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Babcock & Wilcox 1

Kevin RogersMel Albrecht

Michael VarnerBabcock & WilcoxBarberton, Ohio, U.S.A.

Presented to:ICAC NOx ForumMarch 23-24, 2000Washington D.C., U.S.A.

Numerical Modeling for Design Optimization of SCRApplications

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AbstractBabcock & Wilcox (B&W) has utilized numerical modeling

to simulate fluid and combustion phenomena on commercial

contracts for more than a decade. Verification of model predic-

tions with actual field test results has provided valuable feed-

back for model development and refinement. Achieving a highlevel of agreement between field results and the numerical pre-

dictions provides confidence when applying these models as

design tools. This confidence is demonstrated by years of mod-

eling and design practice experience put to use for new and ret-

rofit low NOx

furnace combustion strategies.

Similar challenges of development and verification exist

when extending numerical modeling to include downstream SCR

processes. Modeling approaches are undergoing continual im-

provement predicting component pressure drop, temperature and

chemical specie distributions, mixing efficiencies, and reactor

performance. Building upon prior boiler experience and suc-

cess, numerical modeling is becoming an increasingly valuable

tool for SCR system design optimization efforts.

IntroductionNumerical modeling is an economically effective design and

analysis tool for the simulation of flow, heat transfer and com-

bustion phenomena surrounding boiler components and auxil-

iary equipment. Furnaces, burners, windboxes, steam drums,

pulverizers, electrostatic precipitators, coal nozzles and piping

are only a few examples of the components and equipment that

have been successfully modeled numerically by B&W.

As the collection of project designs and case studies ex-

panded, and as field test results became more available, the de-

gree of success and confidence grew through application expe-

rience and results validation. This type of experience, along-

side the continuum of computer and software development, has

helped to propel the acceptance of numerical modeling by the

power generation industry.

Selective catalytic reduction (SCR) system design has ech-oed a need for similar experience and results validation. Mod-

eling also becomes an instrument capable of mapping the per-

formance characteristics of discrete components or arrange-

ments. Through optimization of the components performance,

an improvement in the overall system design can be obtained.

Retrofit projects in particular can exhibit a strong need for

accurate modeling of flexible design approaches. They are clas-

sically burdened with process conditions and arrangements that

are less than ideal.

Numerical vs PhysicalPhysical modeling has historically been the dominant tech-

nique that has used reduced scale models employed by the sys-tem suppliers, catalyst manufacturers , and end customers. Cata-

lyst sizing and performance criteria have often been set or in-

fluenced by the expected flow and component profiles deter-

mined through physical flow modeling. When specified param-

eters achieve an acceptable value in the physical model, it is

assumed that the field results will be similar. If the acceptance

criteria are based on physical modeling experience, it then be-

comes important to understand how the results could differ when

utilizing a numerical approach. In the end, both modeling ap-

proaches should lead to comparable conclusions that represent

full-scale field operation.

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Numerical modeling offers some advantages over its physi-

cal model counterparts.

In many cases, numerical modeling can more easily and eco-

nomically predict flue gas temperature distribution and

chemical component distributions. (i.e., flue gas NOx distri-

bution, NH3/NOx ratio distribution and mixing phenomena).

Due to its flexibility, numerical modeling permits the study

of an increased number of geometric arrangements or modi-

fications in a more timely manner than is possible with physi-

cal flow modeling.

Numerical modeling can be done at full scale.

Numerical models can be stored for future use and refer-

enced far more efficient ly than physical counterparts. Ware-

housing a physical model is not required.

The input boundary conditions of temperature, flow and com-

ponent concentration profiles can be specified to a higher

level of detail.

Capital investment of model instrumentation and its mainte-

nance/calibration costs are not an issue.

Input conditions can be varied easily to assess system sensitivity.

The data is inherently in an electronic format.

Independent of the modeling approach, the actual inlet con-

ditions and the range of flow/temperature/component mal-dis-

tribution expected should be measured and described as accu-

rately as possible. With downstream predictions influenced by

the upstream inputs, it is sometimes beneficial to move the up-

stream model boundaries to locations where the inlet conditions

are better known or documented.

Validation and Acceptance CriteriaAs modeling efforts on specific projects or case studies ac-

cumulate, a wealth of information begins to build. The result is

a history and case study library that improves verification, as

well as comparative and predictive capabilities.

The variety of SCR system designs and arrangements mod-

eled to date by B&W has aided in the building of such a refer-ence library. To the extent possible and practical, these model

studies are being augmented with field site data and compara-

tive physical modeling results.

Full-scope modeling experience appears increasingly impor-

tant. With the entire process from the boiler through the SCR

modeled, one can develop a more keen understanding of the

direct and peripheral consequences of design changes from the

NOx

generation point to the stack. The ability to manipulate

the design and uncover ways to improve performance with

modifications to the burners, economizers, economizer by-

pass arrangements, flue arrangements, flue internals, reac-

tor design, and even downstream flue gas cleaning equip-

ment, is significantly improved.

The computer models lend themselves to these library-build-

ing efforts, as they are completely and inherently storable in an

electronic data format. Without significant hardship, base models

can be reconfigured to analyze process results from field testing.

It is important that field site measurements be planned to

provide feedback for model refinement and validation work.

However, a high level of comparability to field measurements

is not always paramount. When performing gross level design

optimizations, the use of a model to gauge the relative effec-

tiveness of changes and optional arrangements is often a pri-

mary goal that can be satisfied at reduced accuracy.

With the longer history of use, physical models have achieved

a certain level of acceptability in providing predictions. For this

reason, some numerical validation studies are performed by compar-

ing the results of the numerical approach to those of the physical.

Ideally, each approach should be compared to actual field

results so that each are compared to the real system, not to each

other. Field data taken should be directly compared to model

results. While field data provides the highest level of valida-

tion, it can also become quite costly and requires forethoughton where to place test ports and measurement instrumentation

on the field units. The task of actually collecting the data is

time-consuming and sometimes difficult.

Aside from validation issues, there is typically some given

set of acceptance criteria. These criteria define the target val-

ues which, if achieved, will provide a high level of performance

confidence.

Velocity ProfileThe velocity distribution profile is often reported as a coef-

ficient of variation (Cv) by the following equation:

Cv = 100 /x

where,

Cv = Coefficient of variation expressed as a percent of the

standard deviation about the mean.

= standard deviation

x = mean

Depending on performance requirements, the velocity dis-

tribution requirements at the catalyst inlet can typically range

from 10% to 20% standard deviation from the mean.

The velocity profile through the plane of ammonia injection

can be equally or more important. For a constant ammonia in-

jection flux, variat ions in flue gas velocity through each injec-

tion zone will induce downstream NH3/NOx molar ratio mal-distributions.

TemperatureThe criteria for temperature distribution are typically ex-

pressed as the minimum and maximum deviation about the mean,

rather than as a standard deviation about the mean. The typical

allowable values will range from 20F to 50F.

The average gas temperature can influence the allowable

minimum or maximum. For example, the minimum recom-

mended catalyst operating or ammonia injection temperature

will usually set the allowable minimum. If the average is high,

the allowable deviation on the minus side is greater.

Ammonia to NOx Molar Ratio (NH3/NOx)The numerical approach can easily utilize an inputted inlet

boundary of a field-measured NOx

profile and determine the

NH3/NO

xratio based on predicted dispersion and/or mixing of

injected NH3

downstream.

The criterion for molar ratio at the SCR catalyst face is typi-

cally expressed as a coefficient of variation similar to that for

velocity profiles. The target values can typically range from 5%

to 15% standard deviation from the mean.

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Pressure DropPressure drop was once the primary criterion for many sys-

tem designs. Obscured somewhat by the velocity, temperature

and component profile requirements, it is nonetheless extremely

important.

On retrofit projects, the allowable pressure drop is usually

based on fan or furnace pressure limitations, and the system

operating power-consumption goals. The pressure drop associ-

ated with the catalyst, and at times that associated with a rather

contorted flue arrangement, will rob from pressure drop allow-

able towards the goals of ammonia injection and static mixing.

Comparisons to Physical ModelingComparisons between physical and numerical models have

typically shown an acceptable level of agreement with pressure

drop criteria. An example is a comparison of a physical study

where scaling relationships were used to arrive at full-scale re-

sults from those measured at the model size. Two numerical

models were developed for the comparisons. One was based on

the physical models 1/10th scale, while the other was done in full

scale. Figure 1 provides a view of the arrangement. Pressure com-

parison cases P1 through P3 represent comparable measurement

points between the physical model and that of the numerical. The

comparisons are presented in Tables 1 and 2 below.

The agreement between the predicted pressure drop in the

numerical model and the measured pressure drop in the physi-

cal flow model suggests an adjustment in acceptance criteria

between the two methods is not needed. Each method can be

reasonably assured of providing similar results.

Many numerically-based velocity and temperature profile

results appear to give values that are more on the conservative

side compared to those based on the physical model. Table 3

and Table 4 provide examples. Table 3 represents a particular

arrangement where mixing devices and vane arrangements are

varied from cases V-1 through V-4. Velocity simil itude was not

maintained from the physical to numercal models; however, for

each case, the coefficients of variation for the physical and nu-

merical models were very close in magnitude. The effect of ve-

locity and Reynolds number appear to be more important when

considering dispersion and mixing phenomena as compared to

that of simple turbulent flow pressure drop determination. They

type and order of the rurbulence model used in numerical models

also influences the degree of potential conservatism in the results.

In Table 4 below, cases T-1 through T-3 represent predic-

tions at the catalyst for a constant overall arrangement where

changes in flue internals were made to examining alternate

methods of economizer bypass gas introduction. In this case

the conservatism of the numerical model was significant when

compared to predictions obtained from a physical approach.

Table 1

Physical Model Prediction and Full-Scale Numerical Model Prediction

Pressure Drop Comparison Cases P-1 P-2 P-3Numerical Full-Scale Model Prediction, inches H

2O 0.48 0.18 1.8

Physical Model Full-Scale Prediction, inches H2O 0.50 0.19 1.9

Table 2

Physical Model Measured and 1/10th

Scale Numerical Model Prediction

Pressure Drop Comparison Cases P-1 P-2 P-3Numerical 1/10th Scale Model Prediction, inches H

2O 0.45 0.16 0.54

Physical Model Measurement, inches H2O 0.46 0.17 0.55

Table 3

Physical to Numerical Velocity Profile Comparisons

Velocity Profile Comparison Cases V-1 V-2 V-3 V-4Relative Numerical Model to Field Gas Velocity 1.0 1.0 1.0 1.0Numerical Model Velocity Coefficient of Variation, % rms 17 20 19 22Relative Physical Model to Field Gas Velocity 0.6 0.6 0.6 0.2Physical Model Velocity Coefficient of Variation, % rms 15 17 18 17

Table 4

Physical to Numerical Temperature Extreme Comparisons

Temperature Extreme Comparison Cases T-1 T-2 T-3Relative Numerical Model to Field Gas Velocity 1.0 1.0 1.0Numerical Model Temperature, max & min +/- of the average +59/-49 +148/-80 +56/-62Relative Physical Model to Field Gas Velocity 0.6 0.6 0.6Physical Model Temperature, max & min +/- of the average +10/-28 +60/-36 +32/-34

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System Design OptimizationWith the high performance duty of many contemporary SCR

designs, optimization techniques are of ever-increasing impor-

tance. Numerical modeling has been demonstrating itself as a

very useful tool, not only for the design of a specific project,

but also for product design development and generation of in-

formation to improve optimization methods.

In the past, much less importance was placed on tempera-

ture and chemical component distributions. Flue systems were

designed with splitters and/or vanes to achieve the simple goal

of minimizing pressure drop. Today, the optimization exercise

can become rather convoluted as attempts are made to improve

temperature and component distributions in ways that minimize

degradation of velocity profiles and system pressure loss charac-

teristics.

Achieving uniform distribution profiles at the SCR catalyst

face with regard to NOx concentration, NH3/NOx molar ratio,

velocity and temperature is often complicated by physical space

limitations, arrangement limitations and limits to boiler modi-

fications. In difficult retrofit applications, a unit length of flue

is a precious commodity for the system designer who is focused

on performance goals. The higher the degree of uniformity re-

quired, the more impractical it is to leave a length of flue de-signed for the lowest pressure drop.

Synergy between these mixing and flow distribution tasks is

critical in restricted space system design, especially when com-

plicated by the need for bypass gas introduction to satisfy mini-

mum allowable temperature requirements. For large side-to-side

or top-to-bottom imbalance, the task of minimizing flue length

becomes quite challenging. While this challenge is being tack-

led with both physical and numerical modeling, it is the nu-

merical approaches that appear equipped with the needed flex-

ibility to address the issues in the time required.

Control ModelsSpecially designed models can be developed to substitute

components from the various modeled systems. In this way,

performance estimates of discrete components or sections on a

side-by-side controlled basis can be made. These control mod-

els can be used to map the performance characteristics of the

devices and arrangements, providing tools to manipulate designs

and focus more quickly on an optimized design. These control

models also allow shortcomings of the particular program soft-

ware or model design approach to become more apparent and

identifiable.

An example of using numerical modeling to characterize

components in ways that facilitate system design optimization

is in the area of blend functions. In this case, a dimensionless

blend number can be used to describe the degree of blendingachieved. This blend number Bn

can be described as follows:

Bn = 1 - 1/0

where 0 is the standard deviation of the component at the inlet

of the dispersion or mix zone and 1 is the standard deviation

of the component at the outlet of the dispersion or mix zone.

As the standard deviation at the outlet of the zone (1) drops

with respect to the inlet standard deviation (0), the blend num-

ber will asymptotically approach a value of 1. Therefore, the

higher the blend number, the greater the degree of blend.

However, a high blend number does not necessarily indicate

a high blend or mixing efficiency. It may have been achieved

by very long lengths or by high energy consumption. There are

three primary degrees of freedom. One is the process result,

such as the degree of homogeneity required. The others are

length and energy. As the length variable drops, the energy re-

quirement increases and vice-versa. If you find you are not

achieving the process result within the allowable length and

pressure drop, then a higher efficiency system must be sought

or the required process result must be lowered.

Both the spatial and energy efficiencies of devices and ar-

rangements can be assessed to provide insight into the optimi-

zation exercise. Spatial efficiencies provide an indication of the

degree of blend achievable per unit length or volume. Energy

efficiency values provide an indication of the degree of blend

per unit energy dissipation rate. A bend, an expansion or con-

traction, and even a straight length of flue can perform a static

mixing function. Alone, their efficiency is often low in terms of

achieving a blending process result. However, when augmented

with internals designed to capitalize on the arrangement and

the flow profiles throughout, their efficiency with regard to

blending can be enhanced. Test programs utilizing numerical

modeling as an analytical tool can be performed to map these

characteristics.

Optimization ExercisesModel construction and run time are major considerations.

Higher order turbulence models require both greater capital in-

vestments in software and computing power, as well as the time

requirements to construct the model and run cases to conver-

gence. With an increasing demand for fast project execution,

the project schedule can often dictate the allowable model de-

tail and number of iterations. While reductions in program de-

tail can save time, they also tend to generate conservatism in

the results. With simpler numerical models demonstrating them-

selves as compelling tools for assessment of the performancechanges versus configurat ion changes, one has to be very astute

to not over- or under-detail the model structure.

The output can be configured in ways that facilitate post-

processing by either system performance programs or optimi-

zation exercises. For example, the output of the model can be

configured for direct input into reactor performance profiling

programs. In this way, the reactor performance based on the

catalyst activity, volume and inlet profile variations can be predicted

to assess allowable operating and control system response tolerances.

Often a target value for NH3/NOx molar ratio at the catalyst

face is first attempted by adjustment to the ammonia injection

grid design and/or flow profiles through the plane of injection.

Figure 2 provides a hypothetical case of resultant molar ratio

distribution at the catalyst face. In this example the NH3/NOxcoefficient of variation at catalyst face is approximately 17%

when no static mixing or ammonia injection zone flux adjust-

ments are made. For visualization purposes, Figure 2 shows the

concentration to each zone as a simple percent deviation about the

mean for each of forty-two zones across a reactor inlet cross-section.

In this case, the target molar ratio coefficient of variation at

the catalyst face was 7%. Fortunately, the system design could

tolerate some pressure drop expenditure for velocity profile

adjustment upstream of the ammonia injection grid (AIG) and

on a static mixing function downstream of the AIG. After sev-

eral options were modeled, the best distribution achievable

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within the allowable pressure drop was a 9% coefficient of varia-

tion. Figure 3 depicts this distribution as the percent deviationabout the mean.

In both cases, no ammonia injection grid tuning is used; that

is, the ammonia flow to each injection grid zone is identical

and constant. The ammonia concentration profile produced at

the reactor inlet cross-section from individual injection zones

was predicted to facilitate AIG flow tuning. Figure 4 provides

an indication of the coverage a single injection zone has in this

particular arrangement.

Modeling of a case where the flow of ammonia to each in-

jection zone is biased to help account for NOx, and primarily

velocity maldistribution at the ammonia injection plane, gave

confidence that the target value could be achieved with a pre-

dicted 5.3% standard deviation from the mean. Figure 5 depicts

this distribution as the percent deviation about the mean.Models are also valuable in optimizing ammonia injection

zone quantity and geometry. The projected dispersion pattern

at the catalyst face based on variations to ammonia injection

zone design can be analyzed to provide the maximum control-

lability with the minimum zone quantity. Not only does this

knowledge assist in design optimization efforts, but it can give

insight to field start-up crews on the projected responsiveness

of each ammonia zone flow control valve.

SummaryNumerical modeling will be continually influenced by ad-

vancements in programming and computing power. Equally

important, however, is the degree of application-specific expe-

rience, which often proves to be the key to a successful optimi-

zation in a timely fashion.

Verification of model predictions with actual field test re-

sults has and will continue to provide valuable feedback for

model development and refinement. Confidence in the numeri-

cal modeling approach will be increased as further field results

and numerical predictions are obtained and compared. As model

libraries are expanded and comparisons are documented, the

application of numerical models for design optimization will

be similarly improved.

Figure 1 Contours of static pressure on a plane through theSCR system.

0.0

-0.5

-1.0

-1.5

-2.0

Static Pressure (in. H2O)

Figure 2 NH3/NO

xratio without static mixing or injection zone

biasing.

Deviation from Mean, %

Figure 3 NH3/NO

xratio with static mixing and no injection zone

biasing.

Deviation from Mean, %

Figure 4 AIG control profi le.

NH3, ppmdv

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Copyright 2000 by The Babcock & Wilcox Company,All rights reserved .

No part of th is work may be publi shed, translated or reproduced in any form or by any means, or incorporated into any informat ion retrieval system,without the written permission of the copyright holder. Permission requests should be addressed to: Market Communications, The Babcock &Wilcox Company, P.O. Box 351, Barberton, Ohio, U.S.A. 44203-0351.

Disclaimer

Although the information presented in this work is be lieved to be reliable , this work is pub lished with the underst anding that The Babcock & WilcoxCompany and the authors are supplying general information and are not attempting to render or provide engineering or professional services.

Neither The Babcock & Wilcox Company nor any of its employees make any warranty, guarantee, or representat ion, whether expressed or implied,with respect to the accuracy, completeness or usefulness of any information, product, process or apparatus discussed in this work; and neither The

Babcock & Wilcox Company nor any of i ts employees shall be l iable for any losses or damages with respect to or result ing f rom the use of , or theinability to use, any information, product, process or apparatus discussed in this work.

Figure 5 Mixing plus biasing.

Deviation from Mean, %

In addition to specific project modeling and optimization,

numerical modeling is becoming an increasingly valuable tool

for mapping and characterizing the performance of discrete com-

ponents within the system. As the knowledge of the performance

of discrete components and arrangements grows, so does the

ability to select and choose the most appropriate design path in

the most expeditious manner.

References1. K. Rogers, M. Milobowski, and B. Wooldridge, Perspec-tives on Ammonia Injection and Gaseous Static Mixing in SCR

Retrofit Applications. Presented at EPRI-DOE-EPA Combined

Utility Air Pollutant Control Symposium, Atlanta, Georgia,

August 16-20, 1999.

2. A. Sayre and M. Milobowski, Validation of Numerical

Models of Flow Through SCR Units. Presented at EPRI-DOE-

EPA Combined Utility Air Pollutant Control Symposium, At-

lanta, Georgia, August 16-20, 1999.