NDLI: Intelligent Method For BOF Endpoint [P]&[Mn] Estimation

Content Provider	IEEE Xplore Digital Library
Author	Jun Tao Shusheng Ouyang Xin Wang
Copyright Year	2006
Description	Author affiliation: Shanghai Baosight Software Co. Ltd. (Jun Tao; Shusheng Ouyang; Xin Wang)
Abstract	Effective controlling of phosphorus [P] content and manganese [Mn] content of steel bath is one of main tasks of basic oxygen furnace (BOF) steelmaking progress, but there isn't any real-time means to measure steel bath [P] content or [Mn] content. In this paper, two models are introduced to estimation the bath endpoint [P] and [Mn] for BOF steelmaking progress. Metallurgical mechanism model is applied which is based on the raw material information and process information, for the case of slow analyzing speed of in-blow sublance sample. And neural network model is used which is based on the in-blow sample analyzing result and BOF dynamic process information, for the fast analyzing speed case. Finally the simulation results proved these models are effective
Starting Page	7802
Ending Page	7806
File Size	263953
Page Count	5
File Format	PDF
ISBN	1424403324
DOI	10.1109/WCICA.2006.1713488
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-06-21
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Manganese Information analysis Steel Oxygen Furnaces Neural networks Electric variables measurement Software measurement Raw materials Intelligent control Neural Network Basic Oxygen Furnace Steelmaking Model
Content Type	Text
Resource Type	Article

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