基于人工智能技术的火电厂燃煤锅炉智能燃烧优化研究及应用

doi:10.11959/j.issn.2096-6652.201925

Abstract

Abstract:

In order to reduce the NO_xemission concentration and coal consumption of coal-fired boilers in thermal power plants,the sample increment quantum neural network and an improved quantum bee colony algorithm were proposed.The quantum sample incremental feed-forward neural network can dynamically establish a comprehensive model of Nitrogen and Oxygen emission concentration and boiler coal consumption of coal-fired boiler,and can realize rolling optimization of the model.Based on the established comprehensive model,the optimization of the primary and secondary air volume and coal and the opening degree of each secondary air valve were realized by using the improved quantum bee colony algorithm.Based on the above two methods,a set of intelligent combustion optimization software for coal-fired boiler was developed and applied to the 330 MW boiler of a thermal power plant.The test results show that the Nitrogen and Oxygen emission concentration and the coal consumption of the boiler have been reduced in varying degrees.It is shown that the modeling method and the optimization algorithm are effective.

Key words: NO_xemission, boiler, neural network, quantum computation, bee colony algorithm

CLC Number:

TP323

Peifeng NIU, Yunpeng MA, Xinxin ZHANG, et al. Research and application on combustion optimization of coal-fired boiler in thermal power plant based on artificial intelligence technology[J]. Chinese Journal of Intelligent Science and Technology, 2019, 1(2): 163-170.

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References 12

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Research and application on combustion optimization of coal-fired boiler in thermal power plant based on artificial intelligence technology

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