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.

摘要 影响钢材产品质量的主要问题之一为合金钢中存在杂质，其中磷（P）是干扰炼钢进程的主要杂质元素。磷浓度升高会严重损害钢结合体的物理完整性，进而威胁最终产品的质量。锰铁脱磷工艺通过碳热反应实现，该反应需在供氧过程中控制锰挥发与氧化锰的还原。因此，本研究针对某炼钢企业的锰铁脱磷工艺，提出一款预测模型，可用于估算最终精炼阶段的磷浓度水平。本次选用人工神经网络（Artificial Neural Network）模型，因其是受人类神经系统启发的计算模型；本研究采用结合莱文贝格-马夸特（Levenberg-Marquadt）算法与柯尔莫哥洛夫（Kolmogorov）定理的神经网络架构，以优化估算方法。所构建的模型表现优异，百分比误差仅为0.09%。依托该估算模型，可预先评估特定磷浓度水平对FeMnMC的影响，且具备较高的可靠性。