2019 Pittsburgh AISTech

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Intelligent Steelmaking Based on Advanced Analytics: Reducing Operational Costs of a BOF (Room 315)

Due to the complexity of the steelmaking environment, process control models must account for many uncertainties that could potentially happen during the process. Data mining and data analytics tools have become essential to extract knowledge from large amounts of sensor raw data in order to improve process monitoring and decision-making strategies. Tenova, in partnership with Microsoft, has embarked to expand plant data analytics capabilities with the aim of improving the accuracy of its predictive models. This paper will address the optimization of a BOF static charge model using advanced analytics techniques applied to real operating conditions.