NDLI: Modeling of coke consumption in sintering process based on GA wavelet neural network

Content Provider	IEEE Xplore Digital Library
Author	Weng Wei Wei Chen Xin Wu Min Cao Wei Hua
Copyright Year	2013
Description	Author affiliation: Sch. of Inf. Sci. & Eng., Central South Univ., Changsha, China (Weng Wei Wei; Chen Xin; Wu Min; Cao Wei Hua)
Abstract	The combustion of coke provides the main energy for sintering process production, but there is lack of coke consumption indicators and effective prediction methods in the sintering process till now, so that it is very hard to optimize the sintering process correctly in order to reduce the coke consumption. Based on the analysis on the mechanism of iron ore powder sintering, a reasonable coke consumption indicator is defined. The factors affecting the consumption of coke are determined through the integrated use of the mechanism analysis and gray correlation analysis methods. As the sintering process' features such as time delay, time variance and nonlinearity are considered, a GA-WNN predictive model is developed, in which the genetic algorithm is used to optimize the connection weights, expansion factors and translation scaling factors in order to overcome wavelet neural network's shortcoming-that it is often trapped into local minimum. The GA-WNN model is applied to calculate the coke consumption in a simulation. The result demonstrates that GA-WNN provides an effective way to predict the coke consumption, which serves as a basis to optimize sintering process and reducing the coke consumption.
Starting Page	1860
Ending Page	1865
File Size	345843
Page Count	6
File Format	PDF
ISBN	9789881563835
ISSN	19341768
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-07-26
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	TCCT, CAA
Subject Keyword	Iron ore powder sintering Powders Neural networks Wavelet neural network Genetic algorithm Process control Educational institutions Iron Coke consumption Electronic mail Genetic algorithms
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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