Title :
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
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);
Conference_Titel :
Control Conference (CCC), 2010 29th Chinese
Conference_Location :
Beijing
Print_ISBN :
978-1-4244-6263-6
