Authors : M.M. Lafifi , B. Boulebtateche , S. Afifi , Z. Oualia , R. Lakel , M. Bedda and F. Labed
Abstract: In this study, we try to answer a certain number of questions raised in the electric arc furnace control. In this study, we mention the studied choice of the physical model which approaches best the Electric Arc Furnace (E.A.F). The nonlinear model uses the conductance like electric parameter of adjustment, whereas usually one uses the resistance of arc as parameter of adjustment although its accessibility remains implicit. In order to answer the preset objectives, i.e. to control the process of the E.A.F. and this with an acceptable stability in the respect of the constraints and minimization of energy, it is proposed a structure of control by neural inverse model implemented by a Multi-Layer Perceptron network (MLP). The technique of training data base uses the retro propagation of error gradient algorithm. An application on MATLAB SIMULINK made it possible to simulate this structure; convincing results are recorded.
M.M. Lafifi , B. Boulebtateche , S. Afifi , Z. Oualia , R. Lakel , M. Bedda and F. Labed , 2006. Physical Modeling and Control of Electric Arc Furnace by Neural Inverse Model . Asian Journal of Information Technology, 5: 1028-1033.