An improved Kalman particle swarm optimization for modeling and optimizing of boiler combustion characteristics. Issue 4 (24th April 2023)

• Record Type: Journal Article
• Authors: Liang, Jing; Guo, Hao; Chen, Ke; Yu, Kunjie; Yue, Caitong; Li, Xia
• Is Part Of: Robotica. Volume 41:Issue 4(2023)
• Page Start: 1087
• Page End: 1097
• Publication Date: 2023-04-24
• Subjects: circulating fluidized bed boiler -- particle swarm optimization -- extreme learning machine -- Kalman filtering; Robots -- Periodicals; 629.89205
• DOI: 10.1017/S026357472200145X
• Languages: English
• ISSNs: 0263-5747

Abstract

Abstract: 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.