4.6 Article

A parametric study of layered bed PSA for hydrogen purification

期刊

CHEMICAL ENGINEERING SCIENCE
卷 63, 期 21, 页码 5258-5273

出版社

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.ces.2008.07.017

关键词

Hydrogen purification; Layer PSA; Mathematical modelling; Dynamic simulation; Adsorption; Separations

资金

  1. HY2SEPS [SES6-019887]

向作者/读者索取更多资源

The production of high purity hydrogen (99.99+%) at reduced cost is an important and sought target. This work is focused on the separation of hydrogen from a five component mixture (H-2/CO2/CH4/CO/N-2) by pressure swing adsorption. A complete mathematical model that describes the dynamic behaviour of a PSA unit is presented. This model is applied in the study of the behaviour of both single column and four columns PSA processes with layered activated carbon/zeolite beds and with an eight steps cycle. In the single column simulation, a 99.9994% purity hydrogen stream is attained at the end of the feed step for a process hydrogen recovery of 51.84% and a productivity of 59.6mol(H2)/kg(ads)/day. The multicolumn simulation predicts a hydrogen recovery and purity, respectively, of 52.11% and 99.9958%. The influence of feed flow rate, purge to feed ratio and lengths of both adsorbent layers on the system performance is assessed. It is shown that the introduction of the zeolite layer improves both the purity and recovery of the process. Reduced models are formulated based on the sequential identification of controlling resistances in the complete model. The predictions of the reduced models are evaluated by comparing their results with those obtained from the complete model. it is shown that the model that merely takes into account the micropore resistance (described by the LDF model) and assumes thermal equilibrium only between the gas and solid phases satisfactorily predicts the behaviour of the pressure swing adsorption unit. (c) 2008 Elsevier Ltd. All rights reserved.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.6
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据