Biased minimax probability model and its application in prediction of gasoline properties

被引：0
|
作者：
He K.-X. [1 ,2 ]
Liu J.-J. [1 ]
Wang X.-B. [1 ]
Su Z.-Y. [1 ]
机构：
[1] College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao, 266590, Shandong
[2] Key Laboratory of Advanced Control and Optimization for Chemical Processes, East China University of Science and Technology, Shanghai
来源：
Kongzhi Lilun Yu Yingyong/Control Theory and Applications | 2020年 / 37卷 / 08期
基金：
中国国家自然科学基金; 中国博士后科学基金;
关键词：
Dynamic modeling; Gasoline blending; Machine learning; Minimax probability machine; Process systems;
D O I：
10.7641/CTA.2020.90913
中图分类号：
学科分类号：
摘要：
The online prediction of gasoline properties is mostly realized by the near-infrared quantitative analysis model which established by the unbiased estimation method. However, the range of the positive and negative deviations of the cumulative prediction error is difficult to control, which will seriously affect the operation of gasoline blending optimization control. To deal with this issue, a biased estimation method is proposed for the online prediction of gasoline properties. Firstly, a biased minimax probability regression model is proposed based on minimax probability machine. Then, based on just-in-time learning approach, a local modeling and updating strategy is developed for the biased regression model to improve its adaptive ability. Finally, experiments are carried out with the gasoline data collected from a domestic oil refinery. The results show that the present method has obvious advantages compared with traditional algorithms, and it is beneficial to improve the operation rate of the optimal control system of gasoline blending. © 2020, Editorial Department of Control Theory & Applications South China University of Technology. All right reserved.
引用
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页码：1799 / 1807
页数：8
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