Accurately predicting the oxygen supply in the basic oxygen furnace (BOF) steelmaking process is crucial for improving product quality, enhancing production efficiency and reducing costs. In this study, oxygen supply prediction models based on the K-nearest neighbours (KNN) algorithm were developed and compared using actual production data from the BOF. To further improve the accuracy of oxygen supply predictions for the BOF, this study optimises the algorithm using two methods: distance weighting and feature vector weighting based on the Pearson correlation coefficient. Experimental results indicate that the feature vector weighting method based on the Pearson correlation coefficient achieves better optimisation results compared to the distance weighting KNN algorithm. The prediction accuracy for oxygen supply reached 77.6% for an oxygen consumption error within ±200 Nm3, 89.8% for ±300 Nm3, and 95.7% for ±400 Nm3; additionally, this optimisation improved the endpoint hit rate for oxygen supply by 10.3% compared to the original algorithm. This model provides an effective reference for actual production and offers reliable insights for optimising other algorithms. Future research could further explore the application of other machine learning algorithms in oxygen supply prediction to achieve higher accuracy and broader applicability, thereby advancing the intelligent development of the BOF steelmaking process.
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