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Full Terms & Conditions of access and use can be found at http://www.tandfonline.com/action/journalInformation?journalCode=tcfm20 Download by: [123.201.241.73] Date: 08 November 2015, At: 16:58 Engineering Applications of Computational Fluid Mechanics ISSN: 1994-2060 (Print) 1997-003X (Online) Journal homepage: http://www.tandfonline.com/loi/tcfm20 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 & 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 Study of Ethylene Flare Operations Using Numerical Simulation, Engineering Applications of Computational 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 Copyright 2014 Taylor and Francis Group LLC Published online: 19 Nov 2014. Submit your article to this journal Article views: 46 View related articles View Crossmark data

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Page 1: Parametric Study of Ethylene Flare Operatio Using Numerical Simulation.pdf

Full Terms & Conditions of access and use can be found athttp://www.tandfonline.com/action/journalInformation?journalCode=tcfm20

Download by: [123.201.241.73] Date: 08 November 2015, At: 16:58

Engineering Applications of Computational FluidMechanics

ISSN: 1994-2060 (Print) 1997-003X (Online) Journal homepage: http://www.tandfonline.com/loi/tcfm20

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

Copyright 2014 Taylor and Francis GroupLLC

Published online: 19 Nov 2014.

Submit your article to this journal

Article views: 46

View related articles

View Crossmark data

Page 2: Parametric Study of Ethylene Flare Operatio Using Numerical Simulation.pdf

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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Engineering Applications of Computational Fluid Mechanics Vol. 8, No. 2 (2014)

212

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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Engineering Applications of Computational Fluid Mechanics Vol. 8, No. 2 (2014)

213

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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Engineering Applications of Computational Fluid Mechanics Vol. 8, No. 2, pp. 211–228 (2014)

214

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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215

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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217

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).

REFERENCES

1. American Petroleum Institute (2008).

ANSI/API Standard 537. Flare Details for General Refinery and Petrochemical Service.

Washington, DC, USA.

2. Barlow RS, Karpetis AN, Frank JH, Chen,JY

(2001). Scalar profiles and NO formation in laminar opposed flow partially premixed

methane/air flames. Combustion and Flame

127: 2102-2118.

3. Baukal CE, Schwartz RE (2001). The John Zink Combustion Handbook. New York, CRC

Press.

4. Bourguignon E, Johnson MR, Kostiuk, LW (1999). The use of a closed loop wind tunnel

for measuring the efficiency of flames in

crossflow. Combustion and Flame 119: 319-334.

5. Castiñeira D, Edgar T (2008). CFD for

simulation of crosswind on the efficiency of

high momentum jet turbulent combustion flames. J. Environ. Eng. 134(7): 561–571.

6. Castiñeira D, Edgar, TF (2006). CFD for

simulation of steam-assisted and air-assisted flare combustion systems. Energy and Fuels

20: 1044-1056.

7. Davis SG, Law, CK (1998). Determination of and fuel structure effects on laminar flame

speeds of C1 to C8 hydrocarbons.

Combustion Science and Technology 140(1):

427-449. 8. Davis SG, Law CK, Wang H (1999).

Reaction mechanism of C3 fuel combustion.

http://ignis.usc.edu/Mechanisms/C3/c3.html. 9. EPA (1986). United States Government Code

of Federal Regulations - Standards of

Performance for New Stationary Sources.

General Control Device and Work Practice Requirements, 40CFR § 60.18.

10. EPA (1991). Section 13.5: Industrial Flares.

AP 42, Compilation of Air Pollutant Emission Factors, Volume 1: Stationary Point and Area

Sources.

11. Johnson MR, Kostiuk LW (2000). Efficiencies of low momentum jet diffusion

flames in crosswinds. Combustion and Flame

123:189-200.

12. Johnson MR, Kostiuk LW (2002). A parametric model for the efficiency of a flare

in crosswind. Proceedings of the Combustion

Institute 29: 1943-1950. 13. Kee RJ, Rupley FM, Miller JA, Coltrin ME,

Grcar JF, Meeks E, Moffat HK, Lutz AE.

Lewis DG., Smooke MD, Warnatz J, Evans GH, Larson RS, Mitchell RE, Petzold LR,

Reynolds, WC, Caracotsios M, Stewart W

(2007). CHEMKIN Release 4.1.1.

CHEMKIN® Software, RD00411-C01-004-001. R. Design.

14. Kostiuk L, Johnson M, Thomas G (2004).

Flare Research Project Final Report, University

Dow

nloa

ded

by [

123.

201.

241.

73]

at 1

6:58

08

Nov

embe

r 20

15

Page 19: Parametric Study of Ethylene Flare Operatio Using Numerical Simulation.pdf

Engineering Applications of Computational Fluid Mechanics Vol. 8, No. 2 (2014)

228

of Alberta, Canada.

15. Law CK, Makino A, Lu TF (2005). On the off-stoichiometric peaking of adiabatic flame

temperature with equivalence ratio. The 4th

Joint Meeting of the U.S. Sections of the

Combustion Institute. 16. Lou HH, Martin CB, Chen D, Li XC, Li KY,

Vaid H, Kumar AT, Singh KD, Bean Jr DP

(2011). A reduced reaction mechanism for the simulation in ethylene flare combustion.

Clean Technology and Environmental Policy

14: 229-239. 17. Mayers BF, Bartle ER (1969). Reaction and

ignition delay times in the oxidation of

propane. AIAA Journal 7(10): 1862-1869.

18. McDaniel M (1983). Flare Efficiency Study. Report No. 600/2-83-052, United States

Environmental Protection Agency.

19. Miller JA, Branch MC, McLean WJ, Chandler DW, Smooke MD, Kee RJ (1985).

Proceedings of the Twentieth Symposium

(International) on Combustion, Pittsburgh, Pennsylvania, The Combustion Institute,

USA.

20. Miller JA, Mitchell RE, Smooke MD.Kee RJ

(1982). Proceedings of the Nineteenth Symposium (International) on Combustion,

Pittsburgh, Pennsylvania, The Combustion

Institute, USA. 21. Miller JA, Smooke,MD, Green, RM, Kee RJ

(1983). Kinetic modeling of the oxidation of

ammonia in flames. Combustion Science and

Technology 34: 149-176. 22. Petrova MV, Williams FA (2006). A small

detailed chemical-kinetic mechanism for

hydrocarbon combustion. Combustion and Flame 144(3): 526-544.

23. Pohl JH (1984/1985). Evaluation of the

Efficiency of Industrial Flares. EPA600-2-85-95 and 106, USA.

24. Poudenx P, Kostiuk LW (1999). An

investigation of the mean plume structures of

a flare in a crosswind. Presented at the Canadian Section of the Combustion Institute.

Edmonton, Alberta, Canada.

25. Qin Z, Yang H, Gardiner C (2001). Measurement and modeling of shock-tube

ignition delay for propene. Combustion and

Flame 124: 246-254. 26. Reynolds WC (1986). The Element Potential

Method for Chemical Equilibrium Analysis:

Implementation in the Interactive Program

STANJAN. Department of Mechanical Engineering, Stanford University, USA.

27. Samuelsen S, McDonell V, Chen J, Jermakian

V (2003). Correlation of Ignition Delay with

Fuel Composition and State for Application

to Gas Turbine Combustion. Irvine, CA, University of California, USA.

28. Schultz E, Sheperd J (2000). Validation of

Detailed Reaction Mechanisms for

Detonation Simulation. Explosion Dynamics Laboratory Report FM99-5. Pasadena, CA,

California Institute of Technology, USA.

29. Seinfeld JH, Pandis S (2006). Atmospheric Chemistry and Physics - From Air Pollution

to Climate Change, John Wiley and Sons.

30. Singh KD, Dabade T, Vaid H, Gangadharan P, Chen D, Lou, HH, Li KY, Li XC, Martin

CB (2012). Computational fluid dynamics

modeling of industrial flares operated in a

stand-by mode. Industrial and Engineering Chemistry Research 51(39): 12611–12620.

31. Smith GP. Golden GM, Frenklach M,

Moriarty NW, Eiteneer B, Goldenberg M, Bowman T, Hanson RK, Song S, Gardiner

WC, Lissianski VV, Qin Z (2000). GRI-

Mech. Retrieved 3 October 2011, from http://www.me.berkeley.edu/gri_mech/.

32. Spakovszky ZS (2013). Unified:

Thermodynamics and Propulsion. http://

aeroastro.mit.edu/faculty-research/faculty-list/ zoltan-s-spakovszky.

33. Stone D, Whalley LK, Heard DE (2012).

Tropospheric OH and HO2 radicals: field measurements and model comparisons.

Chemical Society Reviews 19: 6348-6404.

34. UT Austin (2011). TCEQ Flare Study Final

Report. TCEQ PGA No. 582-8-86245-FY09-04 and Task Order No. UTA10-000924-

LOAT-RP9, The University of Texas at

Austin, The Center for Energy and Environmental Resources, USA.

35. UT/TCEQ/John Zink (2008). Project, PGA

No. 582-8-862-45-FY09-04, Tracking No. 2008-81. Quality Assurance Project Plan,

The University of Texas at Austin, The

Center for Energy and Environmental

Resources, USA.

Dow

nloa

ded

by [

123.

201.

241.

73]

at 1

6:58

08

Nov

embe

r 20

15