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

Lee Vinagre Monteiro,Angelo Marcio Oliveira Santanna

doi:10.1590/s1517-707620200002.1008

Lee Vinagre Monteiro, Angelo Marcio Oliveira Santanna

Open Access

https://doi.org/10.1590/s1517-707620200002.1008

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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. Keywords: Ferromanganese alloys, dephosphorization process, Neural networks, Kolmogorov theorem. 1

Highlights

Assuring the quality of processes and products remains a constant challenge for companies, and this is increasingly becoming a fundamental requirement for their endurance
After the correlation test was applied it became clear that from the 21 initial variables only 4 variables were significantly correlated with P level output variables at the final refining process, as: the initial phosphorus level (P*), the initial carbon level (C*), the manganese oxide level (MnO) and the loads composition
We propose to determine a prediction model for Ferromanganese refining process in a steelmaker industry that allows estimating the phosphorus concentration levels

Summary

Introduction

Assuring the quality of processes and products remains a constant challenge for companies, and this is increasingly becoming a fundamental requirement for their endurance. This is no different in steelmaking companies. The refining process adopted by the company subject to this study uses high-purity oxygen to reduce carbon levels in High-Carbon Ferromanganese (FeMnHC) originating Medium-Carbon Ferromanganese (FeMnMC) which has higher market value. During this process there is alteration in levels of other elements, including P. The main advantages of ANNs are: (i) the capacity of approximating the behavior of non-linear physical phenomena, not requiring profound comprehension statistical and complex statistical treatment of the modeled data and; (ii) the capacity of learning any input/output variables of continuous form [4]

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