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 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.

中文翻译：

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使用 QSPR 和多任务学习预测 SE-SMR 的组合吸附剂和催化剂材料

吸附增强蒸汽甲烷重整（SE-SMR）工艺是一种新兴的低碳制氢技术。迄今为止，合适的催化材料以及具有高捕获能力的 CO ₂吸附剂的开发减缓了该过程的升级。在这项研究中，为了帮助开发用于 SE-SMR 的组合吸附剂催化剂材料 (CSCM)，提出了一种涉及定量结构-性能关系分析 (QSPR) 的新方法。通过数据挖掘，已经开发了两个数据库来预测最后一个循环容量（g _{CO 2} /g_吸附剂) 和甲烷转化率 (%)。多任务学习 (MTL) 被应用于 CSCM 属性的预测。本研究数据中的模式也产生了进一步的见解；彩色散点图能够显示输入数据中的某些模式，以及有关如何开发最佳材料的建议。通过对实际与预测图的结果进行整理，提出了可能导致开发在最后循环容量和甲烷转化率方面具有良好性能的 CSCM 的原材料和合成条件。