Prediction of combined sorbent and catalyst materials (CSCM) for SE-SMR, using QSPR and multi-task learning

Simple item page
dc.contributor.authorNkulikiyinka, Paula
dc.contributor.authorWagland, Stuart T.
dc.contributor.authorManovic, Vasilije
dc.contributor.authorClough, Peter T.
dc.date.accessioned2022-07-19T09:13:16Z
dc.date.available2022-07-19T09:13:16Z
dc.date.issued2022-06-23
dc.description.abstractThe process of sorption enhanced steam methane reforming (SE-SMR) is an emerging technology for the production of low carbon hydrogen. The development of a suitable catalytic material, as well as a CO2 adsorbent with high capture capacity, has slowed the upscaling of this process to date. In this study, to aid the development of a combined sorbent catalyst material (CSCM) for SE-SMR, a novel approach involving quantitative structure–property relationship analysis (QSPR) has been proposed. Through data-mining, two databases have been developed for the prediction of the last cycle capacity (gCO2/gsorbent) and methane conversion (%). Multitask learning (MTL) was applied for the prediction of CSCM properties. Patterns in the data of this study have also yielded further insights; colored scatter plots were able to show certain patterns in the input data, as well as suggestions on how to develop an optimal material. With the results from the actual vs predicted plots collated, raw materials and synthesis conditions were proposed that could lead to the development of a CSCM that has good performance with respect to both the last cycle capacity and the methane conversion.en_UK
dc.identifier.citationNkulikiyinka P, Wagland S, Manovic V, Clough P. (2022) Prediction of combined sorbent and catalyst materials (CSCM) for SE-SMR, using QSPR and multi-task learning, Industrial and Engineering Chemistry Research, Volume 61, Issue 26, July 2022, pp. 9218-9233en_UK
dc.identifier.issn0888-5885
dc.identifier.urihttps://doi.org/10.1021/acs.iecr.2c00971
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/18190
dc.language.isoenen_UK
dc.publisherAmerican Chemical Societyen_UK
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.titlePrediction of combined sorbent and catalyst materials (CSCM) for SE-SMR, using QSPR and multi-task learningen_UK
dc.typeArticleen_UK

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
combined_sorbent_and_catalyst_materials-2022.pdf
Size:
4.34 MB
Format:
Adobe Portable Document Format
Description:
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.63 KB
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections

Staff publications (SWEE)