Neural network for roller gap setup in rolling steel mill

Author

Cui, Jianjiang ; Xiao, Wendong ; Xu, Xinhe ; Wu, Wenbm

Author_Institution

Control & Simulation Center, Northeastern Univ., Shenyang, China

Volume

2

fYear

2000

fDate

2000

Firstpage

1135

Abstract

In this paper the methods to control steel strip thickness setup are analyzed for rolling process. By considering many factors that influence the steel strip thickness accuracy, the final thickness error functional formula is obtained. A BP neural network prediction model of final thickness error is presented, high order algorithm is adopted. We train the neural network according to steel strip classification. The combination of this model with others enhances greatly thickness accuracy control

Keywords

backpropagation; neural nets; process control; rolling; steel industry; thickness control; BP neural network prediction model; backpropagation; high-order algorithm; roller gap setup; rolling process; rolling steel mill; steel strip classification; steel strip thickness; steel strip thickness setup control; thickness accuracy control; thickness error functional formula; Automatic control; Feedback control; Intelligent networks; Milling machines; Neural networks; Predictive models; Slabs; Steel; Strips; Thickness control;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Control and Automation, 2000. Proceedings of the 3rd World Congress on

Conference_Location

Hefei

Print_ISBN

0-7803-5995-X

Type

conf

DOI

10.1109/WCICA.2000.863418

Filename

863418