A Predictive Model of Sinter Chemical Composition and Its Application

Author

Wang, Jiesheng ; Wang, Wei

Author_Institution

Res. Center of Inf. & Control, Dalian Univ. of Technol.

Volume

1

fYear

0

Firstpage

4856

Lastpage

4860

Abstract

It is necessary to predict sinter quality in order to realize optimization of technology parameters in sintering process. A predictive model is proposed by combining hybrid Takagi-Sugeno fuzzy model and particle swarm optimization algorithm to predict the quality indexes (FeO content and basicity R) of the finished sinter mineral. The gray relation analysis (GRA) method is used to analyze the factors influencing finished sinter quality. The simulation shows that the method can optimize the structure parameters of the T-S fuzzy model and shorten the learning time. The predictive model was tested by actual industrial data and a relatively satisfactory prediction result was obtained

Keywords

chemical variables control; closed loop systems; fuzzy control; learning (artificial intelligence); particle swarm optimisation; sintering; FeO content; Takagi-Sugeno fuzzy model; basicity; gray relation analysis; learning; particle swarm optimization; sinter chemical composition prediction; sinter quality; sintering process; Chemical technology; Humidity; Iron; Minerals; Optimization methods; Particle swarm optimization; Prediction algorithms; Predictive models; Takagi-Sugeno model; Temperature; Gray relation analysis; Particle swarm optimization; Sintering process; T-S fuzzy model;

fLanguage

English

Publisher

ieee

Conference_Titel

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

Print_ISBN

1-4244-0332-4

Type

conf

DOI

10.1109/WCICA.2006.1713307

Filename

1713307