Enhancement of first carbon hit rate in converter steelmaking through integrated learning-based data cleansing

Abstract

First carbon hit rate (FCHR) is an essential indicator of steel converter smelting, reflecting the proportion of steel tapping completed without additional oxygen blowing. However, significant data loss has occurred due to equipment ageing and worker operations, resulting in difficulties in analysing the FCHR. This paper uses mechanism analysis and feature screening to determine the model input, predicts and fills in abnormal data through ensemble learning, and then optimises it through data transformation. Finally, the Stacking model predicts the FCHR, with a training accuracy of up to 94.5% and a test set accuracy of 90.5%. In addition, the authors also conducted a predictive study on oxygen consumption, and the hit rate performed well under different error thresholds, with a maximum of 97.9%. These results provide powerful decision support for steel production and effectively overcome the challenges of data missingness.