文献类型：期刊文献

英文题名：A novel adaptive algorithm with near-infrared spectroscopy and its application in online gasoline blending processes

作者：He, Kaixun[1,2];Qian, Feng[1];Cheng, Hui[1];Du, Wenli[1]

机构：[1]E China Univ Sci & Technol, Key Lab Adv Control & Optimizat Chem Proc, Minist Educ, Shanghai 200237, Peoples R China;[2]E China Univ Sci & Technol, Sch Informat Sci & Engn, Shanghai 200237, Peoples R China

年份：2015

卷号：140

起止页码：117

外文期刊名：CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS

收录：;EI(收录号：20242616484592);WOS:【SCI-EXPANDED(收录号:WOS:000349062500012)】；

基金：This work is supported by the Major State Basic Research Development Program of China (2012CB720500), the National Natural Science Foundation of China (U1162202), the National Science Fund for Outstanding Young Scholars (61222303), and the Shanghai Leading Academic Discipline Project (B504). We thank the editor and anonymous reviewers for their valuable comments and suggestions to help improve the quality of our paper.

语种：英文

外文关键词：Near-infrared spectroscopy; Gasoline blending; Adaptive model

摘要：Near-infrared (NIR) spectroscopy has been widely used to estimate product qualities. Although numerous studies on NIR modeling methods have been conducted, few papers have reported the online application of an NIR spectrometer in the gasoline blending process. This study presents a novel adaptive modeling method to establish an NIR model for the gasoline blending process. This method is based on the local learning and recursive modeling framework. Based on the framework, the proposed method can adjust the model structure from two aspects: (i) in sampling intervals, the model is updated with a local learning strategy, and the weights of the training samples can be gradually adjusted; and (ii) when new reference samples become available, the new data pairs are selected and added to the training data set based on an effective evaluation mechanism. The high performance of the proposed algorithm was demonstrated through a spectroscopic data set from a real gasoline blending process. The research octane number (RON), as the most important properties of gasoline, was estimated. Several modeling methods such as recursive partial least squares (RPLS), partial least squares (PLS), and locally weighted PLS were utilized for comparison. The results show that the proposed approach produce more accurate results than the traditional RPLS and locally weighted PIS algorithms. (C) 2014 Published by Elsevier B.V.