Improved LS-SVM Boiler Combustion Model based on Affinity Propagation

In the global effort to promote green energy policies, understanding and optimizing boiler combustion processes in coal-fired power plants is crucial. During unit start-ups, shutdowns, and load deep peak regulation, significant energy-saving potential can be harnessed in boilers. This paper focuses...

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Bibliographic Details
Published in:IEEE access 2024-01, Vol.12, p.1-1
Main Authors: Yan, Ming, Wang, Liang, Zhang, Meiling, Shi, Pan
Format: Article
Language:English
Subjects:
Affinity
AP clustering algorithm
Boiler efficiency
Boilers
Carbon
carbon content in fly ash
Clean energy
Clustering
Clustering algorithms
Coal-fired power plants
Combustion efficiency
Energy efficiency
Fly ash
Green energy
hybrid modeling
Hybrid power systems
LS-SVM
Oxygen
oxygen content of flue gas
Performance prediction
Support vector machines
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Summary:In the global effort to promote green energy policies, understanding and optimizing boiler combustion processes in coal-fired power plants is crucial. During unit start-ups, shutdowns, and load deep peak regulation, significant energy-saving potential can be harnessed in boilers. This paper focuses on a 600MW supercritical coal-fired power unit and presents an improved Least Squares Support Vector Machine (LS-SVM) model with refined initial parameters. By combining the improved LS-SVM with Affinity Propagation (AP) clustering, a combustion efficiency model for boilers is constructed. The experimental results demonstrate that the AP-based improved LS-SVM model not only reduces computational complexity and training time but also enhances predictive accuracy and generalization performance.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2024.3372660