文献类型：期刊文献

英文题名：A Novel Integrated Approach to Characterization of Petroleum Naphtha Properties From Near-Infrared Spectroscopy

作者：Yu, Huijing[1,2];Du, Wenli[1,2];Lang, Zi-Qiang[3];Wang, Kai[1,2];Long, Jian[1,2]

机构：[1]East China Univ Sci & Technol, Sch Informat Sci & Engn, Shanghai 200237, Peoples R China;[2]East China Univ Sci & Technol, Key Lab Smart Mfg Energy Chem Proc, Minist Educ, Shanghai 200237, Peoples R China;[3]Univ Sheffield, Dept Automat Control & Syst Engn, Sheffield S1 3JD, S Yorkshire, England

年份：2021

卷号：70

外文期刊名：IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT

收录：;EI(收录号：20212010347147);WOS:【SCI-EXPANDED(收录号:WOS:000688313500003)】；

基金：This work was supported in part by the National Natural Science Foundation of China through the Basic Science Center Program under Grant 61988101, in part by the International (Regional) Cooperation and Exchange Project under Grant 61720106008, in part by the National Natural Science Fund for Distinguished Young Scholars under Grant 61725301, in part by the China Scholarship Council under Grant 201906745024, and in part by the Shanghai Institute of Intelligent Science and Technology, Tongji University, China.

语种：英文

外文关键词：Correlation analysis (CA); data processing; machine learning (ML); near-infrared (NIR) spectroscopy; petroleum naphtha characterization; predictive models

摘要：This article presents a study about the rapid characterization of petroleum naphtha properties based on near-infrared (NIR) spectroscopy. The major challenge of this problem is the low prediction accuracy and poor robustness of predictive models caused by significant noise and insufficient sample data. To address this challenge, a machine learning (ML) method and a correlation analysis (CA) method are applied, respectively. The ML approach uses wavelet transform to reduce noise and kernel partial least square (kPLS) algorithm to deal with the non-linearities. The CA method utilizes the correlation relationship between petroleum naphtha samples and real-time NIR data to solve robustness problem. In order to exploit the advantages of both methods, a novel integration approach is then proposed, which systematically integrates the ML and correlation methods for both good accuracy and robustness. Application studies on NIR spectroscopy data from industry have been conducted. The results confirm the issues with only use of the ML or CA method and demonstrate the advantages of the proposed integrated approach and its potential in industrial applications.