Component Content Soft-Sensor in Rare-Earth Extraction Based on PSO and LS-SVM

Author

Xiang, Zhengrong ; Liu, Songqing

Author_Institution

Sch. of Autom., Nanjing Univ. of Sci. & Technol., Nanjing

Volume

6

fYear

2008

fDate

18-20 Oct. 2008

Firstpage

392

Lastpage

395

Abstract

To tackle the problem that the component content is difficult to detect online, an online prediction method of component content in the rare-earth extraction process using soft sensors based on least squares support vector machines (LS-SVM) is proposed. Particle swarm optimization algorithm (PSO) is presented to select the parameters of LS-SVM and the kernel function. The result of simulation indicates that this method is effective. Compared with the method base on neural network, the method based on LS-SVM is more effective to realize online prediction of the component content in the rare earth extraction process.

Keywords

least squares approximations; metallurgy; neural nets; particle swarm optimisation; process control; rare earth metals; support vector machines; LS-SVM; PSO; component content soft-sensor; kernel function; least squares support vector machines; neural network; online detection; online prediction method; particle swarm optimization algorithm; rare-earth extraction process; Accuracy; Artificial neural networks; Data mining; Feeds; Least squares methods; Neural networks; Sampling methods; Solvents; Support vector machines; Training data; LS-SVM; Particle Swarm Optimization; rare earth extraction; soft sensor;

fLanguage

English

Publisher

ieee

Conference_Titel

Natural Computation, 2008. ICNC '08. Fourth International Conference on

Conference_Location

Jinan

Print_ISBN

978-0-7695-3304-9

Type

conf

DOI

10.1109/ICNC.2008.249

Filename

4667865