DocumentCode :
527856
Title :
The quality prediction of iron ore pellets in grate-kiln-cooler system using artificial neural network
Author :
Feng, Junxiao ; Qiao, Yang
Author_Institution :
Sch. of Mech. Eng., Univ. of Sci. & Technol. Beijing, Beijing, China
Volume :
4
fYear :
2010
fDate :
10-12 Aug. 2010
Firstpage :
1906
Lastpage :
1909
Abstract :
A model of three single layer back propagation(BP) artificial neural networks has been established to predict the compression strength of final pellets and dried pellets, the same as the shatter strength of the green pellets, according to the production data from SHOUGANG Mining Company. The Levenberg-Marquardt optimization arithmetic was used to train the model with the production data. After training, the error of the prediction result is less than 3%. The developed model can meet the requirement from production with a high accuracy and a wide flexibility.
Keywords :
backpropagation; blast furnaces; cooling; kilns; neural nets; optimisation; production engineering computing; quality control; Levenberg-Marquardt optimization arithmetic; SHOUGANG Mining Company; blast furnace; compression strength; grate-kiln-cooler system; iron ore pellets quality prediction; shatter strength; single layer back propagation artificial neural networks; Artificial neural networks; Blast furnaces; Iron; Kilns; Predictive models; Production; Training data; artificial nerural network; grate-kiln-cooler; pellet; quality prediction;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Natural Computation (ICNC), 2010 Sixth International Conference on
Conference_Location :
Yantai, Shandong
Print_ISBN :
978-1-4244-5958-2
Type :
conf
DOI :
10.1109/ICNC.2010.5584645
Filename :
5584645
Link To Document :
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