Software Systems in Clean Coal

Demonstration Projects

TOPICAL REPORT NUMBER 17 December 2001

TOPICAL REPORT NUMBER 17

A report on three projects conducted under

separate cooperative agreements between:

The U.S. Department of Energy and

• CQ Inc.

• Southern Company Services

• New York State Electric & Gas Corporation

December 2001

Software Systems

in Clean Coal

Demonstration Projects

Software Systems

in Clean Coal

Demonstration Projects

Executive Summary........................................................................................ 1

Background..................................................................................................... 2

Process Optimization ...................................................................................... 2

Coal Quality ExpertTM .................................................................................... 7

Generic NOx Control Intelligent SystemTM ................................................... 12

Plant Economic Optimization AdvisorTM ....................................................... 15

Market Potential of Software Systems ........................................................... 17

Conclusions .................................................................................................... 17

Bibliography ................................................................................................... 18

Contacts for CCT Projects and U.S. DOE CCT Program .............................. 19

List of Acronyms and Abbreviations ............................................................. 20

1

Executive

Summary

The Clean Coal Technology (CCT) Demonstration Program is a government and industry co-funded effort to demonstrate a new

generation of innovative coal utilization processes in a series of facilities built across the

country. These projects are conducted on a

commercial scale to prove technical feasibility and provide the information required for

future applications.

The goal of the CCT Program is to furnish

the marketplace with a number of advanced,

more efficient coal-based technologies that

meet strict environmental standards. These

technologies will mitigate the economic and

environmental barriers that limit the full utilization of coal.

To achieve this goal, beginning in 1985, a

multi-phased effort consisting of five separate

solicitations has been administered by the

U.S. Department of Energy's (DOE) National

Energy Technology Laboratory (NETL).

Projects selected through these solicitations

have demonstrated technology options with

the potential to meet the needs of energy

markets while satisfying relevant environmental requirements.

This report discusses the development of

three computer software systems under the

CCT Program for application to the optimization of coal utilization technologies. These

systems are as follows: • Coal Quality Expert (CQETM), a program that provides detailed analyses of

the impacts of coal quality, operational

changes, and/or environmental compliance alternatives on emissions, performance, and power production costs.

• Generic NOx Control Intelligent System

(GNOCISTM), an advanced softwarebased system that optimizes boiler operation to achieve reduced NOx emissions

while improving unit performance.

• Plant Economic Optimization Advisor

(PEOATM), a system designed to assist

in meeting emissions regulations while

optimizing overall plant economic performance.

These projects incorporate aspects of artificial intelligence (AI), which involves computer-based decision-making processes that

mimic those of the human brain.

The most successful of the three computer

programs is GNOCISTM, which has achieved

significant market penetration in commercial

applications. GNOCISTM has been installed

on units representing more than 10,000 MWe

of generation capacity in both the United

States and Europe, and more installations are

planned. Work is in progress as part of the

CCT Program to further extend the capabilities of GNOCISTM to the optimization of

other aspects of power plant operation.

CQETM offers significant benefits in the

selection of coal-based fuels and in the design

and operation of coal-fired power systems. It

has the capability of predicting power plant

performance with a minimum number of benchscale tests, resulting in lower cost to achieve

the desired assessments compared with traditional approaches.

The demonstration of PEOATM at Milliken

Station was discontinued because of operating

problems. Although this particular application

of AI was not successful, the concept of using

neural networks for power plant optimization

is fundamentally sound, and future developments of this technology will undoubtedly

prove to be feasible.
2

Software Systems

in Clean Coal

Demonstration Projects

Background

The Clean Coal Technology (CCT)

Demonstration Program, sponsored by the

U.S. Department of Energy (DOE) and

administered by the National Energy Technology Laboratory (NETL), has been conducted since 1985 to develop innovative,

environmentally friendly coal utilization

processes for the world energy marketplace.

The CCT Program, which is co-funded

by industry and government, involves a series of commercial-scale demonstration

projects that provide data for design, construction, operation, and technical/economic

evaluation of full-scale applications. The goal

of the CCT Program is to enhance the utilization of coal as a major energy source.

The CCT Program has also opened a

channel to policy-making bodies by providing data from cutting-edge technologies to

aid in formulating regulatory decisions. DOE

and the participants in several CCT projects

have provided the Environmental Protection

Agency (EPA) with data to help establish

NOx emissions targets for coal-fired boilers

subject to compliance under the 1990 Clean

Air Act Amendments (CAAA).

Process

Optimization

With increasing competition and tightening emissions standards, it has become

imperative that power plant operators continually seek ways to increase efficiency

consistent with achieving environmental

goals. A number of commercial programs

have been developed to aid in attaining the

desired process optimization, using increasingly sophisticated computer-based technologies.

Instrumentation

Essential to the development of optimization systems is accurate measurement of process variables, including temperature,

pressure, flow rate, and process stream properties. In recent years, the field of process

instrumentation has seen many technical

advances in reliability and accuracy. Without precise measurement of critical variables, optimization of plant efficiency or

pollution control systems cannot be achieved.

Specifically applicable to pollution control technologies, efforts are under way to

develop instruments that measure concentra-
3

Milliken Station

tions of ammonia (NH3), nitrogen oxides (NOx),

mercury (Hg), and particulates in power plant

stack gas. The use of continuous emissions monitoring systems (CEMS) has been mandated by

EPA under the CAAA; this in turn is dependent

on accurate measurement of pollutants.

Software Development

Of equal importance is performance monitoring software to track variables such as process

temperatures and flow rates, steam conditions

and flow rates, and power production. Although

progress has been made in the development of online software for performance monitoring, such

systems have not achieved universal acceptance

in the electric power industry. This Topical Report covers development of control and optimization software as part of the CCT Program.

At the same time, some work is in progress

outside the CCT Program, such as an in-house

system developed by AEP Generating Co. and

installed at their Cardinal Station at Brilliant, Ohio.

This Windows-based system uses off-the-shelf software to track several hundred data points to permit

calculation of plant component efficiencies and overall heat rate. To improve some of the performance

calculations, instruments demonstrating higher accuracy were added to monitor key variables.

Applications of Optimization in Electric

Power Generation

Several areas involved in power generation are

candidates for process optimization. For example,

experience with CCT projects involving combustion

modification has shown a tendency toward increased

levels of unburned carbon (UBC) in power plant fly

ash. This is often expressed in terms of loss on

ignition (LOI). Because the analytical procedures for

LOI and UBC are not the same, the terms are not

strictly synonymous. However, these acronyms are

often used interchangeably, and this report follows

that practice.
4

Data Output

Axon

Data Input

Dendrite

Neuron Synapse Neuron

Processing

Unit

Processing

Unit Connector

Input Output

Axon: sends message

Transmitting

Neuron

Receiving

Neuron

Dendrite:

receives message

Synapse:

transfer connector

Biological Neural Network

Artificial Neural Network

Response Stimulus

Increased UBC increases the amount of

coal needed to generate a given amount of

electricity, thereby decreasing power plant

energy efficiency. Of equal importance is the

fact that UBC is undesirable from the standpoint of fly ash marketability. Fly ash can be

sold as aggregate for concrete, among other

things, provided that the UBC content meets

specifications. In addition, higher UBC content decreases the resistivity of the fly ash

particles, thus adversely affecting electrostatic precipitator (ESP) performance. Although UBC can be reduced by operational

changes, such as increasing coal fineness, a

significant problem still exists and is receiving considerable attention.

Experience has also indicated that combustion modification leads to difficulties in

boiler optimization. This is a result of several

factors, including heightened awareness of

the impact of combustion conditions on NOx

emissions and plant efficiency and increased

sensitivity of combustion conditions to process adjustments.

The adverse impacts of using low-NOx

burners (LNBs) can sometimes be mitigated

by burner optimization; however, these measures are typically performed at only one

operating condition and cannot fully take

into account changes in plant operating conditions over extended periods. Further, NOx

emissions can vary with time by as much as

±20% around the long-term average. A system that reduces the magnitude of these variations and drives NOx emissions down towards

the lower end of this range could help achieve

continuous compliance without additional

controls. Such a strategy could also provide

NOx credits for averaging or trading programs.

Optimization Approaches

A number of approaches to process optimization are being investigated, including

the use of artificial intelligence (AI), which

involves computer-based decision-making

processes that mimic those of the human

brain. One example of AI is neural networks,

which “learn” from experience and can adapt

to changing environments. The use of neural

networks for process optimization is a relatively recent development, and only a limited Analogy between biological neural

networks and artificial neural networks
5

Artificial intelligence (AI) encompasses several modes, including:

• Fuzzy logic, which involves

evaluating process variables in

accordance with approximate

relationships that have been

determined to be sufficiently

accurate to meet the needs of the

control system.

• Expert systems, which incorporate

existing knowledge about process

variables to provide a basis for

deciding appropriate settings for

optimum performance.

• Neural networks, which mimic the

capacity of the human brain to

handle complex nonlinear

relationships and “learn” new

relationships.

Neural networks are considered

by some to be best suited as advisors, i.e., advanced systems that

make recommendations based on

various types of data input. These

recommendations, which change as

power plant operations change, suggest how plant equipment or technologies can be optimized.

Neural networks are a class of

mathematical algorithms that simulate the operation of biological

neurons.The neural network “learns”

the relationships between operating

Artificial Intelligence

conditions, emissions, and performance parameters by processing test

data. In the training process, a neural

network develops a complex nonlinear function that maps the system inputs to the corresponding outputs.

This function is passed on to a mathematical minimization algorithm that

finds optimum operating conditions.

In the evolution of AI systems, neural networks surpass their older cousins, expert systems and fuzzy logic.

The least specific type of AI software,

fuzzy logic, is equipped with a set of

approximate rules used whenever

“close enough is good enough.” Elevators and camera autofocusing systems are primary examples of fuzzy

logic systems. Fuzzy logic stops an

elevator at a floor when it is within a

certain range, not at a specific, definite point.

Expert systems, on the other hand,

learn by following a set of preestablished rules written in codes or computer language. Expert systems are

programmed to incorporate established relationships between input

and output information based on detailed knowledge of a specific process.

Neural networks are the latest type

of AI to enter the power plant industry.

As computational devices that use organized principles of biological nervous systems, neural networks do

not assume relationships. Instead,

they determine relationships by analyzing data, learning much the way

humans do, i.e., by example and

repetition. When it assigns an incorrect output to correspond with a

given input, the network corrects itself by changing the matrices embedded in the program. While it can

take considerable time to complete

this learning curve — depending on

the complexity of the problem —

neural networks outperform any

other current approach to recognizing patterns and relationships.

Modeled after the human brain,

which is made up of interconnected

neurons that contain a limited

amount of information, neural networks are constructed of simple

data processing units with limited

capabilities. When connected together, they form the most powerful

AI system known, capable of such

performance as estimating the

lifespan of mechanical parts, diagnosing malfunctions in automobiles,

and recognizing human speech.

Most importantly, they are “smart”

systems that are beginning to mimic

the complex learning ability of the

human brain.
6

Process Optimization in Electric Power

Generating Plants

Today's competitive power plant environment has placed renewed

emphasis on process optimization. Efficient power generation must

take into account many factors including unit output, emissions, and

fuel properties as well as operating costs. Power plants embody a mix

of control problems that can be very challenging. In addition to internal

control loops, optimization involves consideration of the rapid changes

in load, including frequent startups and shutdowns resulting from close

coupling to the power grid. This variability in load in turn is exacerbated

by deregulation of the power industry.

Artificial intelligence (AI) has become a significant factor in the development of process controls. AI is being used to improve power plant operation by selection of optimum control settings to achieve the balance

required among the numerous variables involved. Neural networks are

the most advanced form of AI at present.

Application of AI depends on state-of-the-art engineering tools for

modeling and controlling multivariable systems, which in turn make extensive use of the improved computing power that has only recently become available commercially. These approaches are made possible

through utilization of the latest advances in Information Technology (IT).

number of commercial applications to power

production have been made to date. However,

neural networks represent cutting-edge technology that should see increasingly wide use in the

years ahead.

As indicated above, a typical optimization

problem in power plant operation involves minimizing NOx emissions without exceeding specifications for LOI. Reducing NOx emissions by

combustion modification, such as by use of

LNBs, inherently results in increased LOI because of reduced carbon burnout. Some power

plants have been unable to meet stringent NOx

emissions specifications without exceeding target levels of LOI required for fly ash sales. AI

has begun to provide the optimization capability

needed to meet these divergent criteria. AI is

being employed to achieve operating conditions

required to handle the tradeoffs between NOx

reduction and combustion efficiency, and it does

so in a manner beyond the capabilities of a

human operator.

Neural networks are able to model multivariate, nonlinear relationships on a continuous basis, thereby providing the necessary feedback

and control required by complex processes such

as combustion in power plant boilers. They can

provide reliable real-time recommendations

based on performance objectives provided by

the user. A further application of AI is to operate

in a supervisory mode, where control settings are

implemented through existing digital control

systems (DCS).

Software Developments

This report focuses on software developed as

part of the CCT Program, including a computer

program for predicting the effect of coal quality

on power plant performance, an AI program

initially applied to the optimization of combustion modification technologies for NOx emissions reduction, and a program designed to

achieve least-cost operation of an entire power

plant. These projects are described in the following sections.
7

Nonlinear, multivariate constrained

optimization (Pavilion Technologies

bulletin)

Coal Quality

Expert (CQETM)

Introduction

The project Development of the Coal

Quality ExpertTM was selected in December

1988 under Round I of the CCT Program.

Participants included ABB Combustion Engineering, Inc. (ABB/CE) and CQ Inc., with

co-funding from Black & Veatch (B&V),

EPRI (formerly the Electric Power Research

Institute), and The Babcock & Wilcox Company (B&W). The project was completed

and the final report was issued in June 1998.

Project Description

The CQETM project was undertaken to

develop a software product that could be

used either as a stand-alone work station or as

a network application by utilities, coal producers, and equipment manufacturers to perform detailed analyses of the impacts of coal

quality, capital improvements, operational

changes, and/or environmental compliance

alternatives on emissions, performance, and

power production costs.

The project involved (a) characterization

and cleanability analyses of various coals,

(b) bench- and pilot-scale combustion testing of the coals in question, and (c) full-scale

utility demonstration tests. Data obtained

from these tests were used to develop the

algorithms, subroutines, and programs included as part of the CQETM technology.

The project was originally conceived by

EPRI to incorporate the results and products

of several ongoing R&D projects into computer software that would become a standard

for studying fuel-related issues in the power

industry. The CQETM model, which addresses

the effects of fuel quality from the coal mine

to the busbar and the stack, is an integration

and improvement of predecessor software

tools, particularly the Coal Quality Impact

Model (CQIM). It was designed to answer

critical questions to be considered before a

utility can be certain that it is operating its

power plants within emissions limitations at

the lowest cost.

The CQETM software is composed of (1)

technical models that evaluate performance

issues; (2) environmental models that examine emissions and regulatory issues; and (3)

financial models that relate performance to

costs, including consumables (fuel, sorbents,

etc.), waste disposal, and operating and maintenance. CQETM is designed to quantify these

relationships, using a minimum of coal quality

information as input data.

CQETM utilizes several previously existing

models. These codes and models were developed by EPRI (with DOE support in some

cases) as part of other programs and were not

the focus of the CQETM project. The previously existing models are described as follows:

Coal Quality Impact Model (CQIM):

CQETM uses the CQIM code to evaluate performance of many auxiliary systems in coalfired power plants. The CQIM code can also
8

CQETM process flow diagram

be used to perform maintenance/availability,

derating, sensitivity, and economic analyses

as well as to model coal cleaning, blending,

and transportation.

NOx Prediction Model (NOxPERT):

This model predicts NOx emissions as a

function of coal parameters, operating data,

and furnace type.

Common Systems Evaluator: This program models equipment systems serving more

than one unit at a plant.

Acid Rain Advisor (ARA): ARA was

designed to assist the user in evaluating options for compliance with the CAAA. ARA

provides the means to rapidly select combinations of SO2 reduction technologies at various units in a system, while viewing

system-wide results. ARA can be used either

on a stand-alone basis, in conjunction with

CQIM, or within CQETM.

Boiler Expert: The CQETM software incorporates a boiler expert model, which consists of the SLAGGO and FOULER routines

developed by PSI PowerServe and the University of North Dakota Energy and Environmental Research Center. CQETM expands

boiler performance modeling beyond CQIM

capabilities to interface with and use results

from these routines, which are described in

the following paragraphs

Slagging Expert (SLAGGO):SLAGGO

simulates the entire cycle of ash formation,

deposit initiation, growth and removal processes based on coal properties and boiler

design and operating parameters. SLAGGO

consists of several models and submodels

including ash formation, ash transport, deposit growth, thermal properties, and deposit

removal models as well as mineral matter

transformation and alkali vaporization

submodels; pyrite kinetics were excluded.

Coal properties, boiler internal aerodynamics, and transport mechanics are accounted

for to predict changes in cleanliness of the

waterwall and superheater tubes in the furnace. Deposit removal by sootblowing is

also modeled.

Fouling Expert (FOULER): FOULER

predicts convective pass fouling based on

boiler design, temperature and gas distributions, ash size and composition distributions,

and sootblowing and load drop parameters.

Thermal resistivities of each heat exchange

section are utilized to iteratively calculate

boiler temperature profiles, and a cleanliness

factor is determined from the difference in

heat transfer between dirty and clean tubes.

Time intervals between sootblowing cycles

can be optimized with FOULER.

Project Objectives

The goal of this project was to develop

and demonstrate a software system that could

be run on a personal computer to provide coal

burning utilities with a predictive tool to

assist in the selection of optimum quality

coal for a specific boiler based on operational

efficiency, cost, and environmental emissions. Data for the algorithms to be included

in the CQETM model were obtained from

bench- , pilot- , and full-scale boiler field

tests.

Waste

Clean

Coal

Selected

Coals

Anthracite

Subbituminous

Lignite

Bituminous

Pilot-Scale

Boiler

Full-Scale

Boiler

Bench-Scale

Testing

Computer

Expert

Model

Data Evaluation

and Correlation
9

Comparison of coal quality assessment methods

Software Description

Traditional methods of evaluating coals

for power plant use involve testing on a large

scale over a considerable period of time.

CQETM, however, permits the use of much

smaller quantities of coal over significantly

shorter time periods. By incorporating algorithms based on large amounts of available

data, CQETM has the capability of predicting

power plant performance with a minimum

number of bench-scale tests of cleanability

and fuel characterization. The net result is a

much lower cost to achieve the desired assessments.

CQETM allows users to construct models

representing their actual unit configurations,

capture complex interactions between economic and technical analyses, and quantify

the economic effects of coal cleaning and

power plant performance.

Development of the Software

CQETM includes more than 100 algorithms

based on the data generated in this project.

The design philosophy incorporates sophisticated modeling techniques, including object-oriented design, coupled with an object

database management system that permits

different views into the data based on the

specific situation being evaluated.

To develop the software tool, the CQETM

project included numerous supporting tasks

to collect and analyze data to form the basis

for the algorithms, methodologies, and

submodels and to verify the accuracy and

integrity of the software.

Coal Characterization Studies

CQ Inc. conducted 13 detailed coal

cleanability characterization studies to provide baseline data for the CQETM model.

This step involved extensive investigations

of physical and chemical properties of all

components of the coal and assessments of

the theoretical potential for removing ashforming, sulfur-bearing, and trace elementbearing minerals associated with the coal.

Long Assessment Period High Costs (6-12 Months)

Shorter Assessment

Period

Relatively

Low Costs

June

January

SMTWTFS

CONVENTIONAL

January

SMTWTFS

CONVENTIONAL

Bench-Scale

Cleaning & Fuel

Characterization

Reliable

Predictions

by

CQIM

CQETM

CQETM

Comparison

of Clean

and Baseline

Performance

Bench-Scale

Cleaning & Fuel

Characterization

Pilot-Scale

Tests on

Cleaned Coal

Field Tests

on

Cleaned Coal

Pilot-Scale

Tests on

Baseline Coal

Field Tests

on

Baseline

Baseline

Fuel

Characterization

Large Coal

Requirements

Much Less

Coal
10

These coal characterizations were completed in

conjunction with field combustion testing at the

following sites: • Public Service of Oklahoma's Northeastern Station

• Mississippi Power Company's Jack Watson

Station

• Northern States Power Company's Allen S.

King Station

• Alabama Power Company's Gaston Station

Additional combustion testing was done at

New England Power Company's Brayton Point

Station.

Pilot-Scale Combustion Tests

Pilot-scale combustion tests were conducted

to support the coal cleanability characterization

and field testing efforts. Fifteen different coals

or coal blends from six different power plants

were tested in ABB/CE's Fireside Performance

Test Facility and B&W's Small Boiler Simulator. The purpose of this phase of the work was to

provide data useful in predicting full-scale boiler

combustion performance from pilot-scale tests

while providing detailed quantitative performance data for CQETM algorithm development.

Utility Boiler Field Tests

Boiler field testing was vital in establishing

correlations between field- , pilot- , and benchscale testing that were used to develop CQETM

algorithms and models. Correlations for fouling

and slagging sample analyses were of special

interest. Comprehensive test burn evaluations

were performed at the utility test sites listed

above. CQIM models, one for each boiler tested,

were developed. The main objectives of this

phase were to: • Evaluate the accuracy of CQIM predictions vs. test data • Assess the benefits of calibrating CQIM

using detailed test burn data

• Identify elements of the CQIM predictions

in which test burn results of improved

equipment models could be used to

enhance the predictive capabilities of

CQIM and CQETM

The conclusions from the field tests address such issues as relative slagging/fouling

rates for baseline and alternative coals,

sootblowing requirements, and furnace exit

gas temperature (FEGT). In addition to coal

cleaning, which is an essential ingredient of

strategies for improved coal utilization,

CQETM also incorporates a wide range of

factors involved in power generation from

coal.

Results

After completion of the tests, a CQETM

prototype model was showcased at trade

shows and at an international coal conference in London, England in 1993. Final model

development and verification were performed

in 1994 and a CQETM alpha version was

released in March 1995. A beta version was

issued in June 1995 and Version 1.1 was

released in June 1996. Another beta version

was released in 1999.

The CQETM program has been made available to EPRI member utilities and is operational. Further refinement of the algorithms

and input/output techniques will be performed

as continued use is made of the program.

Three products are available: use licenses,

consultant licenses, and commercialization

licenses. Further information can be found

on the VistaTM Fuels Web server:

http://147.182.5.102/. VistaTM is a joint venture of EPRI and B&V.

Commercialization of the

Technology

The ARA software became available in

1992, with two commercial sales made, one

in 1993 and one in 1995. Debugging of the

CQETM software proceeded through the end

of the project. CQETM has been distributed to

about 35 utilities in the United States and one

in the United Kingdom through membership

in EPRI. B&V executed the first CQETM
11

commercialization license, and CQ Inc. is

also licensed to commercialize the program.

Under the terms of that license, B&V and CQ

Inc. are working collaboratively to sell use

and consultant licenses worldwide.

Product Advantages

With its array of capabilities, CQETM has

significant value to power generation companies, equipment manufacturers, environmental assessment firms, litigators, fuel

suppliers, government organizations, and

engineering firms.

Built on the foundation of CQIM, CQETM

brings new dimensions to the fuel analysis

and decision-making process:

• Flexible modeling capabilities allow

users to represent a broad range of equipment and system arrangements.

• Specialized applications address specific processes, including fuel purchasing, clean air compliance, and

engineering support.

• Common data and output across a network support overall corporate fuel assessment needs.

CQETM has the capability of predicting

power plant performance with a minimum

number of bench-scale tests. The result is

a much lower cost to achieve the desired

assessments compared with traditional approaches.

Conclusions

Development of the CQETM model included a large number of bench-scale coal

characterization tests, pilot-scale combustion tests, and full-scale utility boiler field

tests. The resulting CQETM software offers

significant benefits in the selection of coalbased fuels and the design and operation of

coal-fired power systems. By incorporating

algorithms based on available data, CQETM

has the capability of predicting power plant

performance with a minimum number of

bench-scale tests of cleanability and fuel

characterization.

The coal characterization tests performed

in the course of this study measured the

effectiveness of physical coal cleaning in

removing ash-forming minerals, pyritic sulfur, and trace elements from bituminous and

subbituminous coals. The ARA software,

developed as part of the CQETM project, can

be used to select CAAA compliance strategies, either within CQETM or as a stand-alone

program.

NOx emissions and efficiency

at Hammond Unit 4
12

Generic NOx

Control Intelligent

System (GNOCISTM)

Introduction

AI has the potential to aid in optimizing boiler

operation with a view to reducing emissions,

minimizing UBC, and maximizing overall power

plant efficiency. The Generic NOx Control Intelligent System (GNOCISTM), is an AI program

developed to provide process control that achieves

NOx reduction consistent with economic and

operational constraints.

Project Description

The project Demonstration of Advanced Combustion Techniques for a Wall-Fired Boiler was

selected in September 1988 under CCT Round II.

The Participant was Southern Company Services,

Inc. (SCS) and the test site was at Georgia Power

Company's Plant Hammond, Unit 4, at Coosa,

Georgia. Georgia Power and Southern Company Services are subsidiaries of Southern

Company, a large producer of electricity

from coal in the United States. The primary

objective of the demonstration at Hammond

Unit 4 was to determine the long-term effects

of commercially available wall-fired lowNOx combustion technologies on NOx emissions and boiler performance. The original

goals of this project have been completed

and several technical reports have been issued.

A subsidiary goal of the project was to

evaluate advanced instrumentation and controls applied to combustion processes. One

of the systems tested was GNOCISTM. Since

completion of these activities, the scope has

been expanded to include further development of GNOCISTM for power plant optimization. This extension of the CCT project is

now underway.

Description of GNOCISTM

GNOCISTM is an enhancement to a plant's

DCS targeted at improving utility boiler efficiency and reducing emissions. GNOCISTM

development was funded by a consortium

consisting of EPRI, PowerGen, Southern

Company, URS Corporation (formerly Radian International), U.K. Department of Trade

and Industry, and U.S. DOE. GNOCISTM

utilizes a neural network model of the combustion characteristics of the boiler that reflects both short- and long-term trends in

boiler operating characteristics.

A constrained-nonlinear optimizing procedure is applied to identify the best operating set points for the plant. These

recommended set points are implemented

automatically without operator intervention

(closed-loop), or, at the plant manager’s discretion, conveyed to the plant operators for

implementation (open-loop). The software

is designed for continuous on-line use and is

applicable to units burning coal, gas, or oil,

and all combustion firing geometries.

GNOCISTM

Historical

Data

Optimizer

Model

Sensor

Validation

Advice/

Control

Instructions

Plant Data

Major elements of GNOCIS TM
13

Georgia Power Company’s Plant

Hammond Unit 4

GNOCISTM is offered commercially by

PowerGen, URS, and Southern Company.

As of December 2001, GNOCISTM has

been installed on units representing more

than 10,000 MWe of generation capacity

in both the United States and Europe. Although results have been site specific,

NOx emission reductions of more than

10% and efficiency improvements of more

than 0.5% are not uncommon.

These commercial applications to date

have shown that in many instances, simultaneous NOx reduction and improved boiler

efficiency can be achieved. Given the (1)

relatively low cost of optimization compared to other control technologies and (2)

increasing volume of documented results,

optimization has proven itself to be an

increasingly viable alternative for NOx

reductions and heat rate improvements.

Developmental Testing at

Kingsnorth and Gaston

Prior to demonstrating GNOCISTM as part

of the CCT project at the 500-MWe Hammond

Unit 4, development and testing of the technology was conducted at PowerGen's 500-

MWe Kingsnorth Unit 1 and at Alabama

Power Company's 250-MWe Gaston Unit 4.

Kingsnorth Station is situated on the banks

of the Medway estuary in Kent, England. It is

a coastal site with four 500-MWe tangentially

fired units that were commissioned between

1970 and 1973. It is supplied with coals from

a variety of worldwide sources. This plant

was chosen for trials because (1) the use of

LNBs had created LOI problems, (2) it had a

modern control system with which

GNOCISTM could be interfaced, and (3) the

range of coal feeds provided a challenging

application.
14

For the Kingsnorth plant, GNOCISTM was

designed to make recommendations for excess oxygen, burner tilt, and feeder speeds.

At this site, testing showed that implementing GNOCISTM recommendations provided

a 4 percentage point reduction in LOI with no

increase in NOx emissions. Alternatively,

NOx emissions were reduced by 10% while

maintaining acceptable LOI.

Alabama Power's Gaston Unit 4 served as

the other initial development site for

GNOCISTM. This wall-fired unit is equipped

with LNBs and uses Eastern bituminous coal

from various sources as its fuel. At this site,

GNOCISTM was designed to operate in either

closed- or open-loop fashion. Controlled variables were grinding mill coal flow and excess oxygen level, while parameters being

optimized were NOx emissions, boiler efficiency, and LOI. For the Gaston plant, boiler

efficiency improvements of 0.5 percentage

point and LOI reductions of about 3 percentage points were obtained.

Hammond Demonstration

The objective of the commercial demonstration at Plant Hammond was to evaluate

and demonstrate the effectiveness of advanced digital control/optimization methodologies as applied to the combustion

modification technologies for NOx abatement installed during the CCT project,

namely, LNBs and advanced overfire air

(OFA).

The major tasks for the Hammond project

included design and installation of the DCS,

instrumentation upgrades, advanced controls/

optimization design and implementation, and

plant characterization of the unit both before

and after activation of the advanced strategies. GNOCISTM testing at Hammond commenced during the winter of 1995 in both

open-loop/advisory and closed-loop/supervisory modes, representing one of the first

applications anywhere of AI for commercial

scale power plant control.

Using results from unit testing and projected unit load profiles, GNOCISTM provided a range of benefits depending on

whether the unit is operated to minimize

NOx emissions, maximize overall plant efficiency, or minimize LOI. For the minimum

NOx case, the annual savings (based on fuel

cost savings only) were projected to be

$238,000, increasing to $392,000 for the

maximum efficiency case and decreasing to

$191,000 for minimum LOI. At a typical

GNOCISTM installation cost of $250,000,

these savings represent an excellent return

on investment.

Future Work

Since its installation at Kingsnorth,

Gaston, and Hammond, GNOCISTM has been

applied to many more sites. GNOCISTM has

now been installed on units representing more

than 10,000 MWe of generation capacity,

and other installations are planned. This is

one of the major successes of the CCT Program. Enhancements and other applications

of GNOCISTM continue to be pursued.

In the extension of the CCT project at

Plant Hammond, now underway, work is

being conducted to expand the scope of the

optimization envelope to encompass other

power plant processes including sootblowing

and ESP operation, as well as overall plant

efficiency. This developmental effort has

been driven largely by the success of

GNOCISTM as applied to boilers and the

potential of further improvements in emissions control and plant performance. This

extension has an expected completion date

of March 2002.
15

Plant Economic

Optimization

Advisor (PEOATM)

Introduction

The Milliken Clean Coal Technology

Demonstration Project was selected in September 1991 under Round IV of the CCT

Program. The primary goal of this project

was to demonstrate SO2 and NOx removal

from power plant stack gas by a combination

of advanced technologies. It was conducted

at New York State Electric & Gas

Corporation's (NYSEG) Milliken Station

(now owned and operated by AES). Operation was initiated in June 1995 and completed in May 1999.

The Saarberg-Holter-Umwelttechnik

(S-H-U) process was used for SO2 control,

along with LNBs for NOx control. In addition, deep staged reburning using micronized coal was tested in this project, but this

technology was not made a permanent part of

the Milliken Station system. The S-H-U process is described in Topical Report No. 12,

Advanced Technologies for the Control of

Sulfur Dioxide Emissions from Coal-Fired

Boilers, June 1999, and micronized coal

reburning is described in Topical Report No.

14, Reburning Technologies for the Control

of Nitrogen Oxides Emissions from CoalFired Boilers, May 1999. Both the SO2 control and the NOx control demonstrations

were highly successful in meeting project

goals.

As part of the project, work was also done

on the development of PEOATM, an on-line

electronic performance system. DHR Technologies, Inc., was responsible for the

PEOATM effort.

Description of PEOATM

PEOATM was designed to assist plant personnel in meeting the requirements of Title

IV of the CAAA and in optimizing overall

The Clean Coal Technology (CCT)

Program of the U.S. Department of Energy (DOE), a model of government and

industry cooperation, supports DOE's

mission to foster a secure and reliable

energy supply system in the United

States that is environmentally and economically sustainable. The CCT Program represents an investment of over

$5 billion in advanced coal-based technology, with industry and state governments providing a significant share —

66% — of the funding. With 26 of the 38

projects having completed operations,

the CCT Program has already yielded

Clean Coal Technologies that are capable of meeting existing and emerging

environmental regulations and competing in a deregulated electric power marketplace.

The CCT Program provides a portfolio of process options that will enable

continued use of the United States' huge

economically recoverable coal reserves

(over 270 years at current consumption

rates) to meet the nation's energy needs

economically and in an environmentally

sound manner.

As the new millennium begins, many

of the Clean Coal Technologies have

reached commercial status. Industry

stands ready to employ them both domestically and internationally to respond

to the energy and environmental demands of the 21st century. For existing

power plants, there are cost-effective

environmental control devices to minimize emissions of sulfur dioxide (SO2),

nitrogen oxides (NOx), and particulate

matter (PM). The CCT Program has

taken a pollution prevention approach as

well, providing technologies that remove

pollutants or their precursors from coal

before combustion.

The Clean Coal Technology Program

Also ready is a new generation of

technologies that can produce electricity

and other commodities, such as steam

and synthesis gas, at high efficiencies

consistent with concerns about global

climate change.

Additionally, new technologies have

been introduced into major coal-using industries, such as steel production, to enhance environmental performance.

Thanks in part to the CCT Program, coal

— abundant, secure, and economical

throughout much of the world — can continue in its role as a key component in

supplying U.S. and world energy needs.

The CCT Program also has global

importance in providing clean and efficient coal-based technologies to a burgeoning energy market in developing

countries. World energy consumption is

expected to increase 63% by 2020, and

coal, the predominant indigenous fuel in

much of the world, will be the fuel of

choice for electricity production. CCT

processes offer a cost-effective means

to mitigate potential environmental problems associated with this unprecedented energy growth.

Most of the CCT demonstrations

have been conducted at commercial

scale, in actual user environments, and

under circumstances typical of commercial operations. Each project addresses

one of the following four market sectors:

• Advanced Electric Power

Generation

• Environmental Control Devices

• Coal Processing for Clean Fuels

• Industrial Applications

The software programs described in

this Topical Report were developed under Environmental Control Devices and

Coal Processing for Clean Fuels.
16

plant economic performance. This demonstration was intended to show the capability

of PEOATM to smoothly integrate with the

power plant DCS, performance monitoring

instruments, and information systems on a

variety of network topologies, operating systems, and hardware platforms.

PEOATM was proposed as a means of

integrating key aspects of plant information

management and analysis to assist plant

personnel with process optimization, including emissions monitoring and compliance.

PEOATM is designed to incorporate all major

aspects of plant operation, including steam

generator and turbine, emissions control, heat

transfer, auxiliary systems, and waste management. PEOATM was intended primarily

for use by plant operators but could also be

useful for engineers and managers.

PEOATM automatically determines and

displays key operational and control set

points for optimized cost operation. The

system provides operators with on-line

emissions monitoring and diagnostic capabilities, along with rapid access to reports

and trend information.

PEOATM is a neural networking system

that uses optimization algorithms to evaluate

key emissions data, such as NOx, SO

2, O2

,

CO, CO

2, LOI, and opacity; other operational parameters, such as boiler and turbine

operation, gypsum sales, and emissions

credits; and the impacts of coal quality and

coal mixing. The system provides “whatif” capabilities to allow users to employ

the optimization features to evaluate various operating scenarios.

In addition to providing optimized set

point data, PEOATM also provides plant operators and engineers with expert advice and

information to help optimize total plant performance. It is an open, client/server-based

system using object-oriented development

technologies.

PEOATM models the plant and then uses

this model to find the least cost operating

conditions given certain constraints. As discussed previously, a neural network “learns”

the input/output relationships by using past

data. Therefore, the data must be valid to

achieve the expected results from the network model.

Originally, an on-line performance monitor (OPM) was to be installed to provide data

validation for PEOATM, as well as calculations and graphical representations of plant

performance. However, the OPM system was

dropped from the demonstration program for

cost reasons, with the result that PEOATM

was required to perform its own calculations

and data validation had to be done manually.

Results

To accomplish the data verification that

had initially been intended for OPM, NYSEG

reviewed all pertinent data points to ensure

that the information was accurate. Two initial tests of PEOATM were conducted, one

short-term and one longer-term. The results

were promising but inconclusive. After a

limited amount of additional testing of the
17

system, it was determined that difficulties

experienced in applying PEOATM at Milliken

were not easily remedied, and the optimization effort was discontinued.

Although this particular application of neural networks to power plant optimization was

not successful, the concept is fundamentally

sound, and future developments of this technology will undoubtedly prove to be feasible.

DHR Technologies has sold six modules of

their PEOATM system, although these were

in applications not related to electric power

generation.

Market Potential

of Software

Systems

With a wide range of capabilities, CQETM

is of potential interest to power generation

companies, equipment manufacturers, environmental assessment firms, litigators, fuel

suppliers, government organizations, and

engineering firms.

Significant market penetration of

GNOCISTM has already been achieved, and

additional applications are anticipated. Partly

as a result of successes to date in minimization of NOx emissions, further development

of GNOCISTM technology is in progress with

a view to extending its capability to control

other aspects of boiler operation. This is

likely to result in many more applications in

the electric power generation industry.

Conclusions

Under the CCT Program, significant

progress has been made in the development

of computer software for optimization of

electric power generation.

The CQETM software developed under

this Program offers significant benefits in the

selection of coal-based fuels and in the design

and operation of coal-fired power systems.

By incorporating algorithms based on available data, CQETM has the capability of predicting power plant performance with a

minimum number of bench-scale tests of

cleanability and fuel characterization. The

result is a much lower cost to achieve the

desired assessments compared with traditional approaches.

With GNOCISTM installations representing more than 10,000 MWe of generation

capacity, the development of this AI system

represents one of the major successes of the

CCT Program. Designed as a low-cost alternative to major capital expenditures for retrofits, GNOCISTM software offers continuous

optimal operation of boilers over a wide

range of conditions. Enhancements to

GNOCISTM are underway to increase its range

and scope of application in the generation of

electric power.
18

“500 MW Demonstration of Advanced WallFired Combustion Techniques for the Reduction of Nitrogen Oxide (NOx) Emissions

from Coal-Fired Boilers — Phase 4 - Digital

Control System and Optimization,” Southern Company Services, 1998.

“500 MW Demonstration of Advanced WallFired Combustion Techniques for the Reduction of Nitrogen Oxide (NOx) Emissions

from Coal-Fired Boilers — CFD Modeling

of SNCR Performance in Georgia Power

Company's Hammond Unit 4 and Wansley

Unit 1,” Southern Company Services, 1998.

PEOATM

Comprehensive Report to Congress, Clean

Coal Technology Program, “Milliken Clean

Coal Technology Demonstration Project,”

proposed by New York State Electric & Gas

Corporation, U.S. DOE, September 1992.

Topical Report No. 12, “Advanced Technologies for the Control of Sulfur Dioxide

Emissions from Coal-Fired Boilers,” U.S.

DOE, June 1999.

Topical Report No. 14, “Reburning Technologies for the Control of Nitrogen Oxides

Emissions from Coal-Fired Boilers,” U.S.

DOE, May 1999.

New York State Electric & Gas Corporation,

Final Report, “Milliken Clean Coal Technology Demonstration Project,” April 1999.

Bibliography

CQETM

Comprehensive Report to Congress, Clean

Coal Technology Program, “Development

of the Coal Quality Expert,” proposed by

Combustion Engineering, Inc. and CQ Inc.,

U.S. DOE, May 1990.

C.D. Harrison, “Coal Quality Expert: Status

and Software Specifications,“ First Annual

Clean Coal Technology Conference (Cleveland OH), November 1992.

D.C. O'Connor, “The CQE Project: Producing Innovative Software for Economical

Deployment of Coal Technologies,” Third

Annual Clean Coal Technology Conference

(Chicago IL), September 1994.

C.D. Harrison, D.B. Kehoe, D.C. O'Connor,

and G.S. Stallard, “CQE: Integrating Fuel

Decisions,” Fourth Annual Clean Coal Technology Conference (Denver CO),

September 1995.

Clean Coal Today, “CQ Inc. Software Tool

Eases Utility Decision-Making,” Fall 1996.

C.D. Harrison D.B. Kehoe, D.C. O'Connor,

and G.S. Stallard, “Recent Experience with

the CQETM,” Fifth Annual Clean Coal Technology Conference (Tampa FL), January

1997.

CQ Inc., Final Report, “Development of

a Coal Quality Expert,” June 20, 1998.

GNOCISTM

Comprehensive Report to Congress, Clean

Coal Technology Program, “Demonstration

of Advanced Combustion Techniques for a

Wall-Fired Boiler,” proposed by Southern

Company Services, U.S. DOE, November

1989.

“500 MW Demonstration of Advanced WallFired Combustion Techniques for the Reduction of Nitrogen Oxide (NOx) Emissions

from Coal-Fired Boilers - Field Chemical

Emissions Monitoring: Overfire Air and

Overfire Air/Low NOx Burner Operation —

Final Report.” Prepared by Radian Corporation for Southern Company Services, 1993.

Topical Report No. 5, “Reducing Emissions

of Nitrogen Oxides via Low-NOx Burner

Technologies,” U.S. DOE, September 1996.

J.N. Sorge, C.L. Larrimore, M.D. Slatsky,

W.R. Menzies, S.M. Smouse, and J.W.

Stallings, “500 MW Demonstration of Advanced Wall-Fired Combustion Techniques

for the Reduction of Nitrogen Oxide Emissions from Coal-Fired Boilers,” Fifth Annual

Clean Coal Technology Conference (Tampa

FL), January 1997.

“Generic NOx Control Intelligent System

(GNOCIS) — Final Report,” Southern Company Services and PowerGen, 1997.

“500 MW Demonstration of Advanced WallFired Combustion Techniques for the Reduction of Nitrogen Oxide (NOx) Emissions

from Coal-Fired Boilers — Online Carbonin-Ash Monitors/Survey and Demonstration,” Southern Company Services, 1997.

J.N. Sorge, M.D. Slatsky, J. Noblett, and

G.H. Warriner, “Combustion Optimization

Using GNOCISTM,” Sixth Clean Coal Technology Conference (Reno NV), April 1998.

“500 MW Demonstration of Advanced WallFired Combustion Techniques for the Reduction of Nitrogen Oxide (NOx) Emissions

from Coal-Fired Boilers — Final Report

(Phase 1 - 3B),” Southern Company Services, 1998.
19

Contacts for CCT

Projects and U.S.

DOE CCT Program

Participant Contacts

Clark Harrison, President

CQ Inc.

160 Quality Center Rd.

Homer City PA 15748

(724) 479-3503

(724) 479-4181 fax

clarkh@cq-inc.com

John N. Sorge, Project Manager

Southern Company Services, Inc.

14N-8195

P.O. Box 2625

Birmingham AL 35202-2625

(205) 257-7426

(205) 257-5367 fax

jnsorge@southernco.com

James Harvilla, Project Manager

New York State Electric & Gas Corporation

Corporate Drive - Kirkwood Industrial Park

P.O. Box 5224

Binghamton NY 13902-2551

(607) 762-8630

(607) 762-8457 fax

jharvila@spectra.net

U.S. Department of Energy Contacts

C. Lowell Miller

Director, Coal Fuels & Industrial Systems

U.S. Department of Energy, FE-24

Germantown MD 20874-1290

(301) 903-9451

(301) 903-2238 fax

lowell.miller@hq.doe.go

Joseph B. Renk

Project Manager - CQETM

National Energy Technology Laboratory

P.O. Box 10940

Pittsburgh PA 15236-0940

(412) 386-6406

(412) 386-4775 fax

joseph.renk@netl.doe.gov

James R. Longanbach

Project Manager - Wall-Fired Project

National Energy Technology Laboratory

P.O. Box 880

Morgantown WV 26507-0880

(304) 285-4659

(304) 285-4469 fax

james.longanbach@netl.doe.gov

James U. Watts

Project Manager - Milliken Project

National Energy Technology Laboratory

P.O. Box 10940

Pittsburgh PA 15236-0940

(412) 386-5991

(412) 386-4775 fax

james.watts@netl.doe.gov
20

List of Acronyms and Abbreviations

ABB/CE........................................................................ ABB Combustion Engineering, Inc.

AI .......................................................................................................... artificial intelligence

ARA ......................................................................................................... Acid Rain Advisor

B&V ............................................................................................................. Black & Veatch

B&W ...............................................................................The Babcock & Wilcox Company

Btu .......................................................................................................... British thermal unit

CAAA .......................................................................... Clean Air Act Amendments of 1990

CCT. .................................................................................................Clean Coal Technology

CEMS ................................................................. continuous emissions monitoring systems

CO............................................................................................................... carbon monoxide

CO2 ................................................................................................................. carbon dioxide

CQETM ................................................................................................... Coal Quality Expert

DCS ..................................................................................................... digital control system

CQIM......................................................................................... Coal Quality Impact Model

DOE ............................................................................................U.S. Department of Energy

EPA ....................................................................... .U.S. Environmental Protection Agency

EPRI ............................................................formerly the Electric Power Research Institute

ESP. ................................................................................................. electrostatic precipitator

FEGT ........................................................................................ furnace exit gas temperature

GNOCISTM ........................................................... Generic NOx Control Intelligent System

Hg ............................................................................................................................. mercury

LNB. ............................................................................................................ low NOx burner

LOI ................................................................................................................ loss on ignition

MWe ......................................................................................... megawatts of electric power

NH3 .......................................................................................................................... ammonia

NOx ............................................................................................................... nitrogen oxides

NYSEG........................................................... New York State Electric & Gas Corporation

O2 ................................................................................................................................ oxygen

OFA. ..................................................................................................................... overfire air

OPM ........................................................................................ on-line performance monitor

PEOATM.................................................................... Plant Economic Optimization Advisor

SCS ........................................................................................... Southern Company Services

S-H-U .................................................................................Saarberg-Holter-Umwelttechnik

SO2 .................................................................................................................. sulfur dioxide

UBC ............................................................................................................. unburned carbon

This report is available on the Internet

at U.S. DOE, Office of Fossil Energy’s

website: www.fe.doe.gov and and on the

Clean Coal Technology Compendium

website:

www.lanl.doe.gov/projects/cctc

To Receive

Additional

Information

To be placed on the Department

of Energy's distribution list for future

information on the Clean Coal Technology Program, the demonstration

projects it is financing, or other Fossil

Energy Programs, please contact:

Robert C. Porter

Director, Office of Communication

U.S. Department of Energy, FE-5

1000 Independence Ave. SW

Washington DC 20585

(202) 586-6503

(202) 586-5146 fax

robert.porter@hq.doe.gov

Otis Mills, Jr.

Public Information Office

U.S. Department of Energy

National Energy Technology

Laboratory

P.O. Box 10940

Pittsburgh PA 15236-0940

(412) 386-5890

(412) 386-6195 fax

otis.mills@netl.doe.gov