化工学报2025，Vol.76Issue(3)：1093-1101,9.DOI:10.11949/0438-1157.20241001
机器学习辅助高通量筛选金属有机骨架用于富碳天然气中分离CO2
Machine learning-assisted high-throughput screening approach for CO2 separation from CO2-rich natural gas using metal-organic frameworks
摘要
Abstract
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,C2 H6,C3 H8,H2S).关键词
金属有机骨架/高通量筛选/CO2分离/机器学习/分子模拟/富碳天然气Key words
metal-organic frameworks/high-throughput screening/CO2 separation/machine learning/molecular simulation/CO2-rich natural gas分类
化学化工引用本文复制引用
周印洁,吉思蓓,何松阳,吉旭,贺革..机器学习辅助高通量筛选金属有机骨架用于富碳天然气中分离CO2[J].化工学报,2025,76(3):1093-1101,9.基金项目
国家重点研发计划项目(2021YFB40005) （2021YFB40005）
