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An improved Kalman particle swarm optimization for modeling and optimizing of boiler combustion characteristics

Published online by Cambridge University Press:  24 October 2022

Jing Liang
School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, China
Hao Guo
School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, China
Ke Chen*
School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, China
Kunjie Yu
School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, China
Caitong Yue
School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, China
Xia Li
School of Mathematics and Information Technology, Hebei Normal University of Science and Technology, Qinhuangdao 066004, China
*Corresponding author. E-mail:


With the rapid development of the national economy, the demand for electricity is also growing. Thermal power generation accounts for the highest proportion of power generation, and coal is the most commonly used combustion material. The massive combustion of coal has led to serious environmental pollution. It is significant to improve energy conversion efficiency and reduce pollutant emissions effectively. In this paper, an extreme learning machine model based on improved Kalman particle swarm optimization (ELM-IKPSO) is proposed to establish the boiler combustion model. The proposed modeling method is applied to the combustion modeling process of a 300 MWe pulverized coal boiler. The simulation results show that compared with the same type of modeling method, ELM-IKPSO can better predict the boiler thermal efficiency and NOx emission concentration and also show better generalization performance. Finally, multi-objective optimization is carried out on the established model, and a set of mutually non-dominated boiler combustion solutions is obtained.

Research Article
© The Author(s), 2022. Published by Cambridge University Press

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