文献类型：期刊文献

中文题名：A New Sample-Selection and Modeling Method Based on Near-Infrared Spectroscopy and Its Industrial Application

英文题名：A new sample-selection and modeling method based on near-infrared spectroscopy and its industrial application

作者：He, Kai-Xun[1]; Cheng, Hui[1]; Qian, Feng[1]

机构：[1] Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China

年份：2014

卷号：31

期号：2

起止页码：207

中文期刊名：Journal of Donghua University(English Edition)

外文期刊名：Journal of Donghua University (English Edition)

收录：EI(收录号：20143318074337);Scopus

基金：National Natural Science Foundations of China(Nos.U1162202,61222303);National High-Tech Research and Development Program of China(No.2013AA040701);the Fundamental Research Funds for the Central Universities and Shanghai Leading Academic Discipline Project,China(No.B504)

语种：英文

中文关键词：gasoline blending;near-infrared spectroscopy;sample selection;modeling method

外文关键词：Neural networks - Principal component analysis - Gasoline - Blending - Infrared devices - Least squares approximations

摘要：Near-infrared( NIR) spectroscopy has been widely employed as a process analytical tool( PAT) in various fields; the most important reason for the use of this method is its ability to record spectra in real time to capture process properties. In quantitative online applications,the robustness of the established NIR model is often deteriorated by process condition variations,nonlinear of the properties or the high-dimensional of the NIR data set. To cope with such situation,a novel method based on principal component analysis( PCA) and artificial neural network( ANN) is proposed and a new sample-selection method is mentioned. The advantage of the presented approach is that it can select proper calibration samples and establish robust model effectively. The performance of the method was tested on a spectroscopic data set from a refinery process. Compared with traditional partial leastsquares( PLS),principal component regression( PCR) and several other modeling methods, the proposed approach was found to achieve good accuracy in the prediction of gasoline properties. An application of the proposed method is also reported.
Near-infrared (NIR) spectroscopy has been widely employed as a process analytical tool (PAT) in various fields; the most important reason for the use of this method is its ability to record spectra in real time to capture process properties. In quantitative online applications, the robustness of the established NIR model is often deteriorated by process condition variations, nonlinear of the properties or the high-dimensional of the NIR data set. To cope with such situation, a novel method based on principal component analysis (PCA) and artificial neural network (ANN) is proposed and a new sample-selection method is mentioned. The advantage of the presented approach is that it can select proper calibration samples and establish robust model effectively. The performance of the method was tested on a spectroscopic data set from a refinery process. Compared with traditional partial least-squares (PLS), principal component regression (PCR) and several other modeling methods, the proposed approach was found to achieve good accuracy in the prediction of gasoline properties. An application of the proposed method is also reported. Copyright ? 2014.