quality assurance project plan cfd modeling for ut/tceq low...

February 2011, Rev. 4

Air Quality Research Program Project 10-022

Quality Assurance Project Plan

CFD Modeling for UT/TCEQ Low BTU & Low Flow Rate Flare Tests

Prepared by:

Daniel Chen, PI

Helen Lou, Co-PI Kuyen Li, Co-PI

Dan F. Smith Department of Chemical Engineering, Lamar University

Christopher Martin, Co-PI Department of Chemistry & Biochemistry, Lamar University

X. Chang Li, Co-PI Department of Mechanical Engineering, Lamar University

1.2 Distribution List Jim MacKay TCEQ Project Liaison Air Quality Division Texas Commission on Environmental Quality Austin, Texas Mr. Vincent M. Torres Project Manager Air Quality Research Program The University of Texas at Austin, Austin, Texas Dr. Daniel H. Chen Principal Investigator Lamar University, Beaumont, Texas 1.3 General Requirements This project is a secondary data project. A secondary data project involves the gathering and /or use of existing environmental data for purposes other than those for which they were originally collected. This document is based on EPA’s National Risk Management Research Laboratory (NRMRL) QAPP for secondary data project and satisfies a Category III level of QA.

2. Table of Contents

Section Description Page No

1 Title and Approval Sheets 1 1.1 Approval Page 2 1.2 Distribution List 3 1.3 General Requirements 3 2 Table Of Contents 4 3 Project Description and Objectives

3.1 Project Description 5 3.2 Project Objectives 8 4 Organization and Responsibilities

4.1 Project Personnel 9 4.2 Project Schedule 12 5 Project Approach

5.1 Computational Fluid Dynamics 18 5.2 Combustion Mechanism Generation 19 5.3 Combustion Mechanism Validation 23 5.4 Geometry Creation 23 5.5 Secondary Data needed 24 5.6 Secondary Data Source 26 5.7 Physical/Turbulence Models 26 6 Project Quality Metrics

6.1 Quality Requirements 28 6.2 CFD Model Uncertainty 32 7 Data Analysis, Interpretation and Management

7.1 Cross project review by air quality research program 34 7.2 Results comparison 34 8 Data Reporting and Quality Assurance & Control Procedures

8.1 Data Storage & Reporting 36 8.2 Control Flare Test data 36 8.3 Mechanism & Fluid Dynamics Uncertainties 36 8.4 Quality Assurance & Control Procedures 37 9 References 38

Quality Assurance Project Plan

3. PROJECT DESCRIPTION AND OBJECTIVES

3.1 Project Description

Current methodologies for calculating speciated and total VOC emissions from

flaring activities generally apply a simple mass reduction to the VOC species sent to the

flare1. In most cases 98% is used as the destruction and removal efficiency (DRE) for the

flare without any intermediate VOC species generated or emitted by the combustion

process. Basic combustion chemistry demonstrates that many intermediate VOC species

are formed by the combustion process. While it is assumed that a flare operating under

its designed conditions and in compliance with 40 CFR 60.18 may achieve 98% DRE or

higher a flare operating outside of these parameters may have a DRE lower than 98%2.

Other factors that may affect DRE and the combustion efficiency (CE) include

environmental factors such as cross wind, ambient temperature, and humidity3,4,5

In this project, computational fluid dynamics (CFD) methods based on CHEMKIN –

CFD and FLUENT are used to model low-Btu, low-flow rate propylene/TNG/nitrogen

flare tests conducted during September, 2010 in the John Zink test facility in Tulsa,

Oklahoma6. In these flare performance tests, plume measurements using both remote

sensing and direct extraction were carried out to determine flare efficiencies and

concentration/location of regulated and photo chemically important pollution species for

air-assist and steam-assist flares. Various combinations of fuel BTU and flow rates were

performed under open-air conditions. This proposed project will primarily use CFD

modeling as a predicting tool for the Tulsa flare performance tests. However, if the test

data is available by May 31, 2011, the CFD modeling will be further compared with the

flare performance data, i.e., flare efficiencies, and other measurement data (e.g., species

concentrations) reported in the TCEQ Comprehensive Flare Study Project. If the

difference between the model value and the test data is within the combined measurement

and prediction uncertainty limit (e.g., ±39% for CE & ±30%% for CO, and a factor of 3

for minor species such as formaldehyde, details see Sec. 7.2), the model value and the

test data will be considered as in good agreement. This modeling tool has the potential to

help TCEQ’s on-going evaluation on flare emissions and to serve as a basis for a future

SIP revision7.

Lamar University will model the data collected from the TCEQ Comprehensive Flare

Study Project (PGA No. 582-8-862-45-FY09-04)6 using computational fluid dynamics

(CFD) programs. The modeling programs used by Lamar University shall be CHEMKIN

and FLUENT.

GRI-Mech 3.0 is an optimized mechanism designed to model natural gas (C1)

combustion, including NO formation and reburn chemistry while USC is an Optimized

Reaction Model of C1-C3 Combustion but lacks chemistry needed to define NO

formation for flaring in air. So the inclusion of NO formation chemistry from the GRI

mechanism will make the USC mechanism suitable for modeling Tulsa test flares (that

burn fuel C1 & C3 and measure NO emission). Ideally the combined and individually

optimized components can be optimized again as a whole. From an engineering point of

view, since that bulk of the mechanism (USC) has been optimized, we only need to

demonstrate this mechanism is applicable and is validated. Indeed, this mechanism has

been tested against methane, ethylene, and propylene experimental data like laminar

flame speed, adiabatic flame temperature, and ignition delay8. We will also add a couple

of NOx species that are important to atmospheric chemistry (NO2 and HONO) to the

existing mechanism and a comparison with lab data will be carried out to evaluate this

new mechanism.9

Lamar University will acquire the operating and design data of the flare tests

conducted at the John Zink facility in Tulsa, OK from the University of Texas. These

input data should include the geometry of the steam-assist and air-assist flares used in the

tests (AutoCad sketch with data preferred), meteorological data (cross-wind

speed/direction, humidity, temperature), and the operating data (aeration, steaming, exit

velocity, waste gas/pilot fuel species) available from the data acquisition system. If

Lamar University acquires the flare test data conducted at the John Zink facility in Tulsa,

OK from the University of Texas by May 31, 2011, the test data will be compared with

the model results to see if they are in good agreement, i.e., within the combined

uncertainty limits. The flare test data include performance data: Combustion Efficiency

(CE)/ Destruction & Removal Efficiencies (DRE) and other measured data:

concentration/location/path of monitored emission species (VOC, CO/CO2, and NOx)

and plume temperature/ location/path. Regulated/monitored flare emissions include O2,

NO, NO2, CO, CO2, CH4, C2H2 (Acetylene), C2H4 (Ethylene), C3H6 (Propylene),

CH2O (Formaldehyde), C2H4O (Acetaldehyde), and C3H6O (Acetone) will also be

predicted10.

Combustion efficiency is defined as the percentage of flare emissions that are completely

oxidized to CO2. It can be written mathematically as:

100SootTHCCOCO

CO CE%2

2 ×+++

= (4.1)

Where:

CO2 - parts per million by volume of carbon dioxide

CO - parts per million by volume of carbon monoxide exiting from the flare

THC - parts per million by volume of total hydrocarbon exiting from the flare

Soot - parts per million by volume of soot as carbon

Soot is eliminated from industrial flares by adding appropriate amounts of steam or air

and that is the reason it can be equal to zero in the above equation.

The destruction & removal efficiency is given as (using propylene as an

example):

Propylene Destruction Eff. = (Amount of C3H6 fed – Amount of unburned C3H6)×100 (4.2)

Amount of CH4 fed

3.2 Project Objectives

The proposed project will:

1) Model the low-BTU, low-flow rate Propylene/TNG/Nitrogen flare tests conducted

during September 2010 in Tulsa, Oklahoma for the TCEQ Comprehensive Flare

Study Project, using the detailed reaction mechanisms and Fluent CFD software.

2) Predict the test results: flare efficiencies (DRE/CE) and emissions using the CFD

modeling

3) Compare the CFD prediction results with the flare test data (efficiencies and

emissions) if available by May 31, 2011.

4. ORGANIZATION AND RESPONSIBILITIES

4.1 Project personnel

• Dr. Daniel Chen, Principal Investigator

o Work Plan, Flare Test Operation/Design/Performance data, Model Development/

Base Case Presentations, CFD modeling & Post processing, Comparison CFD Prediction

and Flare Test Data , and Reporting (Tasks 1, 2, 5C, 6, 7, 8)

• Dr. Helen Lou, Co-PI

o Work Plan, Hardware/Software acquisition, Data Storage, Mechanism

Development, and Reporting (Tasks 1, 3, 4B, 8)

• Dr. Kuyen Li, Co-PI

o Work Plan, CFD Modeling & Post processing, and Comparison CFD Prediction

and Flare Test Data , and Reporting (Tasks 1, 6A, 6C, 7, 8)

• Dr. Christopher Martin, Co-PI

o Work Plan, Combustion Mechanism Development, CFD modeling & Post

processing, and Reporting (Tasks 1, 4A, 6A, 6C, 8)

• Dr. X. Chang Li, Co-PI

o Geometry generation, CFD boundary conditions, Physical/Turbulence Model

Selection & Parameter Evaluation, and Reporting (Tasks 1, 5A, 5B, 5D, 8)

Table 4.1 Task Chart: Task vs. Responsible Faculty

No. Tasks Responsible Faculty

Chen Lou K. Li Martin X. Li 1 Work Plan X X X X X 2 Collection of Flare

Operation/Design/Performance Data X

3 Hardware/Software Acquisition and Data Storage

X

4 Combustion Mechanism Development

a Combustion Mechanism Generation X b Combustion Mechanism Validation X 5 CFD Model Development a Geometry Creation & Boundary

Conditions X b Physical/Turbulence Model

Selection & Parameter Evaluation X c Model Development Presentation X d CFD Model Calibration X 6 CFD Modeling & Post Processing a Modeling Base Case X X X b Base Case presentation X c Modeling rest of the cases X X X

7 Comparison CFD Prediction and Flare Test Data X X

8 Reports a Monthly Report X X X X X b Draft Final Report X X X X X c Final Report X X X X X

Duties of the Students

A. PhD Graduate Students: 2

Working on Combustion Mechanism Generation & Evaluation, CFD Model

Development, Physical/Turbulence Model Selection & Parameter Evaluation, CFD

Model Calibration, and Comparison of CFD Prediction and Flare Test Data (Tasks 4A,

4B, 5B, 5D, 7)

B. MS Research Assistants: 3

Working on Geometry Generation, Input Data File Preparation, CFD Modeling &

Post Processing, Comparison of CFD Prediction and Flare Test Data, and Data Storage.

(Tasks 2, 3, 5A, 6A, 6C, 7)

AQRP - Project Manager

Vincent M. Torres, MSE, PE, MAC

Air Quality Research Program

The University of Texas at Austin

10100 Burnet Road, Bldg. 133 (MC R7100)

Austin, TX 78751

TCEQ - Project Liaison

Air Quality Division/Air Modeling and Data Analysis Section

Texas Commission on Environmental Quality

Mail Code 164, P.O.Box 13087

12118 Park 35 Circle, Bldg. E

Austin, Texas 78711-3087

4.2 Project Schedule

The tasks are outlined below with corresponding deliverable date. The schedule is also

summarized in Table 4.1 & Table 4.2 as a matrix.

Task 1: Work Plan

Lamar University will submit a work plan with a work statement, QAPP, and budget.

The organization and task responsibilities are given below and in Section 5.

Responsible Faculty: D. Chen, H. Lou, K. Li, C. Martin, X. Li

Deliverable 1: Work Plan

Deliverable Date: February 9, 2011

Task 2: Flare Test Operation/Design/Performance Data

Lamar University will acquire the operating and design data of the flare tests conducted

at the John Zink facility in Tulsa, OK from the University of Texas. These data should

include the geometry of the steam-assist and air-assist flares used in the test (AutoCad

sketch with data preferred), meteorological data (cross-wind speed/direction, humidity,

temperature), and the operating data (aeration, steaming, exit velocity, waste gas/pilot

fuel species) available from the data acquisition system. If Lamar University acquires the

test data conducted at the John Zink facility in Tulsa, OK from the University of Texas

by May 31, 2011, the test data will be compared with the model results. The flare

Design/test Operation data from UT will be organized into input files for Fluent

simulations such as geometry generation, fuel/steam/flow/crosswind specifications. Flare

test data, if received by May 31, 2011, will be used to carry out Task 7. We need a base

case of air-assist flare and another base case for steam-assist flare. For each base case, we

need to run 2 more air (or steam) flow rates. We also run 2 more LHV cases for each

flare. So the total number of cases will be 2 (base cases) + 2 (additional air flow rates) +

2 (additional steam flow rates) + 2 (LHVs in air-assist flare) + 2 (LHVs in steam-assist

flare) =10 cases.

Responsible Faculty: D. Chen

Deliverable 2: Included in Monthly Reports

Deliverable Date: March 31, 2011.

Task 3: Hardware/Software Acquisition and Data Storage

Responsible Faculty: H. Lou

Deliverable 3: Included in Monthly and Final Reports

Deliverable Date: Same as the due date for Task 8

Task 4: Combustion Mechanism Development

The details of combustion mechanism development are given in Sections 6.2 - 6.3. The

role of individual faculty is given below and Section 5.

4A Combustion Mechanism Generation

Responsible Faculty: C. Martini (Combustion Mechanism Generation)

Deliverable 4A: Included in Monthly Reports

Deliverable Date: The existing 50 species mechanism is fully functional. The next

version mechanism (USC + GRI 3.0) with additional NOx species will be delivered by

April 30, 2011.

4B Combustion Mechanism Validation

Responsible Faculty: H. Lou

Deliverable 4B: Included in Monthly Reports

Deliverable Date: The data pertinent to the validation (or performance evaluation) of

the new mechanism (USC + GRI 3.0) with additional NOx species will be delivered in

April 30, 2011.

Task 5: CFD Model Development

The details of the CFD model development are given in Sections 6.4 - 6.7.

5A. Geometry Creation & Boundary Conditions

The details of the Geometry Creation & Boundary Conditions are given in Sections 6.4.

Responsible Faculty: X. Li

Deliverable 5A: Included in Monthly Reports

Deliverable Date: March 31, 2011

5B Physical/Turbulence Model Selection & Parameter Evaluation

The details of the Physical/Turbulence Model Selection & Parameter Evaluation are

given in Sections 6.7.


Deliverable 5B: Included in Monthly Reports


5C Model Development Presentation

The PI will provide a presentation to the AQRP Project Manager and the staff to review

the model development (Tasks 5A & 5B).


Deliverable 5C: Model Development Presentation


5D CFD Model Calibration

The selection of physical/turbulence models and parameters will be checked against

literature flare test data11,12,13 and will be varied if necessary to validate the CFD

modeling used in this project. The chosen physical/turbulence models, model parameters,

and simulation results will be delivered at the conclusion of this subtask.


Deliverable 5D: Included in Monthly Reports

Deliverable Date: June 30, 2011

Task 6: CFD Modeling & Post Processing

6A Modeling Base Case

Implement developed CFD modeling framework to run the selected Base Case.

Responsible Faculty: K. Li (CE & DRE), D. Chen (NOx Species) & C. Martin (VOC

Species)

Deliverable 6A: Included in Monthly and Final Reports

Deliverable Date: April 30, 2011

6B Base Case Presentation

The PI will present the results of the base case modeling (Task 6A) to the AQRP manager

and staff.


Deliverable 6B: Base Case Presentation

Deliverable Date: April 30, 2011

6C Modeling Rest of the Cases

Implement developed CFD modeling framework to run the 10 chosen Tulsa, OK Flare

test cases. The post-processing of CFD will predict flare efficiencies and the extent of

flare emissions.

Responsible Faculty: K. Li (CE & DRE), D. Chen (NOx Species) & C. Martin (VOC

Species)

Deliverable 6C: Included in Monthly and Final Reports

Deliverable Date: Same as the due date for Task 8.

Task 7: Comparison CFD Prediction and Flare Test Data

Comparison will be carried out if the data is provided by UT by May 31, 2011. The CFD

model will be validated using literature data.

Responsible Faculty: D. Chen (Species), K. Li (CE & DRE)

Deliverable 7: Included in Monthly and Final Reports

Deliverable Date: August 31, 2011

Task 8: Reports

8A Monthly Project Report


Deliverable 8A: Monthly Project Report

Deliverable Date: 8th day of the month after the issue of the work order

8B: Draft Final Project Report


Deliverable 8B: Draft Final Project Report

Deliverable Date: July 20, 2011

8C Final Project Report


Deliverable 8C: Final Project Report

Deliverable Date: August 31, 2011

Table 4.2 Task Chart: Task vs. Schedule

No. Tasks Months

1 2 3 4 5 6 7 8 1 Work Plan X X 2 Collection of Flare

Operation/Design/Performance Data X X X

3 Hardware/Software Acquisition and Data Storage

X X X X X X X X

4 Combustion Mechanism Development

a Combustion Mechanism Generation X X X X b Combustion Mechanism Validation X X 5 CFD Model Development a Geometry Creation & Boundary

Conditions X X b Physical/Turbulence Model

Selection & Parameter Evaluation X X c Model development presentation X d CFD Model Calibration X X X X X 6 CFD Modeling & Post Processing X X X X X X X a Modeling base case X X X b Base Case presentation X c Modeling rest of the cases X X X X X

7 Comparison CFD Prediction and Flare Test Data X X X

8 Reports a Monthly Report X X X X X X b Draft Final Report X c Final Report X

5. PROJECT APPROACH

5.1 Computational Fluid Dynamics (FLUENT)

FLUENT is a state-of-the-art computer program for modeling fluid flow and heat

transfer in complex geometries. Its solver has a wide span of modeling capabilities, for

example, steady-state or transient flows; heat transfer, including forced, natural, and

mixed convection, conjugate (solid/fluid) heat transfer, and radiation; inviscid, laminar,

and turbulent flows; and volumetric sources of mass, momentum, heat, and chemical

species. Furthermore, FLUENT can simulate the mixing and reaction of chemical

species, including homogeneous and heterogeneous combustion models and surface

deposition/reaction models. Basically, the Navier-Stokes equations as well as equations

for mass, energy and species transport are needed to be solved. The conservation

equations of mass, momentum and energy in a time-averaged steady-state format are

given as follows.

( ) mii

Sρux

=∂∂ (5.1)

( ) ( ) jjiijij

jjii

Fu'u'ρ-τxx

Pgρuρux

+∂∂

+∂∂

−=∂∂ (5.2)

( ) hipii

ipi

SμΦT'u'ρc-xTλ

xTuρc

x++

∂∂

∂∂

=∂∂ (5.3)

Where, ui, T and P are the velocity components, temperature and pressure,

respectively. τij is the symmetric stress tensor defined as

∂∂

−∂∂

+∂

∂=

k

kij

j

i

i

jij x

uδ

32

xu

xu

μτ . (5.4)

The source terms (Sm, Fj and Sh) are used to include the contributions of mass,

momentum and energy from the other phases. µΦ is the viscous dissipation and λ is the

heat conductivity. The equation for species transport is

( ) jjii

jj

iji

iSC'u'ρ-

xC

ρDx

Cρux

+

∂

∂

∂∂

=∂∂ , (5.5)

Where Cj is the mass fraction of the species (j) in the mixture, and Sj is the source

term for this species. Dj is the diffusion coefficient. The terms of ρji u'u' , ρcp T'u'i , and ρ ji C'u'

represent the Reynolds stresses, turbulent heat fluxes and turbulent concentration (or

mass) fluxes; each of them should be modeled properly if the flow is turbulent.

The CHEMKIN mechanism can be interfaced with the software fluent to perform

chemistry and flame calculations at both steady and unsteady state. We can export the

output file of CHEMKIN to Fluent 6.3.26 to further model the combustion.

5.2 Combustion Mechanism Generation

CFD simulation of combustion requires a comprehensive reaction kinetics

mechanism, which takes care of all the reaction pathways and the species that are

produced during and at the end of combustion. CHEMKIN, a reaction engineering

software package, was used to develop the reaction mechanism for the combustion of

ethylene.

Two widely used mechanism, GRI 3.0 and USC (75 species), are available for the

CFD simulation of flaring. The GRI-Mech 3.0 performs well for an extensive range of

combustion conditions, which has been evaluated and shown on their website. The USC

mechanism consisting of 75 species is a comprehensive kinetic model for representing

ethylene and acetylene combustion. It has been evaluated for predicting combustion

properties of both C2 and C3 fuels. However, these are not satisfactory for the

combustion of ethylene for the following reasons: 1. GRI-3.0 mechanism with 53 species

was developed and optimized for the combustion of methane not ethylene. 2. USC

mechanism containing 75 species was optimized for ethylene combustion reactions, but

the absence of NOx producing species in the mechanism does not reflect the reality for

flaring in air.

To overcome this problem, the two reaction mechanisms were combined to create

a more comprehensive mechanism that includes the chemistry of the NOx species and

offers the benefits of optimized USC ethylene combustion mechanism. The combined

GRI-USC mechanism consists of 93 species; and has to be further reduced to 50 species

to satisfy the maximum species limit set by FLUENT 6.3 for the pre-mixed model14. The

reduction of mechanism15 was performed by sensitivity and rate of reaction analyses with

a slight emphasis on aldehyde reactions. This optimized mechanism for the combustion

of C1-C3 hydrocarbons has been tested by the LU team against experimental results like

laminar flame speeds, adiabatic flame temperature, and ignition delay16.

. Fig. 5.1 a & b show the validation results of laminar flame speed vs. equivalence

ratio for methane. Fig. 5.2 a & b give the adiabatic flame temperatures for various

ethylene and air mixtures at STP conditions17. Fig. 5.3 a, b, & c display ignition delay

vs. temperature for propylene combustion16. As can be seen, very good agreements were

obtained between the simulations and the experimental data18.

The existing mechanism optimized for C1-C3 light hydrocarbons will be used

first. In the next version of C1-C3 combustion mechanism, additional NOx species (NO2

and HONO) will be added to the existing mechanism. Then the full combined USC-GRI

mechanism will be subject to the reduction process and the validation process. In addition

to sensitivity and rate of reaction analyses, the Reduced Dimension Mixture feature

offered by Fluent will also be tested to handle this version of combined mechanism19.

Fig. 5.2a Adiabatic flame temp vs. equivalence ratio for h

Fig. 5.1b Laminar flame speed vs equivalence ratio for Propylene

Fig. 5.1a Laminar flame speed vs equivalence ratio for methane

Fig. 5.2b Adiabatic flame temp vs. equivalence ratio for th l

Fig. 5.3b Ignition delay vs. temperature for Ethylene

Fig. 5.3a Ignition delay vs. temperature for Methane

5.3 Combustion Mechanism Validation

Once generated, the reaction mechanism will be put into performance test, i.e.,

the new mechanism will be thoroughly evaluated with literature lab dataError! Bookmark

not defined.. This test will ensure that the results (exit mole fractions) obtained from the

reduced reaction kinetic mechanism are in close agreement with those obtained from the

original mechanism. The validation tests will be performed using CHEMKIN zero

dimensional reactors such as PSR. The results obtained from CHEMKIN i.e. exit

mass/mole fractions will be compared with that of the original mechanism.

5.4 Geometry Creation

A 3-D geometry to be used in the CFD Solver, FLUENT, will be created using

GAMBIT. The geometry will be based on the data collected from UT. It will incorporate

various parameters of the flare layout like tip diameter & stack height. Other operating

parameters to be considered during generation of geometry are the design characteristics,

i.e. steam assist and pilots.

Fig. 5.3c Ignition delay vs. temperature for Propylene

In the FLUENT simulations, different boundaries are given different names as shown in

Table 5.1.

Table 5.1: Description of boundary types Boundary Terminology Description Jet Flare outlet (source of waste gases coming into

the geometry) Cross_Wind One face of the geometry which will act as the

source of crosswind Press_out Outlet (product gases after combustion exit the

geometry from this boundary)

5.5 Secondary Data Needed

Lamar University will collect the following test data from the Comprehensive Flare

Study Project. The data collected shall include but not be limited those listed in Table 5.2.

These data should include the geometry of the steam-assist and air-assist flares used in

the test (AutoCad sketch with data preferred), meteorological data (cross-wind

speed/direction, humidity, temperature), and the operating data (aeration, steaming, exit

velocity, waste gas/pilot fuel species) available from the data acquisition system. If

Lamar University acquires the flare efficiency/emission data of the tests conducted at the

John Zink facility in Tulsa, OK from the University of Texas by May 31, 2011, the

performance data will be compared with the model results (Task 7).

Table 5.2: Flare Operating and Design Data needed for CFD Modeling

Parameters Required

Waste Gas-Inlet Air -Inlet Steam-

Inlet Pilot

Fuel-Inlet Flare

Stack Height n/a n/a n/a n/a Tip Diameter

Velocity n/a Temperature n/a

Pressure n/a Composition Table A n/a Table B n/a

Mass Flow rate n/a Hydraulic Diameter

Table A Flow Composition (specify wt% or vol%)

Table B Flow Composition (specify wt% or vol%)

The flare Design/test Operation data from UT will be organized into input files

for Fluent simulations such as geometry generation, fuel/steam/flow/crosswind

specifications. We need a base case of air-assist flare and another base case for steam-

assist flare. For each base case, we need to run 2 more air (or steam) flow rates. We also

run 2 more LHV cases for each flare. So the total number of cases will be 2 (base cases)

+ 2 (additional air flow rates) + 2 (additional steam flow rates) + 2 (LHVs in air-assist

flare) + 2 (LHVs in steam-assist flare) =10 cases.

Minor elemental mass balance errors may be observed during the post processing

of CFD simulation results. If these errors are higher than the threshold values, (± 1-5%)

then re-meshing the geometry and, boundary adaptation of the geometry will be done.

5.6 Secondary Data Source

The secondary data will be collected from Tulsa flare tests. An example of the test

matrix with planned flow rate, waste gas/pilot fuel composition, and exit velocity, and

lower heating value is given in Table 5.3. Actual test data will be received from UT and

used in the modeling.

Table 5.3: An example of tests performed at the John Zink R&D Facility in Tulsa, OK

Test # S1 Test Point

Time (min.)

Steam/Waste Gas Ratio

Waste Gas flow

Actual Exit

Velocity

% of Flare design

Capacity

Waste Gas composition

Diluted with

LHV

lb/hr ft/s % Prop TNG N2 BTU/Scf S 1.1 20 Incipient SP 2342 1 0.25 100% 0% 0% 2183 S1.1 - S1.2

t from S1.1 - S1.2

2342 1 0.25 100% 0% 0% 2183

S1.2 20 < Snuff 2342 1 0.25 100% 0% 0% 2183

5.7 Physical/Turbulence Models

The standard k-ε model, considered the most robust for a wide range of

applications, will be adopted in this study. The standard k-ε model, which, based on the

Boussinesq hypothesis, relates the Reynolds stresses to the mean velocity as

iji

j

j

itji ρkδ

32

xu

xuμu'u'ρ- −

∂

∂+

∂∂

= (5.6)

where k is the turbulent kinetic energy, and µt is the turbulent viscosity given by

ε/kρCμ 2μt = (5.7)

where Cµ is a constant and ε is the dissipation rate. The equations for the turbulent kinetic

energy (k) and the dissipation rate (ε) are:

( ) ρεGxk

σμμ

xkρu

x kik

t

ii

i−+

∂∂

+

∂∂

=∂∂ . (5.8)

( )kερC

kεGC

xε

σμμ

xερu

x

2

2εk1εiε

t

ii

i−+

∂∂

+

∂∂

=∂∂ . (5.9)

The term Gk is the generation of turbulent kinetic energy due to the mean velocity

gradients. The constants C1ε, C2ε, Cµ, σk, and σε used are: C1ε = 1.44, C2ε = 1.92, Cµ =

0.09, σk = 1.0, and σε =1.3. Note that the constants adopted in the turbulence model may

not be the most appropriate values for the current application. Usually these constants

need to be “tuned” for different flow physics such as separated flow and low-Reynolds

number flow, etc. Since a better knowledge is needed on what values these turbulence

constants should be for the current application, the values of these constants will be kept

unchanged unless there are strong experimental evidences to justify a change.

The above k-ε model is mainly valid for high Reynolds number fully turbulent

flow. Special treatment is needed in the region close to the wall. The enhanced wall

function is one of several methods that model the near-wall flow. In the enhanced wall

treatment, a two-layer model is combined with the wall functions. The whole domain is

separated into a viscosity-affected region and a fully turbulent region by defining a

turbulent Reynolds number, Rey,

ν/ykRe 1/2y = (5.10)

where k is the turbulent kinetic energy and y is the distance from the wall. The standard

k-ε model is used in the fully turbulent region where Rey > 200, and the one-equation

model is used in the viscosity-affected region with Rey < 200. The turbulent viscosities

calculated from these two regions are blended to make the wall functions applicable

throughout the entire near-wall region.

6. PROJECT QUALITY METRICS

6.1 Quality Requirements of the Secondary Data

The quality of the flare measurement data to be collected from the Comprehensive Flare

Study tests (contingent upon availability) is shown in Table 6.1. The accuracy and

precision for the analytical methods are provided in the Quality Assurance Project Plan of

the Comprehensive Flare Study Project. The concentration accuracy generally falls

between 4%- 20% while the plume temperature accuracy is ± 50° C.

Table 6.1: Accuracy of flare modeling input data

S.no. Measurement Precision Accuracy Company

1 Wind Speed,

mph 0.1 m/s 5% Aerodyne

2 Wind Speed,

mph 0.5 m/s +/- 0.6 mph John Zink Co.

LLC

3 Ambient Temperature,

F 0.2 degrees 5% Aerodyne

4 Ambient Temperature,

F n/a +/- 0.54 degrees

F John Zink Co.

LLC

5 Barometric Pressure,

mm Hg 0.5 Torr 3% Aerodyne

6 Barometric Pressure,

psia +/- 0.001 psia +/- 0.008 psia John Zink Co.

LLC 7 Mass Emission rates, lb/hr 5% 5% TRC

8 Total Steam flow rate,

lb/hr +/- 0.2% of reading +/- 0.2% of

reading John Zink Co.

LLC

9 Propylene(IN) flow, lb/hr n/a +/- 27 lb/hr John Zink Co.

LLC

10 Natural gas (IN) flow,

lb/hr n/a +/- 1 lb/hr John Zink Co.

LLC

11 N2 flow (IN),

lb/hr n/a +/- 19 lb/hr John Zink Co.

LLC

Table 6.2: Composition analysis carried out by various companies during the

Comprehensive Flare Study tests at Tulsa, OK

6.2 CFD Model Uncertainty

The USC and GRI mechanisms used in conjunction with CHEMKIN CFD and

FLUENT are all in good agreement with the laboratory experimental data13. Even so,

the uncertainty of measurements (listed for individual species and CE/DRE in

Comprehensive flare test QAPP and report) are somewhat smaller than the uncertainty of

the combined USC/GRI mechanism20,21, which ranges from a factor of 1.2 to 5 for trace

species. and in the order of ± 4% (for CO2) to ±15% (for NO) for major species, Table

6.3. The mechanism uncertainty factor depends on residence, temperature, fuel

composition, species, etc. Fortunately, these mechanism parameters have been optimized

with lab data, mostly within experimental errors. For the sake of simplicity, a typical

uncertainty factor of 2 will be used for trace species (in ppm range). A factor of 2 means

the true value could lie from 0.5* model value to 2*model value. For major species, in

general, these mechanisms can reflect lab data within ± 10% for CO, within ±15% for

NO based on GRI’s assessment and are generally accepted in the combustion chemistry

community.

Recent simulations suggest that the uncertainty in transport coefficients may be

significant. As a result, the uncertainty factor of 3 will be used for the trace species.

CE/DRE Prediction Uncertainty

For relation DCA /= , uncertainty is calculated using

DdDCdCAdA /// +=

The above formula states that the uncertainty (in percentage) of A is the sum of the

uncertainty of C (in percentage) and the uncertainty of D (in percentage).

For relation 321 bbbB ++= , uncertainty is calculated using

321 dbdbdbdB ++=

The above formula states the uncertainty (in value) of B is the sum of the uncertainty of

C (in value) and the uncertainty of D (in value).

Table 6.3: Uncertainties in species prediction & measurement22, 21,6

Species Mechanism Uncertainties

Prediction Uncertainties (Mechanism + CFD)

AQRP Measurement Uncertainties

CO2 4% 6.00% 10%

CO 10% 15.00% 10%

CH4 0.005 0.0075 10%

C3H6 10% 15.00% 10%

H2O 4% 6.00% 15%

H2 10% 15.00% 15%

OH 10% 15.00% 15%

NO 15% 25.00% 5%

O2 0.004 0.006 2%

Note: CH4 & O2 prediction uncertainties are given in mole fractions

Using the uncertainties from Table 6.3 and a fairly typical flare mole fraction data

(CO2: 0.06; CO: 0.01; CH4: 0.03; C3H6: 0.03), the CFD prediction & Tulsa flare test

uncertainties for DRE and CE are estimated as shown in Table 6.4.

Table 6.4 Uncertainty Estimates for Prediction and Measurement

Uncertainties Prediction Measurement

Destruction and Removal Eff (DRE) ±15.00% ±10.00%

Combustion Efficiency (CE) ±19.00% ±20.00%

For CO, the measurement uncertainty is ±15% (IMACC) and the model uncertainty is

±15% (Mechanism +Fluid Dynamics), then the acceptance criterion is ±30%.

7. DATA ANALYSIS, INTERPRETATION AND MANAGEMENT

7.1 Cross-Project Review by Air Quality Research Program (AQRP)

Stage 1 – After the model has been developed and before any simulations are run, the

Principal Investigator shall provide a presentation to the AQRP Project Manager and

Staff to review the model development. This review will focus on, but not be limited to,

the inputs and assumptions used in the development of the model and verify the specific

base case (to be specified by the AQRP Project Manager) that will be used for inter-

comparison of the flare CFD models. No further work shall be performed on this project

until Stage 1 approval has been given by the AQRP Project Manager.

Stage 2 – Upon completion of the simulations, the Principal Investigator shall provide a

presentation of the results of the base case specified in Stage 1 to the AQRP Project

Manager and Staff. This Stage 2 review is a quality assurance assessment of the

performance of the model. The Project Manager will make recommendations on

restrictions for use of the model that shall be included in the final report.

7.2 Results Comparison: CFD vs. Flare Test Data

The report will summarize the validation of analytical results generated from field

sampling at John Zink facility, Tulsa, Oklahoma. Emission rate of all monitored species

will be calculated. Whenever applicable, DRE and CE will be computed. CE, DRE and

Species Concentration model values will be compared vs. Aerodyne, IMACC, Telops and

TRC data when available.

Table 7.1 below present the composition analysis carried out by Aerodyne, Telops,

and IMACCs during the TCEQ Flare tests in Tulsa.

Table: 7.1: Parameters to be compared with the Tulsa Flare Test

Species being Measured at Tulsa Are they considered in the

mechanism Aerodyne Telops TRC IMACC CO

NO2

HCHO (Formaldehyde)

CH3CHO (Acetaldehyde)

HC2H (Acetylene)

CH4

C3H6 (Propylene)

NO

CO2

EPA TO-14 ANALYTES

Oxygenated/ Aromatic and selected olefinic VOC

Ethylene (C2H4)

Water

Ethane

Propane

8. DATA REPORTING AND QUALITY ASSURANCE & CONTROL

PROCEDURES

8.1 Data Storage & Reporting

Lamar University will simulate flaring activities with the design/operating &

meteorological data collected from the UT/TCEQ Comprehensive Flare Study Project

(PGA No. 582-8-862-45-FY09-04) as the input variables using computational fluid

dynamics (CFD) programs. The data comparison, when available, will be carried out

with model uncertainty and the measurement uncertainty in mind. The data will be stored

in external hard drives for three years. The data include various fluent case runs and excel

files containing data analysis.

8.2 Controlled Flare Test Data

Measurement uncertainty mainly depends on accuracy: generally between 4%-20%

for concentrations and ± 50° C for temperatures, Table 6.2.

8.3 Mechanism & Fluid Dynamics Uncertainties

The USC and GRI mechanisms used in conjunction with CHEMKIN CFD and

FLUENT are all evaluated based on the performance of the laboratory experimental

dataError! Bookmark not defined.. In general, these mechanisms can reflect lab data

within ± 10% for CO, within ±15% for NO based on GRI’s assessment and are generally

accepted in the combustion chemistry community. For trace species and fluid dynamics

model uncertainties, see Sec. 6.2.

Recent simulations suggest that the uncertainty in transport coefficients may be

significant. As a result, the uncertainty factor of 3 will be used for the trace species and

the uncertainty factor in Table 6.3 should be multiplied by 1.5 after considering both

mechanism and fluid dynamics uncertainty factors20.

8.4 Quality Assurance/Quality Control Procedures

The QA/QC activities include Project Management, Project Data Acquisition,

Project Assessment/Oversight, and Data Validation and Usability Test1. All of the

developed models, computational programs and project data will be saved to CDs and

removable hard drives regularly for long-term storage. The computers will be well

maintained and subject to the lab safety rules at Lamar University. Project progress will be

internally reviewed by the research group every week. The PI/Co-PIs will carefully

supervise these activities. Each task contains quality assurance/quality control provisions

that involve works being reviewed and checked by PI/Co-PIs and the UT representative

Vincent Torres.

FLUENT/CHEMKIN Simulated results will be rigorously checked for numerical

convergence and elemental mass balance. Field or test flare data will be checked for

accuracy for numerical data entry, unit consistency, and mass balance. The uncertainties

involved in numerical simulations and field data (measurements) will be evaluated and

reflected in all model building. The actual project progress will be checked against the tasks

listed in the proposed milestone chart and the reason for delays, if any, will be documented.

Based on the progress, two to three journal or conference papers are expected to be

generated. The research achievements will also be parts of the theses of the research

associates. The PI/Co-PIs will supervise these activities. Since no experiments will be

performed, the safety of those working on the project will not be affected.

9. REFERENCES

1 Quality Assurance/Quality Control Plan, Quality Assurance Project Plan (QAPP) for Gulf Coast Hazardous Substance Research Center (GCHSRC), Project Number 027LUB0599, June, 1998. 2 Flare efficiency study,EPA-600/2-83-052, Marc Daniel, July 1983 3 J.H. Pohl, Evaluation of the Efficiency of Industrial Flares, 1984/1985, EPA600-2-85-95 and 106 4 David Castiñeira and Thomas F. Edgar, Computational Fluid Dynamics for Simulation of Wind-Tunnel Experiments on Flare Combustion Systems, Energy & Fuels 2008, 22, 1698–1706 5 Passive FTIR Phase I Testing of Simulated and Controlled Flare Systems, FINAL REPORT, (URS/UH/TCEQ, 2004) (http://www.tceq.state.tx.us/assets/public/implementation/air/am/contracts/reports/oth/Passive_FTIR_PhaseI_Flare_Testing_r.pdf) 6 Quality Assurance Project Plan, Texas Commission on Environmental Quality Comprehensive flare Study Project, PGA No. 582-8-862-45-FY09-04, Tracking No. 2008-81 UT/TCEQ/John Zink) 7 Sharma, R., “State of the Ozone State Implementation Plan” Proceeding of Ethylene Producers Conference, pp 392-397, AIChE Spring Houston Meeting 2007 8 Daniel J.Serry, C.T.Bowman, "An Experimental and Analytical Study of Methane Oxidation behind Shock Wavess",Combustion and Flame ,14,37-48(1970). 9 Seinfeld, John H. ; Pandis, Spyros, 2006. Atmospheric Chemistry and Physics - From Air Pollution to Climate Change, 2nd edition John Wiley & Sons 10 US EPA, Office of Air Quality Planning and Standards, Quality Assurance Handbook for Air Pollution Measurement Systems, EP-454/R-98-004, August 1998. 11 URS Corporation. (2004) Passive FTIR Phase I Testing of Simulated and Controlled Flare Systems final report, for Texas Commission on Environmental Quality and University of Houston. 12 Kostiuk, L, Johnson, M and Thomas, G. (2004). University of Alberta Flare Research Project Final Report. 13 David Castiñeira and Thomas F. Edgar, “CFD for Simulation of Steam-Assisted and Air-Assisted Flare Combustion Systems”, Energy & Fuels 20, 1044-1056 (2006) 14 Ferziger, J.H.; Peric, M. Computational Methods for Fluid Dynamics; 3rd edition; Springer: Berlin, 2002

http://www.tceq.state.tx.us/assets/public/implementation/air/am/contracts/reports/oth/Passive_FTIR_PhaseI_Flare_Testing_r.pdf

http://www.tceq.state.tx.us/assets/public/implementation/air/am/contracts/reports/oth/Passive_FTIR_PhaseI_Flare_Testing_r.pdf

15 Tomlin, A.; Pilling, M.; Turányi, T.; Merkin, J.; Brindley, J. Mechanism Reduction for the Oscillatory Oxidation of Hydrogen: Sensitivity and Quasi-Steady-State Analysis. Combust. Flame 1992, 91, 107-130 16 Anjan Tula, CFD Model for Validation of a Combustion Mechanism for Light Hydrocarbons, Master’s thesis, August, 2010, Lamar University, Beaumont, TX. 17 C.K.Law , A.Makino , and T.F.Lu, "On the Off-Stoichiometric Peaking of Adiabatic Flame Temperature ", The 4th J. Meeting of the U.S. Sections of the Combustion Institute,#1844(Mar 2005) 18 Qin.Z, Yang.H, C.Gardiner, " Measurement and modeling of shock-tube ignition delay for propene ", Combustion and Flame ,124, 246-254 (2001) 19 ANSYS Inc., ANSYS 13, User’s Guide. Fluent Inc (2010) 20 David A. Sheen, Xiaoqing You, Hai Wang, Terese Lovas, Spectral uncertainty quantification, propagation and optimization of a detailed kinetic model for ethylene combustion, Proceedings of the Combustion Institute, 32(1), 2009, 535-542 21 Hai Wang, Xiaoqing You, Ameya V. Joshi, Scott G. Davis, Alexander Laskin, Fokion Egolfopoulos & Chung K. Law, USC Mech Version II. High-Temperature Combustion Reaction Model of H2/CO/C1-C4 Compounds. http://ignis.usc.edu/USC_Mech_II.htm, May 2007 22 R.S.Barlow,A.N.Karpetis, J.H.Frank, J.Y. Chen,”Scalar Profiles and NO formation in laminar opposed flow partially premixed methane/air flames” Combustion and flame(2001),

http://ame-www.usc.edu/personnel/wang/index.shtml

http://www.exponent.com/leaders/bios/scott_davis.asp?employeeID=555

http://www.exponent.com/leaders/bios/scott_davis.asp?employeeID=555

http://ame-www.usc.edu/personnel/egolfopoulos/index.shtml

http://ame-www.usc.edu/personnel/egolfopoulos/index.shtml

http://www.princeton.edu/~cklaw/

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