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<p>To address the issue of abnormal energy consumption fluctuations in the converter steelmaking process, an integrated diagnostic method combining the gray wolf optimization (GWO) algorithm, support vector machine (SVM), and K-means clustering was proposed. Eight input parameters—derived from molten iron conditions and external factors—were selected as feature variables. A GWO–SVM model was developed to accurately predict the energy consumption of individual heats. Based on the prediction results, the mean absolute percentage error and maximum relative error of the test set were employed as criteria to identify heats with abnormal energy usage. For these heats, the K-means clustering algorithm was used to determine benchmark values of influencing factors from similar steel grades, enabling root-cause diagnosis of excessive energy consumption. The proposed method was applied to real production data from a converter in a steel plant. The analysis reveals that heat sample No. 44 exhibits abnormal energy consumption, due to gas recovery being 1430.28&#xa0;kg of standard coal below the benchmark level. A secondary contributing factor is a steam recovery shortfall of 237.99&#xa0;kg of standard coal. This integrated approach offers a scientifically grounded tool for energy management in converter operations and provides valuable guidance for optimizing process parameters and enhancing energy efficiency.</p>

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Integrated diagnosis of abnormal energy consumption in converter steelmaking using GWO–SVM–K-means algorithms

  • 期刊论文

  • 发表日期：

    2026年1月6日

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  • Fei-Xiang Dai,
  • Xiang-Jun Bao,
  • Lu Zhang,
  • Xiao-Jing Yang,
  • Guang Chen

摘要

To address the issue of abnormal energy consumption fluctuations in the converter steelmaking process, an integrated diagnostic method combining the gray wolf optimization (GWO) algorithm, support vector machine (SVM), and K-means clustering was proposed. Eight input parameters—derived from molten iron conditions and external factors—were selected as feature variables. A GWO–SVM model was developed to accurately predict the energy consumption of individual heats. Based on the prediction results, the mean absolute percentage error and maximum relative error of the test set were employed as criteria to identify heats with abnormal energy usage. For these heats, the K-means clustering algorithm was used to determine benchmark values of influencing factors from similar steel grades, enabling root-cause diagnosis of excessive energy consumption. The proposed method was applied to real production data from a converter in a steel plant. The analysis reveals that heat sample No. 44 exhibits abnormal energy consumption, due to gas recovery being 1430.28 kg of standard coal below the benchmark level. A secondary contributing factor is a steam recovery shortfall of 237.99 kg of standard coal. This integrated approach offers a scientifically grounded tool for energy management in converter operations and provides valuable guidance for optimizing process parameters and enhancing energy efficiency.