Three-objective optimization of boiler combustion process based on multi-objective teaching–learning based optimization algorithm and ameliorated extreme learning machine

Ma, Yunpeng; Wang, Heqi; Zhang, Xinxin; Hou, Likun; Song, Jiancai

doi:10.1016/j.mlwa.2021.100082

Cited by 6 publications

(1 citation statement)

References 19 publications

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“…Taking boiler thermal efficiency and nitrogen oxide emission as optimization objectives, the optimized operation parameters were obtained through the improved multi-objective particle swarm optimization algorithm. Ma et al [19] built a comprehensive model for boiler thermal efficiency and NO x /SO 2 emissions using an extreme learning machine. After that, a kind of multi-objective modified teachinglearning-based optimization method was proposed to optimize the boiler combustion process parameters.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2023

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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/Nm3, 7.77 mg/Nm3, and 7.03 mg/Nm3, 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.

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Section: Introductionmentioning

confidence: 99%