多孔性
烧结
材料科学
蒸汽重整
甲醇
铜
氢
空间速度
纤维
体积流量
复合材料
化学工程
催化作用
制氢
冶金
热力学
选择性
化学
物理
有机化学
工程类
生物化学
作者
Qinghui Wang,Yang Song,Wei Zhou,Jing‐Rong Li,Zhijia Xu,Yuzhi Ke,Wei Yu,Guanghua Hu
标识
DOI:10.1016/j.apenergy.2018.02.102
摘要
Methanol steam reforming inside micro-reactors is considered as one of the effective approaches for on-board supplying hydrogen for fuel cells. Porous copper fiber sintered felts (PCFSFs) are a new kind of catalyst support for micro-reactors developed in recent years. However, there is a lack of approach to control their porosity configurations due to their random structure. A two-step optimization method was proposed to optimize the PCFSFs’ porosity configuration. Firstly, the topology structures of PCFSFs were optimized based on the best flow distributions obtained from macroscopic numerical analyses, and two kinds of PCFSFs with twelve porosity distributions were fabricated through the multi-step mold pressing and solid-phase sintering method. Secondly, the porosity distributions of the semi-optimized PCFSFs were optimized by investigating their reaction characteristics under different gas hourly space velocities (GHSVs) and reaction temperatures. The results indicated that PCFSFs with porosity distribution along the Left-Right direction (PCFSF-LRs) exhibited better reaction performance than PCFSFs with porosity distribution along the Upside-Underside direction (PCFSF-UUs). The methanol conversion and H2 flow rate for the PCFSF-LRs with porosity distribution of 0.7–0.9–0.8 and 0.8–0.9–0.7 kept on a high level (above 92% and 0.59 mol/h, respectively), regardless of the change of GHSVs and reaction temperatures in most cases. The H2 selectivity of the PCFSF-LR of 0.7–0.9–0.8 was the highest under large GHSVs and all tested reaction temperatures. The demonstrated effect of counteracting, even reversing the conventional influence of the GHSV and temperature on the performance of methanol steam reforming may be attributed to the more uniform flow distribution in the two PCFSF-LRs.
科研通智能强力驱动
Strongly Powered by AbleSci AI