Prediction of silicon content in metal alloy in ferrochrome smelting using data-driven models
DOI:
https://doi.org/10.17159/Abstract
Ferrochrome (FeCr) is a vital component of stainless steels and is commonly produced by smelting chrome ores in submerged arc furnaces. Silicon (Si) is a part of the FeCr alloy originating 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. Complex chemistry of the process and interactions between various factors make Si prediction by fundamental models infeasible. 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 Si content prediction. The presented model includes dimensionality reduction, regularized linear regression and boosting method to reduce 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 test, 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 the plant.
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Copyright (c) 2024 Alexey Vladimirovich Cherkaev, Markus Erwee, Quinn Gareth Reynolds, Stefan Swanepoel
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