Abstract
The argon-oxygen decarburization (AOD) is the refining process of stainless steel to get its final chemical composition through several stages where tons of materials are added and oxygen and inert gas are blown. We present in this paper the design of an empirical model of this process to predict critical values of the decarburization process in order to automate it and enhance the production performance of the AOD. We show that it is possible to build an empirical model, simpler than analytical parameterized models, based on Multilinear Regression or Neural Network Perceptron to predict the amount of oxygen to be blown and the temperature to be reached.
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