基于机器学习和优化算法的数据驱动框架预测用于合成气制烯烃的氧化物-沸石基复合催化体系和反应条件

doi:10.1016/S1872-2067(25)64733-4

催化学报 ›› 2025, Vol. 74: 211-227.DOI: 10.1016/S1872-2067(25)64733-4

基于机器学习和优化算法的数据驱动框架预测用于合成气制烯烃的氧化物-沸石基复合催化体系和反应条件

Mansurbek Urol ugli Abdullaev^a^,^b, Woosong Jeon^a, Yun Kang^c, Juhwan Noh^d, Jung Ho Shin^d, Hee-Joon Chun^e^,^*(), Hyun Woo Kim^c^,^*(), Yong Tae Kim^a^,^b^,^*()

^a韩国化学技术研究院氢能与C1气体研究中心, 大田, 韩国
^b科技大学(UST)先进材料与化学工程系, 大田, 韩国
^c光州科学技术院化学系, 光州, 韩国
^d韩国化学技术研究院化学数据驱动研究中心, 大田, 韩国
^e忠南大学化学系, 大田, 韩国

收稿日期:2024-12-30 接受日期:2025-04-15 出版日期:2025-07-18 发布日期:2025-07-20
通讯作者: *电子信箱: hjchun@cnu.ac.kr (H.-J. Chun),hwk@gist.ac.kr (H. W. Kim),ytkim@krict.re.kr (Y. T. Kim).

Data-driven framework based on machine learning and optimization algorithms to predict oxide-zeolite-based composite and reaction conditions for syngas-to-olefin conversion

Mansurbek Urol ugli Abdullaev^a^,^b, Woosong Jeon^a, Yun Kang^c, Juhwan Noh^d, Jung Ho Shin^d, Hee-Joon Chun^e^,^*(), Hyun Woo Kim^c^,^*(), Yong Tae Kim^a^,^b^,^*()

^aHydrogen & C1 Gas Research Center, Korea Research Institute of Chemical Technology, Daejeon 34114, Republic of Korea
^bDepartment of Advanced Materials and Chemical Engineering, University of Science and Technology (UST), Daejeon 34113, Republic of Korea
^cDepartment of Chemistry, Gwangju Institute of Science and Technology (GIST), Gwangju 61005, Republic of Korea
^dChemical Data-Driven Research Center, Korea Research Institute of Chemical Technology, Daejeon 34114, Republic of Korea
^eDepartment of Chemistry, Chungnam National University, Daejeon 34134, Republic of Korea

Received:2024-12-30 Accepted:2025-04-15 Online:2025-07-18 Published:2025-07-20
Contact: *E-mail: hjchun@cnu.ac.kr (H.-J. Chun), hwk@gist.ac.kr (H. W. Kim), ytkim@krict.re.kr (Y. T. Kim).

摘要/Abstract

摘要：

氧化物-沸石基双功能复合材料(OXZEO)已成为合成气直接制烯烃的潜在高效材料. 然而, 反应参数的实验筛选和优化过程耗时耗物. 为了应对该挑战, 本文提出了一种集成机器学习、贝叶斯优化与实验验证的三阶段数据驱动框架. 采用随机森林、梯度提升决策树和极限梯度提升等集成树模型(R² > 0.87), 成功预测出Zn-Zr和Cu-Mg双元混合氧化物为最优OXZEO体系. 实验验证表明, 该体系与HSAPO-34沸石物理混合后, 轻质烯烃时空收率达3.8-4.6 mmol·g^-¹·h^-¹. 密度泛函理论计算揭示了Zn-Zr与Cu-Mg氧化物的活性差异机制. 本研究开发的框架通过机器学习模型解析16个催化剂与反应条件描述符间的构效关系, 其中氧化物/沸石质量比、反应温度和压力是影响催化性能的关键参数. 结合贝叶斯优化实现多目标参数寻优, 为催化过程设计与优化提供了高效工具, 可推广至其他复杂催化体系的研究.

关键词: 合成气制烯烃, 氧化物-沸石基复合物, 机器学习, 贝叶斯优化, 催化剂和反应工程开发, 反应条件优化, 密度泛函理论

Abstract:

Bifunctional oxide-zeolite-based composites (OXZEO) have emerged as promising materials for the direct conversion of syngas to olefins. However, experimental screening and optimization of reaction parameters remain resource-intensive. To address this challenge, we implemented a three-stage framework integrating machine learning, Bayesian optimization, and experimental validation, utilizing a carefully curated dataset from the literature. Our ensemble-tree model (R² > 0.87) identified Zn-Zr and Cu-Mg binary mixed oxides as the most effective OXZEO systems, with their light olefin space-time yields confirmed by physically mixing with HSAPO-34 through experimental validation. Density functional theory calculations further elucidated the activity trends between Zn-Zr and Cu-Mg mixed oxides. Among 16 catalyst and reaction condition descriptors, the oxide/zeolite ratio, reaction temperature, and pressure emerged as the most significant factors. This interpretable, data-driven framework offers a versatile approach that can be applied to other catalytic processes, providing a powerful tool for experiment design and optimization in catalysis.

Key words: Syngas-to-olefin, Oxide-zeolite-based composite, Machine learning, Bayesian optimization, Catalyst and reaction engineering discovery, Reaction condition optimization, Density functional theory

Mansurbek Urol ugli Abdullaev, Woosong Jeon, Yun Kang, Juhwan Noh, Jung Ho Shin, Hee-Joon Chun, Hyun Woo Kim, Yong Tae Kim. 基于机器学习和优化算法的数据驱动框架预测用于合成气制烯烃的氧化物-沸石基复合催化体系和反应条件[J]. 催化学报, 2025, 74: 211-227.

Mansurbek Urol ugli Abdullaev, Woosong Jeon, Yun Kang, Juhwan Noh, Jung Ho Shin, Hee-Joon Chun, Hyun Woo Kim, Yong Tae Kim. Data-driven framework based on machine learning and optimization algorithms to predict oxide-zeolite-based composite and reaction conditions for syngas-to-olefin conversion[J]. Chinese Journal of Catalysis, 2025, 74: 211-227.

×
扫码分享

导出引用管理器 EndNote|Ris|BibTeX

链接本文: https://www.cjcatal.com/CN/10.1016/S1872-2067(25)64733-4

https://www.cjcatal.com/CN/Y2025/V74/I7/211

图/表 12

Fig. 1. Schematic of the overall three-stage workflow: training a generalized ML model and interpretation (1); discovery of the type and optimal amount of mixed oxide catalysts using ML-BO (2); and screening for OXZEO based on the outputs of the first and second stages (3).

Fig. 2. Specifications of data collection.

Table 1 Experimental and predicted light olefin STYs of Zn-Zr and Cu-Mg binary mixed oxides.

Catalyst	Ox/Zeo ratio	WHSV (mL g^-1 h^-1)	Predicted STY (mmol g_cat^-1 h^-1)	Experimental STY^a (mmol g_cat^-1 h^-1)	Standard error ^b	Absolute t-value ^c	p-value ^c
Zn-Zr	0.5	4000	11.2	9.7	0.04	2.27	0.26
Zn-Zr	0.5	8000	8.4	8.1	0.08	0.14	0.91
Cu-Mg	0.5	4000	6.4	0.8	0.02	1.01	0.5
Cu-Mg	0.5	8000	9.8	2.3	0.11	1.08	0.48

Fig. 3. Alluvial diagram of the STO data set, showing metal types (columns 1-3), synthesis method (column 4), and different zeolites (column 5). Each node shows the frequency. Composite catalyst combinations are shown from left to right.

Fig. 4. Box normal plots representing the mean and quartile values, along with the data distribution of the reaction conditions: WHSV (a), reaction temperature (b), H2/CO ratio in the syngas (c), and reaction pressure (d). The x-axis represents reaction parameters, and the y-axis represents the frequency of data distribution.

Fig. 5. Pearson correlation of features represented as a heatmap. The color of the boxes represents the significance of feature correlations, with ρ-values displayed in each box. Larger absolute ρ-values indicate stronger correlations between feature pairs, while smaller absolute values correspond to weaker correlations.

Fig. 6. Regression plots of the space-time yields (STYs) of light olefins predicted by extreme gradient boosting (XGB) (a), random forest (RF) (b), and gradient boosting decision tree (GBDT) (c), showing R2, root-mean-square error (RMSE), and mean absolute error (MAE) for the train and test data sets and the distribution on gridlines. (d) Feature importance of the best model (GBDT) represented based on normalized Shapley additive explanation (SHAP) values.

Fig. 7. Two-way partial dependence plots (PDP) of the reaction parameters on the light olefin STY: Reaction temperature and Ox/Zeo ratio (a), H2/CO ratio and Ox/Zeo ratio (b), reaction temperature and reaction pressure (c), and reaction temperature and WHSV (d). The surfaces are colored according to their relative light olefin STY values (dark green: low; dark red: high).

Fig. 8. STOx data set analysis: (a) Dependence of CO conversion on CO2 selectivity and oxygenate selectivity. Filled stars, triangle, and circles represent Zn-Al oxide, Cu-Zn-Al oxide, Zn-Zr oxide, respectively. (b) Predictions (blue hollow stars), and experimental data (black hollow circles) are represented as functions of CO conversion vs. light olefin selectivity. The color map indicates light olefin yields. Oxygenates include methanol, ketene, and DME.

Fig. 9. The STY values for catalysts derived from experimental data are compared with those of novel catalysts predicted within a defined range of reaction conditions. The experimental data are depicted as black hollow circles, while the prediction data are illustrated as blue hollow stars. The prediction data are further specified by purple hollow stars, which denote catalysts sampled during the ML-BO process.

Fig. 10. (a) XRD patterns of fresh and spent Cu-Mg and Zn-Zr mixed oxides. CO2-TPD profiles (b) and BET isotherm results (c) for both Cu-Mg and Zn-Zr mixed oxides.

Fig. 11. The first CO hydrogenation step on ZnZrO(101) and CuMgO(100) surface models.

参考文献

[1]	A. M. Bahmanpour, M. Signorile, O. Kröcher, Appl. Catal. B, 2021, 295, 120319.
[2]	X. Pan, F. Jiao, D. Miao, X. Bao, Chem. Rev., 2021, 121, 6588-6609.
[3]	N. Li, F. Jiao, X. Pan, Y. Chen, J. Feng, G. Li, X. Bao, Angew. Chem. Int. Ed., 2019, 58, 7400-7404.
[4]	H. M. Torres Galvis, K. P. de Jong, ACS Catal., 2013, 3, 2130-2149.
[5]	H. M. Torres Galvis, J. H. Bitter, C. B. Khare, M. Ruitenbeek, A. I. Dugulan, K. P. de Jong, Science, 2012, 335, 835-838.
[6]	R. B. Anderson, J. Catal., 1978, 55, 114-115.
[7]	P. J. Flory, J. Am. Chem. Soc., 1936, 58, 1877-1885.
[8]	R. A. Friedel, R. B. Anderson, J. Am. Chem. Soc., 1950, 72, 1212-1215.
[9]	Y. Ding, D. Miao, J. Feng, B. Bai, X. Pan, X. Bao, Appl. Catal. B, 2022, 316, 121628.
[10]	F. Jiao, J. Li, X. Pan, J. Xiao, H. Li, H. Ma, M. Wei, Y. Pan, Z. Zhou, M. Li, S. Miao, J. Li, Y. Zhu, D. Xiao, T. He, J. Yang, F. Qi, Q. Fu, X. Bao, Science, 2016, 351, 1065-1068.
[11]	Y. Fang, H. Sheng, Z. Huang, Y. Yue, W. Hua, W. Shen, H. Xu, ChemCatChem, 2022, 14, e202200200.
[12]	M. Wang, J. Kang, X. Xiong, F. Zhang, K. Cheng, Q. Zhang, Y. Wang, Catal. Today, 2021, 371, 85-92.
[13]	Y. Li, M. Wang, S. Liu, F. Wu, Q. Zhang, S. Zhang, K. Cheng, Y. Wang, ACS Catal., 2022, 12, 8793-8801.
[14]	K. Cheng, W. Zhou, J. Kang, S. He, S. Shi, Q. Zhang, Y. Pan, W. Wen, Y. Wang, Chem, 2017, 3, 334-347.
[15]	F. Jiao, B. Bai, G. Li, X. Pan, Y. Ye, S. Qu, C. Xu, J. Xiao, Z. Jia, W. Liu, T. Peng, Y. Ding, C. Liu, J. Li, X. Bao, Science, 2023, 380, 727-730.
[16]	B. Mahesh, Int. J. Sci. Res., 2020, 9, 381-386.
[17]	P. G. Ghanekar, S. Deshpande, J. Greeley, Nat. Commun., 2022, 13, 5788.
[18]	X. Fan, L. Chen, D. Huang, Y. Tian, X. Zhang, M. Jiao, Z. Zhou, Adv. Funct. Mater., 2024, 34, 2401887.
[19]	L. Chen, X. Zhang, A. Chen, S. Yao, X. Hu, Z. Zhou, Chin. J. Catal., 2022, 43, 11-32.
[20]	J. A. Esterhuizen, B. R. Goldsmith, S. Linic, Nat. Catal., 2022, 5, 175-184.
[21]	K. Vellayappan, Y. Yue, K. H. Lim, K. Cao, J.-Y. Tan, S. Cheng, T. Wang, T. Z. H. Gani, I. A. Karimi, S. Kawi, Appl. Catal. B, 2023, 330, 122593.
[22]	A. Chakkingal, P. Janssens, J. Poissonnier, M. Virginie, A. Y. Khodakov, J. W. Thybaut, Chem. Eng. J., 2022, 446, 137186.
[23]	K. Tripathi, V. Gupta, V. Awasthi, K. K. Pant, S. Upadhyayula, Adv. Sustainable Syst., 2023, 7, 2200416.
[24]	K. S. Chandana, S. Karka, M. K. Gujral, R. Kamesh, A. Roy, J. Environ. Chem. Eng., 2023, 11, 109555.
[25]	J. Li, L. Pan, M. Suvarna, X. Wang, Chem. Eng. J., 2021, 426, 131285.
[26]	L. Breiman, Mach. Learn., 2001, 45, 5-32.
[27]	J.H. Friedman, Anna. Stat., 2001, 29, 1189-1232
[28]	T. Chen, C. Guestrin, Kdd'16: Proceedings of the 22nd Acm Sigkdd International Conference On Knowledge Discovery And Data Mining, 2016, 785-794.
[29]	A. Smith, A. Keane, J. A. Dumesic, G. W. Huber, V. M. Zavala, Appl. Catal. B, 2020, 263, 118257.
[30]	M. Suvarna, T.P. Araujo, J. Pérez-Ramírez, Appl. Catal. B, 2022, 315, 121530
[31]	P. Vanjari, R. Kamesh, K. Y. Rani, Mater. Today Proc., 2023, 72, 524-532.
[32]	F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Mach,. Learn. Res., 2011, 12, 2825-2830
[33]	S. M. Lundberg, S.-I. Lee, Adv. Neural Inf. Process. Syst., 2017, 30,
[34]	B. Shahriari, K. Swersky, Z. Wang, R. P. Adams, N. de Freitas, Proc. IEEE, 2016, 104, 148-175.
[35]	C. K. I. Williams, C. E. Rasmussen, Adv. Neural Inf. Process. Syst., 1996, 8, 514-520
[36]	P. Luong, S. Gupta, D. Nguyen, S. Rana, S. Venkatesh, AI 2019: Adv. Artificial Intelligence, 2019, 11919, 473-484.
[37]	S. Daulton, X. Wan, D. Eriksson, M. Balandat, M. A. Osborne, E. Bakshy, Adv. Neural Inf. Process. Syst., 2022, 35, 12760-12774.
[38]	A. Deshwal, S. Belakaria, J. R. Doppa, International Conference on Machine Learning, PMLR, 2021, 139, 2632-2643.
[39]	N. Gunantara, Cogent Eng., 2018, 5, 1502242.
[40]	H. Gao, S. Zhong, W. Zhang, T. Igou, E. Berger, E. Reid, Y. Zhao, D. Lambeth, L. Gan, M. A. Afolabi, Z. Tong, G. Lan, Y. Chen, Environ. Sci. Technol., 2022, 56, 2572-2581.
[41]	Q. Dong, Y. Yao, S. Cheng, K. Alexopoulos, J. Gao, S. Srinivas, Y. Wang, Y. Pei, C. Zheng, A. H. Brozena, H. Zhao, X. Wang, H. E. Toraman, B. Yang, I. G. Kevrekidis, Y. Ju, D. G. Vlachos, D. Liu, L. Hu, Nature, 2022, 605, 470-476.
[42]	R. J. Murdock, S. K. Kauwe, A. Y. Wang, T. D. Sparks, Integr. Mater. Manuf. Innov., 2020, 9, 221-227.
[43]	R. T. Marler, J. S. Arora, Struct. Multidiscip. Optim., 2004, 26, 369-395.
[44]	J. R. Gardner, M. J. Kusner, Z. E. Xu, K. Q. Weinberger, J. P. Cunningham, ICML., 2014, 937-945.
[45]	A. Lee, J. H. Ghouse, J. C. Eslick, C. D. Laird, J. D. Siirola, M. A. Zamarripa, D. Gunter, J. H. Shinn, A. W. Dowling, D. Bhattacharyya, L. T. Biegler, A. P. Burgard, D. C. Miller, J. Adv. Manuf. Process., 2021, 3, e10095.
[46]	S. Yang, H.-J. Chun, S. Lee, S. J. Han, K.-Y. Lee, Y. T. Kim, ACS Catal., 2020, 10, 10742-10759.
[47]	S. Yang, S. Lee, S. C. Kang, S. J. Han, K.-W. Jun, K.-Y. Lee, Y. T. Kim, RSC Adv., 2019, 9, 14176-14187.
[48]	G. Kresse, J. Hafner, Phys. Rev. B, 1993, 47, 558-561.
[49]	G. Kresse, J. Hafner, Phys. Rev. B, 1994, 49, 14251-14269.
[50]	S. Grimme, J. Antony, S. Ehrlich, H. Krieg, J. Chem. Phys., 2010, 132, 154104.
[51]	G. Henkelman, B. P. Uberuaga, H. Jónsson, J. Chem. Phys., 2000, 113, 9901-9904.
[52]	J. Wang, G. Li, Z. Li, C. Tang, Z. Feng, H. An, H. Liu, T. Liu, C. Li, Sci. Adv., 2017, 3, e1701290
[53]	A. L. MacKay, Acta Crystallogr., 1962, 15, 916-918.
[54]	M. W. Chase, C. A. J. R. Downey, D. J. Frurip, R. A. Mcdonald, A. N. Syverud, J. Phys. Chem. Ref. Data, 1985, 14, 1-1856
[55]	Y.-A. Zhu, D. Chen, X.-G. Zhou, W.-K. Yuan, Catal. Today, 2009, 148, 260-267.
[56]	S. Dang, S. Li, C. Yang, X. Chen, X. Li, L. Zhong, P. Gao, Y. Sun, ChemSusChem, 2019, 12, 3582-3591.
[57]	W. Zhang, S. Wang, S. Guo, Z. Qin, M. Dong, J. Wang, W. Fan, Catal. Sci. Technol., 2022, 12, 2566-2577.
[58]	C. Du, L. Gapu Chizema, E. Hondo, M. Tong, Q. Ma, X. Gao, R. Yang, P. Lu, N. Tsubaki, Chin. J. Chem. Eng., 2021, 36, 101-110.
[59]	H. Chen, Z. Liu, N. Li, F. Jiao, Y. Chen, Z. Zhao, M. Guo, X. Liu, X. Han, X. Pan, X. Gong, G. Hou, X. Bao, Catal. Sci. Technol., 2022, 12, 1289-1295.
[60]	Y. Li, J. Hu, J. Xu, Y. Zheng, M. Chen, H. Wan, Q. Fu, F. Yang, X. Bao, Appl. Surf. Sci., 2019, 494, 353-360.
[61]	F. Meng, X. Li, P. Zhang, L. Yang, G. Yang, P. Ma, Z. Li, Catal. Today, 2021, 368, 118-125.
[62]	F. Meng, X. Liang, L. Wang, G. Yang, X. Huang, Z. Li, Ind. Eng. Chem. Res., 2022, 61, 11397-11406.
[63]	L. Ren, J. Zhang, B. Wang, H. Xu, J. Jiang, Y. Guan, P. Wu, Fuel, 2022, 307, 121916.
[64]	F. Meng, B. Li, J. Zhang, L. Wang, Z. Li, Fuel, 2023, 346, 128351.
[65]	G. Pacchioni, ACS Catal., 2024, 14, 2730-2745.
[66]	Q. Zhang, J. Yu, A. Corma, Adv. Mater., 2020, 32, 2002927.
[67]	C. S. Cundy, P. A. Cox, Micropor. Mesopor. Mater., 2005, 82, 1-78.
[68]	N. Azhari, N. Nurdini, S. Mardiana, T. Ilmi, A. Fajar, I. Makertihartha, Subagjo, G. Kadja.J. CO2 Util., 2022, 59, 101969.
[69]	M. U. U. Abdullaev, S. Lee, T.-W. Kim, C.-U. Kim, Catalysts, 2021, 11, 553.
[70]	F. Meng, P. Zhang, L. Ma, G. Yang, R. Zhang, B. Wang, Y. Hu, Z. Li, Chem. Eng. J., 2023, 467, 143500.
[71]	G. Raveendra, H. Mitta, S. Linglwar, P. Balla, R. Rajendran, B. Ponnala, M. Safdar, P. Vijayanand, New J. Chem., 2023, 47, 7143-7153.
[72]	J. Benesty, J. Chen, Y. Huang, I. Cohen, Noise Reduction in Speech Processing., 2009, 2, 37-38.
[73]	X. Liu, M. Wang, H. Yin, J. Hu, K. Cheng, J. Kang, Q. Zhang, Y. Wang, ACS Catal., 2020, 10, 8303-8314.
[74]	H. Schulz, Catal. Today, 2010, 154, 183-194.
[75]	J. Zhong, J. Han, Y. Wei, Z. Liu, J. Catal., 2021, 396, 23-31.
[76]	Y. Chen, S. Wang, Z. Wei, J. Li, M. Dong, Z. Qin, J. Wang, W. Fan, J. Phys. Chem. C, 2021, 125, 26472-26483.
[77]	J. W. Park, S. J. Kim, M. Seo, S. Y. Kim, Y. Sugi, G. Seo, Appl. Catal. A, 2008, 349, 76-85.
[78]	P. Zhang, F. Meng, X. Li, L. Yang, P. Ma, Z. Li, Catal. Sci. Technol., 2019, 9, 5577-5581.
[79]	S. Senger, L. Radom, J. Am. Chem. Soc., 2000, 122, 2613-2620.
[80]	J. Kanai, J. A. Martens, P. A. Jacobs, J. Catal., 1992, 133, 527-543.
[81]	X. Liu, W. Zhou, Y. Yang, K. Cheng, J. Kang, L. Zhang, G. Zhang, X. Min, Q. Zhang, Y. Wang, Chem. Sci., 2018, 9, 4708-4718.
[82]	S. Wang, P. Wang, D. Shi, S. He, L. Zhang, W. Yan, Z. Qin, J. Li, M. Dong, J. Wang, U. Olsbye, W. Fan, ACS Catal., 2020, 10, 2046-2059.
[83]	P. Zhang, F. Meng, L. Yang, G. Yang, X. Liang, Z. Li, Ind. Eng. Chem. Res., 2021, 60, 13214-13222.
[84]	Y. Ni, Y. Liu, Z. Chen, M. Yang, H. Liu, Y. He, Y. Fu, W. Zhu, Z. Liu, ACS Catal., 2019, 9, 1026-1032.
[85]	A. Cao, Z. Wang, H. Li, A. O. Elnabawy, J. K. Nørskov, J. Catal., 2021, 400, 325-331.
[86]	F. Studt, M. Behrens, E. L. Kunkes, N. Thomas, S. Zander, A. Tarasov, J. Schumann, E. Frei, J. B. Varley, F. Abild-Pedersen, J. K. Nørskov, R. Schlögl, ChemCatChem, 2015, 7, 1105-1111.
[87]	M. R. Dobbelaere, P. P. Plehiers, R. Van de Vijver, C. V. Stevens, K. M. Van Geem, Engineering, 2021, 7, 1201-1211.
[88]	K. Cheng, B. Gu, X. Liu, J. Kang, Q. Zhang, Y. Wang, Angew. Chem. Int. Ed., 2016, 55, 4725-4728.
[89]	J. T. Sun, I. S. Metcalfe, M. Sahibzada, Ind. Eng. Chem. Res., 1999, 38, 3868-3872.
[90]	J. Wang, S. Li, W. Liu, Y. Xiao, Z. Feng, X. Liang, S. Tang, G. Li, C. Dong, F. Pan, C. Li, CCS Chem., 2024, 6, 2996-3007.
[91]	T. Pinheiro Araújo, G. Giannakakis, J. Morales-Vidal, M. Agrachev, Z. Ruiz-Bernal, P. Preikschas, T. Zou, F. Krumeich, P. O. Willi, W. J. Stark, R. N. Grass, G. Jeschke, S. Mitchell, N. López, J. Pérez-Ramírez, Nat. Commun., 2024, 15, 3101.
[92]	W.-D. Hu, C.-M. Wang, Y.-D. Wang, J. Ke, G. Yang, Y.-J. Du, W.-M. Yang, Appl. Surf. Sci., 2021, 569, 151064.
[93]	A. O. Elnabawy, J. Schumann, P. Bothra, A. Cao, J. K. Nørskov, Top. Catal., 2020, 63, 635-648.
[94]	Y.-F. Zhao, R. Rousseau, J. Li, D. Mei, J. Phys. Chem. C, 2012, 116, 15952-15961.
[95]	W. Liu, S. Dang, S. Cheng, Z. Zhang, X. Ding, Y. Shi, P. Ren, W. Tu, Y.-F. Han, ACS Catal., 2024, 14, 7097-7110.
[96]	R. Fang, Q. Zhang, C. Yao, H. Wu, S. Xie, X. Zhang, Q. Wang, J. Lyu, F. Feng, C. Lu, Q. Zhang, X. Li, Appl. Catal. A, 2024, 687, 119969.
[97]	W. Yang, T. T. Fidelis, W.-H. Sun, ACS Omega, 2020, 5, 83-88.
[98]	Y. Ding, F. Jiao, X. Pan, Y. Ji, M. Li, R. Si, Y. Pan, G. Hou, X. Bao, ACS Catal., 2021, 11, 9729-9737.

基于机器学习和优化算法的数据驱动框架预测用于合成气制烯烃的氧化物-沸石基复合催化体系和反应条件

Data-driven framework based on machine learning and optimization algorithms to predict oxide-zeolite-based composite and reaction conditions for syngas-to-olefin conversion

RichHTML

PDF

Supporting Information

可视化

摘要/Abstract

引用本文

使用本文

扫码分享

图/表 12

参考文献

相关文章 15

编辑推荐

Metrics

[1]	安博涵, 李鑫, 刘威龙, 董继鹏, 边锐超, 张璐瑶, 李宁, 高旸钦, 戈磊. W掺杂调控Jahn-Teller效应构建高稳定性W-CoMnP析氢电催化剂[J]. 催化学报, 2025, 74(7): 264-278.
[2]	胡文德, 柯俊, 王仰东, 王传明. 合成气制烯烃双功能催化剂中分子筛拉动效应的微观动力学模拟[J]. 催化学报, 2025, 73(6): 222-233.
[3]	任宪轩, Rozemarijn D. E. Krösschell, 门卓武, 王鹏, Ivo A. W. Filot, Emiel J. M. Hensen. K对Hägg碳化物逆水煤气变换反应作用的理论研究[J]. 催化学报, 2025, 72(5): 289-300.
[4]	马千里, 聂红, 杨平, 刘建强, 高鸿毅, 王薇, 董松涛. 基于机器学习的加氢裂化分子筛催化材料构效关系研究[J]. 催化学报, 2025, 71(4): 187-196.
[5]	焦自浩, 张成翼, 刘亚, 郭烈锦, 王子运. 基于图神经网络的高性能二氧化碳还原高熵合金催化剂预测[J]. 催化学报, 2025, 71(4): 197-207.
[6]	班涛, 王建伟, 余希阳, 田海阔, 高新, 黄正清, 常春然. 机器学习辅助筛选SA-FLP双活性位催化剂用于甲烷与水共转化制甲醇反应[J]. 催化学报, 2025, 70(3): 311-321.
[7]	陈哲, 王涛. 电化学CO还原中低CO覆盖度下的C−C偶联机制: 反应物种最稳定吸附构型随电位的动态变化[J]. 催化学报, 2025, 69(2): 193-202.
[8]	刘京, 马贤迪, 卢政汉, 申东元, 赵阿雅, 韩晸宇, 龙剑平, 周震, 焦梦改, 李国承, 赵恩爱. 靶向构筑高性能单原子铂基氢析出反应电催化剂[J]. 催化学报, 2025, 69(2): 259-270.
[9]	汤雨晴, 陈彦君, Aqsa Abid, 孟子淳, 孙晓颖, 李波, 赵震. 氧化锆催化剂在丙烷催化脱氢中优异性能的再探索: 氧空位的两面性[J]. 催化学报, 2025, 68(1): 272-281.
[10]	刘彩霞, 黄朝俊, 范柏余, 张岩, 房莉静, 王雨禾, 刘庆岭, 王卫超, 陈延国, 张亚伟, 刘建成, 董芳, 张子印. 氯化氢对锑铈基催化剂在NH₃-SCR反应中的促进作用及其机理研究[J]. 催化学报, 2025, 68(1): 376-385.
[11]	穆楠, 薄婷婷, 胡玉高, 徐瑞欣, 刘彦昱, 周伟. 基于铁电In₂Se₃极化切换的N₂还原单原子催化剂[J]. 催化学报, 2024, 63(8): 244-257.
[12]	陈昕煜, 赵聪聪, 任静, 李波, 刘倩倩, 李葳, 杨帆, 陆思琦, 赵宇飞, 颜力楷, 臧宏瑛. 富含氧空位的由多金属氧簇辅助的银基异质结用于高效电催化固氮[J]. 催化学报, 2024, 62(7): 209-218.
[13]	刘洁宇, 郭海强, 熊钰林, 陈星, 于一夫, 王长洪. 基于DFT和机器学习的Pt单原子合金电催化剂的理性设计: 应用于NO至NH₃的转化[J]. 催化学报, 2024, 62(7): 243-253.
[14]	杜仕文, 章福祥. 密度泛函理论在光催化中的普遍应用[J]. 催化学报, 2024, 61(6): 1-36.
[15]	Adel Al-Salihy, 梁策, Abdulwahab Salah, Abdel-Basit Al-Odayni, 卢子昂, 陈孟新, 刘倩倩, 徐平. 用于提升全水解性能的超低含量Ru掺杂NiMoO₄@Ni₃(PO₄)₂核壳纳米结构[J]. 催化学报, 2024, 60(5): 360-375.