文献类型：期刊文献

英文题名：Novel adaptive sample space expansion approach of NIR model for in-situ measurement of gasoline octane number in online gasoline blending processes

作者：Wang, Kai[1,2];He, Kaixun[3];Du, Wenli[1];Long, Jian[1]

机构：[1]East China Univ Sci & Technol, Key Lab Smart Mfg Energy Chem Proc, Minist Educ, Shanghai 200237, Peoples R China;[2]East China Univ Sci & Technol, Sch Informat Sci & Engn, Shanghai 200237, Peoples R China;[3]Shandong Univ Sci & Technol, Coll Elect Engn & Automat, Qingdao 266590, Peoples R China

年份：2021

卷号：239

外文期刊名：CHEMICAL ENGINEERING SCIENCE

收录：;EI(收录号：20212110405611);WOS:【SCI-EXPANDED(收录号:WOS:000661868200007)】；

基金：This work was supported by National Natural Science Foundation of China (Basic Science Center Program: 61988101) , National Natural Science Foundation of China (61973124) and National Natural Science Fund for Distinguished Young Scholars (61725301) .

语种：英文

外文关键词：Gasoline; Just-in-time (JIT) learning; Near-infrared (NIR) spectroscopy; Model updating

摘要：For real-time measurement of gasoline octane number in online gasoline blending processes by near infrared (NIR) spectroscopy, this paper presents an adaptive sample space expansion approach (ASSEA) to improve the real-time measurement accuracy and to shorten the time for establishing the model. To improve the adaptability of the model, a comprehensive index that combines the characteristics of the query samples and the distance between the spectra is adopted to select the guide spectra. A sample construction method is proposed to supplement the original sample set with insufficient sample size. The new spectra are constructed with the labeled spectra based on the guide spectra. Subsequently, the concentration information of the constructed spectra is obtained on the basis of multi-way partial least square method. The proposed ASSEA method is compared with six representative methods. The prediction performance of ASSEA method (RMSE is 0.2442, and R-2 is 0.8767) is significantly better than other methods. And compared with the traditional PLS method, RMSE and R-2 are reduced and increased by 33.4% and 45.9%, respectively. Therefore, its efficiency is validated through an industrial gasoline blending process. (C) 2021 Elsevier Ltd. All rights reserved.