机器学习高效筛选用于CO2/N2选择性吸附分离的沸石材料

doi:10.16085/j.issn.1000-6613.2022-0539

摘要/Abstract

摘要：

目前，针对气体吸附性能的测定及材料设计筛选，传统的实验法耗时耗力，因此分子力学方法中的巨正则蒙特卡洛（GCMC）方法已被广泛应用于该领域中，但日益增长的材料数目使得GCMC方法的计算量越来越高。为解决这一问题，本文提出了一种基于机器学习（ML）方法的吸附材料的筛选框架，包含ML模型的建立、理想化PSA工艺模型筛选材料及GCMC方法的验证三个阶段。首先，建立人工神经网络模型，提出了沸石材料的结构描述符“天然构造单元（NBU）”对特定条件下的气体吸附量进行预测。对于CO₂和N₂气体，分别构建了两个拓扑结构不同的多层前馈神经网络。其次，通过理想吸附溶液理论（IAST）将纯组分的吸附等温线转化为摩尔分数为0.14/0.86的CO₂/N₂二元混合物吸附等温线，并根据一系列吸附材料评估指标筛选出11种最佳沸石材料，并从中选出4种沸石（MON、ABW、NAB和VSV）计算其GCMC的吸附数据。结果表明，它们对N₂的吸附能力远低于CO₂，因此对两种气体的吸附选择性较高，能够很好地从二元混合物中分离CO₂。

关键词: 机器学习, 神经网络, 二氧化碳捕集, 沸石, 巨正则蒙特卡洛

Abstract:

At present, in the determination of gas adsorption performance and material design screening, the traditional experiments consume time and effort, so Grand Canonical Monte Carlo (GCMC) method in molecular mechanics has been widely used. However, the growing number of materials makes the GCMC method more and more computationally intensive, and a framework for screening adsorption materials based on machine learning (ML) method was proposed to solve this problem. The framework included three stages: the building of ML model, material selection of idealized PSA process model and validation using GCMC method. Firstly, artificial neural network models were established, and the structure descriptors "natural building unit (NBU)" of zeolite materials was proposed to predict the adsorption capacity under certain conditions. For CO₂ and N₂, two multi-layer feed-forward neural networks with different topological structures were built. Secondly, the ideal adsorbed solution theory (IAST) can predict the mixture adsorption isotherms of CO₂/N₂ (mole fractions is 0.14/0.86) from pure-component adsorption isotherms, and then 11 "best" zeolite materials were selected by some adsorbent evaluation metrics. Four zeolite materials (MON, ABW, NAB and VSV) were selected and calculate their adsorption data using GCMC method. The results proved that their adsorption capacity for N₂ was much lower than that for CO₂, so they had high adsorption selectivity for the two gases and can well separate CO₂ from binary mixtures.

Key words: machine learning, neural networks, CO₂ capture, zeolite, grand canonical Monte Carlo

中图分类号:

TQ021.8

王璐, 张磊, 都健. 机器学习高效筛选用于CO₂/N₂选择性吸附分离的沸石材料[J]. 化工进展, 2023, 42(1): 148-158.

WANG Lu, ZHANG Lei, DU Jian. High-throughput screening of zeolite materials for CO₂/N₂ selective adsorption separation by machine learning[J]. Chemical Industry and Engineering Progress, 2023, 42(1): 148-158.

图/表 19

图1 基于机器学习的沸石吸附材料筛选框架

图2 LTA沸石骨架示意图

图3 机器学习模型构建流程图

图4 沸石吸附数据的来源组成

图5 模型建立流程

图6 三层前馈神经网络结构示意图

图7 M-P神经元模型

图8 理想化PSA工艺模型示意图

图9 WEI在298K和35bar总压下CO2/N2混合物平衡时的IAST验证

表1 吸附材料评价指标

评估指标	公式
摆动能力 $∆ Q C O 2$ /mol·kg^-1	$∆ Q C O 2$ = $Q C O 2 a d s$ $- Q C O 2$ ^des
吸附选择性 $S a d s, C O 2 / N 2 a d s$	$S a d s, C O 2 / N 2 a d s$ $= Q C O 2 a d s / Q / N 2 a d s y C O 2 / y N 2$
吸附材料选择参数 $S S P, C O 2 / N 2$	$S S P, C O 2 / N 2 = (S a d s a d s, C O 2 / N 2) 2 ∆ Q C O 2 (S a d s d e s, C O 2 / N 2) ∆ Q N 2$
吸附材料性能分数 $A P S C O 2 / N 2$ /mol·kg^-1	$A P S C O 2 / N 2 = S a d s, C O 2 / N 2 a d s$ $∆ Q C O 2$
再生能力R	R $=$ ( $∆ Q C O 2 / Q C O 2 a d s$ ) $× 100$ %

表1 吸附材料评价指标

评估指标	公式
摆动能力 $∆ Q C O 2$ /mol·kg^-1	$∆ Q C O 2$ = $Q C O 2 a d s$ $- Q C O 2$ ^des
吸附选择性 $S a d s, C O 2 / N 2 a d s$	$S a d s, C O 2 / N 2 a d s$ $= Q C O 2 a d s / Q / N 2 a d s y C O 2 / y N 2$
吸附材料选择参数 $S S P, C O 2 / N 2$	$S S P, C O 2 / N 2 = (S a d s a d s, C O 2 / N 2) 2 ∆ Q C O 2 (S a d s d e s, C O 2 / N 2) ∆ Q N 2$
吸附材料性能分数 $A P S C O 2 / N 2$ /mol·kg^-1	$A P S C O 2 / N 2 = S a d s, C O 2 / N 2 a d s$ $∆ Q C O 2$
再生能力R	R $=$ ( $∆ Q C O 2 / Q C O 2 a d s$ ) $× 100$ %

表2 GCMC模拟参数设置

参数	值
模拟类型	MonteCarlo
平衡数	50000
循环周期数	50000
力场类型	GarciaPerez2006
晶格单位	3×3×3
组分	CO₂	N₂
分子类型	TraPPE	TraPPE
摩尔分数	0.14	0.86
平移概率	0.5	0.5
旋转概率	0.5	0.5
重插概率	0.5	0.5
交换概率	1	1

表3 SAR模型的数据划分

数据集	CO₂	N₂
训练集	4704	4848
验证集	588	606
测试集	589	604
可用	5881	6058
空白值	257	80
统计	6138	6138

表4 CO2的模型性能

神经元个数	训练集		验证集		外部测试集		RMSE统计	R²统计
神经元个数	RMSE	R²	RMSE	R²	RMSE	R²	RMSE统计	R²统计
5	0.4227	0.9968	0.1995	0.9940	0.1601	0.9963	0.7823	0.9957
7	0.3028	0.9992	0.1281	0.9968	0.1252	0.9984	0.5562	0.9981
9	0.1216	0.9996	0.1256	0.9972	0.0834	0.9989	0.3305	0.9986
11	0.3681	0.9993	0.2662	0.9973	0.1219	0.9979	0.7562	0.9982
8	0.2360	0.9980	0.2051	0.9913	0.1171	0.9974	0.5581	0.9956
10	0.3638	0.9966	0.2632	0.9941	0.2611	0.9948	0.8881	0.9952

表5 N2的模型性能

神经元个数	训练集		验证集		外部测试集		RMSE统计	R²统计
神经元个数	RMSE	R²	RMSE	R²	RMSE	R²	RMSE统计	R²统计
7	0.0130	0.9991	0.0571	0.9972	0.0316	0.9966	0.1018	0.9976
8	0.0791	0.9983	0.0507	0.9959	0.0565	0.9958	0.1863	0.9967
9	0.0326	0.9996	0.0219	0.9978	0.0270	0.9970	0.0814	0.9981
10	0.0522	0.9997	0.0332	0.9984	0.0429	0.9975	0.1283	0.9985
11	0.1516	0.9979	0.0703	0.9962	0.0514	0.9955	0.2733	0.9965
12	0.0317	0.9997	0.0372	0.9985	0.0271	0.9975	0.0960	0.9986
13	0.0176	0.9997	0.0228	0.9983	0.0284	0.9978	0.0689	0.9986
14	0.0289	0.9998	0.0214	0.9985	0.0270	0.9975	0.0773	0.9986
15	0.1264	0.9990	0.0710	0.9941	0.0680	0.9958	0.2654	0.9963

图10 CO2及N2的训练集、验证集和外部测试集的训练结果

图11 吸附材料评估指标筛选材料

表6 筛选得到的不同组别沸石列表

Ⅰ组	Ⅱ组	Ⅲ组	Ⅳ组
ABW、APC、LOV、MER、MON、NAB、PHI、PUN	AEI、AFY、RHO、SAV	AEN、AFG、AFI、AFN、ASV、ATT、BIK、ESV、HEU、MTW、MVY、NSI、RRO、RWR、UEI、YUG	GIS、SIV、WEI

表7 Ⅰ组沸石的吸附材料指标数值

沸石	$∆ Q C O 2$	R	$S a d s, C O 2 / N 2 a d s$	$S S P, C O 2 / N 2$	$A P S C O 2 / N 2$
MON	4.494542	79.02956	191.4463	5732.045	860.4634
ABW	3.279881	81.96941	133.9630	2715.545	439.3827
APC	3.089023	81.20606	131.6266	2570.451	406.5975
PHI	3.488620	81.73907	102.9267	2181.423	359.0721
PUN	4.489790	89.73103	78.15195	1797.634	350.8858
NAB	4.182718	94.98367	76.82922	1403.537	321.3550
MER	3.277813	83.61190	89.87160	1762.165	294.5823
LOV	3.614247	86.22495	63.68264	770.8370	230.1648
机器学习模型补充数据筛选得到的沸石：
VSV	4.588631	94.35575	83.24993	1971.854	382.0032
RSN	4.073608	91.19332	72.17017	1260.089	293.9930
KFI	3.447833	89.05406	62.91092	871.5884	216.9064

表7 Ⅰ组沸石的吸附材料指标数值

沸石	$∆ Q C O 2$	R	$S a d s, C O 2 / N 2 a d s$	$S S P, C O 2 / N 2$	$A P S C O 2 / N 2$
MON	4.494542	79.02956	191.4463	5732.045	860.4634
ABW	3.279881	81.96941	133.9630	2715.545	439.3827
APC	3.089023	81.20606	131.6266	2570.451	406.5975
PHI	3.488620	81.73907	102.9267	2181.423	359.0721
PUN	4.489790	89.73103	78.15195	1797.634	350.8858
NAB	4.182718	94.98367	76.82922	1403.537	321.3550
MER	3.277813	83.61190	89.87160	1762.165	294.5823
LOV	3.614247	86.22495	63.68264	770.8370	230.1648
机器学习模型补充数据筛选得到的沸石：
VSV	4.588631	94.35575	83.24993	1971.854	382.0032
RSN	4.073608	91.19332	72.17017	1260.089	293.9930
KFI	3.447833	89.05406	62.91092	871.5884	216.9064

图12 GCMC模拟验证候选沸石的混合物吸附等温线

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机器学习高效筛选用于CO₂/N₂选择性吸附分离的沸石材料

High-throughput screening of zeolite materials for CO₂/N₂ selective adsorption separation by machine learning

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