催化学报 ›› 2025, Vol. 74: 211-227.DOI: 10.1016/S1872-2067(25)64733-4
Mansurbek Urol ugli Abdullaeva,b, Woosong Jeona, Yun Kangc, Juhwan Nohd, Jung Ho Shind, Hee-Joon Chune,*(), Hyun Woo Kimc,*(
), Yong Tae Kima,b,*(
)
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).
Mansurbek Urol ugli Abdullaeva,b, Woosong Jeona, Yun Kangc, Juhwan Nohd, Jung Ho Shind, Hee-Joon Chune,*(), Hyun Woo Kimc,*(
), Yong Tae Kima,b,*(
)
Received:
2024-12-30
Accepted:
2025-04-15
Online:
2025-07-18
Published:
2025-07-20
Contact:
*E-mail: 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.
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.
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).
Catalyst | Ox/Zeo ratio | WHSV (mL g-1 h-1) | Predicted STY (mmol gcat-1 h-1) | Experimental STY a (mmol gcat-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 |
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 |
0.5 | 8000 | 9.8 | 2.3 | 0.11 | 1.08 | 0.48 |
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 gcat-1 h-1) | Experimental STY a (mmol gcat-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 |
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 |
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.
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