Nkulikiyinka Paula, Wagland Stuart T, Manovic Vasilije, Clough Peter T
Energy and Power Theme, School of Water, Energy and Environment, Cranfield University, Cranfield, Bedfordshire MK43 0AL, U.K.
Ind Eng Chem Res. 2022 Jul 6;61(26):9218-9233. doi: 10.1021/acs.iecr.2c00971. Epub 2022 Jun 23.
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 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 /g) 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.
吸附增强型蒸汽甲烷重整（SE-SMR）工艺是一种新兴的低碳氢气生产技术。迄今为止，开发合适的催化材料以及具有高捕获能力的CO吸附剂，减缓了该工艺的扩大规模。在本研究中，为了辅助开发用于SE-SMR的复合吸附剂催化剂材料（CSCM），提出了一种涉及定量结构-性质关系分析（QSPR）的新方法。通过数据挖掘，开发了两个数据库，用于预测最后循环容量（g/g）和甲烷转化率（%）。多任务学习（MTL）用于预测CSCM性能。本研究的数据模式也提供了进一步的见解；彩色散点图能够显示输入数据中的某些模式，以及关于如何开发最佳材料的建议。整理实际与预测图的结果后，提出了可能导致开发出在最后循环容量和甲烷转化率方面均具有良好性能的CSCM的原材料和合成条件。
