NDLI: An approach of CBR to intelligent optional control architecture for rare-earth extraction process based on Rough Set

Content Provider	IEEE Xplore Digital Library
Author	Gong Jinhong Yang Hui
Copyright Year	2009
Description	Author affiliation: School of Electronics and Electrical Engineering, East China Jiaotong University, Nanchang, 330013, China (Gong Jinhong; Yang Hui)
Abstract	On the characteristics of complex rare-earth extraction process, difficult system craft operation, achieving hardly automatic control technology, and inaccessibility to on-line component content, the paper lodge a technology for the extraction of rare earth based on the Case Based Reasoning (CBR) technology with Rough-set. It can optimize the manual enactment in the rare-earth extraction process in place of the enactment to feeding liquid flowrate, forecast the information of component content. Also it designs structure of the case and establishes the cases library on the base of the dynamical equilibrium model of the extraction separation process and a large amount of historical data, and simplify the knowledge of the cases library and refines the rules with Rough Set theory, so the cases library is optimized and the speed of retrieval is greatly improved at the same time. The method can achieve the intelligent optimal control for the rare-earth extraction process effectively and obtain good forecast-value of component content. An industrial experiment in the extraction process using this CBR technology with Rough Set proves its effectiveness and accuracy.
Starting Page	1997
Ending Page	2002
File Size	131937
Page Count	6
File Format	PDF
ISBN	9781424427222
DOI	10.1109/CCDC.2009.5191842
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-06-17
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Rough Set Information retrieval Paper technology Data mining Separation processes Case Based Reasoning Design optimization Refining Automatic control Set theory Libraries Optimal Setting Rare Earth Extract Intelligent control
Content Type	Text
Resource Type	Article

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