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 CO 2 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 CO 2 /g sorbent ) 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.

Sustainable coke-resistant Ca-Al nano-sized catalyst for cogenerating of H2 and high value liquid fuels via pyrolysis catalytic steam reforming reaction of the polystyrene-phenol mixture

“…Catalysts for phenol steam reforming and plastic waste cracking reactions need a large surface area, a larger pore structure accessible for gaseous transport, and thermal stability at high temperatures, even when steam is present. One typical candidate for such complex reactions is calcium oxide (CaO) which may trap CO2 generated on-site and increase hydrogen yields 30 . The effectiveness of calcium oxide as an in-bed catalyst for reforming or cracking tars produced by thermally degrading hydrocarbon fuels has long been known 31 .…”