Stacking Ensemble Learning-Assisted Simulation of Plasma-Catalyzed CO2 Reforming of Methane
Abstract
:1. Introduction
2. Methodology
2.1. The Pulsed Discharge Plasma-Catalytic Kinetics Model
2.2. Stacking Ensemble Model
2.3. Data Preprocessing
2.4. Evaluation Metrics
3. Results
3.1. Hyperparameter Tuning with Bayesian Optimization
3.2. Performance of the Stacking Ensemble Model
4. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Researcher | Reactant Gas | Plasma Generation Process | Conclusions |
---|---|---|---|
Xiaoling Wang [6] | CH4, CO2 | DBD | The short pulse rise/fall time can significantly improve the energy efficiency of CH4 and CO2. |
Naama Alhemeiri [7] | CH4, CO2, H2O | DBD, microwave, sliding arc | Plasma catalysis has potential in CO2 conversion, where performance can be improved by optimizing catalysts and diagnostic methods. |
Reza Vakilia [9] | CH4, CO2 | DBD | MOF-based catalysts significantly improve DRM efficiency by optimizing plasma catalyst synergy. |
Asif Hussain Khoja [10] | CH4, CO2 | DBD | DBD has advantages in DRM; optimizing catalyst and reactor configurations can improve performance. |
Type | Species |
---|---|
Molecules | , , , , , , , , , , CO, , CH2O, CH3OH, CH3CHO, CH2CO |
Excited species | (), (), (), (), (), (), (), CO(), CO(), CO(), (a), (b), O(1D), O(1S), (), (), () |
Radicals | , , CH, C, , , , , H, O, OH, CHO, CH2OH, CH3O, C2HO, CH3CO, CH2CHO, , , |
Electron and ions | e, , , , , , , , , , , , , , , , , , , , , |
Surface species | Surf, (s), CO(s), H(s), O(s), H2O(s), OH(s), C(s), (s), (s), CH(s), COOH(s), CH3OH(s), CH2OH(s), CHOH(s), COH(s), CH3O(s), CH2O(s), CHO(s) |
Model | Hyperparameters | BO Range | Optimized Value |
---|---|---|---|
DT | max depth | 1–100 | 75 |
p | 1–5 | 2 | |
min samples leaf | 1–100 | 1 | |
KNN | n neighbors | 1–50 | 12 |
min samples split | 2–3 | 2 | |
metric | Euclidean, Manhattan, Chebyshev, Minkowski | Euclidean | |
XGBoost | learning rate | 0.01–1 | 0.1 |
n estimators | 10–200 | 179 | |
max depth | 1–50 | 45 |
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Pan, J.; Qiao, X.; Zhang, C.; Li, B.; Li, L.; Li, G.; Qin, S. Stacking Ensemble Learning-Assisted Simulation of Plasma-Catalyzed CO2 Reforming of Methane. Electronics 2025, 14, 1329. https://doi.org/10.3390/electronics14071329
Pan J, Qiao X, Zhang C, Li B, Li L, Li G, Qin S. Stacking Ensemble Learning-Assisted Simulation of Plasma-Catalyzed CO2 Reforming of Methane. Electronics. 2025; 14(7):1329. https://doi.org/10.3390/electronics14071329
Chicago/Turabian StylePan, Jie, Xin Qiao, Chunlei Zhang, Bin Li, Lun Li, Guomeng Li, and Shaohua Qin. 2025. "Stacking Ensemble Learning-Assisted Simulation of Plasma-Catalyzed CO2 Reforming of Methane" Electronics 14, no. 7: 1329. https://doi.org/10.3390/electronics14071329
APA StylePan, J., Qiao, X., Zhang, C., Li, B., Li, L., Li, G., & Qin, S. (2025). Stacking Ensemble Learning-Assisted Simulation of Plasma-Catalyzed CO2 Reforming of Methane. Electronics, 14(7), 1329. https://doi.org/10.3390/electronics14071329