Forecast model for dephosphorization process of ferromanganese steels using artificial neural networks

Abstract

One of the main problems affecting the quality of steel products is the existence of contaminants in alloy steel, being phosphorus (P) a major contamination element interfering with the steelmaking process. The increased P concentration levels can severely affect physical integrity of steel bonds, thus threatening the quality of the final product. The dephosphorization process of Ferromanganese consists by carbothermic reaction that involves the control of the manganese volatilization and reduction of manganese oxide in injection of oxygen. Therefore, we propose to forecast model for dephosphorization process of Ferromanganese steels in a steelmaker industry, that allows estimating the phosphorus concentration levels at the final refining process. We chose the artificial neural network models because it is computational models inspired in the human nervous system and an architecture of neural network with the Levenberg-Marquadt algorithm and Kolmogorov theorem for improving the estimation technique. The developed model presented excellent performance with a percentage error of 0.09%. Based on this created estimation model it is possible to estimate the impact of certain P concentration levels in FeMnMC beforehand, with a considerable amount of reliability.

Data Driven Performance Prediction in Steel Making

“…There are some works on the use of neural networks to predict output parameters, such as the temperature of the liquid metal and the volume of oxygen blowing [4], metallurgical length in continuous casting (CC) where the steel solidifies, shell thickness at the end of the mold and the billet surface temperature [5], percentage of phosphorus in the final composition of the steel [6,7]. Mazumdar and Evans [8] provide a complete description of modern steelmaking processes together with physical and mathematical models and solution methodologies based on artificial intelligence.…”

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