With the reduction of raw material quality and the improvement of user requirements, the cost of dephosphorization increases. Steel plants urgently need an efficient and economic dephosphorization process to support the production. To address this issue, first, the migration and distribution of phosphorus in the converter smelting process are described quantitatively. Based on the regular solution model, the influence of slag components and temperature on the dephosphorization capacity of steel slag is explored. Afterward, considering the phosphorus distribution ratio, melting point, and phosphorus capacity of slag, the advantages of the slag‐forming route with higher FeO content in rapid slag formation and dephosphorization are expounded. Based on the guidance of the slag‐forming route, the scheme of controlling oxygen lance position and charging is optimized, and a novel blowing control mode is proposed. Finally, industrial tests on a 120 t converter show that the optimized smelting mode significantly reduces the cost and improves the efficiency. As a result, the lime consumption used to smelt a ton of steel is reduced by 3.67 kg t−1, and the dephosphorization rate is still increased by about 3.07%.

With the high efficiency and automation of converter smelting, it is becoming increasingly important to predict and control the endpoint temperature of the converter. Based on the heat balance, a model for predicting the molten pool temperature in a converter was established. Moreover, the statistical method of multiple linear regression was used to calculate the converter heat loss coefficient, greatly improving the prediction accuracy of the mechanistic model. Using the model, the oxidation process for each element in the molten pool, the melting processes of scrap, and the flux were also calculated. The model could better approximate the actual smelting process. Data from a 130 t converter were collected to validate the model. When the error ranges were limited to ±20 and ±15 °C, the model hit rates were 96 and 86.7%, respectively.

The amount of oxygen blown into the converter directly affects the productivity of the converter and the quality of the molten steel, so it is crucial to accurately predict oxygen consumption. Herein, a hybrid model for predicting oxygen consumption in basic oxygen furnace steelmaking process is established. The oxygen consumption of converter is divided into two types: the determined oxygen consumption and the estimated oxygen consumption. The determined oxygen consumption is calculated by mechanism model. And the estimated oxygen consumption is calculated by statistical model. The method of cluster analysis is also introduced into the model. After clustering the data, a statistical model for predicting the estimated oxygen consumption in each subcategory is established, which further improves the prediction accuracy of the model. After dividing the original data into three categories, the hit rate of the hybrid model is 93.3%, when the relative errors of the model are restricted within 5%. As a result, the hybrid model is fully capable of predicting oxygen consumption in converters.