严玥,江赟,严实.利用RBF网络的火电厂氮氧化物浓度检测方法[J].电子测量与仪器学报,2017,31(1):45-50
利用RBF网络的火电厂氮氧化物浓度检测方法
Detection method of NOx concentration in coal fired power plant using RBF network
  
DOI:10.13382/j.jemi.2017.01.007
中文关键词:  氮氧化物  浓度检测  干扰  神经网络
英文关键词:nitrogen oxides  concentration detection  interferes  neural network
基金项目:国家自然科学基金(61502063)、重庆市教委科研项目(KJ1500639)资助
作者单位
严玥 重庆工商大学检测控制集成系统重庆市市级工程实验室重庆400067 
江赟 重庆工商大学检测控制集成系统重庆市市级工程实验室重庆400067 
严实 重庆川仪分析仪器有限公司重庆400060 
AuthorInstitution
Yan Yue Chongqing Engineering Laboratory for Detection, Control and Integrated System, Chongqing Technology and Business University, Chongqing 400067, China 
Jiang Yun Chongqing Engineering Laboratory for Detection, Control and Integrated System, Chongqing Technology and Business University, Chongqing 400067, China 
Yan Shi Chongqing Chuanyi Analyzer Co. Ltd., Chongqing 400060, China 
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中文摘要:
      火电厂排放气体中的氮氧化物(NO2、NO、N2O等)浓度一直是环保检测的重要指标。针对基于光谱分析的氮氧化物浓度检测精度受到多种因素制约和干扰(如温度、粉尘、水分、电器噪音、光学镜片老化、多组分气体吸收峰值交叉干扰等),很难采用单一方法进行改进的问题。首先设计硬件预处理装置用于气体组分的过滤和提纯,然后利用构建的径向基函数(RBF)网络对传感器测试值进行校正。RBF神经网络的自学习自训练能力省去了传统的对干扰因素进行补偿的研究建模,使得检测中数据处理工作效率更高。随机抽取国内某大型火电厂2015年实际数据进行仿真实验以及预测、分析,综合平均相对误差为0841%,表明方法的有效性。
英文摘要:
      The concentration of nitrogen oxides (NO2, NO, N2O, etc.) in power plant is an important index of environmental protection. Aiming at the problem that the detection accuracy of nitrogen oxides concentration based on spectral analysis could be interfered by all kinds of factors, such as temperature, moisture content, tar, naphthalene, noise of electric devices, optical lens aging, interference at spectral absorption characteristics of polluting gases etc, it is difficult to improve in a single way. At first, the hardware modification is favorable for gas purification and filter. And then, the self learning and self training ability of RBF neural network can save the traditional model for the study of interference factors, and make the data processing more efficient. On the basis of a large thermal power plant’s real data in 2015, the computer simulation and analysis show that this method can improve the accuracy effectively. The overall average deviation is 0.841%.
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