Study on Optimization of Prediction Model of Flue Gas Oxygen Content in Thermal Power Plant Based on Least Squares Support Vector Machine

ZHAO Guoqin; LAN Maowei; LI Yang; ZHOU Yuanxiang; JIANG Zhengwei; GAN Yunhua

doi:10.12096/j.2096-4528.pgt.21088

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Study on Optimization of Prediction Model of Flue Gas Oxygen Content in Thermal Power Plant Based on Least Squares Support Vector Machine

Power Generation Technology Vol. 44, Issue 4, Pages: 534-542(2023)

Power Generation and Environmental Protection|Updated：2025-06-06

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- Study on Optimization of Prediction Model of Flue Gas Oxygen Content in Thermal Power Plant Based on Least Squares Support Vector Machine
- “According to the latest research, a breakthrough has been made in the prediction model of oxygen content in flue gas of a 1000MW ultra supercritical boiler in a power plant. The GA-LSSVM model has high prediction accuracy and strong generalization ability, providing scientific basis for optimizing boiler combustion operation.”
- Affiliations：
  1.Jinghai Power Generation Co., Ltd., Guangdong Energy Group, Jieyang 515223, Guangdong Province, China
  2.School of Electric Power Engineering, South China University of Technology, Guangzhou 510640, Guangdong Province, China
  3.Xi'an Thermal Power Research Institute Co., Ltd., Xi'an 710054, Shaanxi Province, China
- Author bio：
- Funds：
  National Natural Science Foundation of China(51776077);Basic and Applied Basic Research Foundation of Guangdong Province(2020B1515020040)
- DOI：10.12096/j.2096-4528.pgt.21088
  CLC：
- Accepted：
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Study on Optimization of Prediction Model of Flue Gas Oxygen Content in Thermal Power Plant Based on Least Squares Support Vector Machine

DOI：10.12096/j.2096-4528.pgt.21088

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