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计量学报  2021, Vol. 42 Issue (1): 111-116    DOI: 10.3969/j.issn.1000-1158.2021.01.18
  电离辐射、标准物质与生物计量 本期目录 | 过刊浏览 | 高级检索 |
基于RBF神经网络的生物质电站高温气体CO2浓度测量方法
盛伟岸1,张立权1,黄帅1,韩晓娟2,张文彪2
1.大唐长山热电厂, 吉林 松原 131109
2.华北电力大学 控制科学与计算机工程学院, 北京 102206
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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摘要 电站气体浓度测量对实现燃烧优化、提高燃烧效率和火焰品质、减少污染物排放具有重要意义。以CO2气体为例进行研究,基于近红外波段可调谐激光吸收层析成像技术,提出了基于径向基(radial basis function, RBF)神经网络的高温气体CO2浓度测量方法。通过实验获取不同浓度下的CO2吸收可调谐激光光谱信号,计算CO2吸收谱线和原始信号的差值,提取出描述该差异性的统计特征参数作为RBF神经网络的输入,CO2浓度作为RBF神经网络的输出,建立了基于RBF神经网络的高温气体CO2浓度测量仿真模型,通过仿真实例验证了该方法的有效性和正确性。与GRNN神经网络对比分析表明:RBF神经网络法可以有效提高CO2浓度测量精度,为生物质发电高温气体计量提供理论依据。
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盛伟岸
张立权
黄帅
韩晓娟
张文彪
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 wordsmetrology    CO2 concentration    TDLAS    radial basis function neural network    high temperature gas    combustion optimization    biomass power stations
收稿日期: 2018-12-14      发布日期: 2021-01-19
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  
作者简介: 盛伟岸(1988-),男,吉林松原人,主要从事发电厂生产自动化控制专业研究与生产管理工作。Email: 158310319@qq.com
引用本文:   
盛伟岸,张立权,黄帅,韩晓娟,张文彪. 基于RBF神经网络的生物质电站高温气体CO2浓度测量方法[J]. 计量学报, 2021, 42(1): 111-116.
SHENG Wei-an,ZHANG Li-quan,HUANG Shuai,HAN Xiao-juan,ZHANG Wen-biao. CO2 Concentration Measurement Method in High Temperature Gas of Biomass Power Plant Based on RBF Neural Network. Acta Metrologica Sinica, 2021, 42(1): 111-116.
链接本文:  
http://jlxb.china-csm.org:81/Jwk_jlxb/CN/10.3969/j.issn.1000-1158.2021.01.18     或     http://jlxb.china-csm.org:81/Jwk_jlxb/CN/Y2021/V42/I1/111
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