化工学报2025，Vol.76Issue(12)：6453-6464,12.DOI:10.11949/0438-1157.20250680
机器学习驱动分子筛基CO吸附剂设计与优化
Machine learning-driven design and optimization of molecular sieve-based efficient CO adsorbents
摘要
Abstract
Zeolite exhibits great potential for CO adsorption due to its well-organized pore structure and tunable chemical composition.However,numerous influencing factors hinder the design and optimization of adsorbents using traditional experimental methods and theoretical calculations.In this paper,a machine learning strategy of combining the structural characteristics of molecular sieve adsorbents with experimental data was proposed,and a comprehensive data set was constructed to express the structure-activity relationship between molecular sieve structure and CO adsorption performance by integrating literature data and zeolite structure information.Four integrated learning algorithms,gradient enhanced decision tree,random forest,extreme random tree and extreme gradient lifting(XGB),were used to predict CO adsorption,and nested cross validation was used to ensure the prediction accuracy of the evaluation model.The results show that XGB model performs best and shows excellent prediction accuracy.The fragmentation analysis of molecular sieve structure characteristics showed that triangular crystal system,Fd3m space group,near circular pores and interconnected cage structure were conducive to CO adsorption.The alkaline earth metals such as Ca2+and Ba2+in the framework of molecular sieve are conducive to the adsorption of CO.In the metal ion modified molecular sieve,the Cu(Ⅰ)loading has the most significant effect on the CO adsorption performance,and the optimal loading is 13％—15％by weight.When the specific surface area of the adsorbent is 230-400 m2/g and the total pore volume is 0.10-0.15 cm3/g,the adsorbent has the best performance.Its limited pore size is around 4.5 Å and the maximum pore size is in the range of 5.0-5.5 Å,showing relatively good CO adsorption performance.This study provides scientific guidance for the rational design and efficient screening of molecular sieve based co adsorbents.关键词
分子筛/吸附/机器学习/一氧化碳/吸附剂设计Key words
molecular sieve/adsorbent/machine learning/carbon monoxide/adsorbent design分类
化学化工引用本文复制引用
陶玟媛,赵文凯,沈海阔,郭强,肖永厚..机器学习驱动分子筛基CO吸附剂设计与优化[J].化工学报,2025,76(12):6453-6464,12.基金项目
辽宁省化学助剂合成与分离重点实验室基金项目(ZJKF2301) （ZJKF2301）
国家自然科学基金项目(21776028) （21776028）
