Hydrodynamic Profiles Of Computed Tomography-Scanned Polydispersed Beds Produced By Sieving

Abstract

Computational Fluid Dynamics (CFD) models are a valuable tool for design, optimization, and scaling-up of fixed bed chemical reactors. However, the realistic representation of the catalytic bed structure and the mesh quality of the 3D geometry is of paramount importance to improve the accuracy of CFD models. For the former, computed tomography (CT) is a non-destructive method to map and generate the internal structure of actual fixed bed reactors, formed by catalytic particles produced by sieving, thus directly coupling experiments with CFD models. Due to the local topological complexity of these beds, however, meshing their entire volume would lead to exhaustive computational demands. To reduce these, a suitable sample section should be selected, which respects the bulk and radial porosity of the full bed as accurately as possible. Three distinct sample sections were quantified here for their accuracy, identifying that, due to the highly heterogeneous nature of the full beds, sample selection is case sensitive. A selected section was then meshed, and its hydrodynamic profile resolved, to evaluate its mesh independency. The results highlight the importance of choosing a suitable bed section and mesh size to reduce the computational demands, minimise the computational errors, and achieve the desired level of solution detail.

Quantifying the Impact of Intraparticle Convection within Fixed Beds Formed by Catalytic Particles with Low Macro-Porosities

“…In addition, the case studied here uses simplified spherical particles. More complex particle shapes, such as those examined in our CT-scanned bed, ,, will exhibit different local hydrodynamic behavior, which will affect the reactant accessibility into the pores. Consequently, the ability of the PP model to account for both inter- and intraparticle gradients in the local bed and particle structure, velocity, and pressure can be highly valuable to approach such cases.…”

“…In recent years, computational fluid dynamics (CFD) models have been instrumental in the advancement of fixed bed chemical reactors, which are utilized both as investigative and as optimization tools. ,− In particle-resolved CFD (PR-CFD) models, realistic particle arrangements are generated primarily through the discrete element method (DEM), ,, and the individual particles are resolved and meshed. Particle sizes and shapes within the packed bed can either be mono- − or poly-dispersed − in nature, with modern DEM-based beds implementing multiple particle shapes, such as spherical, cylindrical, or Raschig rings. ,,,,− PR-CFD models yield key information regarding the interconnected nature of the physicochemical phenomena during operation of the fixed bed system. ,, Specifically, their ability to consider the impact of the geometrical bed structure to predict the interparticle heat and mass transfer yields highly valuable insights for reactor engineering. − Fixed bed reactors include different levels of “porosity” (Figure ). The particles (gray spheres in Figure a) are immobile, having interconnected gaps between them, referred to as “interparticle network”, where reagents can flow through.…”

“…We focus here purely on catalytic particles with low macro-porosities, i.e., below 0.5, as they exist in-between the spectrum of a completely solid particle and an open-cell foam-type particle. In our previous work, we have investigated the performance of SAPO-34 for EtOH dehydration. ,, The strong acid sites and small micropores of SAPO-34 made it highly selective for ethylene formation. , For this system, we have previously demonstrated the potential of CFD models, developed for ANSYS Fluent, as investigate tools. , Moreover, by mapping the internal structure of the bed, we identified its highly heterogeneous nature, consisting of particles with a wide range of sizes, shapes, and orientations, leading to flow profiles with large velocity and pressure gradients . However, the internal structure of the SAPO-34 particles was not discussed.…”

“…66,67 Moreover, by mapping the internal structure of the bed, we identified its highly heterogeneous nature, consisting of particles with a wide range of sizes, shapes, and orientations, 19 leading to flow profiles with large velocity and pressure gradients. 22 However, the internal structure of the SAPO-34 particles was not discussed. Here, through experimental characterization, the porous nature of the SAPO-34 particles is evaluated.…”