文献类型：期刊文献

英文题名：Near-infrared spectroscopy for the concurrent quality prediction and status monitoring of gasoline blending

作者：He, Kaixun[1,2];Zhong, Maiying[1];Li, Zhi[2];Liu, Jingjing[1]

机构：[1]Shandong Univ Sci & Technol, Coll Elect Engn & Automat, Qingdao 266590, Peoples R China;[2]East China Univ Sci & Technol, Key Lab Adv Control & Optimizat Chem Proc, Minist Educ, Shanghai 200237, Peoples R China

年份：2020

卷号：101

外文期刊名：CONTROL ENGINEERING PRACTICE

收录：;EI(收录号：20202208761023);WOS:【SCI-EXPANDED(收录号:WOS:000555039300011)】；

基金：This research was funded by the National Natural Science Foundation of China (61803234, 61751307, 61873149 and 61733009); the Natural Science Foundation of Shandong Provincial of China (ZR2017BF026); the Fundamental Research Funds for the Central Universities, China; and the Research Fund for the Taishan Scholar Project of Shandong Province of China.

语种：英文

外文关键词：Soft sensor; Training sample selection; Gaussian mixture model; Semisupervised; Gasoline blending

摘要：Gasoline is one of the major products of oil and petrochemical industry. Blending is the final step and key to improve the efficiency of gasoline production. As an important property that can reflect the quality of gasoline products, the research octane number (RON) of final products has been widely used to evaluate the running status of gasoline blending. However, the real-time and direct acquisition of RON from this process is difficult because it have to be determined by running the fuel in a test engine with a variable compression ratio under controlled conditions. This work proposes a data-driven soft sensor based on near-infrared (NIR) spectroscopy for online RON estimation. A modified semisupervised Gaussian mixture algorithm is adopted to automatically discover meaningful modeling samples and initialize the quality prediction model. Besides, a monitoring model is integrated into the quality prediction sensor to monitor the running status and the accuracy of the NIR-based quality prediction sensor. Datasets from a numerical experiment and industrial gasoline blending are provided to reveal the effectiveness and superiorly of the proposed method.