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Journal of the Air & Waste Management Association
ISSN: 1096-2247 (Print) 2162-2906 (Online) Journal homepage: https://www.tandfonline.com/loi/uawm20
Computational fluid dynamics modeling of
laboratory flames and an industrial flare
Kanwar Devesh Singh, Preeti Gangadharan, Daniel H. Chen, Helen H. Lou,
Xianchang Li & Peyton Richmond
To cite this article: Kanwar Devesh Singh, Preeti Gangadharan, Daniel H. Chen, Helen H. Lou,
Xianchang Li & Peyton Richmond (2014) Computational fluid dynamics modeling of laboratory
flames and an industrial flare, Journal of the Air & Waste Management Association, 64:11,
1328-1340, DOI: 10.1080/10962247.2014.948229
To link to this article: https://doi.org/10.1080/10962247.2014.948229
Published online: 20 Oct 2014.
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TECHNICAL PAPER
Computational fluid dynamics modeling of laboratory flames and an
industrial flare
Kanwar Devesh Singh,
1
Preeti Gangadharan,
1
Daniel H. Chen,
1,
⁄Helen H. Lou,
1
Xianchang Li,
2
and Peyton Richmond
1
1
Dan F. Smith Department of Chemical Engineering, Lamar University, Beaumont, TX, USA
2
Department of Mechanical Engineering, Lamar University, Beaumont, TX, USA
⁄Please address correspondence to: Daniel H. Chen, Dan F. Smith Department of Chemical Engineering, Lamar University, Beaumont, TX 77705,
USA; e-mail: daniel.chen@lamar.edu
A computational fluid dynamics (CFD) methodology for simulating the combustion process has been validated with experimental
results. Three different types of experimental setups were used to validate the CFD model. These setups include an industrial-scale
flare setups and two lab-scale flames. The CFD study also involved three different fuels: C
3
H
6
/CH
4
/Air/N
2
,C
2
H
4
/O
2
/Ar, and CH
4
/
Air. In the first setup, flare efficiency data from the Texas Commission on Environmental Quality (TCEQ) 2010 field tests were used
to validate the CFD model. In the second setup, a McKenna burner with flat flames was simulated. Temperature and mass fractions
of important species were compared with the experimental data. Finally, results of an experimental study done at Sandia National
Laboratories to generate a lifted jet flame were used for the purpose of validation. The reduced 50 species mechanism, LU 1.1, the
realizable k-eturbulence model, and the EDC turbulence–chemistry interaction model were used for this work. Flare efficiency, axial
profiles of temperature, and mass fractions of various intermediate species obtained in the simulation were compared with
experimental data and a good agreement between the profiles was clearly observed. In particular, the simulation match with the
TCEQ 2010 flare tests has been significantly improved (within 5% of the data) compared to the results reported by Singh et al. in
2012. Validation of the speciated flat flame data supports the view that flares can be a primary source of formaldehyde emission.
Implications: Validated computational fluid dynamics (CFD) models can be a useful tool to predict destruction and removal
efficiency (DRE) and combustion efficiency (CE) under steam/air assist conditions in the face of many other flare operating variables
such as fuel composition, exit jet velocity, and crosswind. Augmented with rigorous combustion chemistry, CFD is also a powerful
tool to predict flare emissions such as formaldehyde. In fact, this study implicates flares emissions as a primary source of
formaldehyde emissions. The rigorous CFD simulations, together with available controlled flare test data, can be fitted into
simple response surface models for quick engineering use.
Introduction
The most recent Texas Air Quality Studies (TxAQS 2000 and
TxAQS II) revealed that air quality models (Comprehensive Air
Quality Model with extensions [CAMx], Community Multi-Scale
Air Quality [CMAQ]) often significantly underpredict observed
peak O
3
with the current volatile organic compounds (VOC) emis-
sion inventories in the Houston–Galveston–Brazoria area (HGB). It
is generally believed that either unidentified VOC emission inven-
tory sources exist or identified sources are significantly under-
reported. One potential under-reported or non-reported emission
source is due to flare operations (Texas Commission on
Environmental Quality [TCEQ], 2007;Environ,2006; ZEECO,
2007). The more common elevated flares are also subject to cross-
wind effects, which can decrease the combustion efficiency by
reducing the residence time of the combustible materials. The
TCEQ’s ongoing evaluation of flare operations, therefore, is an
important element of a future State Implementation Plan (SIP)
revision (TCEQ, 2009). Flare efficiencies depend on many factors.
Specifically, both destruction and removal efficiency (DREs) and
combustion efficiency (CE) dependontheheatingvalueofthevent
gas, vent gas species, exit velocity, steam injection, assisted air,
fuel–air mixing, and wind speed (Pohl et al., 1984;Pohland
Soelberg, 1985;TCEQ,2000,2007; U.S. Environmental
Protection Agency [EPA], 2009,2012; Singh et al., 2013). As a
result, DRE and CE can drop below the 98% threshold under
certain high air/steam-assisted conditions even when the flare
operation is in compliance with 40 CFR 60.18 (U.S. Government,
2009) as indicated in recent TCEQ and EPA reports (Allen and
Torres, 2011;EPA,2012). A photochemical model used by Al-
Fadhli et al. (2012) predicted an increase in ozone concentrations
by more than 15 ppb at low DREs. Another issue is that the
required flare turndown ratio (more than 15,000 to 1) to accom-
modate various operating modes makes it difficult to maintain high
efficiency at all conditions (Baukal and Schwartz, 2001; Allen and
Torres, 2011;Torresetal,2012).
1328
Journal of the Air & Waste Management Association, 64(11):1328–1340, 2014. Copyright © 2014 A&WMA. ISSN: 1096-2247 print
DOI: 10.1080/10962247.2014.948229 Submitted January 26, 2014; final version submitted July 3, 2014; accepted July 9, 2014.
Color versions of one or more of the figures in the article can be found online at www.tandfonline.com/uawm.
Computational fluid dynamics (CFD) simulations of the com-
bustion process have received considerable attention due to their
potential to closely emulate real-world combustion scenarios.
Such studies include, but are not limited to, the simulation of
internal combustion engines, industrial flares and burners, fire
and explosion, industrial furnaces, and others. The focus of this
work is to model industrial flares and laboratory flames of
similar fuel species and to compare the combustion efficiency
and speciated emissions with experimental data. In recent years,
work on the simulation of open air combustion practices has
been done using different models. For example, Smith (2011)
and Jatale et al. (2012) recently validated their large eddy simu-
lation (LES) model by comparing the CFD simulation results
with experimentally measured flare efficiencies of a 4-inch dia-
meter laboratory flare at the CANMET wind tunnel flare facility.
Castiñeira and Edgar (2008) used the probability density func-
tion (PDF) model to simulate small scale flares in a wind tunnel
setup as well. The simulation results were validated with tem-
perature and concentration profiles of major species. However,
due to a limited number of species (16) considered in the reaction
mechanism, some intermediate species such as OH, HCHO, or
NO were not reported in Castiñeira’s work. On the other extreme,
rigorous reaction mechanisms used for combustion modeling
include as many as 700 reactions and more than 100 species
(Wang et al., 2007). Even though the computational power to
simulate such reactive flows has increased drastically in recent
years, such models are still very computationally expensive.
Hence, reduced mechanisms are necessary to make numerical
modeling practical for turbulent combustion applications.
Lamar University’s research team has combined the GRI-3.0
mechanism (Smith et al., 2000) (optimized for methane) and the
USC-I mechanism (Davis et al., 1999) (optimized for ethylene
but without the NOx species) to obtain a mechanism containing
93 species and 600 reactions. This mechanism was reduced to
create a series of 50-species mechanisms for the combustion of
C
1
–C
3
light hydrocarbons for use in Fluent-Chemkin.
Mechanisms LU 1.0 and LU 1.1 were developed based on
analysis of rate constant, maximum mass fraction, and number
of reactions involved in the reaction pathway. In LU 1.1, CN was
replaced by NO
2
to model the 2010 John Zink flare test (Allen
and Torres, 2011). LU1.1 (Lou et al., 2011; Singh et al., 2012,
2014) used in this study was validated with key laboratory test
data, that is, laminar flame speed (Davis and Law, 1998), adia-
batic flame temperature (Law et al., 2005), and ignition delay
(Mayers and Bartle, 1969). The objective of this work was to
further validate LU 1.1 and the CFD models against three sets of
experimental data involving the combustion of methane, ethy-
lene, propylene, and a combination of the three species. LU1.1
was validated by comparing the predicted concentration profiles
of some important species with lab-scale flame experimental
data. For any detailed combustion reaction mechanism, the abil-
ity to correctly predict concentrations of intermediate species is
important. To evaluate the accuracy of LU1.1, mass fractions of
species like NO, HCHO, and OH were measured along the
height of the flame. These predicted concentration profiles
were then plotted and compared against the experimental data.
Due to the different scale and setup of the three experiments, the
model was validated for both laminar and turbulent flames. The
three experiments are discussed in the next section. One of the
three setups was the Sandia flame in a vitiated coflow. Similar
validation tests of Sandia Flames using various CFD models are
reviewed here. The PDF flamelet model using mixture fraction
theory and (CH
4
–Air) GRI reaction mechanism 2.11 were used
to simulate the Sandia flame (Nik et al., 2010). Tyliszczak (2013)
used an unsteady-state LES model along with the PDF
turbulence-chemistry model coupled with GRI 2.11 reaction
mechanism to simulate the Sandia flame. The diameter of the
lab-scale burner used for Sandia F flame was 18.9 mm. In
Vujanovic (2009), a reduced reaction mechanism was coupled
with the steady-state laminar flamelet combustion model. In all
of these studies, the accuracy of the modeling was limited by the
PDF/flamelet model, which cannot predict slower reactions. In
the present work, a rigorous combustion model, the eddy dis-
sipation concept (EDC) model, was coupled with the 50-species
LU 1.1 reaction mechanism.
To evaluate the reaction mechanism’s capability of modeling
industrial scale flares, test cases from the TCEQ Flare Study project
were modeled. Industrial flares with diameters ranging from 24
inches to 36 inches were used during these tests. To our knowledge,
the previous results reported by Singh et al. in 2012 are the only
validation of CFD simulations with controlled industrial-scale
flares. Prior simulation studies (e.g., Castiñeira 2008)mainlymod-
eled laboratory scale flares with flare diameters of up to 6 inches.
Further, the TCEQ Flare Study tests modeled in this work and
earlier by Singh et al. (2012) were conducted at very low exit
velocities and low heating values. These conditions represent flare
operations in a stand-by mode and have importance in representing
regular flare activities in handling fugitive emissions, venting, and
pressure relief operations.
In addition to the setups mentioned already, the McKenna
burner, a laminar flat flame burner, was also modeled. Such
laminar flame burners are often used for accurately measuring
the concentration of various VOC species. Using the measured
concentration profiles, the reaction mechanism’s ability to pre-
dict the emission of HCHO during combustion process was
validated. Both formaldehyde and acetaldehyde, important radi-
cal producing photochemical species (Seinfeld and Pandis,
2006), were also measured during the TCEQ’s 2010 Tulsa
Flare tests (e.g., Test Number S4.1, Run1).
Numerical Simulation
All the already-mentioned case studies were simulated using
the commercial CFD package ANSYS FLUENT 13.0. To reduce
the computational time, Fluent was run using parallel computing
settings; that is, each case was run on 8 or 12 local parallel
processors.
Governing equations
Computational fluid dynamics mainly involves solving sets of
transport equations using numerical methods like the Green–Gauss
or the least square method. The governing transport equations are
solved for mass, momentum (turbulence), energy and chemical
species. Direct numerical simulation (DNS) for industrial flares is
computationally not feasible because of the time dependent
Singh et al. / Journal of the Air & Waste Management Association 64 (2014) 1328–1340 1329
governing equations. Large eddy simulation (LES) modeling in
which large eddies are explicitly computed in a time dependent
simulation using the “filtered”Navier–Stokes equations can be
applied for industrial flares, but it is computationally expensive.
Therefore, in this study, the more popular Reynolds-averaged
Navier–Stokes (RANS) equations, which govern the transport of
the averaged flow quantities with the whole range of the scales of
turbulence being modeled, are used to model turbulence. The
RANS model is widely used for its reduced computational time
and wide range of practical applications.
The basic governing equations to be solved for RANS com-
bustion modeling are the mass, momentum (turbulence), energy,
and chemical species.
The continuity equation for the RANS model is given in eq 1:
::vðÞ¼0 (1)
where ris the density of the fluid and v is the ensemble-averaged
velocity vector and is the sum of the mean (v
Þand fluctuating
velocities (v0), shown in eq 2,
v¼vþv0(2)
The ensemble-averaged momentum equation is written as
:vivj
¼pþ:viþvjdij
2
3vk
v0v0
(3)
where p is the local pressure term, mis the viscosity of the fluid,
d
ij
is the Kronecker delta function, and v0v0is the Reynolds
stress term.
A common approach, the Boussinesq hypothesis (eq 4), is
used to relate the Reynolds stresses to mean velocity gradients.
This hypothesis is used for low computational cost associated
with the solving of the turbulent viscosity, m
t
. It is used in k-є
turbulent modeling, which is followed in this work. In this case,
two additional transport equations, the turbulence kinetic energy
k and the turbulence dissipation rate є, are solved and m
t
is
computed as a function of k and є.
In many cases, the models based on the Boussinesq hypoth-
esis perform well:
vivj¼t
@vi
@xj
þ@vj
@xi
2
3kþt
@vk
@xk
dij (4)
The turbulent kinetic energy k is defined as
k¼1
2v0v0(5)
For the k-єmodel, the eddy viscosity is calculated by the
Prandtl–Kolamagorov relationship as follows:
t¼Ck2
"(6)
The other governing equation to be solved for is the energy
equation (eq 7). The general form of the energy equation is
presented as
@
@tE
ðÞ
þ:~
vEþp
ðÞðÞ
¼:keff TX
j
hi~
Jiþteff :v
!
þSh
(7)
where keff is effective conductivity, ~
Jiis the diffusion flux of
species i, h
i
is the enthalpy of species i, and teff is the effective
stress tensor.
The last and the most important governing transport equation
to be solved for industrial flare modeling is the species transport
equation,
@
@tYi
ðÞþ:vYi
ðÞ¼:JiþRiþSi(8)
where Y
i
is the local mass fraction of each species and
J
i
is the
turbulent mass diffusion flux. R
i
is the net rate of production of
species iby chemical reaction and S
i
is the rate of creation by
addition from the dispersed phase and any user defined sources.
This equation is computed when the user-defined function of the
reduced mechanism is introduced in the simulation in this study.
The finite-volume method is used to discretize and solve the
governing equations.
Computational domain
All the simulations were performed on three-dimensional (3-
D) computational domains. These 3-D domains were created and
meshed in GAMBIT 2.4.6. Both structured and unstructured
cells were used to discretize the domain. The mesh near the
fuel jet/burner/flare tip was kept very fine as compared to the
rest of the domain. This helped in reducing the number of cells
and hence the computational time. To select the optimum num-
ber of cells, grid independence studies were performed for all
three models. Grid independence studies help to avoid the use of
an excessive number of cells while preserving the accuracy of the
final solution.
Boundary conditions
The boundary conditions specified in the CFD model are
discussed here. Fuel inlet, pilot inlet, and crosswind were speci-
fied as “velocity inlets.”Each velocity-inlet surface was speci-
fied by mass fractions, temperature, and a velocity magnitude.
The flow direction was kept normal to the surface. Turbulence of
the velocity inlet surfaces were specified by turbulence intensity
and hydraulic diameter. The bottom surface of all the domains,
flare stack, and burner surfaces were set as a non-slip wall.For
accurate prediction of turbulent flow near the flare stack bound-
ary surface, the enhanced wall treatment function was used. All
the surfaces from which the flows exit the domain were set as the
pressure outlet. These pressure outlet surfaces were specified by
a gauge pressure value of zero.
CFD models
A pressure-based solver with double precision was used for
modeling. The turbulence was modeled using the realizable k-e
Singh et al. / Journal of the Air & Waste Management Association 64 (2014) 1328–13401330
model. Radiation effects were neglected to reduce the computa-
tional costs. For the pressure–velocity coupling, the widely used
SIMPLE (Patankar, 1980) algorithm was enabled. Discretization
of gradients for constructing values of scalars at the cell faces
were computed using the Green–Gauss cell-based method, and
the pressure staggering option (PRESTO) was used for pressure
discretization. For all the other equations, the first-order upwind
scheme was used initially. However, for better accuracy, this was
later changed to the second-order upwind scheme. Similarly, the
underrelaxation factors were initially set at 0.5 (except for pres-
sure, turbulent viscosity, and body forces, which were kept the
same as default), and were then gradually brought to their default
values with convergence. Other variables defined in the model
are given in Table 1.
Turbulence model. For modeling turbulence in the domain,
the realizable k-emodel was used. The realizable k-emodel
remains the most widely used model to simulate practical appli-
cations. It is derived from the standard k-eturbulence model.
Modified equations for calculating the turbulent viscosity and
dissipation rate are introduced and added to the standard model.
The realizable k-emodel is suitable for a wider range of applica-
tions, gives a more stable solution, and is computationally inex-
pensive at the same time. However, to closely emulate the open-
air flaring process, the LES model should not be overlooked. It is
a transient model that can simulate large eddies in a turbulent
flow. However, due to its very high computational cost, it is not
commonly used for modeling complex combustion processes
involving hundreds of reactions. It normally takes hundreds of
parallel processors and weeks of computational time to simulate
few seconds of a combustion process. For this reason, the realiz-
able k-emodel was preferred over the LES model in this work.
Combustion model. The turbulence–chemistry interaction
model used in this work was the eddy dissipation concept
(EDC) model. Due to their very low computational cost, non-
premixed models like probability density function (PDF) flame-
let models are the most commonly used combustion models.
However, their fundamental assumption of infinitely fast chem-
istry renders them inapplicable for modeling low-Btu, low-exit-
velocity flaring. On the other hand, the EDC model is one of the
most rigorous chemistry–turbulence interaction models. The
EDC model assumes that the reactions in the flame occur in
small turbulent structures called fine scales. To generate these
fine scales in a turbulent reacting flow, the model uses detailed
reaction mechanisms. Unlike other combustion models, both the
kinetic rates and mixing rates are calculated. The slower of the
two is then used as the reaction rate. For this reason, the eddy
dissipation concept is an ideal model for simulating turbulence
combustion. It should be noted that all the cases run using the
EDC approach were completed in two stages. Initially “cold
flow”was simulated, meaning that combustion chemistry was
disabled during this period. Once a converged cold flow was
obtained, the region near the flare stack was patched with a
temperature of 2000 K. The EDC chemistry modeling was
then enabled and the combustion of the fuel started, which
further raised the plume temperature.
Radiation model. Due to the high computational cost, radia-
tion was ignored in the simulation. Radiation is generally used
for modeling industrial furnaces, gas fired heaters, and other
equipment where combustion is used for heat transfer and acts as
source of heat energy. Most applications that utilize radiation
models involve combustion in an enclosed domain. In open-air
combustion systems such as industrial flares and lab-scale bur-
ners, the heat generated by the flare is of no use and is dissipated
into the atmosphere. Using a radiation model can definitely
improve the simulation results but requires a considerable
amount of additional computational time.
Fluent postprocessing
Many specific results were obtained during the postproces-
sing step of the simulation to facilitate the comparison between
the simulations and experimental data. For the TCEQ flare setup
involving a full-scale flare, the predicted and experimental flare
efficiencies were compared. The modeled efficiencies were cal-
culated using the integral flow rates of various species over the
inlet and outlet surfaces of the domain. For the lab-scale flames,
the predicted axial profiles of concentration and temperature
were compared to the experimental ones. A “profile line”at
the center of the geometry was used in both cases. The profile
line provided the measurements of temperature and mole/mass
fractions of various species along the height of the flame.
Data Sources
TCEQ flare: Industrial-scale flare
The model to be validated was developed to simulate turbu-
lent industrial flares under various operating conditions. Hence,
the model was first checked against industrial-scale flare data
obtained from the TCEQ 2010 Flare Study (Allen and Torres,
2011) at the John Zink facility in Tulsa, OK. A similar study
(McDaniel, 1983; Pohl, 1984) was done under the auspices of
the EPA in 1983–1984 and measured the effect of lower heating
value (LHV), exit velocity, and other parameters on the flare
performance. The 2010 TCEQ study was aimed to study the
flare’s performance under low jet velocity, low Btu conditions
(stand-by mode) using the state-of-the-art measurement techni-
que. A 1.05-m-diameter flare was used to combust propylene
gas, along with variable flows of Tulsa Natural Gas (TNG;
Baukal and Schwartz, 2001) and nitrogen (N
2
). Operating para-
meters like air-to-fuel ratio, combustion-zone heating value, and
vent gas flow rate were varied during the flare tests. The mea-
surement of species concentration was done using redundant
Table 1. Variables used in the model
Elemental mass balance error
(C, H, O and N) <5%
High-temperature patch (initial ignition) 1800 K
Turbulence intensity (fuel) 5%
Turbulence intensity (crosswind) 10%
Turbulence model k-epsilon realizable
Singh et al. / Journal of the Air & Waste Management Association 64 (2014) 1328–1340 1331
measurement systems. For example, an extractive sampling
method with a moveable sample collector was used to collect
plume samples during the tests to determine the flare efficien-
cies. Remote-sensing technologies like passive and active
Fourier-transform infrared (PFTIR/AFTIR) spectroscopy and
GasFindIR passive infrared cameras were also used to analyze
the flare plume. Therefore, the test data are believed to be more
accurate than previous studies. The data points to compare
simulation results were taken from Appendix E of the TCEQ
2010 Flare Study Final Report.
Flare efficiencies. Two types of flare efficiencies were mon-
itored and reported during the flare study: DRE and CE.
(1) DRE (destruction and removal efficiency). DRE represents
the percent of the fuel destroyed relative to the amount of
fuel actually sent to the flare. Using C
3
H
6
as an example, it
can be written as
DRE C3H6
ðÞ¼
C3H6fed C3H6plume
C3H6fed
(9)
(2) CE (combustion efficiency). CE, on the other hand, takes
into consideration the percentage of fuel successfully con-
verted into carbon dioxide, the final oxidation product.
It is defined as
CE ¼CO2plume
CO2plume þCOplume þHCsplume
(10)
HCs
plume
in the preceding equation defines the amount of
hydrocarbons present in the plume. Using the data provided in
the TCEQ flare project final report, the flares were modeled in
the CFD Software, ANSYS Fluent (Fluent Inc., 2011). A total of
10 cases from Appendix E of the TCEQ 2010 Flare Study Final
Report were simulated. The details of cases modeled are pro-
vided in Table 2 (Allen and Torres, 2010).
Flare geometry. To keep the geometry simple, a rectangular
domain was built in GAMBIT. The length, width, and height of
the geometry were kept as 30 m, 10 m, and 30 m, respectively.
The flare was located at 5 m from the left side and at the center of
the width. The CFD domain for the simulation of the TCEQ flare
is shown in Figure 1. This configuration provided enough time
for the combustion process to be completed inside the domain.
The height of the flare stack was kept as 10 m and the diameter as
1.05 m. To avoid complex geometry, and hence an unnecessarily
large number of cells, the fuel jet opening was modified. The jet
used for previous study by Singh et al. (2011) had separate fuel
and pilot gas openings. In this work, the fuel and the pilot gas
were provided from a common outlet at the center. The air assist
was injected from the outer ring of the flare stack opening and
the crosswind direction was from left to right.
The largest velocity and species concentration gradients were
located in regions near the stack. To increase the accuracy of the
reacting flow profile, the mesh density near the stack was kept
higher relative to other zones in the domain. The final grid of
840,000 cells was successfully checked for skewness. Skewness
is a commonly used parameter to check the quality of the grid.
The skewness of the grid used for this work was kept under 0.4.
Initially, several grids were prepared for the model. The grid with
minimum computational time and with minimal loss of accuracy
was selected.
McKenna flame: Laminar premixed combustion
Laminar flat flames are one of the flames commonly used to
study combustion chemistry. Flat flame burners like the
McKenna burner (Holthuis & Associates, 2013) are often used
Table 2. Conditions used for TCEQ flare test cases
TCEQ case
number Propylene, lb/hr TNG, lb/hr Nitrogen, lb/hr LHV, Btu/scf
Air-assist flow rate,
lb/hr
Wind speed,
mph
A1.1 919 0 0 2,108 149,173 12.7
A2.1 355 0 0 2,125 83,818 12.8
A2.3 352 0 0 2,108 88,791 10.1
A2.4 353 0 0 2,113 148,799 10.0
A2.5 355 0 0 2,124 119,580 13.3
A3.3 181 18.4 701 334 60,121 11.1
A3.6 181 18.8 704 338 47,494 11.9
A4.3 299 30.3 591 563 66,472 10.7
A6.4 130 12.1 221 585 40,584 14.1
A6.5 130 12.1 221 584 56,594 15.5
Figure 1. Geometry used for the full-scale flare validation.
Singh et al. / Journal of the Air & Waste Management Association 64 (2014) 1328–1340
1332
to produce such flames. Stable flames from such burners provide
an accurate measurement of temperature and concentration of
species formed during the combustion process. A number of
researchers (Zhang et al., 2006; Bhargava and Westmoreland.,
1998) have used these burners to study lab-scale flames. Zhang
et al. at the National Synchrotron Radiation Laboratory, China,
have studied low-pressure C
2
H
4
/O
2
/Ar laminar flames.
Synchrotron photoionization and molecular beam mass spectro-
metry (MBMS) techniques were used to measure species con-
centrations. In this lab scale experimental setup, a 6 cm diameter
McKenna burner was used during the experiment. The burner
used by Zhang et al. is shown in Figure 2. The conditions used
for this McKenna flame are shown in Table 3. The pore Reynolds
number (d*v*r/m) for the modeled flame is 0.1065, which is
well within the laminar region. The Richardson number (g*d/v
2
)
for the same flame is 2.96E-03. Note that the diameter of the
micropores in a McKenna burner is given as 1.0 E–4 m (Holthuis
& Associates, 2013). Also, the Richardson number is the ratio of
buoyancy forces to inertia forces, where a number less than 1
indicates a predominance of inertial forces (Pohl et al, 1984).
The two numbers were calculated using the data given in Table 4.
Geometry. Modeling lab-scale or industrial-scale flames using
a rigorous EDC (eddy dissipation concept) model is computa-
tionally very expensive. The modeling becomes more complex
when a comprehensive reaction mechanism like LU1.1, consist-
ing of 50 species and about 400 reactions, is employed. Hence,
for the sake of simplifying the model, the porous surface of the
burner was replaced with small jet flames spread evenly across
the burner. These small flames coerced together to form a single
laminar and flat flame. The idea was to generate a flame having a
uniform axial profile of temperature and the species’concentra-
tions. The temperature and the species concentration profiles
were measured along the length of the flame. As there were no
radial variations in the species concentration or temperature, the
shape of the burner (square or round) does not affect these
profiles. The geometry used for simulating the flat flame burner
is shown in Figure 3. A square geometry was used for both the
burner and the full domain. Due to the laminar characteristic of
the flame, the grid was finely meshed along the height of the
flame (normal to the burner surface). The rest of the domain had
a relatively coarser mesh. The grid had 611,008 cells and was
successfully checked for skewness.
Sandia flame: Jet flame in a vitiated coflow
Unlike the previous experimental setup, this study simulated
a high jet velocity and a more turbulent flow. The experiment
was performed by Cabra et al. (2005) at the Combustion
Research Facility, Sandia National Laboratory, USA. In addition
to the flow conditions, the fuel composition used was also
different. In their work, Cabra et al. used CH
4
and air as the
reactants for combustion. The setup included a central burner
surrounded by a perforated plate. The actual experimental setup
is shown in Figure 4. The jet fuel composition was kept at 33%
CH
4
and 66% air at a jet velocity of 100 m/sec. The premixed
fuel was maintained at a temperature of 320 K. The perforated
plate acted as a source of “coaxial flow of hot combustion
products from a lean premixed flame.”The combustion products
were obtained from a lean H
2
/Air flame. The coaxial flow was
sent at a velocity of 5.4 m/sec and a temperature of 1350 K. The
idea of providing a coaxial flow was to avoid any interference
from the surrounding air. By keeping the composition and tem-
perature of the coaxial flow similar to the hot combustion pro-
ducts, any interaction in the combustion chemistry could be
avoided. Also, the coaxial flow reduced the mixing of surround-
ing air, which could have led to a leaner fuel and brought down
the flame temperature. Using this setup, Cabra et al. were able to
maintain a very stable and uniform flame. Raman/Rayleigh/
laser-induced fluorescence (LIF) measurement methods were
used to analyze the flame temperature and major species
concentrations.
Instead of pure O
2
(as used in the McKenna flame setup),
air was used for the combustion process. Due to the addition
of N
2
in the fuel, formation of NO
x
wasalsoinvolvedinthe
combustion chemistry. Since almost all flares use air as their
oxidation medium, the Sandia flame is a closer representa-
tion of the combustion chemistry during flaring. Also, the
N
2
in the fuel provided an opportunity to measure the con-
centration of NO, which is an important intermediate in the
combustion process.
Geometry. A cylindrical domain was used for the simulation
of this flame. All of the geometry configurations and boundary
conditions were kept the same as the experimental setup. The
Figure 2. Flat flame burner (McKenna burner).
Table 3. Conditions used for McKenna flame
Fuel composition (mole fractions)
C
2
H
4
O
2
Ar
0.175 0.525 0.300
Exit velocity (m/sec) 0.5757
Temperature (K) 300
Singh et al. / Journal of the Air & Waste Management Association 64 (2014) 1328–1340 1333
details are given in Table 5 and the geometry used is shown in
Figure 5. The grid had 86,360 cells and was successfully
checked for skewness.
Results
The simulation results obtained in the form of flare efficien-
cies, temperature profiles, and concentrations of species (CO
2
,
CO, OH, H
2
O, O
2
,CH
2
O, and NO) were compared with the
experimental data. The details are given next.
TCEQ flare tests
The experimental data collected during the 2010 TCEQ flare
tests included mainly flare efficiencies. The predicted flare
destruction efficiencies, DRE and CE, are compared with field
measurements in Tables 6 and 7, respectively. A good agreement
was found for both flare efficiencies: The average absolute error
for DRE was 4.50% with maximum error being around 10% (see
Table 6), while the absolute average error for CE was 3.04% with
maximum error reaching around 6.7% (see Table 7). In the past,
Table 4. Conditions used to calculate the Reynolds/ Richardson numbers
d (Pore diameter) 1.00E–04 m McKenna Technical Support
r(Mixture density) 0.036 kg/m
3
Aspen Plus Property Set
m(Mixture viscosity) 1.97E–05 kg/m-sec Aspen Plus Property Set
v (Gas velocity) 0.5757 m/sec
g 9.81 m/sec
2
Perry’s Handbook
Figure 3. Geometry used for the simulation of McKenna flame.
Table 5. Conditions used for Sandia flame
Co-flow conditions
H
2
O
2
H
2
OCH
4
N
2
0.0001 0.1193 0.1516 0.0003 0.7287
Co-flow velocity Co-flow temperature
5.4 m/sec 1350 K
Jet conditions
CH
4
O
2
N
2
0.3300 0.1518 0.5182
Jet velocity Jet temperature
100 m/sec 320 K
Figure 4. Experimental setup used at Sandia National Laboratory.
Singh et al. / Journal of the Air & Waste Management Association 64 (2014) 1328–1340
1334
the same test cases were modeled and reported in the 2011 Texas
AQRP (Air Quality Research Program) 10-022 report (for A/F
mass ratio <28) (Chen et al., 2011). In that report, the average
absolute error in the DRE prediction was 13.3%. The EDC
model used in this work employed a new and simplif ied geome-
try. The new model improves DRE of air-assisted low LHV/low
jet velocity propylene/TNG flares significantly compared to the
DRE values reported in the AQRP report (Chen et al., 2011). It
also drastically improves CE prediction compared to the average
absolute error of 29.8% in the 2011 AQRP 10-022 report (for A/
F mass ratio <28). The results clearly validate the CFD model
used, given the uncertainties in both the field measurements and
the numerical simulations.
Figure 6 presents the same comparisons in the form of
calculated versus experimental values for both DRE and
CE. As observed from the plot, the model closely predicts
the flare efficiencies. CE tends to have an evenly distributed
error, while DRE tends to have a higher predicted value. In
Figures 7 and 8, the DRE and CE versus combustion zone
heating value (CZHV) are presented. CZHV is the resultant
heating value of the fuel gas when any assist medium like
air/steam is also taken into account. CZHV in this work was
calculated using eq (11). For higher CZHVs, the model gives
more accurate results compared with lower CZHVs. For this
test case, the error is the largest (about 10%) for DREs
predicted at the lowest heating values:
CZHV ¼PfiHiþmHm
Pfiþmþs(11)
where f
i
is the volume flow rate of the ith component in vent gas,
mthe volume flow rate of makeup fuel, athe volume flow rate of
assisted air, sthe volume flow rate of assisted steam, H
i
the
heating value of the ith component in fuel gas (MJ/m
3
), H
m
the
heating value of the makeup fuel (MJ/m
3
), and CZHV the com-
bustion zone heating value (MJ/m
3
).
McKenna flame burner
Simulation of the flat flame burner using the LU 1.1 mechan-
ism was performed and the validation of the numerical model
Table 6. Comparison of TCEQ measured and simulated DRE (%)
2013 Simulation
TCEQ case number
TCEQ
measurement
DRE
(%)
Error
(%)
A1.1 98.0% 99.8% 1.8%
A2.1 97.2% 99.4% 2.2%
A2.3 96.1% 99.5% 3.6%
A2.4 93.0% 98.0% 5.3%
A2.5 95.1% 97.6% 2.6%
A3.3 88.1% 91.7% 4.1%
A3.6 90.8% 99.3% 9.4%
A4.3 95.2% 94.2% 1.1%
A6.4 92.9% 96.7% 4.0%
A6.5 87.9% 97.4% 10.8%
Average error (absolute) 4.50%
Table 7. Comparison of TCEQ measured and simulated CE (%)
CFD simulation
TCEQ case number
TCEQ
measurement
CE
(%)
Error
(%)
A1.1 96.9% 96.4% 0.5%
A2.1 95.9% 96.9% 1.1%
A2.3 94.4% 94.1% 0.4%
A2.4 89.3% 92.4% 3.4%
A2.5 92.6% 93.0% 0.5%
A3.3 85.2% 81.5% 4.4%
A3.6 88.2% 92.6% 5.0%
A4.3 93.6% 87.3% 6.7%
A6.4 89.2% 84.2% 5.7%
A6.5 81.5% 83.9% 2.9%
Average error (absolute) 3.04%
Figure 5. Three-dimensional geometry used to simulate Sandia flame.
Figure 6. CFD Simulated flare eff iciencies vs. TCEQ measured flare
efficiencies.
Singh et al. / Journal of the Air & Waste Management Association 64 (2014) 1328–1340 1335
using this flame was done by comparing the axial profiles of
temperature and species concentration. The most important para-
meter to be observed is temperature as the combustion chemistry
is highly dependent on it. It can be seen in Figure 9 that the
general trend of the simulated temperature over the length of the
flame was correct. Figures 10–13 compare the mole fractions of
O
2
,C
2
H
4
, and CO
2
. A good agreement was found between the
predicted and experimental profiles of these major species con-
centrations. Although the exit mole fractions (10 mm above
burner) are the same, there is some discrepancy in the CO
2
and
CH
2
O concentrations at the middle of the flame. For the radical-
producing CH
2
O, the EDC model underpredicts the maximum
mole fraction by a factor of 10 and the exiting mole fraction by a
factor of 1.6 (see Figure 14). These discrepancies can be seen as
a cumulative result of uncertainties in the reaction mechanism
and the CFD model.
It should be noted that even though the maximum mole
fractions predicted for some of the species were not equal to
the experimental values, the exit mole fractions were about the
same. The model to be validated is aimed for flare combustion.
Since only the exit mole fractions are used to calculate the flare
efficiencies, the model can be used for that purpose. Further,
even though the CFD model underpredicts the formaldehyde
Figure 7. Comparison of TCEQ measurements and experimental results: DRE
(%) vs CZHV.
Figure 8. Comparison of TCEQ measurements and experimental results: CE (%)
vs CZHV.
Figure 9. Comparison of experimental and simulated temperature prof iles of the
flat flame burner.
Figure 10. Comparison of experimental and simulated O
2
mole fractions.
Figure 11. Comparison of experimental and simulated C
2
H
4
mole fractions.
Singh et al. / Journal of the Air & Waste Management Association 64 (2014) 1328–1340
1336
concentrations, the mere existence of formaldehyde (and maybe
at a higher concentration) as an incomplete combustion product
supports the view that flares can be a primary source of formal-
dehyde emission.
Sandia flame: High-velocity jet flow
Figure 15 compares the temperature profiles of the simulated
and experimental flames. The simulation predicted the general
trend of observed data very well compared to the experimental
flame data. Figure 16 compares the CH
4
mass fractions along the
height of the flame. It can be seen that the combustion of CH
4
is
slow for the first few millimeters above the flame. However, at an
x/D value of 20 mm, the simulated and experimental results are
in total agreement. As shown by Figures 17–19, the mass frac-
tions of CO
2
, CO, and H
2
O are in very good agreement with the
experimental data. The maximum mass fractions of almost all
these products occur at the same height above the burner, that is,
Figure 14. Comparison of experimental and simulated CH
2
O mole fractions.
Figure 15. Comparison of experimental and simulated temperature prof iles of
the jet flame.
Figure 16. Comparison of experimental and simulated CH
4
mass fractions.
Figure 17. Comparison of experimental and simulated CO
2
mass fractions.
Figure 12. Comparison of experimental and simulated CO
2
mole fractions.
Figure 13. Comparison of experimental and simulated CO mole fractions.
Singh et al. / Journal of the Air & Waste Management Association 64 (2014) 1328–1340 1337
x/D ¼60 mm. In summary, the EDC model accurately predicts
the profiles of temperature and concentrations of major species
(CH
4
,CO
2
, CO, and H
2
O).
Two important radicals, OH and NO, were also compared
with the experimental data. OH and NO are by-products of the
combustion process and an important source for the formation of
atmospheric O
3
(Seinfeld and Pandis, 2006). The model predicts
the mass fraction of trace species NO and OH (at the core of the
flame) within a factor of 3.5, as shown in Figures 20 and 21.
Similar to CO
2
, the exit mole fractions of both the radicals are
equal to the measured values. The discrepancy can be seen
around the middle of the flame, which again is due to the
uncertainty in the reaction mechanism.
Discussion
Though the simulation results were reasonably close to the
experimental data, there is some room for improvement. The
modeling of lab-scale and industrial-scale flares can be improved
by using more rigorous models. The most important factor
affecting the simulation results is the kinetic modeling. CFD
models and sufficient computational resources allow the use of
a more detailed kinetic mechanism, which can improve the
accuracy of predicted concentration profiles. Improvements
can be made in the geometry of the flare tip. The ring-shaped
geometry used as the fuel and pilot gas source can be optimized
by increasing the flow area to improve the prediction of DREs. In
this work, the open air flaring process was modeled using steady-
state models. For further improvements, unsteady-state models
like LES models can be used.
In general, the CFD model used to simulate the three flames
provided good results. In this work, a comprehensive reaction
mechanism LU1.1 was used in conjunction with the most rigor-
ous turbulence model and turbulence–chemistry interaction
models available to simulate industrial-scale flares at the stand-
by mode for the first time. The methodology proves to be
applicable to industrial-scale flaring in that the errors of the
predicted DREs and CEs are within 5% of the measured effi-
ciencies. For lab-scale flames, concentrations of major species,
for example, C
2
H
4
,CH
4
,CO
2
, CO, and H
2
O, were accurately
predicted. On the other hand, the study also observed some
discrepancies in the prediction of certain radicals and trace
species such as HCHO, OH, and NO.
The two possible reasons for these discrepancies are the
uncertainties in the original reaction mechanisms and error in
predicting the correct rate of reactions at high temperatures
(>2000 K). The latter may be a result of reduction of the com-
bined mechanism. Although the reaction mechanism was suc-
cessful in predicting the temperature profile and most of the exit
mole fractions, there is still some scope for improvement. A
Figure 21. Comparison of experimental and simulated NO mass fractions.
Figure 18. Comparison of experimental and simulated CO mass fractions.
Figure 19. Comparison of experimental and simulated H
2
O mass fractions.
Figure 20. Comparison of experimental and simulated OH mass fractions.
Singh et al. / Journal of the Air & Waste Management Association 64 (2014) 1328–1340
1338
more comprehensive reaction mechanism that may include all
the species and reactions needed to accurately predict intermedi-
ates can be employed. In addition to the reaction mechanism,
inclusion of radiation in the model can also help in improving the
accuracy of the results. These changes can definitely help in
better prediction of the combustion process, but will demand
significantly higher computational resources.
Even though the CFD model underpredicts the formaldehyde
concentrations compared to the McKenna flat flame, the mere
existence of formaldehyde as an incomplete combustion product
supports the view that flares can be a primary source of formal-
dehyde emission. In fact, formaldehyde concentrations were
measured in the range of 200–1200 ppbv in the 2010 flare
study (Test S4.1R1). Acetaldehyde was also detected in the
similar concentration levels (Allen and Torres, 2011). These
data and their ratio to CO and propylene can be utilized in future
flare modeling work.
Conclusion
The EDC model with a new and simplified geometry
improves the accuracy of DRE calculations for air-assisted low
LHV/low jet velocity propylene/TNG flare tests with an average
error of 4.6%, compared to 13.3% published in the 2011 AQRP
10-022 final report (for A/F mass ratio <28; Singh et al., 2013).
The EDC model also improves the CE prediction accuracy with
an average error of 2.6%, compared to 29.8% in the 2011 AQRP
10-022 report (for A/F mass ratio <28).
For the lab-scale CH
4
/Air mixture (Sandia) flame, the EDC
model accurately predicts the profiles of temperature and con-
centrations of major species (CH
4
,CO
2
, CO). The model pre-
dicts the mass fraction of trace species NO and OH (at the core of
the flame) within a factor of 3.5. In the case of C
2
H
4
/O
2
/Ar
flame, a good agreement was found between the predicted and
experimental profiles of temperature, C
2
H
4
,CO
2
, and other
major species concentrations. The EDC model underpredicted
the maximum and exiting model fractions of CH
2
O by a factor of
10 and 1.6, respectively. OH, another intermediate species, was
found to be overpredicted in the model by a factor of 5.
Funding
This work is supported by the State of Texas and the authors
gratefully acknowledge financial support from TCEQ Supplemental
Environmental Program (SEP agreement 2009-009) and the Texas
Air Research Center (TARC grant 079LUB0096A).
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About the Authors
Kanwar Devesh Singh and Preeti Gangadharan each hold a Ph.D. in chemical
engineering from Lamar University, Beaumont, TX.
Daniel H. Chen and Helen H. Lou are professors and university scholars, and
Peyton Richmond is an associate professor at Dan F. Smith Department of
Chemical Engineering, Lamar University, Beaumont, TX.
Xianchang Li is an associate professor in the Department of Mechanical
Engineering, Lamar University, Beaumont, TX.
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