A reduced reaction mechanism for the simulation in ethylene flare combustion

ORIGINAL PAPER

A reduced reaction mechanism for the simulation in ethyleneflare combustion

Helen H. Lou • Christopher B. Martin • Daniel Chen •

Xianchang Li • Kyuen Li • Hitesh Vaid • Anjan Tula Kumar •

Kanwar Devesh Singh • Doyle P. Bean Jr.

Received: 6 January 2011 / Accepted: 31 May 2011

� Springer-Verlag 2011

Abstract Industrial ethylene flares are considered to be a

probable major source of volatile organic compounds

(VOCs) such as formaldehyde. VOCs are chemicals that

are responsible for the formation of other atmospheric

pollutants like ozone. Due to the difficulty and cost of field

measurements, on-line monitoring is not practical and other

methods must be employed. Current methodologies for

calculating speciated and total VOC emissions from flaring

activities generally apply a simple mass reduction to the

VOC species sent to the flare that does not consider the

production of incomplete combustion or other intermedi-

ates. There arises a need of a speciation study for the

inspection of these flare for their emission. However, most

of the detailed kinetic mechanisms for the speciation study

of flaring events are too complex, consist of large number

of reactions and species, and also are computationally

expensive. A reduced mechanism will thus be desirable

for improving computational efficiency. In this study, a

reduced mechanism for simulating ethylene flare combus-

tion is presented. By retaining the important features of the

detailed mechanism in the form of elementary reactions,

and satisfying the species constraint of commercial CFD

packages, the reduced mechanism, thereby, is useful for

speciation study of flaring event.

Keywords Combustion � Reduced mechanism �Ethylene flare

Introduction

According to the US Environmental Protection Agency

(EPA), industrial operations dispose of 100 million tons of

pollutants into the atmosphere every year. The flaring

emissions during chemical plant operations generate huge

amounts of CO, CO2, NOx, volatile organic compounds

(VOC) such as formaldehyde and highly reactive VOC

(HRVOC) (defined in Texas air quality regulation as eth-

ylene, propylene, isomers of butene and 1,3-butadiene).

VOCs mixed with NOx have been identified associating

with high concentrations of ozone observed in Texas

Houston/Galveston area, which violates the National

Ambient Air Quality Standards (NAAQS) for ozone (Nam

et al. 2008; EPA 2010). Industrial flare emissions from

ethylene plants are considered as a probable major source

of HRVOCs (USEPA 2010).

Results from the most recent Texas Air Quality Model

Simulation of Houston study indicated that neither the

current HRVOC emission inventory (EI) nor adding more

HRVOC in EI could explain the high ozone scenarios in

the Houston–Galveston Area (Byun et al. 2007; Jiang and

Fast 2004). These results also indicated that the models

were very limited in their ability to predict radical inter-

mediates and formaldehyde (CH2O or HCHO), which

could be a great radical source if it was produced as a

primary source from flaring (Byun et al. 2007). However,

no HCHO has been reported as a primary emission from

H. H. Lou (&) � D. Chen � K. Li � H. Vaid �A. T. Kumar � K. D. Singh � D. P. Bean Jr.

Dan F. Smith Department of Chemical Engineering,

Lamar University, Beaumont, TX 77710, USA

e-mail: [email protected]

C. B. Martin

Department of Chemistry and Physics, Lamar University,

Beaumont, TX 77710, USA

X. Li

Department of Mechanical Engineering, Lamar University,

Beaumont, TX 77710, USA

123

Clean Techn Environ Policy

DOI 10.1007/s10098-011-0394-9

flaring. All of the VOC emissions in the inventories are

assumed to be reactive organic gas (ROG), and a emissions

processing software, SMOKE (Simon et al. 2007) was used

to convert the VOC to total organic gas (TOG) before

converting the emissions into speciation for the air quality

models (TCEQ 2007).

Research has been performed on the underrated emis-

sions in experimental flares (Kostiuk et al. 2000; USEPA

1983; URS corporation 2004; Castineira and Edgar 2008).

These scientific findings by the TexAQS2000 study

(NOAA Aeronomy Laboratory 2003) have shown that

ambient VOC emissions sampled by aircraft did not coin-

cide with the annual emissions inventory. Consequently, it

was inferred that some VOC sources were in the emission

inventory or may be recognized as sources, but were sig-

nificantly under reported.

Flare emissions may vary based on actual flare operation

and there may be more variables that affect flare operation

than were identified in the previous studies. Thus, it is

desirable to be able to determine speciated emissions and

combustion efficiency during actual operation (Peters and

Kee 1987). Field measurement tests have also been done

using techniques such as Passive Fourier Transform Infrared

(PFTIR) and Resonance-Enhanced Multiphoton Ionization

(REMPI). PFTIR was used to measure emissions and com-

bustion efficiency from an actual flare, a study sponsored by

TCEQ (Simon et al. 2007). Due to the problems such as

minimum detection limit and accuracy of the system setup,

development of a practical and reliable routine detection of

VOC’s through field measurement is still under study. Field

measurements are also difficult and costly, which contrib-

utes to the current lack of field deployment. Therefore, a

reliable and practical alternative, such as a computational

model for flare speciation, is greatly needed.

Computational methods employed to predict the amount

of under-estimated VOCs, like formaldehyde, require

solving the governing chemical transport equations with

detailed kinetic mechanisms. Detailed chemical kinetic

mechanisms for some of the fuels are available, but they

contain tens to hundreds of species and hundreds to thou-

sands of reactions. Hence, solving equations of hundreds of

species with such complicated mechanisms coupled with

continuity, momentum, energy, radiation, and gravity

equations in CFD grid becomes computationally expensive

and in most cases not feasible. At the same time, the

capacity of the commercial CFD packages like ANSYS

FLUENT 6.3.26 is limited and cannot handle more than 50

species in the simulation. Therefore, there is a need for the

reduction of the detailed kinetic mechanism in a way that

the number of species falls below the allowable limit of

commercial CFD packages. While still retaining important

features of the detailed mechanism, thus making the

chemistry less restrictive and also improving the

computational efficiency. The reduced mechanism pro-

posed in this article can be used further for modeling eth-

ylene flare combustion process in a follow up study.

A review on available methods for the reduction

of detailed chemical kinetic mechanism

Modeling turbulent combustion requires expensive com-

putational resources and thus there is a need of reduced

mechanisms. There are several ways to reduce detailed

mechanism. Among them, the two major methods are the

skeletal reduction and time scale analysis. Skeletal mech-

anisms are on the same form as detailed mechanisms with

the standard Arrhenius elementary reactions. Skeletal

reduction is achieved with different methods including

sensitivity analysis, principal component analysis, lump-

ing, genetic algorithms, optimization, and adaptive reduc-

tion. On the other hand, time scale reduction is based on the

quasi steady-state approximation method (QSSA) (Peters

1988) and the partial equilibrium (PE) method. Other

methods based on QSSA have been reported in several

literatures (Androulakis 2000; Bhattacharjee et al. 2003;

Petzold and Zhu 1999; Chen 1988).

Research efforts have been conducted in both fields and

can be found in literatures (Simon et al. 2007; Brink and

Kilpinen 1999). Work was also done to reduce reaction

mechanism for combustion applications (Peters and Rogg

1993). These techniques reduce the number of reactions too

much by forming global reactions, which may not be able to

take advantage of the involved elementary reactions.

Another drawback is that these techniques may give reduced

mechanism in codes which are difficult to manage due to the

difference in programming language interface. Some of the

software packages for automatic mechanism reduction are

discussed here briefly, which are listed in the following

sections.

KINALC

KINALC is a postprocessor to CHEMKIN (Tomlin et al.

1992) based simulation programs developed by the Com-

bustion Simulations group at the University of Leeds

(Tomlin et al. 1997). It basically conducts three types of

analysis: processing sensitivity analysis results, extraction

of information from reaction rates, and stoichiometry, and

provides information about redundant and QSS species.

Species that are the least connected to the important spe-

cies can be eliminated from the mechanism. KINALC

facilitates the accurate selection of QSSA species by pre-

dicting the instantaneous error of QSSA species (URS

Corporation 2004).

H. H. Lou et al.

123

RIOT (Range Identification and Optimization Toolkit)

RIOT is a software tool to reduce the number of species and/

or reactions in a reaction mechanism, while maintaining user

specified tolerances on the accuracy of the reduced mecha-

nism (Schuchardt et al. 2005). RIOT is very handy at per-

forming an initial reduction of very large models, but it lacks

the ability of working with irreversible reactions, which is

the case with most combustion mechanisms.

Computational Singular Perturbation (CSP)

CSP developed by Lam and Goussis is based on a metic-

ulous timescale analysis, where fast and slow subspaces of

the chemical source term are defined and a reduced

mechanism with less stiffness is formulated. The CSP

method constructs reduced mechanisms consisting of

‘‘virtual global steps,’’ with rates as linear combinations of

the rates of the elementary reactions that form the detailed

mechanism (Neophytou et al. 2004).

CARM (Computer Assisted Reduction Mechanism)

CARM is an interactive code used for automatic generation of

reduced chemistry following the procedure developed by Chen

(NOAA Aeronomy Laboratory 2003). The base of CARM

code is the general idea of quasi-steady-state (QSS) assump-

tion for reducing the complex reaction mechanism. It identifies

the quasi-steady-state species by the criterion in Eq. 1:

xpk � xc

k

��

��

max xpk

��; xc

k

��

� �� d; ð1Þ

where the production rate, xpk ; and consumption rate, xc

k; of

the kth species are given by Eqs. 2 and 3, respectively.

xpk ¼

XI

i¼1

max mki; 0ð Þqi ð2Þ

xck ¼

XI

i¼1

min mki; 0ð Þqi: ð3Þ

The relative error d is usually far less than 1 and set

between 10-2 and 10-3. mki is the stoichiometric coefficient

of the kth species in the ith reaction, qi is the rate of

progress variable for the ith reaction step, and I is the total

number of reaction steps. CARM is used with CHEMKIN

and other flame codes to develop and test reduced

mechanisms. Once the reduced mechanism is built,

CARM automatically generates the source code in

FORTRAN, which can be used in CHEMKIN or can be

used as an individual mechanism (Chen et al. 1997).

In the present work, a new methodology based on the idea

of skeletal mechanism and the basic facts of mass action

kinetics was developed for the formation of reduced mech-

anism. This methodology utilizes simulation result of

CHEMKIN PSR reactor, which is a zero-dimensional

kinetic solver. The simulation result consists of information

on species concentration, residence time, temperature, and

pressure. The main advantage of this methodology lies in the

fact that it preserves the important reaction pathway in their

elementary form and produces reduced reaction mechanism

in standard CHEMKIN format and hence it is easy to use

without any user programming. The reaction mechanism file

needed for CHEMKIN was a combined mechanism formed

using two widely used chemical kinetic mechanisms, i.e.,

GRI-3.0 (Smith et al. 2000) and USC (Wang and Laskin

1998). The use of two mechanisms to form a combined

mechanism has been detailed later in this study.

Introductory information about the reduction

procedure

Two widely used mechanism, GRI 3.0 and USC (75 species),

are available for the CFD simulation of flaring. The GRI-

Mech 3.0 performs well for an extensive range of combus-

tion conditions, which has been evaluated and shown on their

website. The USC mechanism consisting of 75 species is a

comprehensive kinetic model for representing ethylene and

acetylene combustion. It has been evaluated for predicting

combustion properties of both C2 and C3 fuels. However, due

to some of their limitation, as described below, none of them

were used independently for the present work.

1 GRI-3.0 mechanism (with 53 species) was developed

and optimized for the combustion of methane and not

ethylene. A few aspects of natural gas combustion

chemistry are not described by GRI-Mech 3.0; these

include soot formation and the chemistry involved in

selective non-catalytic reduction of NO. The latter may

be important in natural gas reburning at lower

temperatures.

2 USC mechanism (containing 75 species) was optimized

for ethylene combustion reactions, but the absence of

NOx producing species in the mechanism was not

desirable.

To overcome this problem, both of the reaction mech-

anisms were combined so as to yield a mechanism which

could satisfy all the above-mentioned criteria. Consider-

ation of the NOx producing species contained in the GRI

mechanism gives more satisfactory results for the com-

bustion of ethylene than the USC mechanism alone. The

combined GRI-USC mechanism consists of 93 species;

however, to run the mechanism for CFD modeling, the

CFD simulation package, FLUENT (Lu and Law 2005),

can only handle a maximum of 50 species. Thus, the

A reduced reaction mechanism for the simulation in ethylene flare combustion

123

number of species in the combined GRI-USC mechanism

has to be reduced from a total of 93 to at least 50 species,

while still maintaining the accuracy in the predictability of

the reduced mechanism.

To reduce the number of species from the combined

mechanism, a new algorithm, which is illustrated in Fig. 1,

was developed for screening out those species not critical

to the accuracy of the reduced mechanism. The algorithm

basically consists of three major steps, as shown in Fig. 2.

The major steps are time-based sensitivity analysis,

examination of species in the reaction pathway, and pro-

gress variable analysis. These steps are interlinked through

an iterative process as explained in this section. Sensitivity

analysis was conducted using solution file containing data

for exit mole fraction of species at a range of different

residence time. The solution file was obtained by solving

the mechanism in a PSR reactor model of CHEMKIN at

conditions specified in Table 1.

Outline for the reduction procedure

This section presents the outline of the basic algorithm

described in Fig. 1. The methodology involved the

inspection of species based on the following factors, in

order to determine the criticality of a particular species and

its fate in the reduced mechanism. The factors considered

for the evaluation of species are discussed below in detail.

Step 1: Time-based sensitivity analysis of each species

was performed at a total of four different residence times

for each intermediate reduction. These residence times are

typical in an open flare.

i. 2.00E-05 s

ii. 9.00E-05 s

iii. 5.00E-01 s

iv. 1.00E?00 s

The general trend of the species concentration over time

was analyzed, and the maximum mole fraction of each

species was used to identify the critical species. The

highest mole fraction was thus used as one of the factor for

the identification of critical species.

USC + GRI Species

Contains nitrogen?

MMF > 500 ppb

# of rxns 20

MMF > 25 ppb

Keep

Remove

MMF > 100 ppb

# of rxns 8

# of rxns 10

Keep

# of rxns 17

Any HCHO rxns?

Rate constant in slowest quartile

Remove

Legends

No

Yes

Flow

MMF Maximum mass

Fig. 1 Decision tree for identifying the critical species

Time based sensitivity analysis

Reaction Pathway Analysis

Reaction rate constant analysis

Combined mechanism consisting of 93 species

CHEMKIN4.1.1 PSR

reactor

Output file containing mole fraction of

species at the exit of the PSR reactor

Reduced reaction

mechanism

Fig. 2 Overview of the algorithm

H. H. Lou et al.

123

The species with extremely low values of maximum mole

fraction were considered least critical and removed from the

combined mechanism in each of the subsequent steps.

Step 2: The total number of reactions, in which a par-

ticular species is involved, were listed and counted. The

importance of this can be seen in Eq. 4, which presents the

net molar reaction rate of a species. Species which were

involved in a large number of reactions were considered

CRITICAL due to the possibility of their relatively higher

net molar rate of production. Species with a moderate

number of reactions were further analyzed, while the

remaining with few reactions (e.g., 1 or 2 non-HCHO

reactions) was removed.

net molar reaction rate of species

¼Xnr

i¼1

rate of production: ð4Þ

In mathematical form, it is expressed as:

rrk ¼Xnr

i¼1

aiqi; ð5Þ

where ai is the stoichiometric coefficient of the species in

the reaction and qi is the progress variable containing the

concentration terms.

The numbers of reactions corresponding to each species

that also involved HCHO were counted. Species involved

in a very low number of HCHO reactions were considered

least critical in the reaction pathway of formaldehyde and

therefore were removed. Those species with high or mod-

erate number of HCHO reactions were kept in the reduced

mechanism. The species with an intermediate number of

HCHO reactions were further analyzed in Step 3.

Step 3: Values of the rate constant: Forward rate con-

stant of each reaction (involving the species under con-

sideration and HCHO) was calculated and analyzed by

assuming the bath temperature to be 2100 K. The assumed

bath temperature is approximately equal to the temperature

at the center of the flare. If the species under consideration

involve mostly the reactions that have low values of rate

constant and also have low concentration trend as dis-

cussed above, the particular species were removed. For-

ward rate constant and concentration of species can be

linked to progress variable as shown in Eq. 6.

Table 1 Input conditions for the CHEMKIN PSR reactor

Inlet fuel and oxidizer temperature 500 K

Equivalence ratio of fuel to oxidizer 1.0

Reactor temperature 1700 K

Table 2 List of species involved in the mechanisms

Mechanism Number

of species

Species list

Mechanism-1 93 N2, H2O, CO, CO2, O2, OH, H2, H, O, C2H4, C2H2, CH2CO, CH3, C2H3, CH2O, NO, HCO, HCCO, CH2, CH,

HO2, C, C2O, CH4, C2H, CH2*, H2CC, HCN, HCCOH, CH2OH, CH2CHO, N, HNCO, H2O2, HCNO, C4H2,

C3H3, C3H2, CH3OH, NCO, NH, CH3CHO, pC3H4, aC3H4, N2O, CH3O, C2H5, aC3H5, NH2, CN, NNH,

C2H3CHO, HNO, CH3CO, NH3, C4H4, i-C4H3, HOCN, NO2, H2C4O, C4H, C3H6, n-C4H3, HCNN, C2H6,

CH3CCH2, H2CN, CH3CHCH, cC3H4, C6H2, nC3H7, C4H6, i-C4H5, n-C4H5, l-C6H4, iC3H7, C4H7, C6H3,

c-C6H4, C5H5, C3H8, A1, C6H5O, C6H5OH, C5H6, C5H4O, C5H4OH, C5H5O, C3H7




C2H3CHO, HNO, CH3CO, NH3, C4H4, i-C4H3, HOCN, NO2, H2C4O, C4H, C3H6, n-C4H3, HCNN, C2H6,

CH3CCH2, CH3CHCH, cC3H4, C6H2, C4H6, i-C4H5, l-C6H4, iC3H7, C4H7, C3H8, A1, C6H5OH, C5H5O




C2H3CHO, HNO, CH3CO, NH3, C4H4, i-C4H3, HOCN, NO2, H2C4O, C3H6, n-C4H3, HCNN, C2H6,

CH3CHCH, C6H2, C4H6, C5H5O


HO2, C, C2O, CH4, CH2*, H2CC, HCN, HCCOH, CH2OH, CH2CHO, N, HNCO, H2O2, HCNO, C4H2, C3H3,

C3H2, CH3OH, NCO, NH, CH3CHO, pC3H4, aC3H4, N2O, CH3O, C2H5, aC3H5, NH2, CN, NNH, C2H3CHO,

HNO, NH3, C4H4, HOCN, NO2, C3H6, HCNN, C2H6, C5H5O


HO2, CH4, CH2*, H2CC, HCN, CH2OH, CH2CHO, N, HNCO, HCNO, C4H2, NCO, NH, pC3H4, aC3H4, N2O,

CH3O, C2H5, aC3H5, NH2, CN, NNH, HNO, NH3, HOCN, NO2, C3H6, HCNN, C2H6, C5H5O


CH4, H2CC, HCN, CH2OH, CH2CHO, N, C4H2, NCO, NH, N2O, CH3O, C2H5, aC3H5, NH2, NNH, HNO,

NH3, NO2, C3H6, C2H6


123

Table 3 Exit gas mole fraction

at the residence time

of 2.00E-05 s

CHEMKIN mole fraction results

Mechanism 1 2 3 4 5

# Species 93 70 60 50 40

N2 0.6978 0.6978 0.6979 0.6981 0.6982

AR 0.00E?00 0.00E?00 0.00E?00 0.00E?00 0.00E?00

C2H2 3.16E-03 3.16E-03 3.15E-03 3.40E-03 3.72E-03

CH2CHO 3.88E-05 3.89E-05 3.93E-05 3.87E-05 3.22E-05

CH2CO 1.66E-03 1.66E-03 1.68E-03 1.61E-03 1.43E-03

CH2O 4.94E-04 4.94E-04 4.94E-04 4.80E-04 2.17E-04

CO 7.60E-02 7.59E-02 7.59E-02 7.50E-02 7.46E-02

CO2 2.05E-02 2.05E-02 2.04E-02 2.13E-02 2.18E-02

H 3.74E-03 3.74E-03 3.74E-03 3.87E-03 3.87E-03

H2 2.19E-02 2.19E-02 2.19E-02 2.15E-02 2.15E-02

H2CC 2.76E-05 2.76E-05 2.76E-05 2.85E-05 2.06E-05

H2O 7.58E-02 7.58E-02 7.57E-02 7.62E-02 7.62E-02

HNO 6.52E-09 6.19E-09 4.71E-09 5.68E-09 1.68E-09

N2O 3.15E-08 3.15E-08 3.15E-08 3.12E-08 1.76E-08

NO 3.43E-06 3.72E-06 4.13E-06 4.93E-06 2.14E-06

O 1.03E-03 1.03E-03 1.03E-03 1.04E-03 1.12E-03

O2 8.59E-02 8.59E-02 8.59E-02 8.53E-02 8.56E-02

OH 2.68E-03 2.68E-03 2.68E-03 2.85E-03 2.93E-03

Table 4 Exit gas mole fraction

at the residence time of

1.00E?00 s

* in CH2* shows that it is

singlet methylene when

compared to CH2, which is

triplet methylene

CHEMKIN mole fraction results

Mechanism 1 2 3 4 5

# Species 93 80 70 60 50

N2 0.7364 0.7364 0.7364 0.7364 0.7362

AR 0.00E?00 0.00E?00 0.00E?00 0.00E?00 0.00E?00

C2H2 5.62E-06 5.61E-06 5.6E-06 5.6E-06 4.85E-06

C2H3 2.66E-08 2.65E-08 2.65E-08 2.65E-08 2.61E-08

CH2* 8.96E-10 8.96E-10 8.96E-10 8.99E-10 1.17E-09

CH2CHO 2.44E-10 2.45E-10 2.45E-10 2.45E-10 2.59E-10

CH2CO 4.89E-07 4.88E-07 4.88E-07 4.90E-07 5.36E-07

CH2O 2.30E-07 2.30E-07 2.30E-07 2.31E-07 2.59E-07

CO 3.67E-03 3.68E-03 3.68E-03 3.68E-03 3.93E-03

CO2 1.27E-01 1.27E-01 1.27E-01 1.27E-01 1.27E-01

H 8.85E-05 8.87E-05 8.87E-05 8.89E-05 1.04E-04

H2 1.07E-03 1.07E-03 1.07E-03 1.07E-03 1.15E-03

H2CC 2.21E-10 2.21E-10 2.21E-10 2.21E-10 2.26E-10

H2O 0.1291 0.1291 0.1291 0.1291 0.129

HNO 3.77E-09 3.78E-09 3.60E-09 3.12E-09 3.81E-09

HO2 3.78E-07 3.80E-07 3.80E-07 3.80E-07 3.16E-07

N 3.78E-10 3.79E-10 3.66E-10 2.15E-10 2.59E-10

N2O 8.27E-08 8.28E-08 8.25E-08 8.03E-08 8.45E-08

NO 4.66E-05 4.66E-05 4.44E-05 5.48E-05 6.22E-05

O 3.68E-05 3.69E-05 3.69E-05 3.70E-05 4.37E-05

O2 2.24E-03 2.24E-03 2.24E-03 2.24E-03 2.38E-03

OH 5.51E-04 5.51E-04 5.51E-04 5.52E-04 6.01E-04

H. H. Lou et al.

123

qi ¼ kfi � Xi; ð6Þ

where kfi is the forward rate constant and Xi is the con-

centration of species i.

The above equation shows that (qi) can be used as a

substitute for the combination of the forward rate constant

and concentration. As a result of it, we can use (qi) as an

index for identifying critical species. The forward rate

constants kfi

� �

of individual reactions are calculated in a

spreadsheet using Arrhenius equation as given below.

kfi ¼ AiTbi exp

�Ei

RcT

� �

; ð7Þ

where Ai, bi, and Ei are pre-exponential factor, temperature

exponent, and activation energy, respectively, which are all

supplied in the reaction mechanism file for all the

reactions.

In Step 1, the species falling below a specific maximum

mole fraction limit were removed to trim the mechanism.

This step was followed by a manual inspection of species

for their involvement in the number of reactions and also in

reactions linked, directly or indirectly, with pollutant of

interest. Eventually, the intermediate mechanism was

analyzed for progress variable (qi), which takes into

account the rate constant and the maximum concentration.

The intermediate mechanism was then re-run in the

CHEMKIN PSR reactor using the same set of condition as

supplied for the combined mechanism. This was done in

order to check the accuracy of exit mole fraction predic-

tion, as compared to the combined mechanism. If the

results were within acceptable tolerance, the intermediate

mechanism was sent for further reduction. Intermediate

reduced mechanisms were formed after completion of

iteration of the reduction procedure. Consequently, several

reduced mechanisms were obtained from the reduction

procedure. After each step of species removal, the resulting

mechanism was re-run in CHEMKIN PSR reactor at the

specified condition shown in Table 1. This method allows

the effect of each species removal to be analyzed.

Results

This section discusses the effect of species removal at each

step on the accuracy of the reduced mechanism. Several

mechanisms other than the combined 93 species mecha-

nism were produced using the reduction algorithm. The

lists of species of some of the mechanisms are shown in

Table 2.

Each mechanism was simulated in CHEMKIN PSR

reactor with the same set of condition as mentioned above,

but the residence time was varied. The results (exit gas

mole fraction) of each mechanism were then compared to

the original 93 species mechanism, to check its accuracy.

Tables 3 and 4 show the exit gas mole fraction of some

critical species at residence time of 2.00E-05 and

1.00E?00 s, respectively. It is clear that the accuracy of

the reduced mechanism decreases with each trim. Bulk

species such as N2 and O2 show good agreement, but the

errors are increasing among intermediate species such as

CH2O and NO. Desired accuracy of the mechanism will be

subjective to the system of interest. The targeted end use of

the reduced mechanism in this study is the speciation study

of the flaring system. Such study requires prediction of

bulk, intermediate, and radical species with sufficient

accuracy. Hence, the mechanism with 50 species was

selected due to its highest accuracy among the mechanism

which meets the computational restriction. Other systems

with only flow dynamics or heat generation modeling can

opt for the mechanism with 40 species.

The accuracy of the reduced mechanism is demonstrated

by the absolute error in the mole fraction of each species

analyzed. As shown in Table 5, the highest error in the exit

gas mole fraction is in the order of E-09. For the most

critical species HCHO, the mole fraction value has an error

Table 5 Errors in the reduced mechanism as compared to the ori-

ginal mechanism

Highest errors in reduced mechanism mole fractions*

S. no. Compound Full mechanism

(93 species)

Mechanism

(50 species)

Absolute

error

1 C3H8 3.90E-11 4.08E-09 4.04E-09

2 C3H3 1.14E-07 4.28E-07 3.14E-07

3 CH3CHO 3.88E-07 7.27E-07 3.40E-07

4 CH3O 9.73E-08 4.11E-08 -5.63E-08

5 HNCO 4.26E-07 2.31E-07 -1.94E-07

6 NH2 2.83E-08 1.54E-08 -1.29E-08

7 i-C4H3 1.63E-08 8.90E-09 -7.38E-09

8 CH 5.23E-06 7.27E-06 2.04E-06

9 HCNN 8.29E-09 1.12E-08 2.95E-09

10 C3H6 3.09E-07 4.17E-07 1.08E-07

11 pC3H4 6.83E-07 9.16E-07 2.33E-07

12 HCN 2.90E-06 3.77E-06 8.75E-07

13 aC3H4 3.83E-07 4.98E-07 1.15E-07

14 C4H4 3.31E-08 2.48E-08 -8.21E-09

15 n-C4H3 3.70E-09 2.78E-09 -9.14E-10

16 NO 2.67E-05 3.28E-05 6.19E-06

17 N 1.78E-07 2.15E-07 3.72E-08

18 CH2OH 1.40E-06 1.12E-06 -2.81E-07

19 aC3H5 1.31E-07 1.57E-07 2.60E-08

20 NCO 4.39E-08 5.19E-08 8.01E-09

… … … … …44 CH2O 7.16E-05 7.08E-05 1%

*Residence time = 9E-05 s, T = 500 K, % stoich., air = 100%


123

0.00E+00

5.00E-06

1.00E-05

1.50E-05

2.00E-05

2.50E-05

3.00E-05

3.50E-05

4.00E-05

4.50E-05

1.00E-05 1.00E-04 1.00E-03 1.00E-02 1.00E-01 1.00E+00

Mol

e F

ract

ion

Residence Time (seconds)

93 species

80 species

70 species

60 species

50 species

HCHO (500K, 100% Air)

Fig. 3 Comparison of HCHO

mole fraction versus residence

time for all the mechanism

0.00E+00

1.00E-05

2.00E-05

3.00E-05

4.00E-05

5.00E-05

6.00E-05

7.00E-05

1.00E-05 1.00E-04 1.00E-03 1.00E-02 1.00E-01 1.00E+00

Mol

e F

ract

ion


93 species

80 species

70 species

60 species

50 species

NO(500K, 100% Air)

Fig. 4 Comparison of NO mole

fraction versus residence time

for all the mechanism

H. H. Lou et al.

123

0.00E+00

5.00E-07

1.00E-06

1.50E-06

2.00E-06

2.50E-06

3.00E-06

3.50E-06

4.00E-06

1.00E-05 1.00E-04 1.00E-03 1.00E-02 1.00E-01 1.00E+00

Mol

e F

ract

ion


93 species

80 species

60 species

50 species

70 species

HCN(500K, 100% Air)

Fig. 5 Comparison of HCN

mole fraction versus residence

time for all the mechanism

0.00E+00

5.00E-06

1.00E-05

1.50E-05

2.00E-05

2.50E-05

3.00E-05

3.50E-05

4.00E-05

4.50E-05

1.00E-05 1.00E-04 1.00E-03 1.00E-02 1.00E-01 1.00E+00

Mol

e F

ract

ion


93 species

80 species

70 species

60 species

50 species

CH2CHO (500K, 100% Air)

Fig. 6 Comparison of

CH2CHO mole fraction versus

residence time for all the

mechanism


123

percentage of as low as 1%. Therefore, it can be concluded

that this mechanism is fairly accurate when considered for

the prediction of formaldehyde formation during the flaring

event involving ethylene combustion.

To further investigate the accuracy of the mechanism,

some of the species besides HCHO, i.e. NO, HCN,

CH2CHO, and CH3CHO, were also analyzed. This analysis

was conducted by comparing the mole fraction of each

species at different residence time for the combined and all

other reduced mechanisms. The comparison of the results

is illustrated in Figs. 3, 4, 5, 6, and 7 for species HCHO,

NO, HCN, CH2CHO, and CH3CHO, respectively. For

instance, Fig. 3 shows the variation of exit gas mole frac-

tion for HCHO at different residence time, and Table 6

shows the data points used in Fig. 3. The five different

curves in Fig. 3 represent different reaction mechanisms

with different numbers of species. All the curves show

good agreement with the 93 species reaction mechanism at

most of the residence times. This clearly shows that even

though the number of species was gradually reduced from

93 to 50, the reduced mechanism agrees with the original

combined mechanism.

Figure 4 shows a similar type of curve for NO mole

fraction versus residence time, calculated using different

mechanisms. Again, all of the curves are in close agree-

ment with each other. It should be noted that the highest

error noted here is in the order of 1.00E-05, which is very

low. Similar observations can be made for other species.

Comparison with other methods

The reduction procedure presented here has some unique

features and advantages, as compared to other methods. It

has the advantage of handling reversible reaction when

compared to RIOT. The reaction mechanism can be

reduced targeting a set of particular species. For example,

simulation which needs to evaluate radical emission for

ozone formation will need to focus reduction of mechanism

around VOCs such as formaldehyde and NOx.

Also the formation of global reactions steps, as with

CARM and CSP, reduces the system capability of taking

advantages of elementary reaction steps and hence may

result in undershoot or overshoot in prediction of radical

species. This undershoot phenomena was observed with a

0.00E+00

2.00E-06

4.00E-06

6.00E-06

8.00E-06

1.00E-05

1.20E-05

1.00E-05 1.00E-04 1.00E-03 1.00E-02 1.00E-01 1.00E+00

Mol

e F

ract

ion


93 species

80 species

70 species

60 species

50 species

CH3CHO (500K, 100% Air)

Fig. 7 Comparison of

CH3CHO mole fraction versus

residence time for all the

mechanism

Table 6 Comparison of HCHO mole fraction versus residence time

for all the mechanisms

Residence time (s) 2.00E-05 9.00E-05 5.00E-01 1.00E?00

93 species 4.94E-04 7.16E-05 1.58E-06 4.89E-07

80 species 4.94E-04 7.16E-05 9.32E-07 4.88E-07

70 species 4.94E-04 7.16E-05 1.58E-06 4.88E-07

60 species 4.94E-04 7.15E-05 1.58E-06 4.90E-07

50 species 4.80E-04 7.08E-05 5.36E-07 5.36E-07

H. H. Lou et al.

123

50 species reduced mechanism for ethylene, formed using

CARM. The formation of global reaction steps resulted in

under predicting of radical species such as formaldehyde

and NO by at least 14%. This undershoot was just under

1% for current method as shown in Table 5.

Conclusion

A general reaction mechanism reduction methodology was

developed so that a reduced mechanism can be formed

from any given detailed mechanism with different condi-

tions of interest. A reduced reaction mechanism of ethylene

was formed to overcome the computational limit of com-

mercial software. Numerical experiments demonstrate that

it is in full agreement with the combined mechanism. The

reduced reaction mechanism will enable speciation study

of HVOC’s such as formaldehyde during ethylene flaring

event through CFD simulation.

Acknowledgments The authors gratefully acknowledge the finan-

cial support from TARC (Texas Air Research Center), TCEQ (Texas

Commission on Environmental Quality), AQRP (Air Quality

Research Project) Project No.10-022 and SEP (State Environmental

Policy) Agreement No. 2009-009.

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A reduced reaction mechanism for the simulation in ethylene flare combustion

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