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Engineering Applications of Computational FluidMechanics
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Parametric Study of Ethylene Flare OperationsUsing Numerical Simulation
Kanwar Devesh Singh, Preeti Gangadharan, Tanaji Dabade, Varun Shinde,Daniel Chen, Helen H. Lou, Peyton C. Richmond & Xianchang Li
To cite this article: Kanwar Devesh Singh, Preeti Gangadharan, Tanaji Dabade, Varun Shinde,Daniel Chen, Helen H. Lou, Peyton C. Richmond & Xianchang Li (2014) Parametric Studyof Ethylene Flare Operations Using Numerical Simulation, Engineering Applications ofComputational Fluid Mechanics, 8:2, 211-228, DOI: 10.1080/19942060.2014.11015508
To link to this article: http://dx.doi.org/10.1080/19942060.2014.11015508
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Engineering Applications of Computational Fluid Mechanics Vol. 8, No. 2, pp. 211–228 (2014)
211
PARAMETRIC STUDY OF ETHYLENE FLARE OPERATIONS USING
NUMERICAL SIMULATION
Kanwar Devesh Singh
#, Preeti Gangadharan
#, Tanaji Dabade
^, Varun Shinde
#
Daniel Chen#*
, Helen H. Lou#, Peyton C. Richmond
# and Xianchang Li
^
#Dan F. Smith Department of Chemical Engineering, Lamar University, Beaumont, TX 77710, USA
^Department of Mechanical Engineering, Lamar University, Beaumont, TX 77710, USA
*E-Mail: [email protected] (Corresponding Author)
ABSTRACT: In addition to CO2 and H2O, industrial flares may also release Volatile Organic Compounds
(VOCs), NOx, and CO among others. Since experimental measurements of these emissions are expensive, rigorous
computational fluid dynamics (CFD) simulations and the accrued correlations are viable tools to understand and analyze factors affecting flare operations. In this paper, parametric studies of air and steam assisted ethylene flares
based on CFD modeling were employed to investigate important flare operating parameters such as vent gas
velocity, crosswind velocity, stoichiometric air ratio, steam-to-fuel ratio and heat content of the vent gas. The CFD
modeling utilized a 50-species reduced mechanism (LU 1.1) based on rigorous combustion chemistry. Validation
results of LU 1.1 are also presented. The destruction/removal efficiency and the combustion efficiency (DRE & CE)
were computed along with HRVOCs/VOCs/NOx emission rates to quantify the flare performance. Correlations
between DRE/CE and major parameters (crosswind, jet velocity, and combustion zone heating value) were
developed using the results obtained from the case studies. A modified combustion zone heating value definition was
proposed to compute a comprehensive heating value in the combustion zone.
Keywords: C2H4, air/steam assisted flares, flare efficiency/emissions, combustion mechanism
1. INTRODUCTION
Flaring is widely used in the upstream energy,
refining, and chemical process industries to
relieve pressures, vent unwanted gases, and then safely dispose them to the environment. This open
air combustion system oxidizes the fuel gases into
carbon dioxide and water vapor and hence avoids the contamination of air with harmful gases that
cause air pollution and climate change. However,
complications arise due to the significant effects
on flare performance of a wide range of parameters such as the fuel to air and fuel to
steam ratios (Castiñeira and Edgar, 2006), jet
velocity, net heat content of the fuel, crosswind velocity (Castiñeira and Edgar, 2008), etc. When
flare performance deteriorates, incomplete
combustion takes place which produces more combustion byproducts such as CO, aldehydes,
HOx, and NOx (Seinfeld and Pandis, 2006). The
oil and gas industry processes millions of cubic
feet of hydrocarbon gases every day so a slight decrease in flare performance means a release of
tens of thousands of cubic feet of such byproducts
into the atmosphere. The common indicators used to quantify flare performance are Destruction and
Removal Efficiency (DRE) and Combustion
Efficiency (CE) (Baukal and Schwartz, 2001).
A common industrial practice (American Petroleum Institute, 2008) for calculating VOC
emissions from flaring events is to assume 98%
DRE. According to EPA regulations, a 98% DRE
or higher (McDaniel, 1983) can be achieved if the flares are operated according to 40 CFR Section
60.18 (EPA 1986). A flare not complying with
these regulations may not achieve a 98% or higher DRE (Pohl, 1984/1985). But recent flare
studies done by the University of Texas
(UT/TCEQ/John Zink, 2008; UT Austin, 2011)
suggest otherwise. The flare field tests, conducted in Tulsa, Oklahoma (John Zink Hamworthy
Combustion facilities), used different
combinations of fuel heat content/LHV and flow rates. The final report (UT Austin, 2011) showed
DREs lower than 98% even when the flare was
operated in compliance with the EPA regulations. The comprehensive flare study covered various
tests simulating flare operations in a standby
mode for which the vent gas flow rates were kept
very low. The flares during the tests were conducted at a tiny fraction (0.1 - 0.25% ) of the
full capacity. Other operating modes like startup,
shutdown, or emergency are not represented by such low jet velocities.
To achieve the goal of a 98% DRE and to ensure
the proper operation of flares, the effect of many
Received: 7 Feb. 2013; Revised: 14 Oct. 2013; Accepted: 4 Dec. 2013
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operating parameters needs to be well understood.
To the authors' knowledge, for example, the effect of vent gas velocity under different crosswind
velocities has not been quantified, and neither has
the air/steam-to-fuel ratio. Clearly, operating a
flaring system under the most favorable conditions can help reduce the emissions into the
atmosphere and may even save the use of
supplemental fuel (e.g., methane) and steam. In the past, experimental setups (Poudenx and
Kostiuk, 1999; Johnson and Kostiuk, 2002;
Kostiuk et al., 2004) and CFD modeling (Barlow et al., 2001) have been used to study high jet
velocity flares. This paper will summarize the
effects of different operating and meteorological
conditions on flare performance using a commercial CFD package ANSYS FLUENT
13.0.
Flares are classified by the flare tip height (ground or elevated) or by the method of
enhancing mixing at the flare tip (i.e., steam-
assisted, air-assisted, pressure-assisted, or non-assisted). Various flare designs from a simple
stack to a complex steam assisted flare with
multiple steam nozzles are used to optimize
combustion. Two of the most commonly used types, air- and steam- assisted flares, are studied
in this work. As suggested by the name, these
types of flares mix air or steam with the fuel to accomplish smokeless (perceived as satisfactory)
combustion.
1.1 Air-assisted flares
Air-assisted flares, the simpler of the two, use
assist air which is either premixed with the fuel or sent through a ring shaped configuration
(explained below). The air-assist ensures the
availability of sufficient air for complete combustion. The air-assist also provides
additional turbulence to ensure adequate mixing
and hence better combustion.
1.2 Steam-assisted flares
The more complex of the two, steam-assisted flares use steam during the combustion process.
The steam can either be premixed, non-premixed
or a combination of the two. The steam-assisted flares use nozzles at the flare tip to inject non-
premixed steam. Better mixing of fuel, steam and
air caused by high speed injection results in a
more complete combustion. Besides creating a turbulent flame, steam also interacts with the
combustion chemistry. Smoke formation is
drastically reduced when water vapor reacts with
the hydrocarbons and forms CO and CO2. Also,
injecting steam lowers the combustion zone temperature and prevents thermal cracking of
hydrocarbons. In the present study, a simple,
cylindrical flare is used and the fuel and steam are
premixed prior to combustion.
1.3 Flare efficiencies
The two parameters, DRE and CE, used to
monitor the flare performance are discussed
below 1) DRE (Destruction and Removal Efficiency)
DRE represents the percent of the fuel (ethylene
was used in this work, except in some cases,
where it was diluted with nitrogen to lower the CZHV value of the fuel) destroyed relative to the
amount of fuel actually sent to the flare. DRE can
be written as:
2) CE (Combustion Efficiency)
CE, on the other hand, indicates the
conversion of fuel into CO2 rather than other intermediate radicals. It is defined as:
(2)
2. REACTION MECHANISM FOR
COMBUSTION OF C1-C3 HYDROCARBONS
For practical combustion applications where
detailed chemistry is employed, the computational cost is quite high. Accurate simulation of such
processes involves millions of cells and hence a
large memory which considerably increases the
computational time. As such, simplified reaction mechanisms are needed to reduce the time
required for simulations. The reduced mechanism
should predict combustion phenomenon similar to that of the original mechanism using fewer
species. Due to the limitation in the FLUENT
CFD software, one has to select 50 species from the detailed mechanisms when using the EDC
(Eddy Dissipation Concept) model. By choosing
50 species that can closely predict the combustion
chemistry as predicted by the comprehensive mechanism, nearly identical results can be
achieved.
To this end, a reduced reaction mechanism for the combustion of C1-C3 hydrocarbons, LU 1.0, was
developed by Lou et al. (2011). The LU 1.0
mechanism was built upon two widely used reaction mechanisms for the combustion of light
hydrocarbons: GRI-3.0 (Smith et al., 2000) and
USC (Davis et al., 1999). The LU 1.0 mechanism,
DRE = Amount of fuel fed - Amount of fuel in flue gas
Amount of fuel fed to the flare
CE = (Exit flow rate of CO2)actual
(Exit flow rate of CO2)stoichiometric
((((1)
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even though quite satisfactory for predicting VOC
species, does not include one important species, NO2, and its corresponding reactions. Nitrogen
dioxide (NO2) grouped with nitric oxide (NO),
known as NOx, contributes to ground-level ozone
and acid deposition. To address this shortcoming the Lamar research team developed a new
reaction mechanism, LU 1.1, in which one of the
existing species in LU 1.0 was removed and replaced by NO2. Computational experiments
using the CHEMKIN software were conducted to
identify species to be removed. The cyanide (CN) species was found to have a very low exit mole
concentration (~10-27
order) and to involve very
few reactions. Also, its removal had almost no
effect on the simulation results. So, the species CN in LU 1.0 was replaced by NO2 in the new
mechanism. Table 1 shows the complete list of
species involved.
3. MECHANISM VALIDATION
The new LU 1.1 reduced mechanism which has
50species and 335 reactions was validated with
experimental data. The list of species included in
LU1.1 is given in Table 1. Some common performance indicators like Ignition Delay,
Adiabatic Flame Temperature and Laminar Flame
Speed were modeled using the software package CHEMKIN PRO (Kee et al., 2007) for validating
the reduced mechanism against experimental
results. Details of these experimental tests and
validation comparisons are given below.
3.1 Laminar flame speed test
Laminar flame speed of a specific pre-mixed composition of fuel and air is the speed at which a laminar flame propagates. It plays a key role in
characterizing the combustion of air and fuel mixture in different compositions and determines the flammability limits of the mixture. In another study, Miller et al. (1982, 1983 and 1985) verified
combustion chemistry and pollution formation using flame models.
The flame-speed calculation model involves a
freely propagating flame. This configuration at a
certain inlet temperature and pressure gives the
flame speed of the fuel-air mixture. The flame
speed can be modeled as a 1-dimensional flow
using the software package CHEMKIN PRO.
CHEMKIN results for the new reduced
mechanism were compared with experimental
data from Davis and Law (1998) and the LU 1.0
mechanism. The temperature and pressure in the
Flame Speed Calculation model were taken as
1atm and 298K with equivalence ratio varying
between 0.6 and 1.5. Fig. 1 shows the results
obtained from this model.
42-45 cm/s were the maximum laminar flame
speed reported at equivalence ratios between 1
and 1.1. With an exception of very high
equivalence ratios (1.3-1.4), the experimental and
simulation results were found to be in good
agreement. The average percentage error of
around 3% was lower than observed for the
LU1.0 mechanism, as shown in Table 2.
Fig. 1 Comparison of the experimental (Davis and
Law, 1998) and the simulation results for
laminar flame speed for different equivalence
ratios.
3.2 Adiabatic flame temperature test
Under specific conditions, the maximum
temperature reached by combusting a particular
gas mixture is called the adiabatic flame
temperature. Lower temperatures can be observed
due to heat transfer losses, incomplete
combustion, and dissociation. A stoichiometric
mixture (correct proportions such that all fuel and
all oxidizer are consumed) results in the
maximum adiabatic flame temperature for a given
fuel and oxidizer combination (Spakovszky,
2013). The adiabatic flame temperature of a flare
can be controlled by varying the amount of excess
air.
The phase and chemical equilibrium between gas
and condensed phases can be modeled using the
Equilibrium Reactor model. An element-potential
method is embodied in Stanford’s STANJAN
software (Reynolds, 1986) to calculate the
chemical equilibrium. The calculation involves
the STANJAN library in its routine solution
method. The result depends on the
thermodynamic properties of the species in the
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Table 1 List of species involved in mechanism.
Mechanism No. of Species Species List
Full Mechanism 93
H2, H, O, O2, OH, H2O, HO2, H2O2, C, CH, CH2, CH2*, CH3,
CH4, CO, CO2, HCO, CH2O, CH2OH, CH3O, CH3OH, C2H,
C2H2, H2CC, C2H3, C2H4, C2H5, C2H6, HCCO, CH2CO,
HCCOH, C2O, CH2CHO, CH3CHO, CH3CO, C3H2, C3H3,
pC3H4, aC3H4, cC3H4, aC3H5, CH3CCH2, CH3CHCH, C3H6,
C2H3CHO, C3H7, nC3H7, iC3H7, C3H8, C4H, C4H2, H2C4O, n-
C4H3, i-C4H3, C4H4, n-C4H5, i-C4H5 ̧C4H6, 1,2-C4H6, C4H7, 1-
C4H8, C6H2, C6H3, l-C6H4, c-C6H4, A1, A1-, C6H5O, C6H5OH,
C5H6, C5H5, C5H4O, C5H4OH, C5H5O, N, NH, NH2, NH3, NNH, NO, NO2, N2O, HNO, CN, HCN, H2CN, HCNN,
HCNO, HOCN, HNCO, NCO, Ar, N2
New LU 1.1 Reduced
Mechanism (with NO2)
50
H2, H, O, O2, OH, H2O, HO2, CH, CH2, CH2*, CH3, CH4, CO,
CO2, HCO, CH2O, CH2OH, CH3O, C2H2, H2CC, C2H3, C2H4,
C2H5, C2H6, HCCO, CH2CO, CH2CHO, CH3CHO, C3H3,
pC3H4, aC3H4, aC3H5, C3H6, C3H8, C4H2, n-C4H3, i-C4H3,
C4H4, N, NH, NH2, NO, N2O, HNO, Ar, HCN, HNCO, NCO,
NO2, N2
Table 2 Average and maximum percentage error with respect to experimental results.
Indicators
Percentage Error
Without NO2 (LU 1.0) With NO2 (LU 1.1)
Average (%) Maximum
(%) Average (%)
Maximum
(%)
Laminar Flame Speed Propylene 11.605 22.727 8.058 23.260
Adiabatic Flame Temperature Ethylene 1.138 1.863 0.527 0.911
Ignition Delay Propylene 30.68 44.638 31.25 45.735
Fig. 2 Comparison of experimental (Law et al., 2005)
and simulation results for adiabatic flame
temperature at various equivalence ratios.
user’s chemistry set, initial composition and
conditions. Constant volume and internal energy can be used to perform these calculations. 1000 K
as an initial guess for equilibrium temperature is
needed in order to find the burned gas solution.
All reactants and products must be included for
accurate temperature prediction.
The reduced LU 1.1 mechanism with 50 species, including NO2, was tested for adiabatic
temperature in CHEMKIN using ethylene fuel.
The results were compared with experimental data from Law (Law et al., 2005) and the LU 1.0
mechanism. For validation in CHEMKIN, the
pressure and initial temperature chosen were 1
atm and 298 K, respectively. The Equilibrium actor model with equivalence ratio varying
between 0.5 and 2.0 was considered.
Comparison of the experimental and simulated adiabatic flame temperature results for both
mechanisms is shown in Fig. 2. The maximum
adiabatic flame temperature is located at an equivalence ratio range of 1.0-1.1. The maximum
adiabatic flame temperature is 2380 K for the LU
1.0 mechanism, 2391 K for the LU 1.1
mechanism, and 2400 K for experimental data. In all of these cases, the maximum temperature is
located slightly at the leaner side of the fuel air
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mixtures. From Table 2, it can be seen that the
average and maximum errors for the LU 1.0 mechanism (1.138% and 1.863%) and the LU 1.1
mechanism (0.527% and 0.911%) are quite low. It
can be concluded that there is a good agreement
between the experimental and simulation results.
3.3 Ignition delay time test
The ignition delay of a combustible mixture is
defined as the time interval required for the
mixture to spontaneously ignite at some prescribed set of conditions. It is essentially a
macroscopic measurement of the ignition process.
One of the most widely used techniques for
detailed chemical kinetic mechanisms involves comparing computational predictions of the
ignition-delay times to shock tube experiments
(Mayers and Bartle, 1969; Schultz and Sheperd, 2000; Petrova and Williams, 2006). There are
various ways of defining the ignition time. It is
widely recognized that the ignition of a fuel-air mixture comprises a series of overlapping
physical and chemical processes which have
characteristic times that combine to form an
overall ignition delay time. So, ignition delay is composed of a physical delay and chemical delay
(Samuelsen et al., 2003). Once the physical delay
occurs, the chemical delay time dominates.
Fig. 3 Comparison of the experimental (Qin et al.,
2001) and the simulation results for ignition delay time at various temperatures.
The ignition delay was simulated for propylene
fuel. The reduced mechanism with 50 species including NO2 was used to calculate ignition
delay in CHEMKIN. This was compared with
experimental data from Qin et al. (2001). For
validation in CHEMKIN, the pressure was taken as 4 atm, and the temperature was varied between
1200 K and 1600 K. The fuel composition was
C3H6/O2/Ar = (0.0317: 0.0783: 0.89). The model considered was the closed homogenous reactor.
The results obtained are summarized in Fig. 3.
Fig. 3 compares the experimental and simulation
results of ignition delay time versus 104/T for
propylene-air mixtures with NO2 (LU 1.1), and
without NO2 (LU 1.0). It can be concluded that
ignition delay is directly proportional to inlet
temperature, so the maximum ignition delay occurs at the lowest inlet temperature considered.
From Table 2, it can be seen that the average
percentage deviation of the LU 1.1 mechanism from the experimental results (31.25%) is
comparable with the LU 1.0 mechanism
(30.68%). The simulation results are in reasonably good agreement with the experimental
results.
4. CFD SIMULATION OF ASSISTED
ETHYLENE FLARES
4.1 CFD domain
The CFD domain used in this work is shown in
Fig. 4. A cylindrical domain with the flare stack at the center was used. The radius of the domain
was kept at 80m to provides enough residence
time. The height of the domain was kept at 50m.
The flare stack was 10m high with a diameter of 1.05m. The geometry used, shown in Fig. 4a, was
concentric ring-shaped. The geometry thus
provided two inlet surfaces: Fuel Inlet-2 (the ring) and Fuel Inlet-1 (the rest of the circular area). The
two fuel inlets were specified for air-assist and
fuel inlet differently for each case study. For
steam-assist flares, both inlet surfaces were combined to form a single inlet surface. The
crosswind flows from west towards east of the
domain. The geometry had denser mesh near the stack
compared to the rest of the domain. This was
done to obtain more accurate results. A grid independent study was performed in order to rule
out any deviations in the final results that may
occur due to different grid sizes. Four grids (A, B,
C and D) with different mesh sizes were created and tested for accuracy. Grid A was the base grid
for the remaining three grids. The three grids B, C
and D were created from grid A by increasing the number of cells around the stack area. The DRE
and the carbon mass balance error of each grid
were compared. In addition to the final solution, the time taken to reach the solution was also
checked. The results of the grid independence
tests are given in Table 3. As seen from the
results, the DREs obtained from Grid number C and D are very close. The carbon mass balance
error in Grid C is less than 1%, which was used as
one of the convergence criteria. On the other hand
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the same error for Grid D is around 0.17%. But
the solution time for Grid D is almost double that for Grid C. Hence, Grid C was chosen for the
parametric study.
Table 3 Results of grid independent study.
Grid A Grid B Grid C Grid D
Mesh Size 278,980 408,120 486,000 524,064
DRE (%) 99.981 99.781 99.727 99.719
C Error (%) 4.52 1.67 0.87 0.17
Time (hrs) 66.8 96 120 201.6
4.2 Parametric study cases
This work presents the effect of important flare
operating parameters, V (flare jet velocity); SR (Stoichiometric Ratio, i.e., ratio of actual air to
the stoichiometric air); S/F (mass based steam to
fuel ratio) and CZHV (Combustion Zone Heating Value, MJ/m
3) of the vent gas. The case studies
were divided into subsequent categories as shown
in Table 4.
4.2.1 Previous parametric flare studies
A lot of research has been done to understand flaring and to find out the factors that hinder flare
performance. This includes both flaring
experiments and numerical simulations. Due to the high cost involved, not many studies involved
an experimental setup, but a significant amount of
work has been done at the University of Alberta’s
Combustion and Environment Group. Bourguignon et al. (1999) used a closed loop
wind tunnel setup to measure the flare efficiency.
Later, using the same setup, Johnson and Kostiuk (2000) measured the flare efficiencies of low
momentum jet diffusion flames in crosswind. In a
parametric study, Johnson and Ostiuk (2002)
studied the effects of jet velocity, heat content,
etc., on flare efficiency. A comprehensive correlation to calculate flare efficiency from
various flare operating parameters was presented
in the paper. The correlation is only applicable to
low momentum flares (i.e. with jet velocities lower than 4m/s). Similarly, a recent numerical
simulation by Singh et al. (2012) also studied low
momentum flare test cases conducted during TCEQ’s Comprehensive Flare Study Project.
Castiñeira and Edgar (2008) used a 2-D CFD
model to study the high momentum flares in which the jet velocities were varied between
50m/s and 70m/s. However the study did not
include the effect of heat content and air-to-fuel
ratio. Hence, there is a need for a parametric study to examine the effect of jet velocity, cross wind,
and heating value on flare efficiencies and the
interaction between these parameters. In this study, intermediate jet velocities
(10m/s<V<40m/s) were also covered.
Fig. 4a CFD domain for case studies.
(a) (b) (C)
Fig. 4b Cylindrical geometry: (a) Meshed geometry showing inlet and outlet, (b) Flare stack at center of cylinder, and
(c) Details of flare tip.
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4.2.2 Case studies A and B
In case studies A and B, the effect of crosswind
velocity, jet velocity and their combined effect
were studied using test cases represented in a 6 by
7 matrix. The test cases had six different jet velocities (V = 10, 15, 20, 25, 30 and 40m/s) and
seven different crosswind velocities (U = 5, 10,
15, 20, 25, 30 and 35m/s). Each jet velocity was simulated with the seven specified crosswind
velocities. In the two case studies, Fuel Inlet-1
(see Fig. 4) served as the source of air-assist and Fuel Inlet-2 (see Fig. 4) as the source of fuel gas,
i.e., ethylene.
4.2.3 Case study C
In case study C, the effect of SR, Stoichiometric
Air Ratio was studied. This ratio was varied from 0 to as high as 1.35, as shown in Table 4. The SR
ratio represents the mass ratio of the actual air
supplied to the stoichiometric air. The same non-premixed configuration was used for this case
study where Fuel Inlet-1 served as the source of
air-assist and Fuel Inlet-2 as the source of fuel
gas.
Table 4 Conditions of parametric test cases studies.
Conditions of test cases for case studies A and B
V (m/s) U (m/s) SR Fuel
Temperature (K)
CZHV
(MJ/m3)
10.0 - 40.0 5.0 - 40.0 0.3 400 K 54.53
Conditions of test cases for case study C
V (m/s) U (m/s) SR Fuel
Temperature (K)
CZHV
(MJ/m3)
10.0 5.0 0 – 1.35 400 K
59.49-
41.03
Conditions of test cases for case study D
V (m/s) U (m/s) SR Fuel
Temperature (K)
CZHV
(MJ/m3)
10.0 5.0 0.3 400 K
54.53-
25.88
4.2.4 Case study D
Case study D simulated the effect of the heat
content of the fuel gas. The heat content of any
individual species is defined as the Lower Heating Value (LHV) measured in MJ/m
3. In case
of a mixture of two or more different gases, the
heat content of the resultant fuel gas is measured as the CZHV (Combustion Zone Heating Value).
The conventional method to calculate the CZHV
(Combustion Zone Heating Value) is to consider
the heat content of only the vent gases and any
premixed N2/air/steam. But this conventional method does not reflect the decrease in the CZHV
value due to the amount of air provided as air-
assist (non-premixed). Air supplied as air-assist is
typically non-premixed, but a small percentage will be mixed with the vent gas in the combustion
zone, thereby decreasing the heat content. This
non-premixed air does not provide the same dilution effect as steam (in the case of steam-
assisted flares) because the assist air is not
provided through nozzles directed into the fuel. As a result, assisted air does not completely mix
with fuel but rather partially mixes with fuel
through diffusion and some turbulence caused by
velocity differences. So, in order to take into account this decrease, only a fraction of the non-
premixed air-assist is considered in the
calculation of CZHV. Equation 3 is the general equation used for the calculation of CZHV for
both the air and steam assisted cases. CZHV in
case study C and D was varied from 59.49 MJ/m3
to 41.03 MJ/m3, by diluting the fuel gas with N2
and air. For case study E, the CZHV varied from
59.49 MJ/m3 to 7.46 MJ/m
3.
(3)
where fi: Volume flow rate of i
th component in vent gas
m: Volume flow rate of makeup gas
a: Volume flow rate of assisted air
s: Volume flow rate of assisted steam Hi: Heating Value of the i
th component in fuel gas
(MJ/m3)
Hm: Heating Value of the makeup gas (MJ/m3)
CZHV: Combustion Zone Heating Value (MJ/m3)
xeff : Effective fraction (effective fraction of air-
assist that causes the dilution), 2% is proposed for the 2010 John Zink flare tests in Tulsa,
Oklahoma.
The heat content of air, N2 and steam is taken as 0
MJ/m3. The Lower Heating Value of ethylene,
shown in Equation 3, is taken as 60.20 MJ/m3 [4].
In this case study, Fuel Inlet -2 served as the
source for premixed fuel and nitrogen, while Fuel Inlet-1 provided the air-assist.
4.2.5 Case study E
In this work, the vent gas (fuel) considered was
pure ethylene (LHV = 60.20 MJ/m3). The effect
of increasing the steam to fuel ratio was studied using a CFD simulation with no air-assist. To
change the S/F ratio, the amount of steam was
subsequently increased for each input. A mixture
effi
mii
xasmf
HmHfCZHV
*
**
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of ethylene and steam with a constant flow rate of
1.4903 kg/s corresponding to a jet velocity of 10 m/s was used in all the cases. A crosswind from
west to east with a velocity of 5m/s was provided.
The steam to ethylene (fuel) mass ratio was varied
from 0 (no steam assist) to 4. The combustion zone heating value (CZHV) in the cases was kept
more than 7.46 MJ/m3, which is the lowest
acceptable value for complete combustion without additional fuel (EPA, 1991).
5. CFD METHODOLOGY
5.1 CFD (computational fluid dynamics)
model)
All the modeling for this work was performed
using ANSYS FLUENT 13.0. A 3D model was
used to obtain steady-state solutions. The CFD software was run on multi-core processors to
reduce the computational time. Twelve (12) local
parallel processors were used for each case. For modeling purpose, a double precision and
pressure based solver with Realizable k-ε as the
turbulence model was used. For discretization,
PRESTO and Green-Gauss Cell based methods were used. Initially, the simulation was run using
the first order upwind scheme. After an initial
solution it was shifted to second order. In a similar fashion, the under-relaxation factors
starting from 0.5 were gradually increased to 1.
Among the various chemistry-turbulence
interaction models available, the EDC model was selected. In a recent work (Singh et al., 2012), the
two models (EDC & PDF) were compared for the
numerical simulation of flares. In that work, it was found that the PDF model failed to accurately
model the combustion process during flaring, due
to its underlying assumption of infinitely fast chemistry. The EDC model, even though it is
computationally expensive, is more rigorous and
realistic for light-hydrocarbons combustion. A
brief discussion of the EDC model is given below.
5.2 EDC chemistry-turbulence interaction
model
The Eddy Dissipation Concept model was employed for the turbulence-chemistry interaction
in the domain. The EDC model describes detailed
reactions that take place in turbulent flows.
Though computationally expensive, it is the most rigorous model available. Cell temperature and
species concentration at the current time are taken
as initial conditions for a constant pressure
reactor. The reaction rates are governed by the
Arrhenius rate equation. Numerical integration of the reaction rates is done with the help of the in
situ adaptive tabulation (ISAT) algorithm to
reduce the computational time. A two step
approach was used to model the test cases. An initial cold flow solution with combustion
chemistry disabled was followed by hot flow. In
the hot flow, chemistry in the turbulent flow was described using the EDC model. To initiate highly
exothermic reactions between any fuel and air,
activation energy is required. For this purpose, the cells near the flare tip were initialized with a
temperature of 2000K, high enough to start the
combustion process.
5.3 Fluent post processing
In this last step, results in the form of mass flow rates and contours were obtained from the
converged solution. Using these value the flare
efficiencies, CE and DRE, were calculated. The flow rate of each species was integrated over all
inlet and outlet surfaces. The mass fluxes of fuel
and CO2 at all the boundaries were used in
Equations 1 and 2 to calculate the two flare efficiencies.
6. ETHYLENE FLARE SIMULATION
RESULTS
6.1 Case studies A and B (effect of U and V)
To examine the effect of crosswind on DRE/CE
for each jet velocity, the data were plotted in Figs.
5 and 6. To clearly distinguish the trend for each jet velocity, both plots are divided into two parts:
5a/5b and 6a/6b. The “a” parts of the plots show
the data for the jet velocities 10 m/s, 15 m/s and 20 m/s and the “b” parts of the plots show the
same for 25 m/s, 30 m/s and 40 m/s. As expected,
an increase in crosswind velocity reduces the flare
efficiency. Comparing plot 5a with 5b and 6a with 6b, it can be seen that, within this jet velocity
range, the effect of crosswind velocity is stronger
when the jet velocities are higher. In particular, consider the worst case scenario, where the jet
velocity is abnormally high, say 35 m/s, with an
unusual crosswind velocity of 40 m/s, the destruction and removal efficiency, the DRE can
come down to as low as 73%. Similarly, the
combustion efficiency drops down to less than
70%. On the other hand, the effect of jet velocity on the
combustion efficiency (at constant crosswind
velocities) has a different trend. In Fig. 7a,
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combustion efficiency is plotted against jet velocities for crosswind velocities of 10, 15 and 20m/s and for crosswind velocities of 25, 30 and 40m/s in Fig. 7b. Fig. 7a shows no or negligible effect on the CE with increase in jet velocities. But for higher crosswinds, in Fig. 7b, the CE at low jet velocities (around 5m/s) is as low as 50%-60%. The CE value then increases to up to 80% with increasing jet velocity and then again starts decreasing. Thus, there exists an optimal jet velocity zone roughly between 15 to 25 m/s according to this CFD study. However, the exact range of the optimal jet velocity further depends on crosswind velocity and heat content. Crosswind bends the flare at low jet velocities while high jet velocity causes dilution of the fuel in the vertical direction by sucking in too much air. A larger CZHV sustains a higher acceptable jet velocity as well as a wider optimal jet velocity range.
(a)
(b) Fig. 5 Case study A: DRE(%) vs. crosswind velocity
(m/s).
(a)
(b) Fig. 6 Case study A: CE(%) vs. crosswind velocity
(m/s). 6.2 Case study C (effect of SR)
The flare efficiencies for various Stoichiometric Ratios (SR) are plotted in Fig. 8. There is a steady decrease in flare performance with increasing amounts of air-assist. The maximum DRE and CE reported are 99.76% and 97.08%, respectively. Both the DRE and CE keep decreasing with additional air-assist. It can be observed that the DRE remains above 98% even at an SR of 0.369. At the same SR, the CE hovers around 92%. However, though the DRE remains well above 90% at SR =1.35, the CE goes down to as low as 83%. A strong correlation between the two efficiencies and the SR is observed. The correlations can be found in Equations 4 and 5. The R-square values of the correlations for DRE and CE are 0.9195 and 0.9435, respectively.
(5) 001.1*00569.0 SRDRE
9796.0*1228.0 SRCE (4)
(5)
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where SR = ratio of actual assisted-air to stoichiometric air. Equation 5 is valid only for V = 10 to 25m/s and U = 5 to 40m/s, for air-assisted ethylene flares. Although a prior TCEQ report (UT Austin, 2011) showed some higher flare efficiencies compared to Equation 4 & 5, there was a similar linear trend between CE/DRE and SR as observed in the CFD modeling for the air-assist flares.
(a)
(b)
Fig. 7 Case study B: CE(%) vs. jet velocity (m/s).
Fig. 8 Case study C: DRE/CE vs. Stoichiometric Air Ratio.
Fig. 9 Case study D: DRE/CE vs. combustion zone heating value (MJ/m3).
6.3 Case study D (effect of CZHV)
The CZHV of the vent gas was varied from 54.53 MJ/m3 to 25.88 MJ/m3 (ethylene diluted with nitrogen). As expected, the maximum flare efficiency was reported for the highest heat content test case. Fig. 9, which includes data from Cases C and D, shows the effect of CZHV on flare efficiencies. It can be noted that the flare performance, in contrast to the linear decrease observed in the previous case study, decreases dramatically here. The DRE remains above 90% at a CZHV of around 37.30 MJ/m3. Then, it rapidly decreases to 80% at 26.11 MJ/m3. The same trend can be seen in CE, which goes down to 68%. Again, strong correlations between the flare efficiencies and CZHV were observed. The flare efficiencies for different values of CZHVs can be calculated using Equations 6 and 7. The R-square values for the two equations are 0.9035 and 0.9264, respectively.
(6) (7)
where CZHV = Combustion Zone Heating Value of the fuel gas (MJ/m3). Equations 6 and 7 are valid only for V = 10 to 40m/s and U = 5 to 40m/s, for air-assisted ethylene flares.
6.4 Correlation for flare efficiency calculation
One of the objectives of this work was to develop a correlation that can be used to calculate flare efficiencies using parameters such as jet velocity, crosswind velocity and heat content. Therefore, data from case studies A, B, C and D were fitted into a single parametric equation, shown in Equation 8. The correlation includes three input variables: u (crosswind velocity in m/s), v (jet velocity in m/s) and CZHV (common zone heating value in MJ/m3 as defined in Equation 3).
428.0)0373.0/(*0417.0 CZHVCE
2718.0)0373.0/(*1365.0 CZHVDRE
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The CZHV value used for developing the correlation contains the factor xeff, which has already been explained. The value of xeff(0.024) was determined using the results from Case Studies C and D. The xeff value was then optimized to fit all the cases from Case Studies A, B, C and D. The model was fitted for both DRE and CE. In the case of DRE, the R-Square value of the model was 0.89, which indicates the model has a good fit. The model is valid only under the conditions described in Table 5. The maximum deviation of predicted efficiency was about 9%. For the total sample size of 68, only 4 cases were observed with deviations of 5% or more. On the other hand for CE, the R-square value was 0.91 with a maximum deviation of about 14%. In 9 instances, the deviation between the predicted and observed values exceeded 5%. The general correlation is given below:
The rest of the constant values are provided in Table 6 for DRE and Table 7 for CE.The comparison of the correlated efficiencies with the CFD modeled efficiencies is shown in Figs. 10 and 11. The above mentioned correlation can be used to operate the flares for optimized performance. Adjusting one of the operating parameters, the flare emissions can be drastically reduced. As an example, Figs. 12 and 13 can be used to find the optimum jet velocity to maximize the DRE and CE for different crosswind velocity.
Table 5 Conditions under which correlation (equation 8) is valid.
V (m/s) U (m/s) CZHV (MJ/m3)
10.0 - 40.0 5.0 - 35.0 25.88 – 59.49
Table 6 Values of constants used for DRE in equation 8.
b c d e0.2018 0.0015 -0.0014 -1.24E-06 2.90E-05
f g h i j-7.87E-05 -0.1704 -0.5346 0.8233 1.5190
k l m q n-0.0269 0.0015 0.2676 1.1100 0.0373
Table 7 Values of constants used for CE in equation 8.
a b c d e4.37E-01 1.64E-02 8.97E-04 -1.34E-04 4.84E-05
f g h i j-3.63E-04 6.08E-01 -1.36E-01 1.298 1.17E-01
k l m q n-1.16E-01 3.23E-03 4.044E-01 0.0977 0.0373
Fig. 10 Comparison of correlated and CFD modeled DRE.
Fig. 11 Comparison of correlated and CFD modeled CE.
Fig. 12 Contours for DRE and jet velocity for different crosswind velocity.
Fig. 13 Contours for CE and jet velocity for different crosswind velocity.
22/ ****( veudvcubaCEDRE
22 ////** ujviuhvgvuf
))/(*(*))/(*)/(* 2 mnCZHVqvulvuk (8)
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Fig. 14 Case study A (C2H4 Emission Rate vs. Crosswind Velocity).
Fig. 15 Case study A (CO2 Emission Rate vs. Crosswind Velocity).
Fig. 16 Case study A (CO Emission Rate vs. Crosswind Velocity).
6.5 Emission rates
As a result of incomplete combustion, various HRVOCs/VOCs/HOx/NOx compounds are formed and escape into the atmosphere. In this parametric study, the prediction of the emission rates of 6 species; C2H4, CO2, CO, CH2O, NOx
and HOx were reported. These emission rates are Due to the similar trends seen in the emissions only those for Case Studies A and B are plotted, as shown in Figs. 14 to 19. Each plot shows the change in emission rates with crosswind for each jet velocity. In Figs. 14 and 15, the effect on the C2H4 and CO2
emission rates can be seen. These plots reflect the trends seen in DRE/CE. Note that there was asharp increase in C2H4 emission rates at crosswinds of 30 m/s and 35 m/s and a rapid decrease in CO2, indicating poor flare performance. At the same time, a uniform increase in CO emissions also suggests an incomplete combustion. The trend for CO emission rates is shown in Fig. 16. As observed, the CO emission rates can increase by an order of magnitude, i.e., from 0.02 kg/kg C2H4 to 0.2 kg/kg C2H4, due to increased crosswind velocity.
Fig. 17 Case study A: CH2O emission rate vs. crosswind velocity.
Fig. 18 Case study A: HOx emission rate vs. crosswind velocity.
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Table 8 Flare efficiencies at different S/F ratios and heating values.
Case # Steam flow rate
(kg/s) Steam to Fuel ratio
(S/F)
Combustion Zone Heating Value (CZHV)
(MJ/m3)
Destruction and Removal Efficiency
(DRE) (%)
Combustion Efficiency (CE)
(%)
1 0 0 56.47 98.19% 96.00%
2 0.2981 0.2 43.07 98.39% 96.36%
3 0.5961 0.4 34.81 98.38% 95.73%
4 0.8942 0.6 29.21 98.09% 95.02%
5 1.0432 0.7 27.03 97.17% 93.80%
6 1.3413 0.9 23.53 97.16% 93.58%
7 1.7884 1.2 19.70 95.88% 91.85%
8 2.0864 1.4 17.77 93.41% 89.36%
9 2.2355 1.5 16.94 93.01% 88.89%
10 2.608 1.75 15.17 89.55% 85.33%
11 2.9806 2 13.74 85.67% 80.83%
12 3.3532 2.25 12.55 82.60% 77.88%
13 3.7258 2.5 11.55 80.11% 75.29%
14 4.0983 2.75 10.70 68.89% 62.76%
15 4.4709 3 9.97 67.77% 62.07%
16 4.8435 3.25 9.33 66.32% 61.23%
17 5.2161 3.5 8.76 57.54% 52.73%
18 5.5886 3.75 8.26 49.27% 44.72%
19 5.9612 4 7.82 42.51% 38.73%
The other important species observed is CH2O, which is a major factor in the formation of O3 and
radicals such as OH. Fig. 17 shows the increase in
the formaldehyde emission rates with increase in crosswind velocity. It shows that the CH2O
emission rate can go as high as 4 x 10-03
kg/kg
C2H4. The highest emission rates again occur at the lowest flare efficiencies. On the other hand,
Fig. 18 shows the emission rates of HOx (OH &
HO2). In this case, instead of only at higher jet
velocities, the emission rates increases for all jet velocities. In Fig. 19, a trend similar to that of
CH2O is seen for the NOx formation. The NOx
formation rate is not uniform and increases abruptly at high jet velocities and high
crosswinds.
6.6 Correlation between flare efficiencies
In Fig. 15, DRE is plotted against CE. The aim of
this plot is to identify the lowest CE % that can be reached with the flare operating at DRE higher
than the TCEQ benchmark of 98%. As seen from
Fig. 20, the CE value is in general lower than the corresponding DRE and the difference between
the two is not a constant. Rather, their relation can be described as a linear relationship, Equation 9,
with a R-square value of 0.8572.
(9) Equation 9 is only valid for DRE > 0.98 for air-
assisted ethylene flares
It should be noted that at a DRE value of 98%, the CE can go as low as 91%.
Fig. 19 Case study A: NOx emission rate vs. crosswind velocity.
2915.2*2726.3 DRECE
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Fig. 20 Destruction and removal efficiency vs. combustion efficiency.
Fig. 21 Effect of steam to fuel ratio on efficiency.
Fig. 22 Effect of CZHV on efficiency.
Fig. 23 Effect of steam to fuel ratio on HCHO emission.
Fig. 24 Effect of steam to fuel ratio on C2H4 emission.
Fig. 25 Relation between C2H4 and CO2 emission.
Fig. 26 Relation between HCHO and CO2 emission.
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6.7 Case study E (effect of S/F)
In the simulation results shown in Figure 21, it
can be observed that a DRE of 98% or higher can
only be obtained when the S/F ratio is 0.6 or
below. For an S/F ratio of 1.2 or below, the DRE is above 95% and the CE is above 90%. At higher
S/F ratios, efficiencies decline steeply. Thus,
over-steaming can drastically reduce the efficiency of ethylene flares. Table 8 shows the
efficiency values tabulated versus both steam to
fuel ratio and lower heating value. Increased dilution of the fuel with steam, as the S/F ratio is
increased, reduces the CZHV of the fuel-steam
mixture. The combustion efficiency falls below
90% when the CZHV of the fuel is below 17.90 MJ/m
3, as shown in Fig. 22.
The DRE/CE data show a clear trend with
increasing S/F ratio when the vent gas and crosswind velocity are kept constant. This helps
in the formation of quadratic correlations, such as
those shown below, that can be used over the complete range of S/F ratios between 0 and 4.
R2 = 0.992
R2 = 0.991
where S/F refers to the mass ratio of steam to
fuel.
The correlation of DRE/CE with CZHV can be shown using a slightly complex fit:
(R2 = 0.9914)
(R2 = 0.9926)
where CZHV is in MJ/m3.
6.8 Emission rates
HCHO Emission: Fig. 23 shows the effect of
steam to fuel ratio on the emission of formaldehyde (HCHO), a VOC that leads to
ozone formation in the lower atmosphere. As
shown in Table 9, acetaldehyde emission also follows a similar trend. For S/F ratios of 1 or
lower, the HCHO emission is practically
negligible. However, with increasing amounts of
steam added to the vent gas, the amount of HCHO emitted increases to around 0.0056 kg HCHO/kg
C2H4 fuel burnt for an S/F ratio of 4, the highest
ratio considered in the study. Ethylene Emission: The normalized emission of
ethylene, an HRVOC, at different S/F ratios is
shown in Fig. 24. As the steam to fuel ratio
increases, more C2H4 is emitted relative to the
C2H4 entering as fuel. As evident from Figs. 25 and 26, the C2H4 emission and HCHO emission
have an inverse linear relationship with the CO2
emission from the flare. When combustion
efficiency is low, that is, when less of the ethylene is burnt to CO2, the remainder is either
vented as unburned parent compounds (e.g.,
ethylene) or as incomplete combustion products (e.g., formaldehyde). Without steam assist,
0.0181 kg C2H4 is emitted per kg of vent C2H4
entering the flare, whereas over-steaming to an S/F ratio of 4 leads to 0.575 kg C2H4 emission per
kg of C2H4 vent gas.
NOx Emissions: Fig. 27 shows that NOx emission
decreases steeply with the increase in S/F ratio between 0 and 1. Beyond that, the trend is
uneven, although there appears to be a much more
gradual decline when the S/F ratio is between 1 and 4. Since NO2 is the predominant NOx species
in the plume, its trend can be seen in Fig. 27. The
separate emission results for NO2 and NO are displayed in Table 9. Nitric oxide (NO) emissions
decrease sharply with added steam up to an S/F
ratio of 1-1.2, and then level off.
Fig. 27 Effect of steam to fuel ratio on NOx emission.
Fig. 28 Effect of steam to fuel ratio on HOx emission.
5.96)/(*5.0)/(*4.3(%) 2 FSFSCE
94.95/*10*91.0 )*1340.0(06 CZHVeCE CZHV
(10) (10)
(11)
(12)
(13)
3.98)/(*8.1)/(*9.3(%) 2 FSFSDRE
53.98/*10*1.1 )*1608.0(06 CZHVeDRE CZHV
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Table 9 Normalized species emissions at different S/F ratios.
S/F Ratio 0 0.2 0.4 0.6 0.7 0.9
CO2 3.02E+00 3.03E+00 3.01E+00 2.99E+00 2.95E+00 2.94E+00
OH 4.89E-05 3.74E-05 2.81E-05 1.02E-05 1.00E-05 4.67E-06
HO2 9.20E-05 8.84E-05 6.63E-05 1.77E-04 2.33E-04 3.19E-04
CH3 1.92E-06 1.83E-06 1.59E-06 8.92E-07 1.13E-06 1.32E-06
CO 3.30E-02 3.18E-02 4.25E-02 4.96E-02 5.50E-02 5.76E-02
CH2O 1.28E-04 1.11E-04 1.12E-04 1.07E-04 1.47E-04 2.45E-04
C2H4 1.81E-02 1.61E-02 1.62E-02 1.91E-02 2.83E-02 2.84E-02
CH3CHO 6.33E-06 1.65E-05 1.38E-05 3.79E-05 4.17E-05 8.31E-05
NO 5.81E-03 1.99E-03 1.83E-03 5.48E-04 5.00E-04 3.76E-04
NO2 2.31E-02 2.02E-02 1.57E-02 1.19E-02 1.15E-02 8.30E-03
S/F 1.2 1.4 1.5 1.75 2 2.25
CO2 2.89E+00 2.81E+00 2.79E+00 2.68E+00 2.54E+00 2.45E+00
OH 4.17E-06 3.12E-06 1.85E-06 1.64E-06 2.51E-06 1.98E-06
HO2 2.10E-04 3.43E-04 2.77E-04 5.79E-04 7.64E-04 8.07E-04
CH3 2.26E-06 7.95E-07 1.11E-06 8.64E-07 1.27E-06 7.28E-07
CO 6.64E-02 6.70E-02 6.76E-02 6.88E-02 7.81E-02 7.59E-02
CH2O 5.08E-04 6.89E-04 7.45E-04 1.12E-03 1.40E-03 1.82E-03
C2H4 4.12E-02 6.59E-02 6.99E-02 1.05E-01 1.43E-01 1.72E-01
CH3CHO 1.06E-04 1.26E-04 1.78E-04 1.93E-04 3.68E-04 3.78E-04
NO 2.43E-04 1.11E-04 1.06E-04 6.39E-05 1.69E-04 4.05E-05
NO2 1.01E-02 9.39E-03 1.02E-02 8.06E-03 8.20E-03 1.09E-02
S/F 2.5 2.75 3 3.25 3.5 3.75 4
CO2 2.37E+00 1.97E+00 1.95E+00 1.92E+00 1.66E+00 1.41E+00 1.22E+00
OH 1.38E-06 1.67E-06 6.36E-06 1.02E-06 3.48E-06 8.99E-07 8.55E-07
HO2 7.41E-04 9.28E-04 2.22E-03 2.05E-03 2.67E-03 3.42E-03 3.13E-03
CH3 1.25E-06 1.17E-06 3.39E-06 7.10E-07 8.46E-07 5.20E-07 5.58E-07
CO 7.79E-02 9.65E-02 8.83E-02 7.78E-02 7.12E-02 6.60E-02 5.36E-02
CH2O 1.66E-03 2.56E-03 3.83E-03 3.80E-03 4.64E-03 5.51E-03 5.55E-03
C2H4 1.99E-01 3.11E-01 3.22E-01 3.37E-01 4.25E-01 5.07E-01 5.75E-01
CH3CHO 4.62E-04 7.92E-04 6.81E-04 7.55E-04 1.40E-03 1.51E-03 1.32E-03
NO 4.58E-05 1.33E-04 2.24E-05 1.09E-05 9.95E-06 7.03E-05 1.28E-05
NO2 7.19E-03 1.13E-02 1.04E-02 1.05E-02 7.27E-03 5.41E-03 5.02E-03
HOx Emissions: The hydroxyl radical (OH) is the most important oxidant in the atmosphere and
controls the atmospheric lifetimes of most trace
gases. OH is produced in the photolysis processes of ozone (O3), formaldehyde (HCHO) and nitrous
acid (HONO). OH initiates the oxidation process
of NOx, CO, anthropogenic and biogenic VOCs and the formation of peroxide radicals (Seinfeld
and Pandis, 2006). The hydroperoxyl radical
(HO2) plays a key role in the oxidation of NO to
NO2, and eventually leads to the formation of
ozone (O3) (Stone et al., 2012). The addition of steam to vent gas has the opposite effects on the
emission of the two major HOx radicals, OH and
HO2. Whereas additional steam promotes the formation of HO2, it hinders the formation of OH,
as evident from Fig. 28.
7. CONCLUSIONS
Clear trends between the flare efficiencies and the
operating parameters, i.e., stoichiometric air ratio,
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jet velocity, vent gas heat content, and crosswind
velocity were seen through CFD modeling and were summarized with simple correlations. The
important conclusions of this study are listed
below.
A new mechanism, LU1.1, derived from a
previously reduced mechanism, LU1.0 is introduced. In LU1.1, an important species
NO2 was added by replacing CN. The
mechanism was successfully validated using Laminar Flame Speed, Ignition Delay and
Adiabatic Flame Temperature tests for C1-C3
hydrocarbons.
The heat content of the vent gas has a
considerably larger effect on flare
performance than aeration. A novel factor xeff
was used to calculate CZHV in order to take
into consideration the decrease in heat content due to non-premixed air. At the minimum
CZHV tried in this study (25.88 MJ/m3), the
DRE and CE value dropped to 79% and 67%, respectively.
With decreasing flare performance, there was
a corresponding increase in emission rates as
expected. The formation of NOx, HOx, formaldehyde, CO, and CO2 and emission of
the unburned parent compound C2H4 were
predicted, which in most cases peaked at high
jet velocities or high crosswinds.
Even though operating flares in accordance to
the regulations can yield more than 98%
DRE, it does not ensure good performance.
A flare with DRE greater than 98% may have a CE as low as 91% (as observed from the
correlation between DRE and CE, Equation
9).
The DRE drops below 98% as the S/F ratio is
increased to 0.7, and can go as low as 43%
when 4 times as much steam as fuel is used.
The combustion efficiency drops below 90%
for S/F ratios of 1.4 and above.
ACKNOWLEDGMENTS
The authors gratefully acknowledge the financial
support from TCEQ Supplemental Environmental
Program (SEP Agreement No. 2009-009) and the
Texas Air Research Center (TARC Grant #079LUB0096A).
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