Machine Learning and Reinforcement Learning-Driven Optimization of Carbon Capture and Storage Processes and Their Environmental Impact Assessment
11 kwi 2025
O artykule
Data publikacji: 11 kwi 2025
Otrzymano: 04 lis 2024
Przyjęty: 26 lut 2025
DOI: https://doi.org/10.2478/amns-2025-0841
Słowa kluczowe
© 2025 Xihan Wang, published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.
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Comparison of Optimization Performance in CCS Process
Method | CO2 Capture Efficiency (%) | Energy Consumption (MJ/ton CO2) |
---|---|---|
88.2 | 3.56 | |
90.3 | 3.24 | |
92.1 | 3.15 |
Comparison of Predictive Performance for CO2 Capture Efficiency
Model | MAE | RMSE | |
---|---|---|---|
SVR | 2.31 | 3.85 | 0.79 |
RF | 1.98 | 3.21 | 0.85 |
DNN | 1.74 | 2.92 | 0.88 |
GBDT | 1.56 | 2.67 | 0.91 |
Hybrid (GBDT + DNN) | 1.32 | 2.34 | 0.94 |
Comparison of Computational Efficiency
Method | Execution Time per Step (ms) | Total Optimization Time (s) |
---|---|---|
5.6 | 0.56 | |
74.2 | 7.42 | |
52.7 | 5.27 |