A model for steel billet temperature of prediction of heating furnace

Author

Chen Youwen ; Chai Tianyou

Author_Institution

Key Lab. of Process Ind. Autom. of Minist. of Educ., Northeastern Univ., Shenyang, China

fYear

2010

fDate

29-31 July 2010

Firstpage

1299

Lastpage

1302

Abstract

The estimate of main guide line of steel billet temperature is all along a puzzle in the industry due to the fact that heating furnace is a multivariable nonlinear system with large inertia, net lag and crossed coupling. This thesis proposes billet predictive model between billet temperature variable and heating process variable with improved ELM method. At last, based on actual data from steel industry, reckon model parameter. Analyzing check and error indicate that this model can forecast the temperature of entrance for billet steel before 10~25 minutes, and the error of prediction can satisfy the demand for industrial application.

Keywords

billets; furnaces; learning systems; multivariable control systems; nonlinear control systems; steel industry; billet predictive model; billet temperature variable; extreme learning machine; heating furnace prediction; heating process variable; improved ELM method; multivariable nonlinear system; steel billet temperature; steel industry; Artificial neural networks; Billets; Furnaces; Heating; Machine learning; Predictive models; Temperature measurement; Extreme Learning Machine (ELM); Forecast of Billet Temperature; Heating Furnace; Particle Swarm Optimize (PSO);

fLanguage

English

Publisher

ieee

Conference_Titel

Control Conference (CCC), 2010 29th Chinese

Conference_Location

Beijing

Print_ISBN

978-1-4244-6263-6

Type

conf

Filename

5573535