Study on Multi-Objective Optimization of High-Efficiency and Low-NOx Emissions of Power Station Boilers Based on Least Squares Support Vector Machines

LIANG Zhongrong; LAN Maowei; ZHENG Guo; HE Rongqiang; QU Keyang; GAN Yunhua

doi:10.12096/j.2096-4528.pgt.22108

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Study on Multi-Objective Optimization of High-Efficiency and Low-NO_x Emissions of Power Station Boilers Based on Least Squares Support Vector Machines

Power Generation Technology Vol. 44, Issue 6, Pages: 809-816(2023)

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

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- Study on Multi-Objective Optimization of High-Efficiency and Low-NO_x Emissions of Power Station Boilers Based on Least Squares Support Vector Machines
- “In the field of multi-objective optimization of boiler combustion systems, experts use weighted particle swarm optimization algorithm and MOPSO algorithm to optimize operating parameters, achieving high efficiency and low NOx emissions. MOPSO algorithm has higher optimization efficiency and is more conducive to guiding actual operation.”
- Affiliations：
  1.Zhanjiang Electric Power Co., Ltd., Zhanjiang 524099, Guangdong Province, China
  2.China Energy Engineering Group Guangdong Electric Power Design Institute Co., Ltd., Guangzhou 510663, Guangdong Province, China
  3.School of Electric Power Engineering, South China University of Technology, Guangzhou 510640, Guangdong Province, China
- Author bio：
- Funds：
  National Natural Science Foundation of China(52376108);Basic and Applied Basic Research Foundation of Guangdong Province(2020B1515020040)
- DOI：10.12096/j.2096-4528.pgt.22108
  CLC：
- Accepted：
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Study on Multi-Objective Optimization of High-Efficiency and Low-NO_x Emissions of Power Station Boilers Based on Least Squares Support Vector Machines

DOI：10.12096/j.2096-4528.pgt.22108

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