Abstract

CHERKAEV, A.V.; ERWEE, M.; REYNOLDS, Q.G.  and  SWANEPOEL, S.. Prediction of silicon content of alloy in ferrochrome smelting using data-driven models. J. S. Afr. Inst. Min. Metall. [online]. 2024, vol.124, n.2, pp.67-74. ISSN 2411-9717.  http://dx.doi.org/10.17159/2411-9717/2297/2024.

SYNOPSIS Ferrochrome (FeCr) is a vital ingredient in stainless steel production and is commonly produced by smelting chromite ores in submerged arc furnaces. Silicon (Si) is a componrnt of the FeCr alloy from the smelting process. Being both a contaminant and an indicator of the state of the process, its content needs to be kept within a narrow range. The complex chemistry of the smelting process and interactions between various factors make Si prediction by fundamental models infeasible. A data-driven approach offers an alternative by formulating the model based on historical data. This paper presents a systematic development of a data-driven model for predicting Si content. The model includes dimensionality reduction, regularized linear regression, and a boosting method to reduce the variability of the linear model residuals. It shows a good performance on testing data (R² = 0.63). The most significant predictors, as determined by linear model analysis and permutation testing, are previous Si content, carbon and titanium in the alloy, calcium oxide in the slag, resistance between electrodes, and electrode slips. Further analysis using thermodynamic data and models, links these predictors to electrode control and slag chemistry. This analysis lays the foundation for implementing Si content control on a ferrochrome smelter.

Keywords : ferrochrome; silicon content; machine learning; principal component analysis; gradient boosting; mutual information.

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