期刊
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
卷 16, 期 4, 页码 2799-2808出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TII.2019.2945001
关键词
Petroleum; Data models; Estimation; Adaptation models; Informatics; Process control; Monitoring; Gasoline blending; locally weighted; near-infrared (NIR) spectra; probability regression; soft sensor
类别
资金
- National Natural Science Foundation of China [61803234, 61873149]
- Shandong Provincial Natural Science Foundation of China [ZR2017BF026]
- Postdoctoral Science Foundation of China [2018M632691]
- Research Fund for the Taishan Scholar Project of Shandong Province of China
- Scientific Research Foundation of Shandong University of Science and Technology for Recruited Talents [2017RCJJ056]
Near-infrared (NIR) spectroscopy plays a critical role in online analysis of difficult-to-measure properties of petrochemicals. In industrial applications, a calibration model among NIR spectra and properties must be established. However, it is a challenge to obtain a precision NIR model in the majority of petrochemical processes since industrial data present strong non-Gaussian and uncertainty characteristics. To deal with these problems, in the present work a probabilistic regression modeling method based on a biased minimax probability machine (BMPM) is proposed, without assuming any specific distributions for the data, in this article. In addition, a multiple locally weighted updating approach with a new supervised similarity distance is introduced to cope with process changes. The greatest advantage of the proposed approach is that it has superior capability in dealing with uncertainties and variations. The effectiveness of the method is illustrated through its application in an actual gasoline blending process and a simulated fermentation process.
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