Prediction of Silicon Content in Hot Metal Based on Genetic Algorithms

Author

Zhao, Min ; Liu, Xiangguan ; Luo, Shihua

Author_Institution

Inst. of Syst. Optimum Technique, Zhejiang Univ., Hangzhou

Volume

2

fYear

0

fDate

0-0 0

Firstpage

7771

Lastpage

7774

Abstract

In blast furnace (BF) ironmaking process, hot metal silicon content is an important index. Not only is silicon content a significant quality variable, it also reflects the thermal state of BF. A novel genetic algorithm was proposed. Time lag of each pivotal variable was calculated respectively. Based on the time lag analysis, the genetic algorithm is used to approach the fittest equation that describes the behavior between [Si] and the variables. With the calculated equation, the forecasting accuracy is up to 88%. Data, used in this paper, were collected from No.1 BF at Laiwu Iron and Steel Group Co

Keywords

blast furnaces; delays; genetic algorithms; metals; silicon; steel industry; steel manufacture; Si; blast furnace ironmaking process; genetic algorithm; hot metal silicon content; thermal state; time lag analysis; Algorithm design and analysis; Automation; Blast furnaces; Equations; Genetic algorithms; Intelligent control; Iron; Mathematics; Silicon; Steel; BF ironmaking; genetic algorithm; time lag analysis;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Control and Automation, 2006. WCICA 2006. The Sixth World Congress on

Conference_Location

Dalian

Print_ISBN

1-4244-0332-4

Type

conf

DOI

10.1109/WCICA.2006.1713481

Filename

1713481