Title :
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
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);
Conference_Titel :
Control and Decision Conference (CCDC), 2011 Chinese
Conference_Location :
Mianyang
Print_ISBN :
978-1-4244-8737-0
DOI :
10.1109/CCDC.2011.5968661
