Parametric Studies of Steam Methane Reforming Using a Multiscale Reactor Model

Abstract

This work investigates the influence of porous catalyst structural parameters on a packed bed reactor’s performance, through the application of a multiscale reactor model. Constitutive equations, at the catalytic pellet and packed bed reactor length scales, are derived using the Reynolds transport theorem. Diffusive fluxes in the microscale (catalytic pellet) and macroscale (reactor) domains are calculated using the dusty gas model (DGM) and Stefan–Maxwell model (SMM) equations respectively, while Chapman–Enskog theory is applied to estimate diffusion and viscosity coefficients. Simulations are carried out for a case study on hydrogen production through steam methane reforming, using a finite element numerical scheme. The employed multiscale model enables the computation of catalyst effectiveness factors throughout the reactor, thus quantifying the effect on reactor performance of various catalyst structural characteristics, such as volumetric fraction, tortuosity, thermal conductivity, and mean pore diameter.

Gas-Phase Potassium Effects and the Role of the Support on the Tar Reforming of Biomass-Derived Producer Gas Over Sulfur-Equilibrated Ni/MgAl2O4

“…This could be related to the different reactivities of the molecules, differences in internal mass transfer limitations between the molecules, the way the intrinsic activity toward the different molecules changes on a local scale inside the catalyst particles because of the K concentration profile dynamically changing inside the catalyst particles and throughout the bed during the K-decay stage, and the fact that the K-doped MgAl 2 O 4 support is active toward naphthalene and not CH 4 and C 2 H 4 , as will be discussed later. The calculated effective diffusivities ( D eff ) − for CH 4 , C 2 H 4 , and C 10 H 8 are equal to 4.83 × 10 –7 , 3.65 × 10 –7 , and 1.71 × 10 –7 m 2 /s, respectively, under the experimental conditions of the study, indicating the slower diffusion of larger tar molecules in the catalyst particles. The lower the D eff , the more sensitive the conversion rate becomes to the conditions at the outermost volume of the catalyst particles.…”

On Process Intensification through Membrane Storage Reactors

“…The parameter D e f f is evaluated as D e f f = ε v D AB using the binary diffusion coefficient, D AB , of the transferrable species, which in turn can be evaluated using the Chapman-Enskog solution to the Boltzmann Equation along with a Lennard-Jones potential for estimating the effects of intermolecular forces. Using the formulation established in our previous results [14], the values of D AB for the species present in the considered case study fall in the range of 0.02 ≤ D AB ≤ 0.15 cm 2 s . Values for the characteristic length can be obtained by considering L char to be equal either to the length or the radius of the reactor, which in turn yield Pe values that satisfy Pe ≥ 1 × 10 5 and Pe ≥ 100, respectively, and justify neglecting diffusion in the void domain.…”

“…The values for D a and Θ were chosen by selecting a range of reasonable values for the parameters shown in Equation (8). Fixing the operating pressure P * = 25(bar), temperature T * = 900(K), catalyst density ρ c = 2355.2 kg m 3 [14], and reference reaction rate…”

“…These efforts include the design and sim-ulation of multi-scale reactor models whose constitutive equations are derived through application of conservation laws and the Reynolds Transport Theorem. Through our rigorous mathematical development, we have successfully modeled transport at the reactor and catalyst pellet scales [13], and have implemented advanced transport models, such as the Dusty Gas Model (DGM), Stefan-Maxwell model (SMM), and Chapman-Enskog framework [14]. We have also proposed methodologies for the synthesis of intensified processes, through the aforementioned IDEAS framework that has been used to identify performance limits for reactive separation networks [15], and the Energetically Enhanced Process (EEP) concept that has been used to synthesize energy-intensified flowsheets using MR networks [16].…”

A Numerical Study of Hydrogen Production via High-temperature and Low-temperature Water-Gas Shift Reactors’ System: The Multi-Scale Modeling Approach and Simulation

“…(3) Knudsen diffusion: the transport mechanism defined as the motion of species by concentration variations in the presence of small pore walls. Thermal diffusion is not took into account in this study (da Cruz et al 2017). The resulting DGM is:…”