融合波长选择和异常光谱检测的天然气燃烧过程定量分析方法  

Quantitative Analysis Method of Natural Gas Combustion Process Combining Wavelength Selection and Outlier Spectra Detection

作　　者：曹晖[1] 胡洛娜[1] 周延[2] 

机构地区：[1]西安交通大学电气工程学院电力设备电气绝缘国家重点实验室,陕西西安710049 [2]西安交通大学能源与动力工程学院,陕西西安710049

出　　处：《光谱学与光谱分析》2012年第10期2799-2804,共6页Spectroscopy and Spectral Analysis

基　　金：教育部博士点基金项目(20090201120005);国家自然科学基金项目(61005058)资助

摘　　要：针对天然气燃烧过程的近红外光谱数据,采用了一种融合波长选择和异常光谱检测的定量分析方法。该方法根据偏最小二乘(PLS)模型的系数及预测误差的统计分布,在实现波长选择的同时,完成异常光谱样本的检测。与PLS、先用留一法将异常样本删除后PLS建模(LOO-PLS)、基于PLS的无信息变量消除法(UVE-PLS)以及先用留一法将异常样本删除后使用UVE-PLS建模(LOO-UVE-PLS)相比较,该方法将甲烷预测模型的预测均方根误差(RMSEP)分别降低了14.33%,14.33%,10.96%和12.21%;将一氧化碳预测模型的RMSEP分别降低了67.26%,72.58%,11.32%和4.52%;将二氧化碳预测模型的RMSEP分别降低了5.95%,19.7%,36.71%和4.04%。实验表明,该方法建立的分析物预测模型具有较高的预测能力和较好的稳健性,在大大减少所选波长数量,降低模型复杂度的同时,还能有效地检测出异常光谱样本,减小两者之间的相互影响。The present paper uses a combination method of wavelength selection and outlier spectra detection for quantitative analysis of nature gas combustion process based on its near infrared spectra. According to the statistical distribution of partial least squares （PLS） model coefficients and prediction errors, the method realized wavelength selection and outlier spectra detection, respectively. In contrast with PLS, PLS after leave-one-out for outlier detection （LOO-PLS）, uninformative variable elimination by PLS （UVE-PLS） and UVE-PLS after leave-one-out for outlier detection （LOO-UVE-PLS）, the root-mean-squared error of prediction （RMSEP） based on the method for CH4 prediction model is reduced by 14.33%, 14.33%, 10. 96% and 12.21%;the RMSEP value for CO prediction model is reduced by 67.26%, 72. 58%, 11.32% and 4. 52%; the RMSEP value for CO2 prediction model is reduced by 5.95%, 19.7%, 36.71% and 4. 04% respectively. Experimental results demonstrate that the method can significantly decrease the number of selected wavelengths, reduce model complexity and effectively detect outlier spectra. The established prediction model of analytes is more accurate as well as robust.

关 键 词：天然气燃烧 近红外光谱 波长选择 异常光谱检测 

分 类 号：O657.3[理学—分析化学]