Please use this identifier to cite or link to this item: http://www.repositorio.ufop.br/handle/123456789/10817
Title: Hybrid model associating thermodynamic calculations and artificial neural network in order to predict molten steel temperature evolution from blowing end of a BOF for secondary metallurgy.
Authors: Viana Júnior, Marcos Antônio
Silva, Carlos Antônio da
Silva, Itavahn Alves da
Keywords: Molten steel temperature control
Issue Date: 2018
Citation: JÚNIOR, Marcos Antônio Viana. Hybrid model associating thermodynamic calculations and artificial neural network in order to predict molten steel temperature evolution from blowing end of a BOF for secondary metallurgy. REM - International Engineering Journal, Ouro Preto, v. 71, p. 587-592, out,/dez. 2018. Disponível em: <http://www.scielo.br/scielo.php?script=sci_arttext&pid=S2448-167X2018000400587&lng=en&tlng=en>. Acesso em: 13 fev. 2019.
Abstract: The developed model is an association of thermodynamic calculations for dissolution of alloys, slag formers and the deoxidation reaction in the molten steel with two artificial neural network (ANN) models trained with industrial data, to predict the molten steel temperature drop from the blowing end of the BOF until the first measurement at secondary metallurgy. To calculate the associated energy for deoxidation, an experiment was designed to set up the parameters for oxygen partitioning among deoxidants, with timed aluminum addition during teeming being the main parameter. The temperature control in the teeming stage presented a standard deviation for the error of prediction of 5.46 oC, for transportation from the rinsing station to the secondary metallurgy of 2.79 oC. The association of all calculations presented an error standard deviation of 7.49 oC. The operational validation presented superior accuracy compared with the current method for controlling the temperature, resulting in a reduction in the aluminum consumption for heating at secondary metallurgy with a potential economy of U$ 4.07 million per year for a steel shop producing 5 million tons of steel yearly. The artificial neural network model confirmed its capacity for modeling a complex multivariable process and the separation of thermodynamic calculation provides a better adaptability to different steel grades with different teeming strategies.
URI: http://www.repositorio.ufop.br/handle/123456789/10817
ISSN: 18070353
metadata.dc.rights.license: A REM - International Engineering Journal - autoriza o depósito de cópia de artigos dos professores e alunos da UFOP no Repositório Institucional da UFOP. Licença concedida mediante preenchimento de formulário online em: 12 set. 2013.
Appears in Collections:DEMET - Artigos publicados em periódicos

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