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<p>To address the national dual carbon strategy and to decrease the consumption of iron and steel materials by enterprises, accurate prediction of the converter tapping weight can help to control the amount of ferroalloy addition in tapping. In the actual production process, this relies on manual experience to estimate the converter tapping weight, but the accuracy of the estimated results is not very good. At present, there are many applications of neural networks, but few people make network predictions for the converter tapping weight. In this study, the key factors influencing the converter tapping weight are analyzed by the Pearson correlation method. The kernel principal component analysis–genetic algorithm–backpropagation (KPCA–GA–BP) neural network model has been established to forecast the converter tapping weight. The hidden layer nodes, learning rate, and training times of the model were optimized through a trial-and-error process. The training results show that the hit ratios for the converter tapping weight prediction within error ranges of ± 1&#xa0;t, ± 2&#xa0;t, and ± 3&#xa0;t are 74.0%, 91.0%, and 97.5%, respectively, which significantly surpass the conventional manual predictions. Compared with the traditional artificial prediction, the accuracy of ± 2&#xa0;t and ± 3&#xa0;t has been increased by 56.0% and 37.5%, respectively. At the same time, the established KPCA–GA–BP model has been added to the intelligent system of steelmaking alloy addition, and effectively applied to the production, making the steelmaking process more intelligent.</p>

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Prediction of Converter Tapping Weight Based on KPCA–GA–BP Neural Network Model

  • 期刊论文

  • 发表日期：

    2025年5月14日

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  • Peng Liu,
  • Zhibin Gao,
  • Changwei Fu,
  • Yanping Bao,
  • Lihua Zhao

摘要

To address the national dual carbon strategy and to decrease the consumption of iron and steel materials by enterprises, accurate prediction of the converter tapping weight can help to control the amount of ferroalloy addition in tapping. In the actual production process, this relies on manual experience to estimate the converter tapping weight, but the accuracy of the estimated results is not very good. At present, there are many applications of neural networks, but few people make network predictions for the converter tapping weight. In this study, the key factors influencing the converter tapping weight are analyzed by the Pearson correlation method. The kernel principal component analysis–genetic algorithm–backpropagation (KPCA–GA–BP) neural network model has been established to forecast the converter tapping weight. The hidden layer nodes, learning rate, and training times of the model were optimized through a trial-and-error process. The training results show that the hit ratios for the converter tapping weight prediction within error ranges of ± 1 t, ± 2 t, and ± 3 t are 74.0%, 91.0%, and 97.5%, respectively, which significantly surpass the conventional manual predictions. Compared with the traditional artificial prediction, the accuracy of ± 2 t and ± 3 t has been increased by 56.0% and 37.5%, respectively. At the same time, the established KPCA–GA–BP model has been added to the intelligent system of steelmaking alloy addition, and effectively applied to the production, making the steelmaking process more intelligent.