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Optimization of electrical smelting furnace for magnesia based on PLS model

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.