Computational simulation of SE-SR of methane in a bench-scale circulating fluidised bed reactor: Insights into the effects of bed geometry design and catalyst-sorbent ratios

• CPFD model applied to full-loop simulation of bench-scale SE-SR of methane. • Explored SE-SR behaviour under conditions of low catalyst activity and low sorbent loadings. • Compared the performance of two reactor bed designs. • Low catalyst activity has relatively high impact on performance. Operating sorbent-enhanced steam reforming (SE-SR) of methane in fluidised bed reactors presents a promising pathway for industrial low-carbon hydrogen production. However, further understanding of its complex multi-phase behaviours under certain operating conditions is still needed to guide reactor design and scale-up. This study developed a computational particle fluid dynamic (CPFD) reactor model to study cyclic SE-SR performance. The model was used to simulate scenarios representing potential reductions in catalyst activity and sorbent inventory levels over time by varying catalyst-sorbent ratios. Additionally, the effects of two different bed geometry designs were examined. Results indicate that varying solids ratios influenced reaction progress, with optimised methane conversion and CO 2 capture observed at moderate ratios. Higher sorbent loadings enhanced thermal neutrality but risked increased calciner energy penalties. Bed geometry also influenced localised hydrodynamics. Detailed solids and gas concentration contours provided insight into segregation and spatial product distribution in the two designs.

Authors

Institutions

Publication Details

Journal
Fuel
Published
2024-08-19
DOI
https://doi.org/10.1016/j.fuel.2024.132817
Citations
1
Primary Topic
Chemical Looping and Thermochemical Processes
Type
article
Field-Weighted Citation Impact
0.18

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Computational simulation of SE-SR of methane in a bench-scale circulating fluidised bed reactor: Insights into the effects of bed geometry design and catalyst-sorbent ratios

Carolina Font-Palma, Chinonyelum Udemu
1 citations
Fuel
Chemical Looping and Thermochemical Processes
0.18
article

Computational simulation of SE-SR of methane in a bench-scale circulating fluidised bed reactor: Insights into the effects of bed geometry design and catalyst-sorbent ratios

Carolina Font-Palma, Chinonyelum Udemu
article en
1 citations

Abstract

• CPFD model applied to full-loop simulation of bench-scale SE-SR of methane. • Explored SE-SR behaviour under conditions of low catalyst activity and low sorbent loadings. • Compared the performance of two reactor bed designs. • Low catalyst activity has relatively high impact on performance. Operating sorbent-enhanced steam reforming (SE-SR) of methane in fluidised bed reactors presents a promising pathway for industrial low-carbon hydrogen production. However, further understanding of its complex multi-phase behaviours under certain operating conditions is still needed to guide reactor design and scale-up. This study developed a computational particle fluid dynamic (CPFD) reactor model to study cyclic SE-SR performance. The model was used to simulate scenarios representing potential reductions in catalyst activity and sorbent inventory levels over time by varying catalyst-sorbent ratios. Additionally, the effects of two different bed geometry designs were examined. Results indicate that varying solids ratios influenced reaction progress, with optimised methane conversion and CO 2 capture observed at moderate ratios. Higher sorbent loadings enhanced thermal neutrality but risked increased calciner energy penalties. Bed geometry also influenced localised hydrodynamics. Detailed solids and gas concentration contours provided insight into segregation and spatial product distribution in the two designs.

FuelVol. 377
University of Hull (GB)
University of Hull, EDF Energy, Petroleum Technology Development Fund
Openalex Percentile: Top 53%
Chemical Looping and Thermochemical Processes
0.18
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.