Title

SMS SIEMAG BOF PROCESS MODEL: STABLE AND OPTIMIZED PERFORMANCE UNDER SUBOPTIMAL CONDITIONS

SMS SIEMAG BOF PROCESS MODEL: STABLE AND OPTIMIZED PERFORMANCE UNDER SUBOPTIMAL CONDITIONS

Authorship

Hofmann, Axel; Reichel, Jan; Loginov, Sergey; Das, Satyajit

Hofmann, Axel

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Reichel, Jan

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Loginov, Sergey

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Das, Satyajit

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DOI

10.5151/2594-5300-22824

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Abstract

Automation in the steel industry has played a crucial role in enhancing safety and productivity as well as in facilitating the steelmaking operation. For proper functioning of the Level 2 model, the variation in the input conditions should be minimal. But this is actually very difficult to achieve as most of the steel plants have very high fluctuations in their input conditions, such as hot metal weight and chemistry, scrap type and chemistry, flux and coolant chemistry. These conditions may vary on a heat to heat basis and in some steel plants some of the important parameters are not known. This paper studies the effect of the input conditions on the end-of-blow result, for example errors in hot metal weight and carbon analysis. Trials were performed in steel plants to see the actual conditions and the model was tuned to negate the errors as far as possible. Emphasis was made on a correction calculation based on an in-blow measurement and the detection of the end of blow via waste gas analysis to achieve the carbon and temperature aim.

Automation in the steel industry has played a crucial role in enhancing safety and productivity as well as in facilitating the steelmaking operation. For proper functioning of the Level 2 model, the variation in the input conditions should be minimal. But this is actually very difficult to achieve as most of the steel plants have very high fluctuations in their input conditions, such as hot metal weight and chemistry, scrap type and chemistry, flux and coolant chemistry. These conditions may vary on a heat to heat basis and in some steel plants some of the important parameters are not known. This paper studies the effect of the input conditions on the end-of-blow result, for example errors in hot metal weight and carbon analysis. Trials were performed in steel plants to see the actual conditions and the model was tuned to negate the errors as far as possible. Emphasis was made on a correction calculation based on an in-blow measurement and the detection of the end of blow via waste gas analysis to achieve the carbon and temperature aim.

Keywords

Basic oxygen furnace; Process model; Dynamical decarburization; Automation; Duplex process.

Basic oxygen furnace; Process model; Dynamical decarburization; Automation; Duplex process.