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ISSN : 1225-0112(Print)
ISSN : 2288-4505(Online)
Applied Chemistry for Engineering Vol.35 No.5 pp.429-437
DOI : https://doi.org/10.14478/ace.2024.1042

Multiphysics Simulations of Dry Reforming of Methane in a Fixed Bed Reactor with Different Catalyst Types

Soo-Won Son, Boram Gu†
School of Chemical Engineering, Chonnam National University, Gwangju 61186, Republic of Korea
† Corresponding Author: Chonnam National University School of Chemical Engineering, Gwnagju 61186, Republic of Korea Tel: +82-62-530-1827 e-mail: boram.gu@jnu.ac.kr
July 15, 2024 ; August 3, 2024 ; August 5, 2024

Abstract


Dry reforming of methane (DRM) uses natural gas and carbon dioxide as reactants to produce hydrogen, potentially providing a solution for reducing greenhouse gas emissions and offering an alternative carbon-free energy source. However, since DRM is highly endothermic, its application and commercialization are limited by rapid temperature decrease and resulting catalyst inactivation. Consequently, research into catalyst development and reactor design to mitigate temperature decline is essential. In this study, computational fluid dynamics (CFD) is utilized to develop a multiphysics simulation platform for DRM within a fixed-bed reactor, employing two types of spherical catalysts: eggshell and uniform types. Our findings indicate that the eggshell model can maintain a higher temperature at the reactor’s core than the uniform catalyst model by approximately 20 K. Furthermore, the eggshell model demonstrates superior methane conversion and hydrogen yield due to its ability to suppress excessively rapid reactions. These results underscore the benefits of eggshell catalysts in highly endothermic reactors, such as those used in DRM processes. The developed simulation platform can be used to assess various combinations of reactor and catalyst designs and further optimize their dimensions and operational protocols.


Dry reforming of methane (DRM) , Fixed-bed reactor , Eggshell catalysts , Computational fluid dynamic (CFD) , Greenhouse gas reduction

다양한 촉매 유형에 따른 고정층 반응기에서의 메탄 건식 개질 다중물리 시뮬레이션 연구

손수원, 구보람†
전남대학교 화학공학부

초록


메탄 건식 개질은 천연가스와 이산화탄소를 사용해 수소를 생산하는 방식으로 온실가스 배출을 줄이고 무탄소 에너 지를 제공할 수 있다. 그러나 강한 흡열 반응으로 인해 온도 급강하와 그로 인한 촉매 비활성화가 발생함에 따라 실증 및 상용화에 어려움이 있다. 본 연구에서는 고정층 반응기 내 메탄 건식 개질 공정을 시뮬레이션하기 위해 전산유체 역학 기반의 다중물리 시뮬레이션 플랫폼을 개발하고, 두 종류의 구형 촉매(에그쉘형과 균일형)의 성능을 비교하였다. 에그쉘 모델은 균일 촉매 모델에 비해 반응기 중심부에서 약 20 K 높은 온도를 유지할 수 있음을 보였다. 또한, 에그 쉘 모델은 급속하게 진행되는 반응을 억제하여 전반적인 메탄 전환율과 수소 수율에서 우수한 성능을 나타냈다. 개발 된 시뮬레이션 플랫폼은 다양한 반응기 및 촉매 설계 조합을 평가하고 반응기 크기와 운영 프로토콜을 최적화하는 데 사용될 수 있다.


    1. Introduction

    The continuous population increase and industrial development have led to a rapid increase in energy usage[1]. Most resources traditionally used to support large-scale energy consumption are fossil fuels, which currently play the most significant role in global warming by producing greenhouse gases[2]. As an alternative to fossil fuels, hydrogen has been attracting attention as a carbon-free energy source, which is traditionally produced via natural gas reforming[3]. Among various reforming technologies, steam methane reforming (SMR) is most commonly used in industrial processes[4]. This process, characterized by a relatively high hydrogen to methane ratio (H2/CH4 = 4), is efficient for producing large amounts of hydrogen but requires maintaining high temperatures (approximately 800~900 K) and releases carbon dioxide as a byproduct[5]. In this respect, it counteracts efforts to reduce greenhouse gas (GHG) emissions. On the other hand, dry methane reforming (DRM) can potentially reduce GHG emissions by using carbon dioxide as a reactant. Additionally, the ratio of hydrogen and carbon monoxide produced is 1, offering an additional advantage in further applications such as fuel production[6-9].

    The DRM reaction is highly endothermic, and the reactor commonly used in industry for this endothermic reaction is a fixed bed reactor[10]. Fixed bed reactors are the most commonly used when a heterogeneous catalytic reaction is employed, with catalyst particles randomly stacked inside the reactor. The most common commercially available catalysts have spherical and cylindrical shapes, and their variations have been made by making holes to increase the efficiency of heat transfer and reduce pressure drops or by making grooves to increase the surface area. When designing a packed bed catalytic reactor, the ratio of catalyst diameter to reactor diameter is crucial, especially when spherical materials are packed inside the reactor. Due to flow channelling and pressure drop within the reactor, the particle diameter is considerably limited. If the ratio of reactor diameter and particle diameter exceeds about 8, the pressure drop in the fixed bed reactor becomes too large, diminishing the reactor’s advantages. Additionally, the reactor diameter must not be excessively large because the heat transfer from the wall temperature to the inside of the catalyst is very important due to the substantial endothermic reaction. For this reason, the appropriate diameter ratio of the entire reactor must be maintained at about 4 to 8[11-13]. In spite of this ratio, complete heat transfer to the inside of catalysts might not be achieved, and there is an area in the core of the reactor where the temperature significantly decreases due to large endothermic reaction rates.

    The most significant barrier to the industrial use of DRM is the formation of coke during the reaction[14]. This coking leads to the deactivation of the catalyst, necessitating its frequent replacement, which undermines cost-effectiveness. Notably, the coking reaction tends to occur more vigorously at temperatures lower than those at which the DRM reaction is most active, leading to more significant coking in the cooler areas inside the reactor[15,16]. Therefore, it is crucial to conduct research on internal flow to identify the cooler areas inside the reactor based on the regions of catalytic inactivity that generate cokes. In many experimental studies, temperature drops inside a fixed bed reactor can be observed through axial average temperature data[17]. However, quantifying the degree of temperature decline of internal catalysts is challenging. In such cases where quantitative results within the reactor are needed, computational fluid dynamics (CFD) research can be employed to facilitate the quantitative analysis of internal temperature reduction and coking risk by incorporating the complex physical phenomena present within the reactor, such as fluid flow, heat and mass transfer and heterogeneous chemical reactions[18,19].

    DRM catalysts are typically produced by immersing aluminium support in an aqueous nickel solution, resulting in a homogeneous distribution of active sites in the final catalysts. Alternatively, nickel active sites can thinly be coated over the support, forming an eggshell shape[20]. Eggshell-type catalysts are often utilized when heat transfer within the particles is incomplete and consequently adversely affects the catalyst’s lifespan. In other words, the eggshell type exhibits significant advantages in terms of heat transfer, demonstrating superior heat transport capabilities across short transport or diffusion paths and within catalyst layers[21,22]. Due to these various advantages, it shows excellent performance in methane reforming reactions such as DRM and SMR, with experimental results indicating a considerable advantage over the uniform catalysts in terms of CO2 and CH4 conversion[23-26]. When comparing coke deposition between the single particle eggshell and uniform catalysts, the eggshell structure was found to exhibit delayed coke accumulation compared to the uniform model, and therefore, the eggshell catalyst’s active lifespan was also slightly extended compared to the uniform model[27]. While the advantages of the eggshell model are evident when a single particle exists, research on the overall effect on the reaction when the entire reactor is packed is still lacking.

    In this study, we developed a three-dimensional (3D) multiphysics simulation platform for DRM in a fixed bed reactor packed with two types of spherical catalysts: uniform and eggshell catalysts. The physical models consist of fluid flow, heat and mass transfer in free and porous regions for catalyst-free regions in the reactor and catalyst internals, coupled with chemical reactions and thermodynamics. The numerical simulations allow us to analyze spatial profiles of velocity, pressure, temperature and species concentration, as well as overall reactor performances such as conversion and yield. The developed simulation platform can lay a foundation for a virtual prediction platform via the quantitative assessment of the complex interplay of transport phenomena and chemical reactions under a wide range of operating conditions. Furthermore, model-based optimization can be undertaken to identify the optimal reactor and catalyst designs and operating strategies, such as reactor dimensions, catalyst sizes and eggshell thickness.

    2. Methods

    2.1. Geometry

    In this study, a 3D configuration of a fixed bed reactor packed with catalysts is depicted in Figure 1. The upstream and downstream sections are integrated with the entire packed bed, facilitating flow development and stabilization, thereby enhancing calculation convergence. The total length of the reactor is set to 8 × 10-2 m (the ratio of lengths for each region shown in Figure 1(b)) and the reactor radius is 8.1 × 10-3 m, with the sphere radius being 2 × 10-3 m, which are taken from the literature[28]. The diameter of these particles falls within the typical range utilized in fixed bed reactors. A total of 113 particles within the reactor are arranged randomly, which is achieved using an open source program, Blender. The catalysts are treated as porous media, whereas catalyst-free areas are of free flow.

    2.1. Governing equations

    Model equations are formulated for 3D and steady-state reactors, incorporating the continuity equations and the conversation of momentum, energy and mass (components). Two distinct regions in 3D simulation domains exist: the catalyst’s internal areas as porous media and the rest free fluid region. Momentum loss occurs at the interface between the free fluid and porous catalyst, generating a velocity gradient at the catalyst interfaces[29].

    2.1.1. Momentum, heat and mass transfer in the free fluid region The continuity equations in steady state are expressed as:

    ∇ ⋅ ( ρ u ) = 0
    (1)

    where the subscripts ρ and u represent the density and velocity of fluid, respectively. The conservation equation of momentum is expressed as follows:

    ρ ( u ⋅ ∇ ) u = ∇ ⋅ [ − p I + μ ( ∇ u + ( ∇ u ) T ) ]
    (2)

    where p is the pressure, μ the fluid viscosity and I the identity matrix. The species transport can be expressed as:

    ρ ( u ⋅ ∇ ) ω i − ∇ ( ρ D i ∇ ω i + ρ D i ∇ M i M n − ρ ω i ∑ k M i M n D i ∇ x k ) = 0
    (3)

    where Mn and ωi represent the average molar mass and the mass fraction of component i, respectively, and xi the molar fraction of component i and Di refers to the diffusion coefficient of the component i. The conservation equations for energy are as follows:

    ρ C p u ⋅ ∇ T + ∇ ⋅ ( − k ∇ T ) = 0
    (4)

    where Cp is the heat capacity, k the fluid thermal conductivity and T the temperature.

    2.1.2. Momentum, heat and mass transfer in the porous media region The mass conservation equation in porous media is expressed as follows:

    ∇ ⋅ ( ρ u ) = S m
    (5)

    where Sm is a source term calculated by chemical reactions. The momentum conservation equation in the porous media is given as:

    1 ε ρ ( u ⋅ ∇ ) u ε = ∇ ⋅ [ − p I + μ 1 ε ( ∇ u + ( ∇ u ) T ) − 2 3 μ 1 ε ( ∇ ⋅ u ) I ] − ( μ κ − 1 + β ρ | u | + S m ε 2 ) u
    (6)

    where κ and ε are the permeability and porosity of the porous media, respectively, and β is the Forchheimer coefficient, which accounts for the inertial effects. The conservation of energy is formulated as:

    ρ C p u ⋅ ∇ T + ∇ ⋅ ( − k e f f ∇ T ) = Q
    (7)

    where Q is the heat source generated by chemical reactions and keff the effective thermal conductivity within the porous domain, which can be calculated from the following formulation:

    k e f f = ( 1 − ε ) k c + ε k
    (8)

    where kc is the thermal conductivity of the catalyst. The species transport in the porous media is similar to Eq. (3) except for added reaction source terms due to chemical reactions taking place in the catalyst regions.

    ρ ( u ⋅ ∇ ) ω i − ∇ ( ρ D i e f f ∇ ω i + ρ D i e f f ∇ M i M n − ρ ω i ∑ k M i M n D k e f f ∇ x k ) = R i
    (9)

    where Ri represents the reaction source term for component i and is assigned to the active zone in the catalyst. D i e f f is the effective diffusion coefficient, estimated using the tortuosity of porous media (or porosity)[30].

    2.2. Reaction kinetics

    The DRM reaction (r1) and the reverse water gas shift (RWGS) reaction (r2) simultaneously occur in the active zone of the catalyst.

    Reaction   1 :   C H 4 + C O 2 ↔ 2 C O + 2 H 2 ,   Δ H 298 K = + 247   kJ/mol
    (10)

    Reaction   2 :   C O 2 + H 2 ↔ C O + H 2 O ,   Δ H 298 K = + 41.2   kJ/mol
    (11)

    In this study, the Langmuir-Hinshelwood kinetics are applied[31,32], with the kinetic parameters available in the literature for the catalyst Rh/Al2O3[33-34].

    r 1 = k 1 ⋅ ( K C O 2 , 1 K C H 4 , 1 P C O 2 P C H 4 ( 1 + K C O 2 , 1 P C O 2 + K C H 4 , 1 P C H 4 ) 2 ) ⋅ ( 1 − ( P C O P H 2 ) 2 K 1 P C H 4 P C O 2 )
    (12)

    r 2 = k 2 ⋅ ( K C O 2 , 2 K H 2 , 2 P C O 2 P H 2 ( 1 + K C O 2 , 2 P C O 2 + K H 2 , 2 P H 2 ) 2 ) ⋅ ( 1 − P C O P H 2 O K 2 P C O 2 P H 2 )
    (13)

    where P with its subscript indicates the partial pressure. The kinetic parameters are listed in Table 1.

    2.3. Computational details

    Apart from geometry generation, meshing, model set-up, computation and result post-processing are undertaken using the commercial software COMSOL Multiphysics 6.1. For mesh generation, a significant challenge emerges due to the mesh being too densely created in areas where solids are very close to each other or the reactor walls, leading to an excessive increase in the number of mesh elements. For the benefit of computational cost and efficiency, a common approach involves preventing the spheres from contacting each other by reducing their size slightly, in our case, by 0.5%. While this strategy may not perfectly replicate the actual internal structure, it proves its benefits, especially when computation demands are high due to numerous variables to be resolved, as in our cases involving velocity, pressure, concentrations and temperature in both free and porous regions in addition to multiple chemical reactions and thermodynamics calculations. This approach results in using approximately 800,000 mesh elements for both geometries.

    The feed composition is set to be CH4:CO2:N2 = 1:1:8 with an inlet velocity of approximately 0.886 m/s, which results in a laminar flow. The outlet pressure was prescribed to be 1 bar, equivalent to atmospheric pressure. In this study, the reactor wall temperature (Twall) and inlet temperature (Tin) of the reactor are maintained to be equal, varied at 923, 973, 1023, 1073 and 1123 K, to examine the distribution of internal cold zones as the temperature increases. Parameters and variables for the reactor design and operating conditions can be found in Table 2. All steady-state solutions are achieved with a residual goal < 10-6 using a workstation with AMD EPYC 7763 CPU @2.45 GHz 64 cores and 512 GB RAM.

    3. Results and discussion

    3.1. Velocity profiles and pressure drop

    Velocity magnitude distributions and velocity vectors in the free region are shown in Figure 2. The maximum velocity is observed between the particles at approximately 4.5 m/s, substantially exceeding the inlet velocity by about 7.5-folds. Due to the same packing configuration for both types of catalysts, areas with high velocities are virtually identical, with slight differences in velocity magnitudes. As can be seen in Figure 3(b), the eggshell model exhibits lower velocities in the inter-particle regions. For both cases, velocities within the catalyst particles are very low.

    Additional insights are provided by examining radial velocity data displayed in Figure 3(a), which confirms that the velocity within the eggshell model is lower than that recorded in the uniform catalyst model. Minor discrepancies between the two models can be attributed to changes in gas properties, such as viscosity and density variations induced by the catalytic reaction. This detailed examination of flow dynamics in packed bed reactors emphasizes the significant impact of the physical structure and reaction-induced modifications in gas properties on fluid movement, thereby providing crucial considerations for enhancing reactor design and operational efficiency.

    Figure 3(a) shows average velocities in the radial direction for each case, where zero dimensionless radial position indicates the reactor centre. The velocity trend is related to the voidage inside the reactor. In high voidage areas, the velocity is low, while in low voidage areas, the velocity is shown to be high. Eggshell and uniform cases do not exhibit significant differences in the velocity patterns, albeit slight discrepancy in the velocity magnitude, attributed to different permeability of neighbourhood catalysts. In fixed bed reactors, pressure drops are associated with operational costs, which are influenced by the catalyst characteristics and reactor designs. Therefore, the prediction of pressure drop is of paramount importance. A comparative analysis is conducted between the Ergun Equation[39] and the observed axial pressure drops across two different types of particles, shown in Figure 3(b). The disparity in pressure drop between the two particle types was found to be negligible, with a maximum difference of approximately 2 Pa. Although CFD results predict a slightly higher pressure drop value than the Ergun equation (approximately 5.4% greater at specific packing locations), this difference is not significant. Due to the endothermic reaction facilitated by the DRM catalysts within the fixed bed reactors. Minor changes in the properties of the gas phase can happen, depending on the extent of the reaction within the catalysts. These modifications may, in turn, affect the pressure drop across the reactor. Although the Ergun equation appears to be capable of predicting the overall pressure drop, the nonlinearity observed within the reactor cannot be captured by it. Also the discrepancy between the CFD and Ergun equation results would be larger for the larger-sized reactor and higher catalytic activity.

    3.3. Temperature distributions and cold zone analysis

    As previously mentioned, the rapid decline in catalyst temperature due to the endothermic DRM reaction significantly promotes coke deposition, leading to catalyst deactivation. Therefore, maintaining the catalyst’s effectiveness requires a thorough understanding of the temperature distribution within the fixed-bed reactor. In this context, the temperature distribution is analyzed both axially and radially for the evaluated models to determine the extent of temperature reduction. Additionally, cold zones are quantified, and temperature analysis is performed by separating the solid catalyst and fluid components.

    When the inlet and wall temperatures are 923 K, as shown in Figure 4(a), an examination of the axial average temperature distribution along the reactor length reveals a significant decrease of about 100 K near the initial point of packing within the reaction zone. This initial stage of the reaction shows a reduction exceeding 120 K. Subsequently, the temperature of both catalysts continues to decrease gradually until the reaction concludes. Beyond the packing zone, the temperature begins to rise again, facilitated by convective heat transfer from the wall temperature. Consequently, the eggshell model maintains a higher temperature compared to the uniform model, highlighting its enhanced thermal regulation within the reaction environment. Maintaining a higher temperature in the DRM reaction is crucial for sustaining high reaction rates and facilitating more reactions while reducing the risk of coke production and catalyst inactivation. This suggests that the eggshell- type catalysts can be advantageous in highly endothermic reactions.

    Additionally, the temperature profile within the packing area exhibits fluctuations due to fluid penetration into the catalyst and subsequent heterogeneous reactions there. The temperature in both catalyst models decreases progressively towards the center and then increases from the center back to the exterior. This fluctuating pattern shown in Figure 5(a) indicates dynamic temperature changes within the catalyst. The uniform model, where reactions occur deeper within the center, shows a more significant temperature increase inside the particle compared to the eggshell model, which exhibits a slightly less pronounced effect. According to the axial and radial temperature profiles within the reactor depicted in Figures 5(a) and 5(b), both models share a similar pattern due to identical packing structures. However, the temperature in the center of the eggshell reactor is maintained at approximately 20 K higher, gradually reaching a fixed temperature of 923 K towards the wall.

    A quantitative assessment is performed to provide a more detailed analysis of the temperature distribution within the reactor. The first evaluation aims to define the cold zone, which is assessed by dividing the area approximately 150 K lower than the inlet temperature by the volume of the entire packing area. As illustrated in Figure 5(c), at lower temperatures, the cold zone ratio of the uniform model is nearly twice as high compared to the eggshell case. However, as the temperature increases, the difference between the two models diminishes, and the eggshell model consistently exhibits a lower cold zone ratio than the uniform model, underscoring its advantage for the DRM reaction. The second evaluation involves analyzing the average temperatures of the solid (catalysts) and fluid areas within the packing zone for each catalyst model, as shown in Figure 5(d). Relative to the total fluid content, the reaction in the solid area resulted in a slight temperature decrease, approximately 10 to 20 K. When comparing the two reactor models, higher reactor temperatures led to larger discrepancies between the eggshell and uniform models, both in the fluid and solid phases. For all temperatures, the differences in fluid temperatures between the eggshell and uniform models are shown to be smaller than those in the solid temperatures, possibly due to the reactions taking place in the solid phase. It is also shown that discrepancies between the fluid and solid regions for the uniform cases are larger than those for the eggshell cases. For example, at 973 K, the eggshell model results in 23.54 K, while the uniform model results in 26.79 K. Also, at 1173 K, the eggshell has the fluid-solid temperature difference at 47.18 K and the uniform case at 52.48 K. The fluid-solid temperature differences are predicted to be larger at higher temperatures.

    3.4. Concentration distributions, conversions and yields

    An analysis of the internal concentration distribution was conducted based on the reactions occurring within the fixed-bed reactor. Figures 6(a) and 6(b) illustrate the concentration distributions of CH4 and H2 for both catalyst models in the yz plane of the fixed bed reactor at an inlet temperature of 973 K. Similar to the temperature results presented in Figure 4(b), the uniform model shows that as the fluid diffuses towards the center of the catalyst, the reaction intensifies in the center, leading to the highest consumption of CH4 and the greatest production of H2. In contrast, in the eggshell model, the reaction predominantly occurs at the shell, not at the inner center, resulting in a concentration that does not focus at the catalyst’s center, as can be seen in Figure 6(c). This leads to a relatively uniform concentration distribution throughout the catalyst bed, highlighting the differences in reaction dynamics between the two catalyst models. This is visually demonstrated in the comparative distributions of CH4 and H2 concentrations in Figures 6(b) and 6(d); a clear contrast in colours can be observed between the internal catalytic region and the fluid region.

    Furthermore, the eggshell model facilitates significantly more reactions than the uniform model, resulting in greater consumption of the reactant CH4 and higher production of H2, as can be seen in the axial concentration profiles shown in Figure 7. This is attributed to regulating the endothermic DRM and RWGS reaction rates in the eggshell reactor by preventing excessively rapid reactions from taking place within the dense active catalytic regions and consequently maintaining higher temperatures throughout the reactor.

    This advantage is corroborated by the conversion of CH4 and the yield of H2. The uniform model displays conversions ranging from approximately 7.5% to 16%, depending on the temperature, while the eggshell model exhibits conversions from about 11% to 25%. Comparing these data, the eggshell model shows a distinctly higher conversion of approximately up to 37%, underscoring its superior efficacy in the DRM reaction. In terms of H2 yield, the eggshell reactors consistently result in higher yields than the uniform models, although there is a negligible variation in the H2 yield with respect to temperature.

    4. Conclusion

    In this study, multiphysics simulations are performed for DRM in a 3D fixed bed reactor packed with spherical catalysts, focusing on comparing eggshell and uniform catalyst types. The developed simulation platform accounts for the random arrangement of spherical catalysts, combined free and porous regions for fluid flow, mass and heat transfer, thermodynamics for gas mixture properties and Langmuir kinetics for DRM and RWGS.

    When comparing pressure drops predicted by our simulation platform with the Ergun equation, a widely used empirical relation for pressure drops, it can be concluded that a realistic representation of a packed bed is necessary for accurate pressure drop predictions. Although the overall pressure drops predicted by the Ergun equation and our model are comparable in the studied small reactor, larger-scale reactors could result in larger errors when the Ergun equation is used. Simulation results reveal that the eggshell model excels in maintaining a higher temperature throughout the reactor compared to the uniform model, facilitating an overall increase in the DRM reaction rate and a reduction in coke formation. These phenomena are indirectly evidenced by the CH4 conversion and H2 yield.

    Future research will aim to incorporate the kinetics of coke deposition to accurately account for catalyst inactivation and the resulting degradation in reactor performance. Additionally, the developed simulation platform will be extended for a scale-up study to provide insights and guidelines for reactor design and operating protocols.

    Acknowledgements

    This study was financially supported by Chonnam National University (Grant number: 2021-2189).

    Authors

    Soo-Won Son; B.Sc., Student, School of Chemical Engineering, Chonnam National University, Gwnagju 61186, Republic of Korea; llstvincentll@naver.com

    Boram Gu; Ph.D., Associate Professor, School of Chemical Engineering, Chonnam National University, Gwangju 61186, Republic of Korea; boram.gu@jnu.ac.kr

    Figures

    ACE-35-5-429_F1.gif
    Schematic overview of catalyst-packed fixed bed reactors with two types of catalysts, eggshell and uniform particles. (a) The schematic of a multi-tube reactor and (b) zoomed-in view of the single packed-bed composed of catalyst-free upstream and downstream and the packing region in between.
    ACE-35-5-429_F2.gif
    Velocity distributions in the yz plane of the fixed bed reactor. The flow direction is the negative z. (a) Uniform and (b) eggshell catalysts.
    ACE-35-5-429_F3.gif
    Simulation results of velocity and pressure drop. (a) Average velocity in the radial direction and (b) average pressure drop in the axial direction of the reactor.
    ACE-35-5-429_F4.gif
    Temperature distributions in the yz plane of the fixed bed reactor: (a) Eggshell and (b) uniform cases.
    ACE-35-5-429_F5.gif
    Simulation results of average temperatures in the fixed bed reactor at the wall and inlet temperature at 923 K (a) in the axial and (b) in the radial direction within the reactor. Simulation results for varied wall and inlet temperatures: (c) cold zone ratios and (d) volume-averaged temperatures of the catalyst and fluids.
    ACE-35-5-429_F6.gif
    Concentration distributions in the yz plane of the fixed bed reactor at an inlet temperature of 973 K. CH4 concentration distributions in the (a) eggshell and (b) uniform models and H2 concentration distributions in the (c) eggshell and (d) uniform models.
    ACE-35-5-429_F7.gif
    Average species concentrations in the axial direction: (a) CH4, (b) CO2, (c) H2 and (d) H2O.
    ACE-35-5-429_F8.gif
    Overall reactor performance indicators: (a) conversion of reactant CH4 and CO2 for varied temperatures and (b) the yield of H2 relative to feed CH4.

    Tables

    Reaction Kinetics Parameter
    Operating Conditions and Catalyst Properties

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