Optimization of electrical smelting furnace for magnesia based on PLS model

Author

Yingwei, Zhang ; Yunpeng, Fan ; Du Wenyou

Author_Institution

Key Lab. of Integrated Autom. of Process Ind., Northeastern Univ., Shenyang, China

fYear

2011

fDate

23-25 May 2011

Firstpage

2665

Lastpage

2669

Abstract

In this paper, a new modeling approach is proposed by adding the partial least squares (PLS) model between input variables and state variables. The contribution is obtained more accurate system information. This approach is applied on temperature control in the electrical smelting furnace for magnesia (ESFM) by combining iterative learning control (ILC) and particle swarm optimization (PSO) algorithm, the simulation results show that the algorithm is effective.

Keywords

electric furnaces; iterative methods; learning (artificial intelligence); least squares approximations; magnesium compounds; particle swarm optimisation; smelting; temperature control; PLS model; electrical smelting furnace for magnesia; input variables; iterative learning control; partial least square model; particle swarm optimization algorithm; state variables; temperature control; Computational modeling; Data models; Input variables; Mathematical model; Process control; Production; Simulation; iterative learning control (ILC); partial least squares (PLS); particle swarm optimization (PSO);

fLanguage

English

Publisher

ieee

Conference_Titel

Control and Decision Conference (CCDC), 2011 Chinese

Conference_Location

Mianyang

Print_ISBN

978-1-4244-8737-0

Type

conf

DOI

10.1109/CCDC.2011.5968661

Filename

5968661