当前位置: X-MOL 学术Adv. Mater. Sci. Eng. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Crude Oil Source Identification of Asphalt via ATR-FTIR Approach Combined with Multivariate Statistical Analysis
Advances in Materials Science and Engineering ( IF 2.098 ) Pub Date : 2020-07-06 , DOI: 10.1155/2020/2025072
Ruibo Ren 1 , Wenmiao Fan 1 , Pinhui Zhao 1 , Hao Zhou 1 , Weikun Meng 1 , Ping Ji 2
Affiliation  

The types of crude oil for producing asphalt have a decisive influence on various performance measures (including aging resistance and durability) of asphalt. To discriminate and predict the crude oil source of different asphalt samples, a discrimination model was established using 12 greatly different infrared (IR) characteristic absorption peaks (CAPs) as predictive variables. The model was established based on diverse fingerprint recognition technologies (such as principal component analysis (PCA) and multivariate logistic regression analysis) by using attenuated total reflectance-Fourier transform infrared spectroscopy (ATR-FTIR). In this way, the crude oil source of different asphalt samples can be effectively discriminated. At first, by using PCA, the 12 CAPs in the IR spectra of asphalt samples were subjected to dimension reduction processing to control the variables of key factors. Moreover, the scores of various principal components in asphalt samples were calculated. Afterwards, the scores of principal components were analysed through modelling based on multivariate logistic regression analysis to discriminate and predict the crude oil source of different asphalt samples. The result showed that the logistic regression model shows a favourable goodness of fit, with the prediction accuracy reaching 93.9% for the crude oil source of asphalt samples. The method exhibits some outstanding advantages (including ease of operation and high accuracy), which is important when controlling the source and quality and improving the performance of asphalt.

中文翻译:

ATR-FTIR方法与多元统计分析相结合的沥青原油来源识别

生产沥青的原油类型对沥青的各种性能指标(包括耐老化性和耐久性)具有决定性的影响。为了区分和预测不同沥青样品的原油来源,使用12个大大不同的红外(IR)特征吸收峰(CAPs)作为预测变量,建立了鉴别模型。该模型是基于多种指纹识别技术(例如主成分分析(PCA)和多元logistic回归分析),使用衰减全反射-傅里叶变换红外光谱(ATR-FTIR)建立的。这样,可以有效地区分不同沥青样品的原油来源。首先,通过使用PCA,对沥青样品的红外光谱中的12个CAP进行降维处理,以控制关键因素的变量。此外,计算了沥青样品中各种主要成分的分数。然后,通过基于多元逻辑回归分析的建模方法分析主成分的得分,以区分和预测不同沥青样品的原油来源。结果表明,逻辑回归模型显示出良好的拟合度,对沥青样品原油来源的预测精度达到93.9%。该方法具有一些突出的优点(包括易于操作和高精度),这在控制来源和质量以及改善沥青性能时非常重要。此外,计算了沥青样品中各种主要成分的分数。然后,通过基于多元逻辑回归分析的建模分析主成分的得分,以区分和预测不同沥青样品的原油来源。结果表明,逻辑回归模型显示出良好的拟合度,对沥青样品原油来源的预测精度达到93.9%。该方法具有一些突出的优点(包括易于操作和高精度),这在控制来源和质量以及改善沥青性能时非常重要。此外,计算了沥青样品中各种主要成分的分数。然后,通过基于多元逻辑回归分析的建模方法分析主成分的得分,以区分和预测不同沥青样品的原油来源。结果表明,逻辑回归模型显示出良好的拟合度,对沥青样品原油来源的预测精度达到93.9%。该方法具有一些突出的优点(包括易于操作和高精度),这在控制来源和质量以及改善沥青性能时非常重要。通过多元logistic回归分析,通过建模分析主成分的得分,以区分和预测不同沥青样品的原油来源。结果表明,逻辑回归模型显示出良好的拟合度,对沥青样品原油来源的预测精度达到93.9%。该方法具有一些突出的优点(包括易于操作和高精度),这在控制来源和质量以及改善沥青性能时非常重要。通过多元logistic回归分析,通过建模分析主成分的得分,以区分和预测不同沥青样品的原油来源。结果表明,逻辑回归模型显示出良好的拟合度,对沥青样品原油来源的预测精度达到93.9%。该方法具有一些突出的优点(包括易于操作和高精度),这在控制来源和质量以及改善沥青性能时非常重要。
更新日期:2020-07-06
down
wechat
bug