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CO2 Concentration Measurement Method in High Temperature Gas of Biomass Power Plant Based on RBF Neural Network |
SHENG Wei-an1,ZHANG Li-quan1,HUANG Shuai1,HAN Xiao-juan2,ZHANG Wen-biao2 |
1. Datang Changshan Thermal Power Plant, Songyuan, Jilin 131109, China
2. School of Control Science and Computer Engineering, North China Electric Power University, Beijing 102206, China |
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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.
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Received: 14 December 2018
Published: 19 January 2021
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