Abstract: The control of end-point temperature and composition in converter steelmaking is an important operation in the later stage of blowing. The accurate temperature and carbon prediction is very important. In order to improve the end-point carbon and temperature hit rates of the converter blowing, main input variables were determined with characteristic correlation analysis method and the converter end-point prediction models based on the XGBoost algorithm were built up. The models were verified from the actual converter production data.In comparison with the results obtained from BP and optimized BP neural network models, it was found that the XGBoost algorithm model can achieve a high end-point hit rates under the premise of ensuring a fast convergence speed. In order to further improve the hit rates of end point temperature and carbon, the several important parameters in the XGBoost algorithm model were adjusted accordingly. Finally, when the temperature deviations were ±15 ℃ and ±10 ℃,the hit rates of end point with XGBoost algorithm model were 95.84 % and 91.69 %, respectively. When the deviations of carbon mass fraction in molten steel were ±0.015 % and ±0.01 %, the end point hit rates were 93.31 % and 87.84 %, respectively.

Key words: converter steelmaking, end point prediction, XGBoost algorithm, hit rate, taping temperature, taping carbon content