(671c) Microscale CFD Simulation of a Packed-Bed Catalytic Reactor for the Production of Hydrogen Via Aqueous Phase Reforming

Aqueous phase reforming (APR) is a promising alternative for the sustainable production of hydrogen from wastewater via the valorization of the contained organic compounds (e.g., from the food industry), or crude glycerol from biodiesel production plants. APR allows for large energy savings, since it does not require any evaporation of the reactants, and has lower working temperatures (typically from 220 to 270 °C).¹ The typical reactor configuration is that of a fixed-bed, with catalytic particles packed in a tubular container.

The objective of this work is to investigate this process from the experimental and computational viewpoints, and especially the role of hydrogen mass transfer in APR performance, hitherto unexplored.

Having chosen the platinum-based catalyst on activated carbon support, a kinetic model capable of characterizing the generation of the main products and by-products was developed from experimental data to understand the interplay between reaction and transport phenomena. This kinetic model will then be employed in the CFD simulations, performed on a three-dimensional model.

These geometries are digital replicas of the catalyst pellet packing generated with the open-source code Blender. This in-silico approach allows for a faster and cheaper evaluation of different geometrical configurations to facilitate geometrical optimization, as opposed to more time-consuming experiments, such as micro-computed tomography.

Then, pore-scale fluid flow CFD simulations were performed with OpenFOAM (left figure). Subsequently the nonreactive transport of the chemical species is studied to explore the influence of the geometry on diffusive and advective transport. Later, we implemented the kinetic model to evaluate these effects also in the context of reactive transport.

This will allow a study to be carried out on a macroscale system with reduced dimensionality to optimize reactor configuration and pellets geometry, thus improving the performances achievable by this process.

PNRR M4C2,Investimento1.4-Avviso.n.3138del16/12/2021-CN00000013NationalCentreforHPC,BigDataandQuantumComputing(HPC)-CUPE13C22000990001

(1) Zoppi, Giulia, et al. Catalysis Today 387 (2022): 224-236.