DocumentCode :
1676623
Title :
Soft-sensor model of mill load based on rough set and RBF neural network
Author :
Zhang, Yong ; Wang, Yukun
Author_Institution :
Sch. of Electron. & Inf. Eng., Liaoning Univ. of Sci. & Technol., Anshan, China
fYear :
2010
Firstpage :
4333
Lastpage :
4336
Abstract :
Most of the mill concentrator determine the mill load according to the noise and the ball mill operating current, and it´s low accuracy. So a mill load forecasting soft-sensor model based on the fundamental factors that reflecting the mill load is researched, Rough set theory is apply to optimization modeling data, applications RBF neural networks buliding mill load soft-sensor model and train the network Through adaptive clustering method. Test results show that the mathematical model can meet the mill load forecasting accuracy, this method laid a good foundation to enhance the level of mill load control and reduce equipment failure rates .
Keywords :
ball milling; pattern clustering; production engineering computing; radial basis function networks; rolling mills; rough set theory; RBF neural network; adaptive clustering method; ball mill operating current; equipment failure rates; mathematical model; mill concentrator; mill load forecasting; rough set theory; soft-sensor model; Accuracy; Adaptation model; Artificial neural networks; Data models; Load modeling; Mathematical model; Predictive models; Adaptive Clustering; Ball mill load; RBF neural network; Rough Sets;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligent Control and Automation (WCICA), 2010 8th World Congress on
Conference_Location :
Jinan
Print_ISBN :
978-1-4244-6712-9
Type :
conf
DOI :
10.1109/WCICA.2010.5554025
Filename :
5554025
Link To Document :
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