End-point Temperature Prediction of Converter Steelmaking Based on Key Feature Amplification and Grey Wolf Algorithm Improved Affinity Propagation Clustering

YZ Guo and DF He and XL Li and K Feng, METALLURGICAL AND MATERIALS TRANSACTIONS B-PROCESS METALLURGY AND MATERIALS PROCESSING SCIENCE, 56, 2047-2062 (2025).

DOI: 10.1007/s11663-025-03472-4

To achieve the goal of reducing energy consumption in the steel industry, accurately predicting the temperature of converter steelmaking is critical for controlling the steelmaking process. Due to the complexity of the converter steelmaking process, precisely predicting the end-point temperature remains a significant challenge. To achieve precise control of the end-point temperature of converter steelmaking, a method combining key feature amplification (KFA) with grey wolf optimizer (GWO) improved affinity propagation (AP) clustering algorithm and gradient-boosting decision tree (GBDT) was proposed to establish a steel temperature prediction model. Firstly, the primary factors influencing the temperature of converter steelmaking were identified based on metallurgical mechanisms. Secondly, the maximal information coefficient (MIC) was utilized to eliminate factors with minimal impact on converter steelmaking temperature, thus determining the input variables for the model. Then, the metallurgical mechanisms and MIC calculation results were integrated with AP clustering to amplify the key feature weights of the model. For the parameter optimization problem in AP clustering, the GWO was employed to find the optimal operating condition classification. Finally, GBDT models were constructed for each operating condition dataset, culminating in the establishment of the KFA-GWO-AP-GBDT end-point converter steelmaking temperature prediction model. The results demonstrate that, compared to K-means, AP, and KFA-AP clustering, KFA-GWO-AP clustering exhibits the best performance and can effectively classify operating conditions. Compared to unclustered and other clustered models, the KFA-GWO-AP-GBDT model achieved prediction accuracy rates of 52.18, 83.36, and 96.58 pct within +/- 5 degrees C, +/- 10 degrees C, and +/- 15 degrees C, respectively, showcasing the best overall performance. This model holds significant practical importance for achieving precise control of converter steelmaking temperature, reducing production costs, and enhancing converter efficiency.

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