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）

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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

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