4.7 Article

Experiment and modeling for flux and permeate concentration of heavy metal ion in adsorptive membrane filtration using a metal-organic framework incorporated nanofibrous membrane

期刊

CHEMICAL ENGINEERING JOURNAL
卷 352, 期 -, 页码 737-744

出版社

ELSEVIER SCIENCE SA
DOI: 10.1016/j.cej.2018.07.077

关键词

Membrane adsorption; Modeling; Flux prediction; Prediction of permeate concentration; Transmembrane pressure difference

资金

  1. Natural Sciences and Engineering Council (NSERC) of Canada [463039-2014]

向作者/读者索取更多资源

The environmental consequences of lead ion accumulation have been linked to detrimental health impacts in humans. Hence, removal of heavy metal (lead, Pb) ions by membrane adsorption/filtration was studied in this work using nanofibrous membranes in which the adsorbent metal-organic framework, MOF-808, was embedded. S-shaped breakthrough curves were obtained experimentally when the heavy metal concentration in the permeate was plotted vs the filtration period. Simple model equations that enable the reproduction of the S-shaped breakthrough curve were derived. It was found that the model equations could simulate the experimental data reasonably well. Attempts were further made to correlate the parameters involved in the model equations to the properties of mixed matrix nanofibrous membranes, including the pore size and pore size distribution, membrane thickness, fiber diameter, the adsorption rate constant, the Langmuir adsorption constant and the maximum adsorption capacity. The model equation parameters were also correlated to the operating conditions including the heavy metal concentration in the feed and the transmembrane pressure difference. It is believed that the model equations, despite this simplicity, can provide deeper insight into the membrane adsorption/filtration phenomena. These equations also contribute to the process design for successful removal of heavy metal ions from the environment to improve health factors for humans.

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

4.7
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