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

doi:10.11949/0438-1157.20241001

Abstract

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, C₂H₆, C₃H₈, H₂S).

Key words: metal-organic frameworks, high-throughput screening, CO₂ separation, machine learning, molecular simulation, CO₂-rich natural gas

摘要：

在碳达峰和碳中和目标的推动下，开发绿色化学技术，如利用海上风电电解水生产的绿色氢气和从富碳天然气中分离出来的CO₂合成绿甲醇具有重要的社会经济意义。但如何高效分离海洋富碳天然气中的二氧化碳成为其中的关键技术难点，常规的高通量筛选方法用于金属有机骨架（MOFs）分离实际天然气组分CO₂面临着模型复杂性高、求解时间长的问题。提出了一种机器学习辅助的高通量筛选策略，其在训练集和测试集上的R²值分别超过了0.98和0.92，可用于快速从富碳天然气六元混合物（N₂、CO₂、CH₄、C₂H₆、C₃H₈、H₂S）中分离出CO₂。

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

CLC Number:

Yinjie ZHOU, Sibei JI, Songyang HE, Xu JI, Ge HE. Machine learning-assisted high-throughput screening approach for CO₂ separation from CO₂-rich natural gas using metal-organic frameworks[J]. CIESC Journal, 2025, 76(3): 1093-1101.

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.

Figures/Tables 9

Fig.1 The workflow illustration for screening high-performance MOFs for CO2 separation

Table 1 Lennard-Jones potential energy parameter and atomic charge of adsorbed material

Atom	(ε/k_B)/K	σ/Å	Charge
C_CO₂	27.00	2.80	0.700
O_CO₂	79.00	3.05	-0.350
H_H₂S	50.00	2.50	0.210
S_H₂S	122.00	3.60	0
M_H₂S	0	0	-0.420
N_N₂	36.00	3.31	-0.482
N_COM	0	0	0.964
CH₄	148.00	3.73	0
CH₃	98.00	3.75	0
CH₂	46.00	3.95	0

Table 2 Optimal hyperparameters identified by the grid search method using five-fold cross-validation

Hyperparameter	Hyperparameter space	Optimal value
n_estimators	range(100, 501, 50)	200
max_depth	range(5, 51, 5)	20
min_samples_split	range(2, 12, 2)	2
min_samples_leaf	range(1, 6, 1)	1
criterion	[‘gini’, ‘entropy’]	‘gini’
max_features	[‘auto’, ‘sqrt’, ‘log2’]	‘auto’
class_weight	[‘balanced’]	‘balanced’

Table 3 Performance of RF models in predicting R for CO2 separation

Model	Descriptor type	Training set			Test set
Model	Descriptor type	R²	MAE/(mol·kg^-1)	RMSE/(mol·kg^-1)	R²	MAE/(mol·kg^-1)	RMSE/(mol·kg^-1)
R/%	structural	0.911	0.049	0.071	0.422	0.123	0.184
	chemical	0.983	0.019	0.031	0.898	0.047	0.078
	both	0.986	0.017	0.028	0.922	0.040	0.069

Fig.2 Comparison of predicted R% values with GCMC simulated R% values

Fig.3 The distribution of MOFs in performance metrics

Table 4 Top 10 high-performance MOFs selected according to their structural characteristics and separation performance

CCDC	MOF	LCD/Å	VSA/(m²·cm^-3)	VF	$Δ N_{C O_{2}}$ /(mol·kg^-1)	$S_{C O_{2} / S C}$	TSN/(mol·kg^-1)	R/%
183282	XIGWUF	7.07	3242.86	0.70	7.72	39.74	12.34	88.92
1501708	ETECOX	10.31	1753.04	0.76	5.74	28.92	8.39	90.02
1432726	MAGNEQ	5.31	1670.44	0.52	4.29	50.76	7.32	85.05
808299	NAQRAA	8.82	1966.76	0.65	4.09	33.87	6.26	86.65
264131	PARHAS	6.77	1866.99	0.64	4.41	15.10	5.20	90.88
1494751	ORIVUI	11.75	1483.49	0.71	3.69	21.73	4.94	86.96
791072	RUZDAS	5.27	891.53	0.37	2.73	52.63	4.70	86.76
785296	CUSDIE	13.10	1619.70	0.64	3.65	16.64	4.46	86.62
264132	PARHEW	7.39	2084.34	0.69	3.44	12.48	3.77	91.21
239782	FIFNUE	4.75	943.07	0.46	3.25	14.52	3.77	87.19

Table 4 Top 10 high-performance MOFs selected according to their structural characteristics and separation performance

CCDC	MOF	LCD/Å	VSA/(m²·cm^-3)	VF	$Δ N_{C O_{2}}$ /(mol·kg^-1)	$S_{C O_{2} / S C}$	TSN/(mol·kg^-1)	R/%
183282	XIGWUF	7.07	3242.86	0.70	7.72	39.74	12.34	88.92
1501708	ETECOX	10.31	1753.04	0.76	5.74	28.92	8.39	90.02
1432726	MAGNEQ	5.31	1670.44	0.52	4.29	50.76	7.32	85.05
808299	NAQRAA	8.82	1966.76	0.65	4.09	33.87	6.26	86.65
264131	PARHAS	6.77	1866.99	0.64	4.41	15.10	5.20	90.88
1494751	ORIVUI	11.75	1483.49	0.71	3.69	21.73	4.94	86.96
791072	RUZDAS	5.27	891.53	0.37	2.73	52.63	4.70	86.76
785296	CUSDIE	13.10	1619.70	0.64	3.65	16.64	4.46	86.62
264132	PARHEW	7.39	2084.34	0.69	3.44	12.48	3.77	91.21
239782	FIFNUE	4.75	943.07	0.46	3.25	14.52	3.77	87.19

Fig.4 Crystal structure of the top 10 high-performance CO2 separation

Fig.5 Relationship plots of (a)PLD~Qst0~ΔNCO2， (b) S~LCD~PLD， (c) TSN~ρ~GSA， (d) LCD~ρ~PLD for 2731 MOFs with R≥85%（the color from red to blue refers to the coded value from low to high）

References 34

1	唐春, 周乐懿, 李东升, 等. 绿氢制绿甲醇的技术经济可行性分析[J]. 世界石油工业, 2024, 31(1): 92-99.
	Tang C, Zhou L Y, Li D S, et al. Technical and economic analysis of green hydrogen for green methanol synthesis[J]. World Petroleum Industry, 2024, 31(1): 92-99.
2	Din I U, Alotaibi M A, Alharthi A I, et al. Green synthesis approach for preparing zeolite based Co-Cu bimetallic catalysts for low temperature CO₂ hydrogenation to methanol[J]. Fuel, 2022, 330: 125643.
3	季东, 王健, 王可, 等. 不同CO₂捕集技术的CO₂耦合绿氢制甲醇工艺研究[J]. 化工学报, 2022, 73(10): 4565-4575.
	Ji D, Wang J, Wang K, et al. Process research of methanol production by CO₂ coupled green hydrogen with different CO₂ capture technologies[J]. CIESC Journal, 2022, 73(10): 4565-4575.
4	Wang D L, Meng W L, Zhou H R, et al. Green hydrogen coupling with CO₂ utilization of coal-to-methanol for high methanol productivity and low CO₂ emission[J]. Energy, 2021, 231: 120970.
5	王江涛, 鹿晓斌. CO₂促进"甲醇经济"与"氢经济"共同发展[J]. 现代化工, 2021, 41(7): 14-18, 25.
	Wang J T, Lu X B. Together development of "methanol economy" and "hydrogen economy" driven by CO₂ utilization[J]. Modern Chemical Industry, 2021, 41(7): 14-18, 25.
6	文一如, 付佳, 刘大欢. 基于机器学习的MOFs材料研究进展: 能源气体吸附分离[J]. 化工学报, 2024, 75(4): 1370-1381.
	Wen Y R, Fu J, Liu D H. Advances in machine learning-based materials research for MOFs: energy gas adsorption separation[J]. CIESC Journal, 2024, 75(4): 1370-1381.
7	王玉杰, 李申辉, 赵之平. M-MOF-74吸附分离H₂/He混合物的分子模拟研究[J]. 化工学报, 2022, 73(10): 4507-4517.
	Wang Y J, Li S H, Zhao Z P. Molecular simulation study on adsorption and separation of H₂/He mixtures by M-MOF-74[J]. CIESC Journal, 2022, 73(10): 4507-4517.
8	李建惠, 兰天昊, 陈杨, 等. MOF复合材料在气体吸附分离中的研究进展[J]. 化工学报, 2021, 72(1): 167-179.
	Li J H, Lan T H, Chen Y, et al. Research progress of MOF-based composites for gas adsorption and separation[J]. CIESC Journal, 2021, 72(1): 167-179.
9	Aghaji M Z, Fernandez M, Boyd P G, et al. Quantitative structure-property relationship models for recognizing metal organic frameworks (MOFs) with high CO₂ working capacity and CO₂/CH₄ selectivity for methane purification[J]. European Journal of Inorganic Chemistry, 2016, 2016(27): 4505-4511.
10	Gao W Q, Zheng W Z, Yan K X, et al. Accelerating the discovery of acid gas-selective MOFs for natural gas purification: a combination of machine learning and molecular fingerprint[J]. Fuel, 2023, 350: 128757.
11	Haldoupis E, Nair S, Sholl D S. Efficient calculation of diffusion limitations in metal organic framework materials: a tool for identifying materials for kinetic separations[J]. Journal of the American Chemical Society, 2010, 132(21): 7528-7539.
12	Watanabe T, Sholl D S. Accelerating applications of metal-organic frameworks for gas adsorption and separation by computational screening of materials[J]. Langmuir, 2012, 28(40): 14114-14128.
13	Wilmer C E, Farha O K, Bae Y S, et al. Structure-property relationships of porous materials for carbon dioxide separation and capture[J]. Energy & Environmental Science, 2012, 5(12): 9849.
14	Laha S, Dwarkanath N, Sharma A, et al. Tailoring a robust Al-MOF for trapping C₂H₆ and C₂H₂ towards efficient C₂H₄ purification from quaternary mixtures[J]. Chemical Science, 2022, 13(24): 7172-7180.
15	Yuan J P, Liu X Y, Li X D, et al. Computer simulations for the adsorption and separation of CH₄/H₂/CO₂/N₂ gases by hybrid ultramicroporous materials[J]. Materials Today Communications, 2021, 26: 101987.
16	杨文远, 梁红, 乔智威. 高通量筛选金属-有机框架: 分离天然气中的硫化氢和二氧化碳[J]. 化学学报, 2018, 76(10): 785-792.
	Yang W Y, Liang H, Qiao Z W. High-throughput screening of metal-organic frameworks for the separation of hydrogen sulfide and carbon dioxide from natural gas[J]. Acta Chimica Sinica, 2018, 76(10): 785-792.
17	Chung Y G, Haldoupis E, Bucior B J, et al. Advances, updates, and analytics for the computation-ready, experimental metal-organic framework database: core MOF 2019[J]. Journal of Chemical & Engineering Data, 2019, 64(12): 5985-5998.
18	Willems T F, Rycroft C H, Kazi M, et al. Algorithms and tools for high-throughput geometry-based analysis of crystalline porous materials[J]. Microporous and Mesoporous Materials, 2012, 149(1): 134-141.
19	Li J R, Kuppler R J, Zhou H C. Selective gas adsorption and separation in metal-organic frameworks[J]. Chemical Society Reviews, 2009, 38(5): 1477.
20	Jablonka K M, Ongari D, Moosavi S M, et al. Big-data science in porous materials: materials genomics and machine learning[J]. Chemical Reviews, 2020, 120(16): 8066-8129.
21	Xue X Y, Cheng M, Wang S H, et al. High-throughput screening of metal-organic frameworks assisted by machine learning: propane/propylene separation[J]. Industrial & Engineering Chemistry Research, 2023, 62(2): 1073-1084.
22	Dubbeldam D, Calero S, Ellis D E, et al. RASPA: molecular simulation software for adsorption and diffusion in flexible nanoporous materials[J]. Molecular Simulation, 2016, 42(2): 81-101.
23	Qing W. Study on technology clusters for direct utilization of CO₂-rich natural gas and construction of hybrid system for energy and chemicals production[J]. China Pet Process Petrochemical Technol, 2020, 22(2): 1-9.
24	Cheng M, Wang S H, Zhang Z Y, et al. High-throughput virtual screening of metal-organic frameworks for xenon recovery from exhaled anesthetic gas mixture[J]. Chemical Engineering Journal, 2023, 451: 138218.
25	Shah M S, Tsapatsis M, Siepmann J I. Development of the transferable potentials for phase equilibria model for hydrogen sulfide[J]. The Journal of Physical Chemistry B, 2015, 119(23): 7041-7052.
26	Shah M S, Tsapatsis M, Siepmann J I. Identifying optimal zeolitic sorbents for sweetening of highly sour natural gas[J]. Angewandte Chemie International Edition, 2016, 55(20): 5938-5942.
27	Greathouse J A, Allendorf M D. Force field validation for molecular dynamics simulations of IRMOF-1 and other isoreticular zinc carboxylate coordination polymers[J]. The Journal of Physical Chemistry C, 2008, 112(15): 5795-5802.
28	Haldoupis E, Watanabe T, Nair S, et al. Quantifying large effects of framework flexibility on diffusion in MOFs: CH₄ and CO₂ in ZIF-8[J]. ChemPhysChem, 2012, 13(15): 3449-3452.
29	Pérez-Pellitero J, Amrouche H, Siperstein F, et al. Adsorption of CO₂, CH₄, and N₂ on zeolitic imidazolate frameworks: experiments and simulations[J]. Chemistry-A European Journal, 2010, 16(5): 1560-1571.
30	Rappe A K, Casewit C J, Colwell K S, et al. UFF, a full periodic table force field for molecular mechanics and molecular dynamics simulations[J]. Journal of the American Chemical Society, 1992, 114(25): 10024-10035.
31	Aprea P, Caputo D, Gargiulo N, et al. Modeling carbon dioxide adsorption on microporous substrates: comparison between Cu-BTC metal-organic framework and 13X zeolitic molecular sieve[J]. Journal of Chemical & Engineering Data, 2010, 55(9): 3655-3661.
32	Peng D Y, Robinson D B. A new two-constant equation of state[J]. Industrial & Engineering Chemistry Fundamentals, 1976, 15(1): 59-64.
33	Pedregosa F, Varoquaux G, Gramfort A, et al. Scikit-learn: machine learning in python[J]. Journal of Machine Learning Research, 2011, 12: 2825-2830.
34	Jung Y, Hu J H. A K-fold averaging cross-validation procedure[J]. Journal of Nonparametric Statistics, 2015, 27(2): 167-179.

[1]	Yaqi HOU, Wei ZHANG, Hong ZHANG, Feiyu GAO, Jiahua HU. Optimization of LBM multiphase flow models based on machine learning and particle swarm algorithm [J]. CIESC Journal, 2025, 76(3): 1120-1132.
[2]	Haijun FENG, Bingxuan ZHANG, Jian ZHOU. Predicting and interpreting the toxicity of ionic liquids using graph neural network [J]. CIESC Journal, 2025, 76(1): 93-106.
[3]	Siwen ZHANG, Haiming GU, Shanhui ZHAO. Molecular mechanism study on chemical looping gasification of cellulose over iron oxide nanocluster [J]. CIESC Journal, 2025, 76(1): 363-373.
[4]	Xinyue WANG, Xiaohu XU, Haiyang ZHANG, Chunhua YIN. Study on encapsulation and properties vitamin A acetate/cyclodextrin [J]. CIESC Journal, 2024, 75(S1): 321-328.
[5]	Yanhui DAI, Qizhao XIONG, Qiang FANG, Dongxiao YANG, Yi WANG, Yang CHEN, Jinping LI, Libo LI. In situ steam-assisted method for one-step synthesis of hierarchically porous Cu-BTC [J]. CIESC Journal, 2024, 75(9): 3329-3337.
[6]	Zheming WU, Biyun ZHANG, Renchao ZHENG. Engineering of nitrilase enantioselectivity for efficient synthesis of brivaracetam [J]. CIESC Journal, 2024, 75(7): 2633-2643.
[7]	Hansong QIN, Guoliang LI, Hao YAN, Xiang FENG, Yibin LIU, Xiaobo CHEN, Chaohe YANG. Theoretical study on the adsorption and diffusion behavior of methyl oleate catalytic cracking in hierarchical ZSM-5 zeolite [J]. CIESC Journal, 2024, 75(5): 1870-1881.
[8]	Dongfei LIU, Fan ZHANG, Zheng LIU, Diannan LU. A review of machine learning potentials and their applications to molecular simulation [J]. CIESC Journal, 2024, 75(4): 1241-1255.
[9]	Zheng ZHANG, Wuqiong WANG, Yajing ZHANG, Kangjun WANG, Yuanhui JI. Research progress in theoretical calculation of pharmaceutical formulation design [J]. CIESC Journal, 2024, 75(4): 1429-1438.
[10]	Yiru WEN, Jia FU, Dahuan LIU. Advances in machine learning-based materials research for MOFs: energy gas adsorption separation [J]. CIESC Journal, 2024, 75(4): 1370-1381.
[11]	Ying LIU, Fang ZHENG, Qiwei YANG, Zhiguo ZHANG, Qilong REN, Zongbi BAO. Recent progress in adsorption and separation of xylene isomers [J]. CIESC Journal, 2024, 75(4): 1081-1095.
[12]	Kang ZHOU, Jianxin WANG, Hai YU, Chaoliang WEI, Fengqi FAN, Xinhao CHE, Lei ZHANG. Foam rupture properties of mineral base oils based on molecular dynamics simulation [J]. CIESC Journal, 2024, 75(4): 1668-1678.
[13]	Yujiao ZENG, Xin XIAO, Gang YANG, Yibo ZHANG, Guangming ZHENG, Fang LI, Fengling WANG. Surrogate modeling and optimization of wet phosphoric acid production process based on mechanism and data hybrid driven [J]. CIESC Journal, 2024, 75(3): 936-944.
[14]	Yuxiang CHEN, Chuanlei LIU, Zijun GONG, Qiyue ZHAO, Guanchu GUO, Hao JIANG, Hui SUN, Benxian SHEN. Machine learning-assisted solvent molecule design for efficient absorption of ethanethiol [J]. CIESC Journal, 2024, 75(3): 914-923.
[15]	Fan WU, Xudong PENG, Jinbo JIANG, Xiangkai MENG, Yangyang LIANG. Study on adaptability of molecular dynamics in predicting density and viscosity of natural gas [J]. CIESC Journal, 2024, 75(2): 450-462.

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

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

RichHTML

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 9

References 34

Related Articles 15

Recommended Articles 0

Metrics

Comments