Prediction of desulfurization conformity in KR process based on cost-sensitive and Bayesian optimized XGBoost
KR desulfurization is a typical approach for hot metal pretreatment.With the increasing de-mand of low sulfur steel products,achieving stable control of end sulfur content in molten iron and subsequently reducing the overall cost of desulfurization processes are of paramount importance.A critical aspect of this process is the ability to predict whether the desulfurization endpoint complies with the required standards.Therefore,a modeling method combining a sample class balance process-ing method based on cost-sensitive strategy and Bayesian-optimized extreme gradient boosting(XG-Boost)algorithm with a binary classification analysis method for solving end sulfur prediction problem in KR desulfurization process is proposed.Firstly,the end sulfur content of the desulfurization data is processed into conforming or non-conforming two categories based on the binary classification analysis method.The category sample weights are adjusted based on the cost-sensitive strategy to alleviate the imbalance issue for constructing the feature dataset.Then,using actual production data from a steel company,the model is trained via cross-validation with cost-sensitive strategy and Bayesian-optimized XGBoost with the optimal parameters are selected based on Macro-F1 metric to form the final desulfu-rization conformity prediction model for the KR process,achieving the data prediction for desulfuriza-tion conformity and non-conformity targets.Experimental results comparing with support vector ma-chine(SVM)and back propagation neural network(BPNN)prediction models show that the pro-posed method can effectively deal with the imbalance issue in desulfurization data,showing good practical effects in desulfurization conformity prediction.
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