NDLI: Application of online SVR on the dynamic liquid level soft sensing

Content Provider	IEEE Xplore Digital Library
Author	Bai Shan Jiang Zijian Wang Tong Lai Haozhe
Copyright Year	2013
Description	Author affiliation: Sch. of Electr. Eng., Shenyang Univ. of Technol., Shenyang, China (Bai Shan; Jiang Zijian; Wang Tong; Lai Haozhe)
Abstract	There exist different kinds of drawbacks and security risks in the measurement methods of dynamic liquid level in oilfield, consequently , each of them can't provide reliable protection to the oilfield for its high efficiency production operations and easily to cause significant economic losses . In order to solve the problems above , soft sensing technology is found as a new way to measure the dynamic liquid level of the oilfield instead of original hardware technology. ANN is a kind of soft sensing technology which is earlier to be used as the way to measure the dynamic liquid level , but there are some problems restricted its use of areas, such as easy to fall into local minimization, slow convergence speed and poor generalization ability. According to the oilfield data's characteristics, Online SVR algorithm is proposed to be a solution to measure the dynamic liquid level in this paper. As a kind of soft sensing technology, Online SVR not only has the same performance of ANN, but also can update the forcast model by online . In addition, it also solve the problems of ANN .Online SVR algorithm is verified by the experiment in this paper and the result of this experiment shows that Online SVR has higher precision than ANN . The experimental results also show the Online SVR can be used in measuring the dynamic liquid level in oilfield.
Sponsorship	IEEE Control Syst. Soc.
Starting Page	3003
Ending Page	3007
File Size	707378
Page Count	5
File Format	PDF
ISBN	9781467355339
e-ISBN	9781467355346
DOI	10.1109/CCDC.2013.6561460
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-05-25
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Training Dynamic Liquid Level Online SVR Liquids Heuristic algorithms Artificial Neural Networks Artificial neural networks Soft Sensing Sensors Equations
Content Type	Text
Resource Type	Article

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