

speciation sensors, online carbon-in-ash

sensor (located in the ESP), communications

links or data acquisition and control, and

related computers, controllers, and Pegasusoptimization products.

Baseline testing will be perormed

to establish comparative data or the

operational testing that will ollow in

Phase 3. Ater initial baseline testing,

parametric testing will be perormed to

exercise various combinations o control

variables to determine their eect on

mercury speciation and by-product

generation and to determine overall plant

perormance. These data will be used in

Phase 2 to adjust the neural network oroptimization control.

During Phase 2, sotware installation,

data communications modication, and

distributed control system modication

will be achieved. The test plan data and

historical data (i applicable) will be

evaluated to conrm that no irregularit

exist prior to model development. A

extraneous data (e.g., calibrations) a

eliminated rom the data set, operati

issues and constraints will be review

as part o urther model developme

Control models will be developed characterize the eect o control variab

on the operational characteristics o t

boiler, mercury speciation, and by-produ

generation. Models will be created th

accurately and robustly represent the ee

that changes in the unit have on the outp

to be optimized. Beore the control mod

are implemented in an online system, ofi

simulation will be perormed. The mod

will then be evaluated and demonstrat

to Limestone Power Plant operators a

engineers so their input can be used

nalize the behavior o the models.

Pegasus uses pre-designed and custo

methods or constraining the models und

various design and operational limitatio

These are dynamic constraints that fuctu

with load, number o burners in service, r

o change, etc. Ater the initial modeli

is completed, a shorter series o tests w

be conducted. These will involve setti

up operational parameters to veriy

predictive capabilities o the neural netwo

model and to assure that the model h

been properly trained. During this perithe models will be coarse-tuned. Cont

loops will rst be tested one at a time a

then as groups to deal with the individu

loop characteristics beore dealing with t

interactive characteristics.

At the end o Phase 2, a decision w

be made whether to initiate work und

Phase 3 or to conclude the project a

the successul demonstration o close

loop operability or neural networks a

controllers.

Phase 3 plans include demonstrati

and validation o all systems as well as

comparison o the test results with the proj

objectives. Extended mercury and mu

pollutant testing will be conducted. T

technology packagesthe uel managem

system, combustion and mercury cont

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system, ESP system, ISB system, FGD system,

and intelligent plant (i-Plant) systemwill

all be demonstrated during closed-loop

operation. Operator and engineering training

will also be conducted during Phase 3.

Anticipated Benefts

In this project, Pegasus Technologies and

NRG Texas are attempting to put together

all o the required best-o-class articial

intelligence and simulation technologies

to prove that mercury speciation and

multi-pollutant reduction benets can be

measured, optimized, and controlled. I

successul, Pegasus will demonstrate the

capability o sophisticated control processes

and advanced sensor technologies to

simultaneously reduce harmul emissionso mercury and increase plant eciency.

Increased control o SO2, NOX, and

particulate matter should also result, along

with a reduction in water usage. Since

these technologies are designed to control

and optimize all major acets o power plant

operations, the demonstration is expected

to provide the capability to maximize plant

eciency or electricity production while

reducing mercury emissions. This project

is also expected to address concerns that

higher mercury concentrations in existing

by-products, such as ash, may adverselyaect the commercial value o those by-

products.

This project should demonstrate an

operating environment that simultaneously

oers higher-than-average compliance with

environmental requirements and better

control o emissions, resulting in both a

smaller risk o non-compliance to the utility

and minimization o capital expenditures.

In general, the project is expected to

demonstrate how integrating sensors

and advanced controls into a total plantsolution can lead to improved economics

while being environmentally compliant.

The technologies being demonstrated are

expected to have widespread application

since they can be directly retrotted to the

existing coal feet or integrated into uture

new plant designs.

Demonstration

o Integrated

Optimization Sotware

at the Baldwin EnergyComplex Project

Introduction

As part o the CCPI, sophisticated

computational techniques are being applied

to an Illinois coal-red power plant to show

how new technology can increase power

plant eciency and reliability and reduce air

emissions. NeuCo, Inc., o Boston, MA, is

designing and demonstrating an integratedonline optimization system at the Dynegy

Midwest Generation power plant located

in Baldwin, IL. The Baldwin Energy

Complex (BEC) consists o two 585 MW

cyclone-red boilers with selective catalytic

reduction (SCR) and a 595 MW tangentially

red boiler with low NOX burners.

Dynegy Midwest Generations Baldwin Energy Complex

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20

The ve system optimization modules

being developed include cyclone combustion,

sootblowing, SCR operations, overall unit

thermal perormance, and maintenance

optimization. This project builds on the

NeuCo proprietary ProcessLink technology

platorm. Power plants operate in manydierent conditions and plant processes are

highly complex and interrelated. The goal

o optimization is to continuously assess and

adjust (or provide actionable advice about) the

settings o the many variables aecting plant

perormance so that the optimal balance o

plant emissions, uel eciency, capacity, and

reliability is achieved. The total cost o this

45-month project is estimated at $19 million,

including a 45 percent DOE cost share.

Project Objectives

The overall objective o applyi

integrated optimization sotware is

improve the emissions prole, ecien

maintenance requirements, and pla

asset lie or coal-based power generatiin order to extend the use o abund

coal resources in the United States in

environmentally sound manner. In gener

sotware optimization oers seve

advantages to power plants, includi

the ability to control key parameters on

consistent basis, compensate or chang

in coal quality, optimize controls to m

specic plant objectives, and help

understanding the available data and

use or improved operations. The proj

at BEC will demonstrate and quant

the environmental and emissions beneassociated with deployment o a u

integrated set o sotware solutions

optimization o plant perormance or co

red power generation.

Because retrots, repowering, mod

cations, and new technologies are stead

increasing the complexity o modern pow

plants, an integrated process-optimizati

approach is required to maximize equipm

perormance and minimize operating cos

Optimization solutions are now availab

or a variety o power plant control systemlinking these systems together will provi

overall plant-level optimization that

expected to yield additional benets.

Thereore, the primary objective

this project is to demonstrate integrati

o existing controls and control system

sensors, and computer hardware w

advanced optimization techniques at BE

and to link the individual optimizati

modules through the NeuCo ProcessLink

platorm. Collectively, these modules

expected to provide optimization solutioor this 1,765 MW coal-red power pla

by reducing emissions, increasing pla

eciency, and increasing the availability

the plant or power generation.

The Baldwin Energy Power Plant at sunset

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Project Description

NeuCo is designing and demonstrating

an integrated online optimization sotware

system or the Dynegy Midwest Generation

power plant using advanced computational

techniques that are expected to achieve peakperormance rom the three coal-red units

at the energy complex. NeuCo is using its

ProcessLink technology platorm o neural

networks, expert systems (heuristics), rst

principle models, advanced algorithms, and

uzzy logic to maximize perormances rom

the power plant combustor, soot removal

system, and emission controlsthe rst time

that all o these modules have been integrated

into a computerized process network.

Five separate modules are being

designed and demonstrated by NeuCo atBEC and then integrated to provide unied

plant optimization.

CombustionOpt uses neural network-

based optimization, model predictive

control, and other technologies to

extract knowledge about the combustion

process, determine the optimal balance

o uel and air fows in the urnace,

and respond to changing conditions.

CombustionOpt directly adjusts the

distributed control system to more

consistently position the dampers, burner

tilts, overre air, and other controllable

parameters at their optimal settings or

given sets o conditions, objectives, andconstraints. This module should reduce

uel consumption and NOX emissions; it

should also improve carbon monoxide

control, reduce opacity, and improve loss

on ignition.

SootOpt optimizes sootblowing to

reduce adverse ouling conditions

and unplanned outages that soot and

sootblowing can cause. It also expands

Planned Integration Concept or the Baldwin Energy Complex

eiciency improvements, reduc

emissions, and drives boiler-cleani

actions toward optimal plant heat ra

emissions, and reliability goals. T

SootOpt adaptive neural network mod

identiy the equipment and actions m

eective or achieving plant ecienreliability, and emissions objectiv

and then bias control activity towa

those objectives. The neural mod

work within boundaries dened

expert rules to ensure all applicable un

specic constraints are considered.

SCR-Opt uses neural-network-bas

optimization, model predictive contr

and other technologies to make t

operation o the SCR as eicient

possible. SCR-Opt is expected

minimize ammonia usage and reduNOX emissions.

PerormanceOpt uses a rigoro

rst-principles-based thermodynam

model o the boiler and steam cyc

to conduct both what is and wh

i simulations o unit operatio

Continuously monitoring the actu

versus the expected perorman

levels o key equipment and proce

Control Room at the BaldwinEnergy Complex

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22

conditions, PerormanceOpt detects

when perormance deviates rom what

is achievable under current operating

conditions and calculates the impact o

that deviation so that remedial actions

can be prioritized. PerormanceOpt is

expected to improve heat rate, steamtemperatures, and generating capacity.

MaintenanceOpt helps engineers

manage the entire lie-cycle o reliability,

capacity, and eciency problems more

eciently and eectively. It uses neural

network technology to constantly search

or gaps between actual and expected

behavior across a broad range o

process and equipment health variables.

Its powerul diagnostics knowledge

base also helps to lter alse alarms,

determine the root causes and correctiveactions o identied problems, and aid

in problem resolution and tracking.

MaintenanceOpt is expected to increase

annual power output and assist in

providing lower costs o electricity to the

consumer.

These advanced computational capa-

bilities will be used to comprehensively

optimize a variety o systems within BEC by

using existing control technologies and then

linking these systems to each other. This

innovative project will provide solutionsthat use system-speciic optimization

applications as data sources and actuators.

In general, the overall architecture o this

control platorm is designed to permit

lexible deployment strategies. Rather

than requiring that all data and logic reside

on a single computer, the service model

allows applications to leverage networked

computational resources. The application

architecture is built around interoperable

services that should result in more ecient

plant operations. The planned integration

concept is shown below.

This integrated optimization sotware

project at BEC consists o two phases.

Phase I, which has been completed, entailed

the development and installation o initial

versions o each o the ve optimization

modules, as well as their integration through

the ProcessLink platorm to address theull scope o plant operations and relevant

system interactions.

Extensive operating experience will be

required to quantiy the benets associated

with control system optimization. The goal

during Phase I was to establish each system

and demonstrate its role in unied plant

optimization. Phase I activities ocused

on developing, deploying, integrating,

and testing prototypes or each o the ve

optimization modules; identiying and

addressing issues required or the modulesto integrate with plant operations; and

systematically collecting and assimilating

eedback to improve subsequent module

releases.

The goal o Phase II is to improve

upon the sotware installed and tested in

Phase I and to perorm rigorous analysis

o operating data in order to quantiy the

benets o the integrated system. Phase

II entails quantication o results at BEC;

renement o the sotware installed and

demonstrated in Phase I to support additionalcommercial releases o the ve products;

installation and beta testing at BEC; and

commercialization o the solutions, taking

into account both what is learned at BEC

and eedback systematically incorporated

rom other operators o U.S. coal-red

power generation plants.

During both phases, best practices

iterative sotware development methods

will be applied toward integration,

ull-scale demonstration, and eventual

commercialization o these ve solutions. Allsystem sotware engineering, applications

engineering, and systems integration will

proceed through a multi-step, iterative

process that supports a structured, modu

approach to determining sotware a

hardware requirements and unction

denitions along with various desig

development, test, installation, and start

activities. This iterative developme

process is specically designed to depla commercially viable product as soon

possible, while at the same time applyi

host-site eedback and what is learn

toward maximizing unctionality a

benets in subsequent releases.

Anticipated Benefts

This optimization initiative is expect

to reduce NOX emissions by 5 percent a

increase thermal eciency by 1.5 perce

The increased thermal eciency is expectto reduce emissions o CO2, mercury, a

particulates. Ammonia consumption

expected to be reduced by 15 perce

accompanied by a one-year extension o t

SCR catalyst. The optimization initiat

is expected to also result in improv

power plant capacity and reliability whic

in turn, is expected to increase net annu

electrical power production by 1.5 perce

Consumers should benet through low

electricity costs.

The NeuCo ProcessLink architectuoers plant operators a highly fexib

control system platorm. Optimizat i

modules can be designed and applied

individual subsystems in a plant, leveragi

existing sensors, actuators, and network

computational resourcesand then linki

them to other individual subsystems

provide overall plant-level integration

controls responsive to plant operator a

corporate criteria. This integrated proc

optimization approach will likely be

important tool or plant operators as pla

complexity increases through retrot arepowering applications, installation o n

technologies, and plant modications.

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Conclusions

The U.S. Department o Energy Clean

Coal Technology programs continue to

sponsor projects that develop technologiesor optimizing power plant operation.

These technologies help keep the cost

o electricity low, reduce emissions, and

conserve our uel supply.

The Lignite Fuel Enhancement project

demonstrates a technology that reduces

the moisture content o low-rank coals,

which results in a number o benets to

power plant operation. Coal consumption

is reduced, thereby reducing CO2 and other

emissions. Parasitic power requirements

are also reduced. When the technology isapplied to new plants, capital costs will be

reduced in several major subsystems, such

as SO2 removal, pulverizers, and cooling

towers. This technology achieves these

benets using only waste heat to remove the

moisture rom the uel.

While the Neural Network-Intelligent

Sootblower project did not reach all o its

goals, the NN-ISB control system was ound

to be a sound idea with signicant potential.

The project successully demonstrated a

neural network, closed-loop operation on aull-scale boiler without causing unit upsets

or violating any constraintsand also

achieved operator acceptance.

Although not yet completed, the

Mercury Specie and Multi-Pollutant

Control project and the Demonstrationof Integrated Optimization Software

project have demonstrated the ability o NN

and AI technologies to provide signicant

economic, operational, and environmental

beneits to power plant operation. The

demonstrated technologies are applicable

to all types o coal-red boilers and do not

require the purchase o major equipment.

Given the benets and relatively low cost,

these types o technologies are likely to nd

a ready market.

The continuing development o sot-ware to control overall power plant

operation, or selected aspects o it, has

shown substantial progress in optimizing

power plant perormance. These sotware

packages allow operators to more easily

stay within their emission limits while

improving power plant eiciency and

lowering the cost o power production.

While doing so, the participants have

produced products that are expected to

nd a substantial market both in the United

States and abroad.

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Bibliography

Bullinger, Charles, Mark Ness, and Nenad Sarunac. Coal Creek Prototype Fluidized

Bed Coal Dryer: Perormance Improvement, Emissions Reduction, and Operating

Experience, Paper presented at 31st International Technical Conerence on Coal

Utilization and Fuel Systems, Clearwater, Florida, May 21-25, 2006.

Gollakota, Sai. Great River Energy Project Benets Presentation. Paper presented on

February, 2007.

Great River Energy. Great River Energy Lignite Fuel Enhancement Proposal to DOE

Solicitation DE-PS26-02NT4142 8Public Abstract, August 1, 2002, http://www.

netl.doe.gov/technologies/coalpower/cctc/ccpi/proposal-pd/greabs.pd.

Ness, Mark, and Charles Bullinger. Pre-Drying the Lignite to CREs Coal Creek Statio

May, 2005.

NeuCo, Inc. Technical Progress Reports #11 to the U.S. Department o Energy, Oce o

Fossil Energy, National Energy Technology Laboratory. Cooperative Agreement DEFC26-04NT41768. September 30, 2006.

NeuCo, Inc. Technical Progress Report #12 to the U.S. Department o Energy, Oce o

Fossil Energy, National Energy Technology Laboratory. Cooperative Agreement DE

FC26-04NT41768, February 5, 2007.

Tampa Electric Company and Pegasus Technologies, Inc. Project Perormance and

Review: Neural Network Based Intelligent Sootblowing System, Tampa Electric

Company Big Bend Unit #2. April 2005

U. S. Department o Energy. Clean Coal Technology Programs: Program Update 2006,

September 2006, pp. 3-54, http://www.netl.doe.gov/technologies/coalpower/cctc/ccp

pubs/2006_program_update.pd.

U. S. Department o Energy, Oce o Fossil Energy, National Energy Technology

Laboratory. Advanced Coal Conversion Process Demonstration: A DOE

Assessment, April 2005, http://www.netl.doe.gov/technologies/coalpower/cctc/cctd

bibliography/demonstration/pds/rsbud/NETL-1217_as%20sent%20to%20OSTI.pd

U.S. Department o Energy, Oce o Fossil Energy, National Energy Technology Laborator

Big Bend Power Station Neural Network-Intelligent Sootblower Optimization.

Project Brie, Sept. 2005. http://www.netl.doe.gov/technologies/coalpower/cctc/PPI

bibliography/demonstration/environmental/neural/bigbenddemo.pd

U.S. Department o Energy, Oce o Fossil Energy, National Energy Technology

Laboratory. Big Bend Power Station Neural Network-Intelligent Sootblower

Optimization. Project Fact Sheet, Dec. 2006. http://www.netl.doe.gov/publicationsactsheets/project/Proj233.pd

Contacts or Participants

in CCT ProjectsDavid Wroblewski, Senior VP

Development

Pegasus Technologies100 Seventh Avenue, Suite 210

Chardon, OH 020-25-779

[email protected]

John McDermott, Vice President,

Product ManagementNeuCo, Inc.

200 Clarendon Street, HancockTower, T-1Boston, MA 0211

[email protected]

Charles Bullinger

Great River Energy

275 Third St., SWUnderwood, ND 557-959

[email protected]

Mark Rhode

TECO Energy

P.O.Box 111Tampa, FL 01

1-22-152

[email protected]

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U. S. Department o Energy, Oce o Fossil Energy, National Energy Technology

Laboratory. Big Bend Power Station Neural Network-Sootblower Optimization: A

DOE Assessment, DOE/NETL-2006/1234, June 2006. http://www.netl.doe.gov/

technologies/coalpower/cctc/PPII/bibliography/demonstration/environmental/neural/

BigBendSootblowerPPA_Final_061306.pd


Laboratory. Cleaner Air by the Numbers. TechLine, March 17, 2004. http://www.

netl.doe.gov/publications/press/2004/tl_ccpi_neucoaward.html


Laboratory. Demonstration o Integrated Optimization Sotware at the Baldwin

Energy Complex. Project Brie, 2006. http://www.netl.doe.gov/technologies/

coalpower/cctc/ccpi/bibliography/demonstration/environmental/ccpi_demo.html


Laboratory. Demonstration o Integrated Optimization Sotware at the BaldwinEnergy Complex. Project Fact Sheet, Dec. 2006. http://www.netl.doe.gov/

publications/actsheets/project/Proj221.pd.


Laboratory. DOE, Tampa Electric Sign Agreement to Add Intelligent Computer

System to Florida Power Plant. TechLine, August 21, 2002.TechLine, August 21, 2002. http://www.netl.doe.gov/

technologies/coalpower/cctc/PPII/bibliography/demonstration/environmental/neural/

neural_tech.pd


Laboratory. Mercury Specie and Multi-Pollutant Control. Project Brie, 2006.

http://www.netl.doe.gov/technologies/coalpower/cctc/ccpi/project_bries/CCPI_

Pegasus.pd.


Laboratory. Mercury Specie and Multi-Pollutant Control Project. Project Fact Sheet,

Dec. 2006. http://www.netl.doe.gov/publications/actsheets/project/Proj372.pd


Laboratory. Pegasus Project Selected as Part o Clean Coal Power Initiative.October 28, 2004. TechLine. http://www.netl.doe.gov/publications/press/2004/tl_

ccpi2_pegasus.html

Contacts or U.S.

Departmento Energy CCT Program

John Rockey, Project ManagerNeural Network-Intelligent

Sootblowing Optimization

National Energy TechnologyLaboratory

10 Collins Ferry RoadP.O. Box 0

Morgantown, WV 2507-00

[email protected]

Sai Gollakota, Project Manager

Lignite Fuel Enhancement

National Energy TechnologyLaboratory

10 Collins Ferry RoadP.O. Box 0

Morgantown, WV 2507-000-25-151

[email protected]

George Pukanic, Project Manager

Demonstration o IntegratedOptimization Sotware

National Energy TechnologyLaboratory2 Cochrans Mill Road

P.O. Box 1090Pittsburgh, PA 152-090

12--05

[email protected]

Michael McMillian, Project ManagerMercury Specie and Multi-Pollutant

ControlNational Energy Technology

Laboratory

10 Collins Ferry Road

P.O. Box 0Morgantown, WV 2507-000-25-9

[email protected]

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2

To Receive Additional

Inormation

To be placed on the Departmento Energys distribution list oruture inormation on the Clean

Coal Demonstration Programs,the projects they are nancing,

or other Fossil Energy Programs,please contact:

John L. Grasser, DirectorOce o Communication

FE-5/Forrestal BuildingU.S. Department o Energy

1000 Independence Ave., SW

Washington, DC 2055202-5-0

202-5-51 [email protected]

David J. AnnaOce o Public Aairs Coordination

U.S. Department o EnergyNational Energy Technology

Laboratory

P.O. Box 1090

Pittsburgh, PA 152-09012--12--195 ax

[email protected]

Acronyms and Abbreviations

ACM Advanced calibration monitor

AI Articial intelligence

APH Air preheater

BEC Baldwin Energy Complex

CCPI Clean Coal Power Initiative

CCTDP Clean Coal Technology Demonstration Program

CO2 Carbon dioxide

DOE Department o Energy

ESP Electrostatic precipitator

FGD Flue gas desulurization

FL Fuzzy logic

FMS Fuel management system

GRE Great River Energy

ISB Intelligent sootblowing

MW Megawatt

NETL National Energy Technology Laboratory

NN Neural network

NN-ISB Neural network-intelligent sootblowing

NOx Nitrogen Oxides

OPM Online perormance monitor

PPII Power Plant Improvement Initiative

SCR Selective catalytic reduction

SO2 Sulphur dioxide

TECO Tampa Electric Company

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National Energy

Technology Laboratory

1450 Queen Avenue SW

Albany, OR 97321-2198

541-967-5892

2175 University Avenue South, Suite 201

Fairbanks, AK 99709

907-452-2559

3610 Collins Ferry Road

P.O. Box 880

Morgantown, WV 26507-0880

304-285-4764

626 Cochrans Mill Road

P.O. Box 10940

Pittsburgh, PA 15236-0940

412-386-4687

One West Third Street, Suite 1400Tulsa, OK 74103-3519

918-699-2000

Visit the NETL website at:

www.netl.doe.gov

Customer Service:

1-800-553-7681

U.S. Department o Energy

Oce o Fossil Energy

Printed in the United States on recycled paper

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