Comparison between empirical strategies for predicting endpoint phosphorus content in BOF steelmaking process
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Universidade Federal de Minas Gerais
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Dephosphorization is a reaction of important role in steelmaking process, and the correct adequacy of endpoint phosphorus content would improve the quality and productivity of steel in basic oxygen furnace (BOF) processing. Aiming to meet the required steel specifications and reduce process time, two different empirical strategies were established for predicting the endpoint phosphorus content in BOF steelmaking process: linear regression and neural network. Eight variables that affect the endpoint phosphorus content (selected as output) were determined as the input variables of the models. The performances of predictions were evaluated simultaneously with the sensitivity analysis of the model to variations in the values of its input variables. Sensitivity analysis is essential as it reveals the impact of input variables on results, although it is often neglected due to its complexity and the need for multiple simulations. Integrating sensitivity analysis with prediction techniques allows for identifying key variables and making decisions. Both empirical models are suitable and reliable for decision-making in the process and can be used as tools for predicting the endpoint phosphorus content, where the neural network has higher accuracy. The sensitivity analysis showed that the two variables that most affect the response of the empirical models were the percentage of oxygen volume of oxygen blown until the sub-lance in relation to the estimated total volume, and the phosphorus concentration in the sub-lance.
Abstract
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Estratégias empíricas, Desforização, Rede neural, Forno de oxigênio básico (BOF)
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Dephosphorization, Linear regression, Neural network, Sensitivity analysis
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https://ojs.bonviewpress.com/index.php/AAES/article/view/3358
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