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A new on-line combustion optimization approach for ultra-supercritical coal-fired boiler to improve boiler efficiency, reduce NOx emission and enhance operating safety.

Authors :
Xu, Wentao
Huang, Yaji
Song, Siheng
Yue, Junfeng
Chen, Bo
Liu, Yuqing
Zou, Yiran
Source :
Energy. Nov2023, Vol. 282, pN.PAG-N.PAG. 1p.
Publication Year :
2023

Abstract

To take into account the economy, environment protection and operating safety of the boiler in the combustion optimization process, a new on-line combustion optimization approach for boiler is proposed. The historical combustion data collected from DCS of the coal-fired power plant is preprocessed at first. Then improved biogeography optimization-based long short-term memory neural network (IBBO-LSTM) and similarity measurement method are designed to construct the adaptive dynamic combustion model for boiler with boiler efficiency, NOx emission and the temperature of water wall as outputs respectively. After that improved non-dominated sorting genetic algorithm-Ⅱ (INSGA-Ⅱ) is designed to generate a series of boiler combustion optimization solutions under different running load offline, and improved multi-level fuzzy comprehensive evaluation (IDHGF) is designed to retain the combustion optimization solutions with higher running safety. Meanwhile, the retained optimization solutions are integrated into an optimization cases base. Thereafter, case-based reasoning based on safety enhancement mechanism (CBRSEM) is designed to achieve the online combustion optimization for boiler. Finally, to confirm the effectiveness of the combination of IBBO-LSTM, INSGA-Ⅱ, IDHGF and CBRSEM, different online optimization methods (IBBO-LSTM-INSGA-Ⅱ, IBBO-LSTM-INSGA-Ⅱ-IDHGF, IBBO-LSTM-NSGA-Ⅱ-DHGF-CBR, IBBO-LSTM-NSGA-Ⅱ-IDHGF-CBR, IBBO-LSTM-NSGA-Ⅱ-DHGF-CBRSEM, IBBO-LSTM-NSGA-Ⅱ-IDHGF-CBRSEM, IBBO-LSTM-INSGA-Ⅱ-DHGF-CBR, IBBO-LSTM-INSGA-Ⅱ-IDHGF-CBR) are adopted to optimize a given combustion case. The proposed on-line combustion optimization approach for boiler received satisfied combustion optimization results that the growing for boiler efficiency was 0.653%, and the reduced concentration for NOx emission reached 22.891 mg/m3, and the operating safety raised from 5.592 to 6.913. In conclusion, IBBO-LSTM-INSGA-Ⅱ-IDHGF-CBRSEM can online offer the combustion optimization strategy to the boiler operators to improve boiler efficiency, reduce NOx emission and enhance the running safety of boiler, so that it is suitable for online application. • A new online combustion optimization method for boiler is proposed by combining multi-objective optimization algorithm and performance evaluation theory. • Improved biogeography optimization-based long short-term memory neural network are used to construct the self-adaptive dynamic mathematical mode for boiler combustion system. • Improved non-dominated sorting genetic algorithm-Ⅱand improved multi-level fuzzy comprehensive evaluation are combined to gain a series of combustion optimizations with higher boiler efficiency, lower NOx emission and higher operating safety offline. • Case-based reasoning based on safety enhancement mechanism is used to realize the online combustion optimization of boiler. • The boiler efficiency increased by 0.653%, and the NOx emission reduced by 22.891 mg/m3, and the operating safety raised from 5.592 to 6.913. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
03605442
Volume :
282
Database :
Academic Search Index
Journal :
Energy
Publication Type :
Academic Journal
Accession number :
172042720
Full Text :
https://doi.org/10.1016/j.energy.2023.128748