Optimization of Circulating Fluidized Bed Boiler Combustion Key Control Parameters Based on Machine Learning

Lei Han, Lingmei Wang (), Hairui Yang, Chengzhen Jia, Enlong Meng, Yushan Liu and Shaoping Yin
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Lei Han: School of Computer and Information Technology, Shanxi University, Taiyuan 030006, China
Lingmei Wang: School of Computer and Information Technology, Shanxi University, Taiyuan 030006, China
Hairui Yang: Department of Energy and Power Engineering, Tsinghua University, Beijing 100084, China
Chengzhen Jia: Department of Automation, Tsinghua University, Beijing 100084, China
Enlong Meng: School of Automation and Software, Shanxi University, Taiyuan 030006, China
Yushan Liu: School of Automation and Software, Shanxi University, Taiyuan 030006, China
Shaoping Yin: School of Automation and Software, Shanxi University, Taiyuan 030006, China

Energies, 2023, vol. 16, issue 15, 1-23

Abstract: During the coal-fired circulating fluidized bed unit participation in the peak regulation process of the power grid, the thermal automatic control system assists the operator to adjust the mode focusing on pollutant control and ignoring the economy so that the unit’s operating performance maintains a huge potential for deep mining. The high-dimensional and coupling-related data characteristics of circulating fluidized bed boilers put forward more refined and demanding requirements for combustion optimization analysis and open-loop guidance operation. Therefore, this paper proposes a combustion optimization method that incorporates neighborhood rough set machine learning. This method first reduces the control parameters affecting multi-objective combustion optimization with the neighborhood rough set algorithm that fully considers the correlation of each variable combination and then establishes a multi-objective combustion optimization prediction model by combining the online calculation of boiler thermal efficiency. Finally, the NSGAII algorithm realizes the optimization of the control parameter setting value of the boiler combustion system. The results show that this method reduces the number of control commands involved in combustion optimization adjustment from 26 to 11. At the same time, based on the optimization results obtained by using traditional combustion optimization methods under high, medium, and medium-low load conditions, the boiler thermal efficiency increased by 0.07%, decreased by 0.02%, and increased by 0.55%, respectively, and the nitrogen oxide emission concentration decreased by 5.02 mg/Nm 3 , 7.77 mg/Nm 3 , and 7.03 mg/Nm 3 , respectively. The implementation of this method can help better account for the economy and pollutant discharge of the boiler combustion system during the variable working conditions, guide the operators to adjust the combustion more accurately, and effectively reduce the ineffective energy consumption in the adjustment process. The proposal and application of this method laid the foundation for the construction of smart power plants.

Keywords: machine learning; neighborhood rough sets; circulating fluidized bed boiler; combustion key control parameters optimization (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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