Machine learning-assisted high-throughput screening approach for CO2 separation from CO2-rich natural gas using metal-organic frameworks

被引：0

作者：

Zhou, Yinjie ^{[1
]}

Ji, Sibei ^{[1
]}

He, Songyang ^{[1
]}

Ji, Xu ^{[1
]}

He, Ge ^{[1
]}

机构：

[1] School of Chemical Engineering, Sichuan University, Sichuan, Chengdu

来源：

Huagong Xuebao/CIESC Journal | 2025年 / 76卷 / 03期

关键词：

CO[!sub]2[!/sub] separation; CO[!sub]2[!/sub]-rich natural gas; high-throughput screening; machine learning; metal-organic frameworks; molecular simulation;

D O I：

10.11949/0438-1157.20241001

中图分类号：

学科分类号：

摘要：

Driven by the goal of carbon dioxide peaking and carbon neutrality, it is of great social and economic significance to develop green chemical technologies, such as the substantial use of H2 generated by water electrolysis with offshore wind power and CO2 separated from CO2-rich natural gas to produce green methanol is gaining significant socioeconomic and environmental relevance. However, how to efficiently separate carbon dioxide from marine carbon-rich natural gas has become a key technical difficulty. Conventional high-throughput screening methods for metal organic frameworks (MOFs) to separate actual natural gas component CO2 face the problems of high model complexity and long solution time. Therefore, a machine learning-assisted high-throughput screening strategy is proposed. The R2 values on the training set and the test set are more than 0.98 and 0.92, respectively, which can be used to quickly and efficiently separate CO2 from the actual natural gas of six components (N2, CO2, CH4, C2H6, C3H8, H2S). © 2025 Materials China. All rights reserved.

引用

页码：1093 / 1101

页数：8

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