Research on digital twin modeling method for combustion process based on model reduction
In response to the difficulty in obtaining combustion information within coal-fired boiler furnaces, a method is proposed in this study to improve the reduced-order model using clustering segmentation. This approach aims to rapidly predict the combustion temperature field inside the furnace by estab...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-01-01
|
| Series: | Case Studies in Thermal Engineering |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2214157X24016502 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841555788396494848 |
|---|---|
| author | Yue Zhang Jiale Li |
| author_facet | Yue Zhang Jiale Li |
| author_sort | Yue Zhang |
| collection | DOAJ |
| description | In response to the difficulty in obtaining combustion information within coal-fired boiler furnaces, a method is proposed in this study to improve the reduced-order model using clustering segmentation. This approach aims to rapidly predict the combustion temperature field inside the furnace by establishing a twin model of the combustion temperature field. Initially, the finite volume method is employed to analyze the combustion system of a 600 MW subcritical boiler under various operating conditions. Subsequently, cross-sectional data from burner nozzle positions at each operating condition are extracted. These data are subjected to Proper Orthogonal Decomposition (POD), Spectral Proper Orthogonal Decomposition (SPOD), and Wavelet Transform-POD (WT-POD) for dimensionality reduction to obtain modal data. Comparative analyses are conducted on the modal data obtained from different methods. Furthermore, based on modal data analysis, a Support Vector Machine (SVM) regression model is selected to reconstruct the temperature field. The average absolute error of the reconstructed temperature fields from three methods under different operating conditions is then compared. Finally, the model is refined using clustering segmentation, resulting in an improvement of approximately 0.6 % in reconstruction accuracy. This enhancement demonstrates that the clustered POD-SVR-GA model achieves higher accuracy in reconstructing combustion temperature fields after clustering-based improvements. |
| format | Article |
| id | doaj-art-e0eb0505e0d04310bd2a70139654a89d |
| institution | Kabale University |
| issn | 2214-157X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Case Studies in Thermal Engineering |
| spelling | doaj-art-e0eb0505e0d04310bd2a70139654a89d2025-01-08T04:52:40ZengElsevierCase Studies in Thermal Engineering2214-157X2025-01-0165105619Research on digital twin modeling method for combustion process based on model reductionYue Zhang0Jiale Li1Department of Automation, North China Electric Power University, Baoding, 071000, Hebei Province, China; Hebei Technology Innovation Center of Simulation & Optimized Control for Power Generation, North China Electric Power University, China; Baoding Key Laboratory of State Detection and Optimization Regulation for Integrated Energy System, ChinaDepartment of Automation, North China Electric Power University, Baoding, 071000, Hebei Province, China; Hebei Technology Innovation Center of Simulation & Optimized Control for Power Generation, North China Electric Power University, China; Corresponding author. Department of Automation, North China Electric Power University, Baoding, 071000, Hebei Province, China.In response to the difficulty in obtaining combustion information within coal-fired boiler furnaces, a method is proposed in this study to improve the reduced-order model using clustering segmentation. This approach aims to rapidly predict the combustion temperature field inside the furnace by establishing a twin model of the combustion temperature field. Initially, the finite volume method is employed to analyze the combustion system of a 600 MW subcritical boiler under various operating conditions. Subsequently, cross-sectional data from burner nozzle positions at each operating condition are extracted. These data are subjected to Proper Orthogonal Decomposition (POD), Spectral Proper Orthogonal Decomposition (SPOD), and Wavelet Transform-POD (WT-POD) for dimensionality reduction to obtain modal data. Comparative analyses are conducted on the modal data obtained from different methods. Furthermore, based on modal data analysis, a Support Vector Machine (SVM) regression model is selected to reconstruct the temperature field. The average absolute error of the reconstructed temperature fields from three methods under different operating conditions is then compared. Finally, the model is refined using clustering segmentation, resulting in an improvement of approximately 0.6 % in reconstruction accuracy. This enhancement demonstrates that the clustered POD-SVR-GA model achieves higher accuracy in reconstructing combustion temperature fields after clustering-based improvements.http://www.sciencedirect.com/science/article/pii/S2214157X24016502Model reductionTwin modelTemperature field predictionPODWavelet transformCluster |
| spellingShingle | Yue Zhang Jiale Li Research on digital twin modeling method for combustion process based on model reduction Case Studies in Thermal Engineering Model reduction Twin model Temperature field prediction POD Wavelet transform Cluster |
| title | Research on digital twin modeling method for combustion process based on model reduction |
| title_full | Research on digital twin modeling method for combustion process based on model reduction |
| title_fullStr | Research on digital twin modeling method for combustion process based on model reduction |
| title_full_unstemmed | Research on digital twin modeling method for combustion process based on model reduction |
| title_short | Research on digital twin modeling method for combustion process based on model reduction |
| title_sort | research on digital twin modeling method for combustion process based on model reduction |
| topic | Model reduction Twin model Temperature field prediction POD Wavelet transform Cluster |
| url | http://www.sciencedirect.com/science/article/pii/S2214157X24016502 |
| work_keys_str_mv | AT yuezhang researchondigitaltwinmodelingmethodforcombustionprocessbasedonmodelreduction AT jialeli researchondigitaltwinmodelingmethodforcombustionprocessbasedonmodelreduction |