Pore structure-transport relationships in high-temperature shift catalyst pellets studied by integrated multiscale porosimetry and X-ray tomography

Mousa, Suleiman; Beech, Toby; Softley, Emma; Fletcher, Robin S.; Kelly, Gordon; Viney, Emily; Rigby, Sean P.

Abstract

Diffusion-limited, heterogeneously-catalysed processes mean choices influencing pore structure-transport relationships, made during pellet fabrication, affect product performance. This work shows how the ‘sifting strategy’ can identify the critical aspects of a highly complex catalyst pellet pore structure that control mass transport to construct an idiosyncratic, minimalist model. This is implemented using fully-integrated gas overcondensation, mercury porosimetry and X-ray tomography experiments. It showed high temperature shift (HTS) catalyst pellets had a trimodal pore structure. The second mode, consisting of macropores within the roll-compacted feed particles, controlled mass transport. Knudsen-regime mass transport was shown to be critically-controlled by an incipiently-percolating cluster of these intermediate-sized macropores, as its rate could be drastically reduced via introduction of very few blockages via mercury entrapment. This incipiently percolating network could be represented by a lattice-based, random cluster model. X-ray tomographic images, analysed with an AI segmentation algorithm, validated the proposed model of interpretation for the indirect characterization data.

Citation

Mousa, S., Beech, T., Softley, E., Fletcher, R. S., Kelly, G., Viney, E., & Rigby, S. P. (2024). Pore structure-transport relationships in high-temperature shift catalyst pellets studied by integrated multiscale porosimetry and X-ray tomography. Chemical Engineering Science, 292, Article 120005. https://doi.org/10.1016/j.ces.2024.120005

• Journal Article Type: Article
• Acceptance Date: Mar 11, 2024
• Online Publication Date: Mar 12, 2024
• Publication Date: Jun 15, 2024
• Deposit Date: Mar 14, 2024
• Publicly Available Date: Mar 25, 2024
• Journal: Chemical Engineering Science
• Print ISSN: 0009-2509
• Electronic ISSN: 1873-4405
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 292
• Article Number: 120005
• DOI: https://doi.org/10.1016/j.ces.2024.120005
• Keywords: X-ray computed tomography; Neural network; Adsorption; Porosity; Diffusion
• Public URL: https://nottingham-repository.worktribe.com/output/32464553
• Publisher URL: https://www.sciencedirect.com/science/article/pii/S0009250924003051