Prediction of Combined Sorbent and Catalyst Materials for SE-SMR, Using QSPR and Multitask Learning.

ABSTRACT: The 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 (g_CO2 /g_sorbent) and methane conversion (%). Multitask learning (MTL) was applied for the prediction of CSCM properties. Patterns in the data of this study have

SUBMITTER: Nkulikiyinka P 

PROVIDER: S-EPMC9264356 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature