NDLI: A novel method based on information fusion for modeling burden surface temperature field in blast furnace

Content Provider	IEEE Xplore Digital Library
Author	Liu Zhentao Wu Min Cao Weihua He Yong
Copyright Year	2008
Description	Author affiliation: Sch. of Inf. Sci. & Eng., Central South Univ., Changsha (Liu Zhentao; Wu Min; Cao Weihua; He Yong)
Abstract	A novel method for modeling burden surface temperature field in blast furnace (BF) is presented in this paper. It is based on information fusion and makes full use of temperature information detected from the throat of BF. A dynamic temperature calibration method based on two-point method is taken as the benchmark calibration method, in which the nonlinear error involved is adjusted by an improved BP neural network based on genetic algorithm (GA). Due to the high robustness and better nonlinear approximation ability, the proposed method overcomes the shortages of conventional calibration methods, and gets the distribution model of temperature field with more details. The simulation results and industrial implementation show that, this model of burden surface temperature field can depict distribution of temperature field more exactly, so that it is more effective to understand the distribution of gas flow and instruct the operation of burden distribution.
Starting Page	285
Ending Page	289
File Size	316568
Page Count	5
File Format	PDF
ISBN	9787900719706
DOI	10.1109/CHICC.2008.4605312
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-07-16
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute Of Intelligence M&C Technical Of KMUST
Subject Keyword	Temperature distribution Crossing temperature Artificial neural networks Information fusion Blast furnaces Calibration Electronic mail Proposals Genetic algorithms Infrared image BP neural network Genetic algorithm Temperature calibration
Content Type	Text
Resource Type	Article

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