Optimization of industrial boiler combustion control system based on genetic algorithm. (August 2018)

• Record Type: Journal Article
• Authors: Pan, Hongguang; Zhong, Weimin; Wang, Zaiying; Wang, Guoxin
• Is Part Of: Computers & electrical engineering. Volume 70(2018)
• Page Start: 987
• Page End: 997
• Publication Date: 2018-08
• Subjects: Furnace combustion control system -- Excess air coefficient -- Varying offset dual-crisscross -- Genetic algorithm; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2018.03.003
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: In the combustion process, the traditional furnace combustion control method cannot meet the control requirements with frequent variable loads. Firstly, for the combustion control system with the varying variable bias and bi-double crossing limit, the ranges of bias coefficients are analyzed gradually. Secondly, the objective function is designed based on the excess air coefficient and the main steam pressure deviation signal. Finally, the genetic algorithm is adopted to optimize the bias coefficients to achieve better control performance. The simulation results show that the presented method can effectively improve the response speed and keep the excess air coefficient in the optimal combustion interval.