esp gas flow fundamentals - airflow sciences · 1 airflow sciences corporation esp gas flow...

11Airflow Sciences Corporation

ESP Gas Flow FundamentalsESP Gas Flow Fundamentals

Robert Mudry, P.E.Vice President – EngineeringAirflow Sciences [email protected]

ESP/FF Round Table & ExpositionAugust 12, 2002

Robert Mudry, P.E.Robert Mudry, P.E.Vice President Vice President –– EngineeringEngineeringAirflow Sciences CorporationAirflow Sciences Corporationrmudryrmudry@@airflowsciencesairflowsciences.com.com

ESP/FF Round Table & ExpositionESP/FF Round Table & ExpositionAugust 12, 2002August 12, 2002


OutlineOutline

v Introduction

v ESP Fluid Flow Basics

v Assessing Flow Characteristics

v ESP Flow Modeling

v Case Studies

v Questions

vv IntroductionIntroduction

vv ESP Fluid Flow BasicsESP Fluid Flow Basics

vv Assessing Flow CharacteristicsAssessing Flow Characteristics

vv ESP Flow ModelingESP Flow Modeling

vv Case StudiesCase Studies

vv QuestionsQuestions


IntroductionIntroduction

v Why Worry About Fluid Dynamics?• Strong influence on performance of pollution control

equipment (ESP, FF, SCR, LNB, Scrubber, etc.)

• Relatively low cost performance enhancements are possible

v Example Cases

v About Your Speaker

vv Why Worry About Fluid Dynamics?Why Worry About Fluid Dynamics?•• Strong influence on performance of pollution control Strong influence on performance of pollution control

equipment (ESP, FF, SCR, LNB, Scrubber, etc.)equipment (ESP, FF, SCR, LNB, Scrubber, etc.)

•• Relatively low cost performance enhancements are possibleRelatively low cost performance enhancements are possible

vv Example CasesExample Cases

vv About Your SpeakerAbout Your Speaker


Example CasesExample Cases

v How important is flow distribution?vv How important is flow distribution?How important is flow distribution?

Improved dust capture Improved dust capture reduces opacity to 7%; reduces opacity to 7%; system pressure loss reduced system pressure loss reduced by 5 inches Hby 5 inches H22OO

High opacity (14%) High opacity (14%) and high pressure and high pressure loss cause high loss cause high operating costsoperating costs

Essroc Essroc Materials Materials Nazareth Unit 1Nazareth Unit 1

23% reduction in particulate 23% reduction in particulate emissions allows load emissions allows load increase of 150 MW per unitincrease of 150 MW per unit

High opacity causes High opacity causes 240 MW 240 MW derate derate per per unitunit

Southern California Southern California Edison Mohave Edison Mohave Units 1&2Units 1&2

Full load opacity less than 5%Full load opacity less than 5%Full load opacity 25%Full load opacity 25%Mississippi Power Mississippi Power Watson Unit 5Watson Unit 5

After Flow ImprovementsAfter Flow ImprovementsBaseline PerformanceBaseline PerformancePlantPlant


About Your SpeakerAbout Your Speakerv BSE, MSE Aerospace Engineering – University of Michigan

v 13 years as fluid dynamics consultant to industry

v Involved in 300+ testing/modeling projects

v Institute of Clean Air Companies (ICAC) member

v Author of 6 power industry technical papers

v Registered Professional Engineer – MI, NC, VA

vv BSE, MSE Aerospace Engineering BSE, MSE Aerospace Engineering –– University of MichiganUniversity of Michigan

vv 13 years as fluid dynamics consultant to industry13 years as fluid dynamics consultant to industry

vv Involved in 300+ testing/modeling projectsInvolved in 300+ testing/modeling projects

vv Institute of Clean Air Companies (ICAC) memberInstitute of Clean Air Companies (ICAC) member

vv Author of 6 power industry technical papersAuthor of 6 power industry technical papers

vv Registered Professional Engineer Registered Professional Engineer –– MI, NC, VAMI, NC, VA


OutlineOutline

v Introduction

v ESP Fluid Flow Basics• Gas Velocity Distribution

½ Ductwork½ Collection Region

• Gas Flow Balance• Pressure Drop• Gas Temperature• Gas Conditioning


v ESP Flow Modeling

v Case Studies

v Questions


vv ESP Fluid Flow BasicsESP Fluid Flow Basics•• Gas Velocity DistributionGas Velocity Distribution

½½ DuctworkDuctwork½½ Collection RegionCollection Region

•• Gas Flow BalanceGas Flow Balance•• Pressure DropPressure Drop•• Gas TemperatureGas Temperature•• Gas ConditioningGas Conditioning






Gas Velocity Distribution Gas Velocity Distribution –– DuctworkDuctwork

v Ductwork Design Criteria• Maintain minimum

velocity requirementsto avoid particle dropout

• Provide good flow characteristics to ESP

v Considerations• Horizontal surfaces • Cross sectional area• Bends• Structure

vv Ductwork Design CriteriaDuctwork Design Criteria•• Maintain minimumMaintain minimum

velocity requirementsvelocity requirementsto avoid particle dropoutto avoid particle dropout

•• Provide good flow Provide good flow characteristics to ESPcharacteristics to ESP

vv ConsiderationsConsiderations•• Horizontal surfaces Horizontal surfaces •• Cross sectional areaCross sectional area•• BendsBends•• StructureStructure


Gas Velocity Distribution Gas Velocity Distribution ––Collection RegionCollection Region

v Uniform Flow Concept• ESP inlet & outlet planes

v Industry Standards• ICAC

• % RMS Deviation

v “Skewed” Flow Concepts

vv Uniform Flow ConceptUniform Flow Concept•• ESP inlet & outlet planesESP inlet & outlet planes

vv Industry StandardsIndustry Standards•• ICACICAC

•• % RMS Deviation% RMS Deviation

vv “Skewed” Flow “Skewed” Flow ConceptsConcepts

ICAC: 85% of velocities � 1.15 * Vavg99% of velocities � 1.40 * Vavg

Other: % RMS Deviation � 15% of Vavg


v Flow Control Methods• Vanes, baffles

• Flow straighteners

• Perforated plates

vv Flow Control MethodsFlow Control Methods•• Vanes, bafflesVanes, baffles

•• Flow Flow straightenersstraighteners

•• Perforated platesPerforated plates

Gas Velocity Distribution Gas Velocity Distribution ––Collection RegionCollection Region


Gas Flow BalanceGas Flow Balance

v Industry Standards

v Control Methods

vv Industry StandardsIndustry Standards

vv Control MethodsControl Methods

21 %

35 %

26 %

18 %

Percent of total mass flow through each chamber

ICAC: Flow within each chamber to bewithin ±10% of its theoretical share


Pressure DropPressure Dropv General goal:

• Minimize DP

v Methods• Vanes

• Duct contouring

• Area management

vv General goal: General goal: •• Minimize DPMinimize DP

vv MethodsMethods•• VanesVanes

•• Duct contouringDuct contouring

•• Area managementArea management

Flow

Ductwork redesign saves 2.1 inches H2O over baseline


Gas TemperatureGas Temperature

v Average temperature

v Temperature stratification

v Inleakage

vv Average temperatureAverage temperature

vv Temperature stratificationTemperature stratification

vv InleakageInleakage

Temperature

Res

istiv

ity


Gas ConditioningGas Conditioning

v Modify ash resistivity• SO3, ammonia, others

v Alter gas density, viscosity• Humidification

vv Modify ash Modify ash resistivityresistivity•• SOSO33, ammonia, others, ammonia, others

vv Alter gas density, viscosityAlter gas density, viscosity•• HumidificationHumidification

Low SO3 Concentration

High SO3Concentration

SO3 Concentration

Humidification principle:m = * v * A

m, A = constant= f (T)

If T is reduced, increasesThus v decreases when water is added

Temperature

Res

istiv

ity

5 ppm SO3


OutlineOutline

v Introduction


v Assessing Flow Characteristics• Inspections

• Field Testing½ Ductwork

½ Collection Region

v ESP Flow Modeling

v Case Studies

v Questions



vv Assessing Flow CharacteristicsAssessing Flow Characteristics•• InspectionsInspections

•• Field TestingField Testing½½ DuctworkDuctwork

½½ Collection RegionCollection Region





InspectionsInspections

v Ash Patterns

v Geometry Influence on Fluid Dynamics

v Irregularities

vv Ash PatternsAsh Patterns

vv Geometry Influence on Geometry Influence on Fluid DynamicsFluid Dynamics

vv IrregularitiesIrregularities


Field Testing Field Testing –– DuctworkDuctwork

v Velocity

v Temperature

v Pressure

v Particulate

v Resistivity

v Chemical Species

vv VelocityVelocity

vv TemperatureTemperature

vv PressurePressure

vv ParticulateParticulate

vv ResistivityResistivity

vv Chemical SpeciesChemical Species


Field Testing Field Testing –– Collection RegionCollection Regionv Velocity Distribution

• Cold flow conditions

• Vane anemometer½ Accuracy 1% in 3-10 ft/sec range

½ Lightweight, portable

½ Sensitive to flow angularity, turbulence, dust

vv Velocity DistributionVelocity Distribution•• Cold flow conditionsCold flow conditions

•• Vane anemometerVane anemometer½½ Accuracy 1% in 3Accuracy 1% in 3--10 ft/sec range10 ft/sec range

½½ Lightweight, portableLightweight, portable

½½ Sensitive to flow angularity, turbulence, dustSensitive to flow angularity, turbulence, dust


OutlineOutline

v Introduction



v ESP Flow Modeling• Physical Models

• Computational Fluid Dynamics (CFD) Models

v Case Studies

v Questions




vv ESP Flow ModelingESP Flow Modeling•• Physical ModelsPhysical Models

•• Computational Fluid Dynamics (CFD) ModelsComputational Fluid Dynamics (CFD) Models




ESP Modeling ESP Modeling –– Physical ModelsPhysical Models

v Background

v Theory

v Simulation Parameters (how the model is set up)

v Results Analysis (what you get from the model)

vv BackgroundBackground

vv TheoryTheory

vv Simulation Parameters (how the model is set up)Simulation Parameters (how the model is set up)

vv Results Analysis (what you get from the model)Results Analysis (what you get from the model)


Physical Models Physical Models –– BackgroundBackground

v Utilized for fluid flow analysis for a century … or more?

v Applied to ESPs for decades

v Underlying principle is to reproduce fluid flow behavior in a controlled, laboratory environment

vv Utilized for fluid flow analysis for a century … or more?Utilized for fluid flow analysis for a century … or more?

vv Applied to Applied to ESPs ESPs for decadesfor decades

vv Underlying principle is to reproduce fluid flow behavior in a Underlying principle is to reproduce fluid flow behavior in a controlled, laboratory environmentcontrolled, laboratory environment


Physical Models Physical Models –– TheoryTheory

v Key criteria is to generate “Similarity” between the scale model and the real-world object• Geometric similarity

½ Accurate scale representation of geometry

½ Inclusion of all influencing geometry elements (typically those >4”)

½ Selection of scale can be important

• Fluid dynamic similarity½ Precise Reynolds Number (Re) matching is not feasible

½ General practice is to match full scale velocity but ensure that Re remains in the turbulent range throughout the model

vv Key criteria is to generate “Similarity” between the Key criteria is to generate “Similarity” between the scale model and the realscale model and the real--world objectworld object•• Geometric similarityGeometric similarity

½½ Accurate scale representation of geometryAccurate scale representation of geometry

½½ Inclusion of all influencing geometry elements (typically those Inclusion of all influencing geometry elements (typically those >4”)>4”)

½½ Selection of scale can be importantSelection of scale can be important

•• Fluid dynamic similarityFluid dynamic similarity½½ Precise Reynolds Number (Re) matching is not feasiblePrecise Reynolds Number (Re) matching is not feasible

½½ General practice is to match full scale velocity but ensure thatGeneral practice is to match full scale velocity but ensure that Re remains Re remains in the turbulent range throughout the modelin the turbulent range throughout the model

Re =v Dh


Physical Models Physical Models ––Simulation ParametersSimulation Parameters

v ESP geometry½ 1/8th to 1/16th scale

representation

½ Include features >4” in size

v Flow conditions½ Scaled air flow rate (ambient

temperature)

½ Reproduce velocity profile at model inlet

½ Simulated chemical injection

½ Simulated particle tracking

vv ESP geometryESP geometry½½ 1/8th to 1/16th scale 1/8th to 1/16th scale

representationrepresentation

½½ Include features >4” in sizeInclude features >4” in size

vv Flow conditionsFlow conditions½½ Scaled Scaled airair flow rate (ambient flow rate (ambient

temperature)temperature)

½½ Reproduce velocity profile at Reproduce velocity profile at model inletmodel inlet

½½ Simulated chemical injectionSimulated chemical injection

½½ Simulated particle trackingSimulated particle tracking


Physical Models Physical Models –– Results AnalysisResults Analysis

v Quantitative data available at discrete measurement points• Velocity magnitude, directionality• Pressure (corrected to full scale)• Chemical species concentrations

v Integrated/reduced data• Mass balance between ESP chambers• Comparison to ICAC conditions or

target velocity profiles• Correlation to test data

v Qualitative data• Flow directionality (smoke, tufts)• Particle behavior, drop-out

vv Quantitative data available at discrete measurement pointsQuantitative data available at discrete measurement points•• Velocity magnitude, directionalityVelocity magnitude, directionality•• Pressure (corrected to full scale)Pressure (corrected to full scale)•• Chemical species concentrationsChemical species concentrations

vv Integrated/reduced dataIntegrated/reduced data•• Mass balance between ESP chambersMass balance between ESP chambers•• Comparison to ICAC conditions or Comparison to ICAC conditions or

target velocity profilestarget velocity profiles•• Correlation to test dataCorrelation to test data

vv Qualitative dataQualitative data•• Flow directionality (smoke, tufts)Flow directionality (smoke, tufts)•• Particle behavior, dropParticle behavior, drop--outout


Flow Modeling Flow Modeling ––Computational Fluid Dynamics (CFD)Computational Fluid Dynamics (CFD)

v Background

v Theory

v Simulation Parameters (how the model is set up)

v Results Analysis (what you get from the model)

vv BackgroundBackground

vv TheoryTheory

vv Simulation Parameters (how the model is set up)Simulation Parameters (how the model is set up)

vv Results Analysis (what you get from the model)Results Analysis (what you get from the model)


CFD CFD –– BackgroundBackground

v Developed in the aerospace industry c.1970 (with the advent of “high speed” computers)

v Applied to ESPs for 15+ years

v Underlying principle is to solve the first-principles equations governing fluid flow behavior using a computer

vv Developed in the aerospace industry c.1970 (with the advent Developed in the aerospace industry c.1970 (with the advent of “high speed” computers)of “high speed” computers)

vv Applied to Applied to ESPs ESPs for 15+ yearsfor 15+ years

vv Underlying principle is to solve the firstUnderlying principle is to solve the first--principles principles equations governing fluid flow behavior using a computerequations governing fluid flow behavior using a computer

Sou

rce:

NA

SA


CFD CFD –– TheoryTheory

v Control Volume Approach• Divide the flow domain into distinct control volumes

• Solve the Navier-Stokes equations (Conservation of Mass, Momentum, Energy) in each control volume

vv Control Volume ApproachControl Volume Approach•• Divide the flow domain into distinct control volumesDivide the flow domain into distinct control volumes

•• Solve the Solve the NavierNavier--Stokes equations (Conservation of Mass, Stokes equations (Conservation of Mass, Momentum, Energy) in each control volumeMomentum, Energy) in each control volume

Inflow Outflow

Control Volume or “Cell”

ESP model with 850,000 cells


CFD CFD –– Simulation ParametersSimulation Parameters

v ESP geometry½ Full scale representation½ Include features >4” in size,

more detail if possible

v Flow conditions½ Full scale gas flow rate½ Reproduce velocity profile at

model inlet½ Reproduce temperature profile

at model inlet½ Simulated chemical injection½ Simulated particle tracking

vv ESP geometryESP geometry½½ Full scale representationFull scale representation½½ Include features >4” in size, Include features >4” in size,

more detail if possiblemore detail if possible

vv Flow conditionsFlow conditions½½ Full scale gas flow rateFull scale gas flow rate½½ Reproduce velocity profile at Reproduce velocity profile at

model inletmodel inlet½½ Reproduce temperature profile Reproduce temperature profile

at model inletat model inlet½½ Simulated chemical injectionSimulated chemical injection½½ Simulated particle trackingSimulated particle tracking


CFD CFD –– Results AnalysisResults Analysis

v Quantitative data available at all control volumes• Velocity magnitude,

directionality• Temperature• Pressure• Turbulence• Chemical species

concentrations• Particle trajectories

v Integrated/reduced data• Mass balance between ESP chambers• Comparison to ICAC conditions or target velocity profiles• Correlation to test data

vv Quantitative data available at all control volumesQuantitative data available at all control volumes•• Velocity magnitude, Velocity magnitude,

directionalitydirectionality•• TemperatureTemperature•• PressurePressure•• TurbulenceTurbulence•• Chemical species Chemical species

concentrationsconcentrations•• Particle trajectoriesParticle trajectories

vv Integrated/reduced dataIntegrated/reduced data•• Mass balance between ESP chambersMass balance between ESP chambers•• Comparison to ICAC conditions or target velocity profilesComparison to ICAC conditions or target velocity profiles•• Correlation to test dataCorrelation to test data


OutlineOutline

v Introduction



v ESP Flow Modeling

v Case Studies• Reducing Forced Outages for Hot Side ESP Cleaning• Improving Capture Efficiency to Avoid MW Derates• Gas Conditioning System Design

v Questions





vv Case StudiesCase Studies•• Reducing Forced Outages for Hot Side ESP CleaningReducing Forced Outages for Hot Side ESP Cleaning•• Improving Capture Efficiency to Avoid MWImproving Capture Efficiency to Avoid MW DeratesDerates•• Gas Conditioning System DesignGas Conditioning System Design



Reducing Forced Outages for ESP CleaningReducing Forced Outages for ESP Cleaning

v Hot side ESP

v Southeast U.S.

v 185 MW unit

v ESP cleaning required every 2-3 months to operate within opacity limits

v Unit derate and eventual forced outage as ESP capture performance degrades

vv Hot side ESPHot side ESP

vv Southeast U.S.Southeast U.S.

vv 185 MW unit185 MW unit

vv ESP cleaning required every 2ESP cleaning required every 2--3 months to operate 3 months to operate within opacity limitswithin opacity limits

vv Unit Unit derate derate and eventual forced outage as ESP and eventual forced outage as ESP capture performance degradescapture performance degrades


Reducing Forced Outages for ESP CleaningReducing Forced Outages for ESP Cleaning

v Known problem: Poor side-to-side gas velocity distribution within collection region

v Solution: Expand flow more efficiently in the ESP inlet ductwork

v Result: ESP operates for 12 months without cleaning; no derates due to opacity

vv Known problem: Poor sideKnown problem: Poor side--toto--side gas velocity side gas velocity distribution within collection regiondistribution within collection region

vv Solution: Expand flow more efficiently in the ESP Solution: Expand flow more efficiently in the ESP inlet ductworkinlet ductwork

vv Result: ESP operates for 12 months without Result: ESP operates for 12 months without cleaning; no cleaning; no derates derates due to opacitydue to opacity


Avoiding MW Avoiding MW DeratesDerates

v Cold side ESP

v Western U.S.

v Two 790 MW units

v Undersized ESPs

v Both units regularly derated by 240 MW to operate within opacity limits

vv Cold side ESPCold side ESP

vv Western U.S.Western U.S.

vv Two 790 MW unitsTwo 790 MW units

vv Undersized Undersized ESPsESPs

vv Both units regularly Both units regularly derated derated by 240 MW to operate by 240 MW to operate within opacity limitswithin opacity limits


Avoiding MW Avoiding MW DeratesDerates

v Baseline CFD modeling indicates poor gas velocity distribution within collection region

v Solution: Redesign flow control devices (turning vanes, perforated plates)

vv Baseline CFD modeling indicates poor gas velocity Baseline CFD modeling indicates poor gas velocity distribution within collection regiondistribution within collection region

vv Solution: Redesign flow control devices (turning Solution: Redesign flow control devices (turning vanes, perforated plates)vanes, perforated plates)

v Results• 23% reduction in

particulate emissions

• Output increased by 150 MW per unit

vv ResultsResults•• 23% reduction in 23% reduction in

particulate emissionsparticulate emissions

•• Output increased by 150 Output increased by 150 MW per unitMW per unit


Gas Conditioning System DesignGas Conditioning System Design

v Cold side ESP

v Midwest U.S.

v 422 MW unit

v Humidification system injects water into ESP inlet ductwork

vv Cold side ESPCold side ESP

vv Midwest U.S.Midwest U.S.

vv 422 MW unit 422 MW unit

vv Humidification system injects water into ESP inlet Humidification system injects water into ESP inlet ductworkductwork

v Severe buildup on internal structure causes forced outages and high maintenance costs

vv Severe buildup on internal Severe buildup on internal structure causes forced outages structure causes forced outages and high maintenance costsand high maintenance costs


Gas Conditioning System DesignGas Conditioning System Design

v Baseline CFD modeling indicates water droplets do not evaporate completely before impacting structure

v Solution: Redesign spray nozzles and internal structure

vv Baseline CFD modeling indicates water droplets do Baseline CFD modeling indicates water droplets do not evaporate completely before impacting structurenot evaporate completely before impacting structure

vv Solution: Redesign spray nozzles and internal Solution: Redesign spray nozzles and internal structurestructure

v Results: Minimal material buildup, elimination of forced outages

vv Results: Minimal material Results: Minimal material buildup, elimination of buildup, elimination of forced outagesforced outages


Questions?Questions?

If you would like an electronic copy of this presentation, please contact Rob Mudry as follows:[email protected]. 734-464-8900

esp gas flow fundamentals - airflow sciences · 1 airflow sciences corporation esp gas flow...

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