基于光谱诊断技术的乙醇柴油品质检测方法

发布时间：2018-08-26 08:47

【摘要】：越来越多的人选择汽车出行,这必会带来了不少问题,如日益减少的石油资源和石油需求量增加之间的矛盾,为了缓解矛盾,尽快寻找石油燃料的替代产品。乙醇柴油是柴油替代产品的一种,然而,不同厂商生产的乙醇柴油品质参差不齐,不利于乙醇柴油的推广使用,因此,需要一种便捷的手段对乙醇柴油的品质进行检测。本文采取了光谱诊断技术对乙醇柴油的主要指标做了研究。建立乙醇柴油品质指标的准确可靠定量分析模型,具体结论如下:1.以乙醇柴油为实验目标,利用近红外光谱(near infrared spectroscopy,NIR)技术对乙醇柴油的乙醇含量、密度、粘度进行定量分析,采用五种预处理方法对光谱数据进行处理,并建立了最小二乘支持向量机、主成分回归和偏最小二乘回归三种模型。结果表明:在多元散射校正-平滑预处理下,最小二乘支持向量机对乙醇柴油密度、粘度、乙醇含量的建模效果最好,相关系数分别Rp是0.995,0.995和0.995;RMSEP分别是6.8×10-4,1.13×10-2和0.5714×10-1。2.以乙醇柴油为实验目标,利用中红外光谱(mid-infrared spectroscopy,MIR)技术,对乙醇柴油进行光谱采集与分析。对乙醇柴油MIR原始数据进行不同的预处理,并对光谱数据进行波段筛选,分别建立了乙醇柴油乙醇含量、密度、粘度PLSR模型,得出以下主要结论:综合比较八种变量筛选方法,发现UVE-SPA-CARS-PLS对乙醇含量的建模效果最好,模型预测集的Rp、RMSEP分别为0.978、0.825。变量筛选较原始光谱建立的模型来说,不仅模型输入数量减少,预测效果也有所提高。3.利用拉曼光谱技术,对乙醇柴油进行光谱采集与分析,对乙醇柴油拉曼光谱原始数据进行不同的预处理,并对光谱数据进行波段筛选,分别建立了乙醇柴油乙醇含量、密度、粘度PLSR模型,得出以下主要结论:发现SPA-CARS-PLS对乙醇含量的建模效果最好,模型预测集的Rp、RMSEP分别为0.978、0.825。波段筛选出的波长变量以及建模的结果为以后设计便携式中红外光谱仪打下基础。
[Abstract]:More and more people choose to travel by car, which will bring a lot of problems, such as the contradiction between decreasing oil resources and increasing oil demand. In order to alleviate the contradiction, find alternative products of petroleum fuel as soon as possible. Ethanol diesel is one of the alternative products of diesel fuel. However, the quality of ethanol diesel produced by different manufacturers is not uniform, which is not conducive to the promotion and use of ethanol diesel. Therefore, a convenient method is needed to detect the quality of ethanol diesel. In this paper, the main indexes of ethanol diesel oil were studied by spectral diagnostic technique. The accurate and reliable quantitative analysis model of ethanol diesel quality index was established, and the concrete conclusion was as follows: 1. The ethanol content, density and viscosity of ethanol diesel oil were quantitatively analyzed by using near infrared spectroscopy (near infrared spectroscopy,NIR) technique. Five pretreatment methods were used to process the spectral data. Three models, namely least squares support vector machine, principal component regression and partial least squares regression, are established. The results show that the least square support vector machine (LS-SVM) has the best modeling effect on the density, viscosity and ethanol content of ethanol diesel under the condition of multivariate scattering correction and smoothing pretreatment. The correlation coefficient Rp is 0.995 and 0.995 respectively, and the correlation coefficients are 6.8 脳 10 ~ (-4) ~ 1.13 脳 10 ~ (-2) and 0.5714 脳 10 ~ (-1) ~ (2), respectively. Taking ethanol diesel oil as the experimental object, the spectral acquisition and analysis of ethanol diesel oil were carried out by using mid-infrared spectroscopy (mid-infrared spectroscopy,MIR) technology. The MIR raw data of ethanol diesel were pretreated with different bands and spectral data were screened. The PLSR models of ethanol content, density and viscosity of ethanol diesel oil were established, and the following main conclusions were obtained: comprehensive comparison of eight methods for screening variables. It was found that UVE-SPA-CARS-PLS had the best effect on the modeling of ethanol content, and the Rp,RMSEP of the model prediction set was 0. 978 / 0. 825 respectively. Variable screening is more effective than the original spectral model. Not only the input number of the model is reduced, but also the prediction effect is improved. The spectral data of ethanol diesel oil were collected and analyzed by Raman spectroscopy. The original data were pretreated and the spectral data were screened. The ethanol content and density of ethanol diesel oil were established. The main conclusions of viscosity PLSR model are as follows: it is found that SPA-CARS-PLS has the best effect on modeling ethanol content, and the Rp,RMSEP of model prediction set is 0.978 卤0.825, respectively. The wavelength variables selected from the band and the modeling results lay the foundation for the later design of the portable mid-infrared spectrometer.
【学位授予单位】：华东交通大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：O657.3;TQ517

【参考文献】