Applied Mathematical Modelling 34 (2010) 2312–2322

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Applied Mathematical Modelling

journal homepage: www.elsevier .com/locate /apm

Numerical simulation of methane partial oxidation in the burnerand combustion chamber of autothermal reformer

Hadi Amirshaghaghi a, Akbar Zamaniyan b,*, Hadi Ebrahimi b, Mahshid Zarkesh b

a Islamic Azad University, Islamshahr Branch, No. 159, 7th Boostan St., Pasdaran Ave. P.O. Box 19585/466 Tehran, Iranb Research Institute of Petroleum Industry (RIPI), Gas Research Division, Tehran, Iran

a r t i c l e i n f o a b s t r a c t

Article history:Received 23 August 2008Received in revised form 22 October 2009Accepted 30 October 2009Available online 6 November 2009

Keywords:Autothermal reformingPartial oxidationNumerical simulationCFD

0307-904X/$ - see front matter � 2009 Elsevier Incdoi:10.1016/j.apm.2009.10.039

* Corresponding author. Address: Department ofComplex, P.O. Box 14665-1998, Tehran, Iran. Tel.: +

E-mail address: zamaniyana@ripi.ir (A. Zamaniy

This study aims to model the methane partial oxidation process in the burner and combus-tion chamber of autothermal reactor. The numerical simulation based on this model offersa powerful tool that can assist in reactor design and optimization and scale up of the pro-cess saving expensive pilot work. The steady-state governing equations were solved usingthe SIMPLE algorithm and the effect of turbulence on the mean flow field was accounted forusing the RNG k–e model. A two-step reaction mechanism was used for the gas combustionwith CO as the intermediate species. The reaction rates were modeled using an Eddy-Dis-sipation Model. In terms of the geometrical model, a 3D model for burner was developedwhile an axis-symmetric model for the combustion chamber was implemented to reducethe computational costs. The model formulated was validated against a currently operatingautothermal reactor and then has been used to investigate different aspects of these reac-tors. Results show that effect of oxygen to methane ratio is more than that of feed temper-ature. It is demonstrated that a 60% increase in O2/CH4 ratio causes a 15.4% decrease and42.7% increase in H2/CO ratio and methane conversion, respectively. In contrast, a 60%increase in feed temperature does not have a significant effect on the process.

� 2009 Elsevier Inc. All rights reserved.

1. Introduction

Synthesis gas is a very interesting intermediate product in the chemical industry used for a variety of important processessuch as ammonia and methanol synthesis. Nowadays, increasing efforts are devoted to the development of efficient technol-ogies to exploit the existing resources of natural gas.

There are a number of major processes for synthesis gas generation, but in actuality these technologies are points along aspectrum that spans combustion through steam reforming. Combustion is defined as the process of combining oxygen with ahydrocarbon to form stoichiometric quantities of carbon dioxide and water as products. For a methane fuel, oxygen to thecarbon molar ratio of 2 provides sufficient oxygen to convert all the carbon and hydrogen in the fuel into carbon dioxide(CO2) and water (H2O), respectively. Partial oxidation (POX) is the process in which sub-stoichiometric amount of oxygenis added to the process, resulting in carbon monoxide and hydrogen products instead. Autothermal reforming (ATR) com-bines partial oxidation and steam reforming (SR) in a single process. In autothermal reactor, after the feed gas and steammixture is preheated, it is combusted with an oxidant and passed over a catalyst to bring the steam-methane reforming reac-tion to equilibrium [1]. Independent parameters that affect the performance of an ATR reactor are: inlet feed temperature,steam to methane ratio, oxygen to methane ratio and pressure.

. All rights reserved.

Gas Conversion, Gas Research Division, Research Institute of Petroleum Industry (RIPI), Azadi Sports98 (21) 44739540/59; fax: +98 (21) 44739779.an).

Nomenclature

A, B constant parametersek large eddy mixing time scaleg acceleration of gravity (m s�2)h total enthalpy, J mol�1

hi enthalpy of component i, J mol�1

�g gravity tensor, m s�2

keff effective thermal conductivity, J m�1 si, j indexJj0 diffusion flux of species j0

k indexM molecular weightN number of chemical speciesP pressure (kPa)Ri,r rate of reaction for component i (kmol kg�1 s�1)R universal gas constant (kJ kmol�1 K�1)Sh source term in the energy equationT temperature (K)�u mean velocity vector, m s�1

m0i;r stoichiometric coefficient for reactant i in reactionYP mass fraction of any product speciesYR mass fraction of any reactant species

Greek lettersq density of mixture (kg m�3)m0i;r stoichiometric coefficient for reactant i in reaction��s stress tensor

Subscriptseff effectiveop operation

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There have been numerous studies on POX and ATR processes, carried out both numerically and experimentally. Gray [2]experimentally studied the effect of reactant initial temperature on final product composition. Vanpee [3] conducted anexperimental study on the methane combustion and highlighted the effect of OH in initiation and stability of POX process.Barbieri et al. [4] suggested a mechanism including 87 reactions, which was in agreement with Vanpee’s results. Caron et al.[5] investigated effect of high pressures on combustion of fuel rich mixtures and reported the strong effect of high pressureson reducing methane self ignition temperatures.

Recently, CFD has been used in the several researches and has been a useful tool for predicting the fluids behavior includ-ing transport phenomena. Simulation of fluid flow in burners is a well-known example of application of the CFD.

There are several researches about CFD simulation of the burner in 1D, 2D, and 3D. Most of all are correspond to the spe-cific geometries. For instance, Yapici et al. [6] simulated a two-dimensional axisymmetric combustion chamber using a Flu-ent CFD code to investigate the effects of air/fuel ratio and swirl velocity on the combustion and entropy generation rate.Another example is simulation of recuperative MILD combustion burners by an axisymmetric model that has been consid-ered in 2D [7] or 3D geometries [8].

Effect of input air flow, an important parameter in a burner is the subject of some researchers that applied the CFD tool(e.g. effect of air distribution on burner performance by LaRose and Hopkins [9] and turbulent air flow in a typical boilerwind box segments by Bhasker [10]). The more specific 3D simulations includes considering of the sensitivity of several ma-jor operating variables of Cho et al. [11] on of a large front wall-fired furnace, vaporization during pulverized coal combus-tion on a laboratory-scale single-burner furnace [12], and studies on burner secondary airflow [13].

So far there is no research about 3D simulation of autothermal reformer by considering of burner phenomena.To summarize the argument it could been deduced that in previous studies, the effect of various parameters has been

investigated, mostly using PSR (Perfectly Stirred Reactors [14]) and using one-dimensional models where hydrodynamic ef-fect were not taken into account, which obviously indicates the demand for a general study including the investigation ofcomplex interaction of turbulence and chemical reactions, where this study intends to fill this gap and accomplish a morerealistic understanding of processes inside the reactor. The well known CFD commercial software, FLUENT 6 was used formodeling.

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2. Numerical modeling studies

2.1. Assumptions

In the aim of simplicity some assumptions were made, including steady-state incompressible flow and axisymmetric flowfield in combustion chamber-burner in 3D geometry. Moreover, in the calculation of turbulence terms, in averaged formequations, boussinesq method was used.

2.2. Computational domain

The computational domain covered the entire burner and combustion chamber flow field from the fuel, air and steaminlets to the catalyst bed (see Fig. 1).Since the flow field was axisymmetric in combustion chamber, an axisymmetric swirlmodel was used, but to model the burner a 3D was used because of asymmetric geometry of burner. A total number of100,000 tetrahedral elements and 300,000 triangular elements were used for simulation of burner and combustion chamber,respectively. Efforts were made to keep the wall parameter y+ in the desired range (30–60). A number of meshes were testedto ensure the mesh independence of the numerical solutions.

2.3. Governing equations

The mathematical model of processes inside the reactor is constituted of four different parts: swirling turbulent flow,Heat and mass transport by convection, chemical reactions and radiation. Hence, in order to model the problem five setsof equations should be solved: continuity, momentum, energy (including radiation), species transport and turbulent flowmodeling equations (in time averaged form). The conservation equations for mass, momentum, and total enthalpy, maybe expressed in a coordinate-free form as:

r � ðq�uÞ ¼ 0; ð1Þr � ðq�u�uÞ ¼ �rpþr � ð��sÞ þ q�g; ð2Þ

Fig. 1. Schematic of autothermal rector.

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r � ð�uðqhÞÞ ¼ r � keffrT �X

hj0Jj0 þ sik � �u� �

þ Sh;reaction þ Sh;radiation: ð3Þ

In the above equations, q represents mixture density, �u is mean velocity vector, and hj are total enthalpy and enthalpy of jthspecies and ��s stands for the viscous stress tensor. To take into account the radiation of hot gasses inside the chamber, the P1model was used

2.4. Turbulence modeling

In the current study, the well known RNG k–emodel has been implemented to simulate turbulence. The RNG model inFLUENT provides an option to account for the effects of swirl or rotation by modifying the turbulent viscosity appropriately.In comparison with the standard k model, the smaller destruction of e augments e, reducing k and, eventually, the effectiveviscosity. As a result, in rapidly strained flows, the RNG model yields a lower turbulent viscosity than the standard k–e mod-el. Thus, the RNG model is more responsive to the effects of rapid strain and streamlines curvature than the standard k–emodel, which explains the superior performance of the RNG model for certain classes of flows [15,16].

2.5. Combustion model

Most fuels are fast burning, and the overall rate of reaction is controlled by turbulent mixing. In non-premixed flames,turbulence slowly convects/mixes fuel and oxidizer into the reaction zones where they burn quickly. In such cases, the com-bustion is said to be mixing-limited, and the complex chemical kinetic rates can be safely neglected.

FLUENT provides a turbulence-chemistry interaction model, based on the work of Magnussen and Hjertager [17], calledthe Eddy-Dissipation Model. According to this model, overall reaction rate is controlled by turbulence mixing rate and themean reaction rate of fuel is proportional to the inverse of the time scale of the large-scale eddies characterized by the ratiok/e, and to the smallest of the fuel, oxygen or products concentrations. In this model the reaction rate is given by smaller offollowing two expressions:

Ri;r ¼ m0i;rMw;iAqek

minYR

m0R;r �Mw;k

!; ð4Þ

Ri;r ¼ m0i;rMw;iABqek

RpYp

RNj m00j;rMw;j

!: ð5Þ

In the above equations, N represents number of chemical species in the system, m0i;r is stoichiometric coefficient for reactant iin reaction, YP and YR represent the mass fraction of any product species, P and mass fraction of a particular reactant, R,respectively. Besides A and B are empirical constants and equal to 4.0 and 0.5, respectively.

Moreover, the methane partial oxidation modeled using a two step reduced mechanism as below:

CH4 þ 1:5O2 ! COþ 2H2O; ð6ÞCOþ :5O2 ! CO2: ð7Þ

2.6. Catalyst bed model

To take into account the reactions inside the catalyst bed (methane reforming and water–gas shift), a previously validated1D code [18,19], was implemented (the code’s input included, pressure, temperature and composition of hot gases at theoutlet of combustion chamber).

ðIÞ Steam Reforming : CH4 þH2 O$ COþ 3H2; ð8ÞðIIÞ Water Gas Shit : COþH2 O$ CO2 þH2; ð9ÞðIIIÞ Methanation : CH4 þ 2H2 O$ CO2 þ 4H2: ð10Þ

2.7. Material properties

Since the Eddy-Dissipation Model does not predict intermediate species and dissociation effects and leads to over-predic-tion of temperature, the specific heat of each species was defined as piecewise-polynomial function of temperature to ac-count for radical and intermediate formation. Besides to consider density variation the Incompressible Ideal gas lawimplemented. The density of the air-natural-gas mixture is defined with the ideal-gas mixing law:

q ¼ Pop

RTP

i

miMi

; ð11Þ

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where R is the universal gas constant, mi is the mass fraction of species i, Mi is the molecular weight of species i, and pop is theoperating pressure (taken to be atmospheric).

For other thermal properties of the mixture such as molecular viscosity, thermal conductivity, diffusivity and absorptioncoefficient, the values for air at 900 K were used. The thermal conductivity was 25 W/m K for the stainless steel, and 0.1 W/m K for the ceramic insulation [20].

2.8. Boundary conditions

To complete the modeling procedure we need to set proper boundary conditions for both parts of our model (burner andcombustion chamber).

(a) Burner: In the current study, mass flow inlet and pressure outlet boundary conditions were set for burner inlet andoutlet, respectively. Due to the known mixture composition and temperature of reactants at reactor inlet, the appro-priate values were implemented for mass flow rate and temperature of species.

(b) Combustion chamber: Again mass flow inlet and pressure outlet boundary conditions were used at the inlet and outlet,respectively. Mass flow rate and temperature of species, also is known from previous part simulation results (burner’soutlet). Moreover, to consider the presence of swirl generator at combustion chamber’s inlet, the appropriate tangen-tial velocities were imposed. Considering the steam circulation on the reactor’s outer wall [1], constant temperatureboundary condition was used at reactor walls. Besides to estimate Reynolds stress components and turbulence dissi-pation and estimated turbulence intensity of 10% and hydraulic diameters were used for both parts.

(c) Catalyst bed: After simulation of combustion chamber, the output parameters including pressure, temperature andcomposition of hot gases are entered to a Visual Basic code to be used in the catalytic bed model as mentioned inthe Section 2.6.

3. Solution procedure

Numerical solution of the model equations were performed in finite volume method by using CFD commercial code, Flu-ent 6.0 and SIMPLE algorithm was used for pressure–velocity relationship. A segregated solver with a second-order accurate

Table 1Characterization of reactor feed.

Characteristics of stream Fuel Oxidizer Steam

Temperature, �C 670 230 395Pressure, bar 36 37 47.0Flow rate, kg/h 296,083 114,130. 23,717.0Composition, mol%CO2 3.49 99.8CO 1.81H2 18.37CH4 33.56N2 1.47 0.01H2O 40.77 100.0CH3OH 0.01C2H6 0.1C3H8 0.05Ar 0.01 0.01

Table 2Comparison of plant data and simulation results of rector outlet (products of fixed bed catalytic section).

Parameter Plant Model %Error

Reactor outlet temperature, �C 975 968 0.7Reactor outlet pressure, bar 35 35.04 0.1Products composition (mol%)H2 40.24 39.95 0.7CO 19.16 19.96 4.1CO2 5.66 6.03 6.5N2 1.02 1.02 –CH4 1.27 1.36 7H2O 32.61 31.55 3.3Ar 0.03 .03 –

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scheme was used to resolve the flow field. At convergence, the normalized residuals of flow variables were about or less than10�6 in all test cases. The monitored axial velocities at two points in the shear layer downstream of the combustion chamberremained unchanged at least for the first four digits.

4. Case study and model validation

In the aim of model validation, autothermal reactor of ‘‘Zagros Petrochemical Complex” of Iran has been selected. Table 1represents characteristics and operating condition of this reactor. Unfortunately, there is no data available for fluid flow and

Fig. 2. ATR burner: (a) mesh configuration of the burner; (b) path lines in the burner, colored by temperature (values in the legend are temperature withunit of K). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Fig. 3. Combustion chamber: (a) configuration (white and black networks represent the small meshes); (b) generated meshes of half top part of thechamber. In vicinity of the inlet the meshes are finer than that of outlet (almost is colored by black).

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properties inside the reactor, so temperature and mixture composition of products at the outlet of catalyst bed was used inthe purpose of comparison. The output (pressure, temperature and composition) was used for comparison.

5. Result and discussion

5.1. Numerical model assessment

Table 2 summarizes the results, obtained by developed model and presents a comparison between prediction and plantdata. Results for temperature and pressure show excellent agreement with measured data (below 1%), but less good agree-ment for species mole fractions (within about 6%). This is thought to be due to the neglect of Partial Oxidation detailed mech-anism and intermediate species.

Fig. 2 shows the ATR burner, simulated in this study that includes (a) mesh configuration and (b) pathlines (colored bytemperature). Combustion chamber is presented in Fig. 3 with the specified meshes.

Contours of stream function, velocity (for the case of Table 1) are shown in Figs. 4 and 5. These figures show the presenceof a central recirculation zone and a corner recirculation zone. The recirculation zones are created by the expansion of theswirling inlet flow in the reactor. By increasing the amount of tangential velocity at inlet (by adjusting angle of swirl vanes of

Fig. 4. Stream function contours at combustion chamber (central recirculation zone is clearly visible). The unit of legend values is m/s.

Fig. 5. Contours of mole fraction of O2 at combustion chamber.

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burner), the recirculation zones are also increased. As a result, the hot burnt gases are better mixed with the initial mixture,stabilizing the flame and speeding up the reactions.

There is a clear striation of the flow with very low oxygen concentrations in the center part of the combustion chamberand relatively high concentrations near the walls (Fig. 5). The low oxygen region in the center has, at the same time, highvelocities and high temperatures. These effects are shown in Figs. 6 and 7.

The temperature contours of the ATR reactor (at the operating condition of Table 1), is shown in Fig. 7. Again the presenceof hot spot is observed to develop near the centerline in initial part of the combustion chamber. Moving toward the walls andalong the flow direction, the temperature drops. From these trends, it can be estimated that the methane oxidation reactionmainly occurs in the front part (and near the centerline) of the combustion chamber, which will release a large amount ofheat. The endothermic reactions, such as the methane steam reforming reaction (SR), will absorb the generated heat and theoverall process will proceed automatically.

5.2. Products H2/CO

The ratio of hydrogen to carbon monoxide is a key factor in ATR reforming process. In general for ATR, a ratio within about2.5 is desirable. But regarding to desired application, Hydrogen or Syngas production, this value may change. In our case themaximum Syngas production is of interest, so the optimizations will be made to reduce this ratio. As mentioned above,independent parameters that affect the performance of an ATR reactor are inlet feed temperature and steam to methaneratio.

Fig. 8a shows that H2/CO ratio decreases with increasing O2/CH4 ratio. The reason for this is obvious. This can be ex-plained by the fact that, by increasing the amount of Oxygen at feed, a greater amount of methane would have the

Fig. 7. Temperature contours at combustion chamber. The unit of legend values is K.

Fig. 6. Velocity contours at combustion chamber. The unit of legend values is m/s.

Fig. 8. H2/CO molar ratio of products versus (a) O2/CH4feed (average feed temperature is 810 K) and (b) feed average temperature (O2/CH4 molar ratio atfeed .51).

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chance to participate in reaction with oxygen and partial oxidation (Eq. (12)) tend toward more production of CO andCO2, consequently.

The beneficial effects of increasing inlet feed temperature on maximum CO yield are shown in Fig. 8b. From this fig-ure, it is inferred that, the CO yield would increase slightly due to higher inlet temperatures. On the other hand, higherinlet temperatures may also require specialized construction materials to insure reformer life, so increasing feed O2/CH4

ratio, seems to be more favorable. It is shown that by increasing of O2/CH4, 60% (0.5–0.8), the H2/CO is decreased about15.4 %. And an increase of 42.7 % is obtained by increasing of feed temperature about 60% (Fig. 8b).

5.3. Methane conversion

Several parameters affect methane conversion, including feedstock temperature and O2/CH4 ratio, Pressure and reactorsize. In this investigation the influence of feedstock temperature and O2/CH4 ratio has been studied, assuming a knowngeometry and inlet streams pressure.

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Fig. 9a shows the effect of feed O2/CH4, on methane conversion percent, for an ATR reactor. It indicates that, by increasingthe feed O2/CH4 ratio, the methane conversion rate would increase significantly. This is due to fact that, by increasing theamount of oxygen to fuel, a higher combustion of methane would be occurred and less unreacted methane will remain.

Another parameter which influences methane conversion is, feedstock temperature. In Fig. 9b methane conversion var-iation under operation of the reactors at feed O2/CH4 ratio equal to.53 as a function of feed average temperature are pre-sented. The higher the preheat temperature, the higher is the final combustion temperature [21]. The result in whichhigh methane conversion percent is obtained by preheating of the feedstock is due to the fact that less heat is providedby way of the combustion reaction of methane at the expense of reducing the production of hydrogen and carbon monoxide.Due to lower oxygen consumption and less combustion of methane, higher conversion ratio of methane to synthesis gas andfewer products of carbon dioxide and water vapor are obtained.

According to the Figs. 8 and 9, effect of oxygen to methane ratio is more than that of feed temperature. It is demonstratedthat a 60% increase in O2/CH4 ratio (0.5–0.8) creates a 15.4% decrease and 42.7% increase in H2/CO ratio (Fig. 8) and methaneconversion, respectively. However, for the same value of increase, 60%, in feed temperature (600–960 K), there are only a0.46% decrease in H2/CO and a 0.92% increase in methane conversion. On the other hand, variation of feed temperature isnot effective in this process.

Fig. 9. Methane conversion percent versus (a) O2/CH4 feed (average feed temperature is 810 K) and (b) feed average temperature (O2/CH4 molar ratio atfeed .51).

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6. Conclusions

Non-catalytic partial oxidation of methane has been studied computationally. The model formulated was validatedagainst the plant data and then has been used to investigate different aspects of these reactors. From the simulation datapresented in this paper, a reactor optimization strategy can be deduced; operate at the highest possible O2/CH4 ratio andinlet feed temperature while minimizing heat loss and reactor pressure to achieve the maximum CO production rate. Accord-ing to the results, effect of oxygen to methane ratio was more than that of feed temperature. It was demonstrated that a 60%increase in O2/CH4 ratio (0.5–0.8) creates a 15.4% decrease and 42.7% increase in H2/CO ratio and methane conversion,respectively. However, for the same value of increase, 60%, in feed temperature (600–960 K), there are only a 0.46% decreasein H2/CO and a 0.92% increase in methane conversion. It means that changing of feed temperature is not significant in com-parison to the variation of O2/CH4 ratio of the feed.

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