通过回收C2H6和C3H8来提升天然气的铜基金属有机框架

IF 9.1 Q1 ENGINEERING, CHEMICAL Green Chemical Engineering Pub Date : 2023-03-01 DOI:10.1016/j.gce.2022.04.006
Shao-Min Wang, Qing-Yuan Yang
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引用次数: 4

摘要

天然气处理涉及从甲烷中分离高级烃(C2–C3),这是一项重要的能源密集型操作。在本文中,我们对一种用于从甲烷中分离丙烷和乙烷的创新铜基MOF(Cu-MOF)进行了全面的研究。与甲烷相比,该材料对C2–C3碳氢化合物表现出较高的吸附能力和选择性,这主要是由于其优先吸附C2–C3烃。环境条件下的吸附等温线显示出对C3H8的134.0 cm3/g的显著吸收,以及对C3H8/CH4和C2H6/CH4的204和9的优异选择性。根据理论计算,范德华相互作用和客体分子极化率的差异是影响分离性能的原因。此外,我们还进行了吸附动力学实验、动力学突破和循环实验,以进一步检验分离性能。总体而言,本研究建立了一种用于天然气升级的节能吸附剂。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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A copper-based metal-organic framework for upgrading natural gas through the recovery of C2H6 and C3H8

Natural gas processing involves the separation of higher hydrocarbons (C2–C3) from methane, which is an important and energy-intensive operation. In this paper, we present a comprehensive study of an innovative copper-based MOF (Cu-MOF) for the separation of propane and ethane from methane. The material exhibits a high adsorption capacity and selectivity for C2–C3 hydrocarbons over methane, which is primarily due to its preferential C2–C3 hydrocarbon adsorption. The adsorption isotherms at ambient conditions showed a remarkable uptake of C3H8 of 134.0 cm3/g, as well as excellent selectivity of 204 and 9 for C3H8/CH4 and C2H6/CH4. According to the theoretical calculations, differences in van der Waals interactions and polarizability of the guest molecules were responsible for influencing separation performance. In addition, we conducted adsorption kinetic experiments, dynamic breakthrough, and cycling experiments to further examine the separation performance. Overall, this research establishes an energy-efficient adsorbent for upgrading natural gas.

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来源期刊
Green Chemical Engineering
Green Chemical Engineering Process Chemistry and Technology, Catalysis, Filtration and Separation
CiteScore
11.60
自引率
0.00%
发文量
58
审稿时长
51 days
期刊最新文献
OFC: Outside Front Cover Outside Back Cover Outside Back Cover OFC: Outside Front Cover Integration of physical information and reaction mechanism data for surrogate prediction model and multi-objective optimization of glycolic acid production
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