化工进展 ›› 2023, Vol. 42 ›› Issue (1): 148-158.DOI: 10.16085/j.issn.1000-6613.2022-0539
收稿日期:
2022-04-01
修回日期:
2022-05-12
出版日期:
2023-01-25
发布日期:
2023-02-20
通讯作者:
张磊
作者简介:
王璐(1997—),女,硕士研究生,研究方向为化工系统工程。E-mail:877907576@qq.com。
基金资助:
WANG Lu(), ZHANG Lei(), DU Jian
Received:
2022-04-01
Revised:
2022-05-12
Online:
2023-01-25
Published:
2023-02-20
Contact:
ZHANG Lei
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。
中图分类号:
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.
评估指标 | 公式 |
---|---|
摆动能力 | |
吸附选择性 | |
吸附材料选择参数 | |
吸附材料性能分数 | |
再生能力R | R |
表1 吸附材料评价指标
评估指标 | 公式 |
---|---|
摆动能力 | |
吸附选择性 | |
吸附材料选择参数 | |
吸附材料性能分数 | |
再生能力R | R |
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 |
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 |
神经元个数 | 训练集 | 验证集 | 外部测试集 | RMSE统计 | R2统计 | |||
---|---|---|---|---|---|---|---|---|
RMSE | R2 | RMSE | R2 | RMSE | R2 | |||
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 |
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 | |||
---|---|---|---|---|---|---|---|---|
RMSE | R2 | RMSE | R2 | RMSE | R2 | |||
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 |
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 |
神经元个数 | 训练集 | 验证集 | 外部测试集 | RMSE统计 | R2统计 | |||
---|---|---|---|---|---|---|---|---|
RMSE | R2 | RMSE | R2 | RMSE | R2 | |||
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 |
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 |
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 | |||
---|---|---|---|---|---|---|---|---|
RMSE | R2 | RMSE | R2 | RMSE | R2 | |||
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 |
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 |
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 |
沸石 | R | ||||
---|---|---|---|---|---|
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 | R | ||||
---|---|---|---|---|---|
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 |
1 | CHAO Cong, DENG Yimin, DEWIL Raf, et al. Post-combustion carbon capture[J]. Renewable and Sustainable Energy Reviews, 2021, 138: 110490. |
2 | Seongbin GA, JANG Hong, LEE Jay H. New performance indicators for adsorbent evaluation derived from a reduced order model of an idealized PSA process for CO2 capture[J]. Computers & Chemical Engineering, 2017, 102: 188-212. |
3 | WU Luogang, LIU Jiaqi, SHANG Hua, et al. Capture CO2 from N2 and CH4 by zeolite L with different crystal morphology[J]. Microporous and Mesoporous Materials, 2021, 316: 110956. |
4 | 顾玉明. 氮气在分子筛中吸附性质的理论研究[D]. 南京: 南京大学, 2019. |
GU Yuming. Theoretical study on adsorption properties of nitrogen in zeolites[D]. Nanjing: Nanjing University, 2019. | |
5 | Youn Sang BAE, SNURR Randall Q. Development and evaluation of porous materials for carbon dioxide separation and capture[J]. Angewandte Chemie International Edition, 2011, 50(49): 11586-11596. |
6 | LEPERI Karson, CHUNG Yongchul G, YOU Fengqi, et al. Development of a general evaluation metric for rapid screening of adsorbent materials for postcombustion CO2 capture[J]. ACS Sustainable Chemistry & Engineering, 2019, 7(13): 11529-11539. |
7 | TONG Minman, LAN Youshi, YANG Qingyuan, et al. High-throughput computational screening and design of nanoporous materials for methane storage and carbon dioxide capture[J]. Green Energy & Environment, 2018, 3(2): 107-119. |
8 | WIERSUM Andrew D, CHANG Jong San, SERRE Christian, et al. An adsorbent performance indicator as a first step evaluation of novel sorbents for gas separations: application to metal-organic frameworks[J]. Langmuir, 2013, 29(10): 3301-3309. |
9 | CHUNG Yongchul G, GÓMEZ-GUALDRÓN Diego A, LI Peng, et al. In silico discovery of metal-organic frameworks for precombustion CO2 capture using a genetic algorithm[J]. Science Advances, 2016, 2(10): e1600909. |
10 | RAHMATI Mahmoud, MODARRESS Hamid. Grand canonical Monte Carlo simulation of isotherm for hydrogen adsorption on nanoporous siliceous zeolites at room temperature[J]. Applied Surface Science, 2009, 255(9): 4773-4778. |
11 | LIN Shiru, WANG Yekun, ZHAO Yinghe, et al. Machine-learning-assisted screening of pure-silica zeolites for effective removal of linear siloxanes and derivatives[J]. Journal of Materials Chemistry A, 2020, 8(6): 3228-3237. |
12 | 毛海涛. 混合机器学习与机理建模的香精产品设计[D]. 大连: 大连理工大学, 2020. |
MAO Haitao. Fragrance product design with A hybrid machine learning and mechanistic modeling approach[D]. Dalian: Dalian University of Technology, 2020. | |
13 | WANG Sunchong. Interdisciplinary computing in Java programming[M]//Springer Science+Business Media. New York: Kluwer Academic Publishers, 2003: 81. |
14 | LIU Yue, ZHAO Tianlu, JU Wangwei, et al. Materials discovery and design using machine learning[J]. Journal of Materiomics, 2017, 3(3): 159-177. |
15 | MALEKIAN Arash, CHITSAZ Nastaran. Concepts, procedures, and applications of artificial neural network models in streamflow forecasting[M]//Advances in Streamflow Forecasting. Amsterdam: Elsevier, 2021: 115-147. |
16 | ROSENBLATT F. The perceptron: a perceiving and recognizing automaton[R]. New York: Cornell Aeronautical Lab, 1957. |
17 | WERBOS Paul J. Applications of advances in nonlinear sensitivity analysis[M]//System Modeling and Optimization. Heidelberg: Springer-Verlag, 2005: 762-770. |
18 | RUMELHART David E, HINTON Geoffrey E, WILLIAMS Ronald J. Learning internal representations by error propagation[R]. Defense Technical Information Center, 1985. |
19 | BROOMHEAD D, LOWE D. Multivariable functional interpolation and adaptative networks[J]. Complex Systems, 1988, 2: 321-355. |
20 | ELMAN Jeffrey L. Finding structure in time[J]. Cognitive Science, 1990, 14(2): 179-211. |
21 | YANN LeCun, YOSHUA Bengio. Convolutional networks for images, speech, and time-series[J]. The Handbook of Brain Theory and Neural Networks, 1995: 255-258. |
22 | HINTON Geoffrey E, OSINDERO Simon, Yee Whye TEH. A fast learning algorithm for deep belief nets[J]. Neural Computation, 2006, 18(7): 1527-1554. |
23 | IYER Shachit, DEMIREL Salih E, M M Faruque HASAN. Combined natural gas separation and storage based on in silico material screening and process optimization[J]. Industrial & Engineering Chemistry Research, 2018, 57(49): 16727-16750. |
24 | SIMON Cory M, SMIT Berend, HARANCZYK Maciej. pyIAST: ideal adsorbed solution theory (IAST) Python package[J]. Computer Physics Communications, 2016, 200: 364-380. |
25 | ANUROVA Natalia A, BLATOV Vladislav A, ILYUSHIN Gregory D, et al. Natural tilings for zeolite-type frameworks[J]. The Journal of Physical Chemistry C, 2010, 114(22): 10160-10170. |
26 | Structure Commission of the International Zeolite Association (IZA-SC). Database of Zeolite Structures[DB/OL]. [2022-04-01]. . |
27 | GOODFELLOW I, BENGIO Y, COURVILLE A. Deep learning[M]. Cambridge: The MIT Press, 2016. |
28 | MCCULLOCH Warren S, PITTS Walter. A logical calculus of the ideas immanent in nervous activity[J]. The Bulletin of Mathematical Biophysics, 1943, 5(4): 115-133. |
29 | MYERS A L, PRAUSNITZ J M. Thermodynamics of mixed-gas adsorption[J]. AIChE Journal, 1965, 11(1): 121-127. |
30 | HAND D W, LOPER S, ARI M, et al. Prediction of multicomponent adsorption equilibria using ideal adsorbed solution theory[J]. Environmental Science & Technology, 1985, 19(11): 1037-1043. |
31 | BABARAO Ravichandar, HU Zhongqiao, JIANG Jianwen, et al. Storage and separation of CO2 and CH4 in silicalite, C168 schwarzite, and IRMOF-1: a comparative study from Monte Carlo simulation[J]. Langmuir: the ACS Journal of Surfaces and Colloids, 2007, 23(2): 659-666. |
32 | CESSFORD Naomi F, SEATON Nigel A, Tina DÜREN. Evaluation of ideal adsorbed solution theory as a tool for the design of metal-organic framework materials[J]. Industrial & Engineering Chemistry Research, 2012, 51(13): 4911-4921. |
33 | ROTHER J, FIEBACK T. Multicomponent adsorption measurements on activated carbon, zeolite molecular sieve and metal-organic framework[J]. Adsorption, 2013, 19(5): 1065-1074. |
34 | FRENKEL D, SMIT B. Understanding molecular simulation second edition from algorithms to applications computational science series vol 1[M]. Academic Press, Inc, 2001. |
35 | WILMER Christopher E, FARHA Omar K, Youn Sang BAE, et al. Structure-property relationships of porous materials for carbon dioxide separation and capture[J]. Energy & Environmental Science, 2012, 5(12): 9849-9856. |
36 | SUMER Zeynep, KESKIN Seda. Ranking of MOF adsorbents for CO2 separations: a molecular simulation study[J]. Industrial & Engineering Chemistry Research, 2016, 55(39): 10404-10419. |
37 | DUBBELDAM David, CALERO Sofía, ELLIS Donald E, et al. RASPA: molecular simulation software for adsorption and diffusion in flexible nanoporous materials[J]. Molecular Simulation, 2016, 42(2): 81-101. |
38 | GARCÍA-PÉREZ E, PARRA J B, ANIA C O, et al. A computational study of CO2, N2, and CH4 adsorption in zeolites[J]. Adsorption, 2007, 13(5/6): 469-476. |
39 | HASSE DAVID J, KULKARNI SUDHIR S, SANDERS EDGAR S JR, et al. Method of obtaining carbon dioxide from a carbon dioxide-containing gas mixture by means of a membrane and condensing: EP2512623[P]. 2016-11-02. |
40 | 袁俊鹏, 刘秀英, 李晓东, 等. 沸石分子筛对CH4/H2的吸附与分离性能[J]. 物理学报, 2021, 70(15): 209-218. |
YUAN Junpeng, LIU Xiuying, LI Xiaodong, et al. Molecular simulation for adsorption and separation of CH4/H2 in zeolites[J]. Acta Physica Sinica, 2021, 70(15): 209-218. |
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