Improved LS-SVM Boiler Combustion Model based on Affinity Propagation

• 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
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2024.3372660

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.