Component Content Soft-Sensor Based on RBF Neural Network in Rare Earth Countercurrent Extraction Process

Author

Yang, Hui ; Xu, Yonggang ; Wang, Xin

Author_Institution

Sch. of Electr. & Electron. Eng., East China Jiaotong Univ., Nanchang

Volume

1

fYear

0

fDate

0-0 0

Firstpage

4909

Lastpage

4912

Abstract

In consideration of the difficulty in online measuring the component content in rare earth extraction separation production process, the soft-sensor method based on the radial basis function (RBF) neural networks is proposed to measure the rare earth component content. The parameters of soft-sensor are optimized by the hierarchical genetic algorithms. In addition, application experiment research of this proposed method is carried out in the rare earth separation production process of a corporation. The results show that this method is effective and can realize online measuring for the component content of rare earth in the countercurrent extraction

Keywords

metallurgical industries; mineral processing industry; neurocontrollers; radial basis function networks; rare earth metals; separation; component content soft-sensor; hierarchical genetic algorithms; radial basis function neural networks; rare earth extraction separation production process; Automation; Electric variables measurement; Electronic mail; Genetic algorithms; Intelligent control; Intelligent networks; Neural networks; Production; Roentgenium; RBF neural network; countercurrent extraction; hierarchical genetic algorithms; rare earth; soft-sensor;

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.2006.1713318

Filename

1713318