基于深度信念网络和极限学习机的SO2浓度检测

A Detection Method of SO2 Concentration Based on DBN and ELM

使用差分吸收光谱技术(Differential optical absorption spectroscopy,DOAS)进行工业在线气体检测,在气体浓度较低时,其光谱吸收不明显,信噪比较低,通过传统方法来对工业气体浓度进行反演,预测结果难以满足工业应用具体要求。针对SO2气体的差分吸收光谱特点,采用氚灯作为光源,采集189.73~644 nm波段内的标准浓度SO2的吸收光谱高维数据,选取吸收光谱数据并进行预处理,然后利用训练集数据建立深度信念网络模型进行低维特征提取。在此基础上,利用训练数据的低维嵌入特征构建极限学习机反演模型,实现SO2气体浓度计算,并对该模型进行了有效性测试,从而得到一种更加精确的SO2气体浓度在线检测方法。

Differential optical absorption spectroscopy(DOAS) is widely used for online gas detection in industry.However,when the concentration of industrial gas is low,the spectral absorption is not obvious and the SNR is very low.So if the inversion of industrial gas concentration is carried out by using the traditional methods,it is very difficult to meet the requirements of industrial application.According to the differential absorption spectra of SO2,tritium lamp is used as the light source to collect the high-dimensional data of absorption spectra in 189.73~644 nm band.And after selecting and preprocessing the absorption spectra data,a deep belief network(DBN) model is established based on the training set data to extract the low-dimensional features of the test data.Furthermore,the extreme learning machine(ELM) is constructed by using the lowdimensional embedding characteristics of training data to realize the calculation of the SO2 concentration.The effectiveness of the proposed model is evaluated,and it seems that the method is more suitable for accurate on-line detection of SO2 concentration in industrial field.