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Online optimization for collaborative treatment of CO and multi-pollutant emission reduction based on particle swarm optimization
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Ziwei KANG1, Linghong CHEN1, Yanyan WU1, Jun WU2, Bitao XU3, Hangliang JIN3, Peipei QU3
Thermal Power Generation | 2025, 54(7) : 23 - 32
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Thermal Power Generation | 2025, 54(7): 23-32
• Special topic on “ultra supercritical circulating fluidized bed power generation technology” •
Online optimization for collaborative treatment of CO and multi-pollutant emission reduction based on particle swarm optimization
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Ziwei KANG1, Linghong CHEN1, Yanyan WU1, Jun WU2, Bitao XU3, Hangliang JIN3, Peipei QU3
Affiliations
  • 1.College of Energy Engineering, Zhejiang University, Hangzhou 310027, China
  • 2.Hangzhou Hanglian Thermal Power Co., Ltd., Hangzhou 310018, China
  • 3.Hangzhou Heda Energy Co., Ltd., Hangzhou 310018, China
Published: 2025-07-25 doi: 10.19666/j.rlfd.202410228
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Nowadays, circulating fluidized bed (CFB) coal-fired boilers face challenges in the process of deep peak regulation, such as high CO emission concentrations and the lack of theoretical guidance for collaborative emission reduction of multiple pollutants including NOx and SO2. Taking a 150 t/h CFB coal-fired boiler as the research object, a model for quickly predicting mass concentrations of CO, NOx and SO2 emitted from the furnace is established based on the long short-term memory (LSTM) neural network, the Attention mechanism and the XGBoost algorithm. Moreover, an online emission reduction strategy is proposed by coupling with the particle swarm optimization (PSO) algorithm. 36 298 operational data points from the coal-fired boiler throughout 2023 are selected as training samples. A correlation analysis is conducted between the boiler inspection data and pollutant emission mass concentrations to determine the input parameters for the prediction model. The fitness function and boundary function are determined with the prediction model coupled with the PSO algorithm. Through the calculation of emission reduction optimization model, an online emission reduction optimization strategy for CO, NOx and SO2 mass concentrations of CFB boilers in different load ranges is proposed, and the feasibility of the algorithm in practical boiler tuning applications is evaluated.

CFB boiler  /  long short-term memory neural network  /  PSO algorithm  /  CO  /  multi-pollutant emission reduction
Ziwei KANG, Linghong CHEN, Yanyan WU, Jun WU, Bitao XU, Hangliang JIN, Peipei QU. Online optimization for collaborative treatment of CO and multi-pollutant emission reduction based on particle swarm optimization[J]. Thermal Power Generation, 2025 , 54 (7) : 23 -32 . DOI: 10.19666/j.rlfd.202410228
Funding
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  • Key Research and Development Program of Zhejiang Province(2024C03113)
Appendix
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Year 2025 volume 54 Issue 7
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Article Info
doi: 10.19666/j.rlfd.202410228
  • Receive Date：2024-10-09
  • Online Date：2026-03-06
  • Published：2025-07-25
Article Data
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History
  • Received：2024-10-09
Funding
Key Research and Development Program of Zhejiang Province(2024C03113)
Affiliations
    1.College of Energy Engineering, Zhejiang University, Hangzhou 310027, China
    2.Hangzhou Hanglian Thermal Power Co., Ltd., Hangzhou 310018, China
    3.Hangzhou Heda Energy Co., Ltd., Hangzhou 310018, China
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https://castjournals.cast.org.cn/joweb/rlfd/EN/10.19666/j.rlfd.202410228
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