基于RBF神经网络的生物质电站高温气体CO2浓度测量方法

doi:10.3969/j.issn.1000-1158.2021.01.18

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Abstract The measurement of gas concentration in power plants is of great significance to realize combustion optimization, improve combustion efficiency and flame quality, and reduce pollutant emissions. Taking CO₂ as an example, according to infrared tunable laser absorption tomography technology, a measuring method of CO₂ concentration in high temperature gas based on radial basis function (RBF) neural network is proposed. The CO₂ absorption tunable laser spectral signals at different concentrations were obtained by experiments. The difference between the CO₂ absorption spectrum and the original signal is calculated, and the statistical characteristic parameters describing the difference are extracted, which are regarded as the input of RBF neural network and the CO₂ concentration as the output of RBF neural network. The model of the high temperature gas CO₂ concentration based on RBF neural network is established. The simulation results show that the method is effective and correct. Compared with GRNN neural network, the RBF neural network method can effectively improve the accuracy of CO₂ concentration measurement, and provide a theoretical basis for high temperature gas measurement in biomass power stations.

Key words： metrology CO₂ concentration TDLAS radial basis function neural network high temperature gas combustion optimization biomass power stations

Received: 14 December 2018 Published: 19 January 2021

PACS:	计量学
	CO2浓度
	可调谐二极管激光吸收光谱
	径向基神经网络
	高温气体
	燃烧优化
	生物质电站

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	Articles by authors
	SHENG Wei-an
	ZHANG Li-quan
	HUANG Shuai
	HAN Xiao-juan
	ZHANG Wen-biao

Cite this article:

SHENG Wei-an,ZHANG Li-quan,HUANG Shuai, et al. CO₂ Concentration Measurement Method in High Temperature Gas of Biomass Power Plant Based on RBF Neural Network[J]. Acta Metrologica Sinica, 2021, 42(1): 111-116.

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http://jlxb.china-csm.org:81/Jwk_jlxb/EN/10.3969/j.issn.1000-1158.2021.01.18 OR http://jlxb.china-csm.org:81/Jwk_jlxb/EN/Y2021/V42/I1/111

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