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 performance and quality of steel products are significantly impacted by the alloying element control. The efficiency of alloy utilization in the steelmaking process was directly related to element yield. This study analyses the factors that influence the yield of elements in the steelmaking process using correlation analysis. A yield prediction model was developed using a t-distributed stochastic neighbor embedding (t-SNE) algorithm, a whale optimization algorithm (WOA), and a long short-term memory (LSTM) neural network. The t-SNE algorithm was used to reduce the dimensionality of the original data, while the WOA optimization algorithm was employed to optimize the hyperparameters of the LSTM neural network. The t-SNE-WOA-LSTM model accurately predicted the yield of Mn and Si elements with hit rates of 71.67%, 96.67%, and 99.17% and 57.50%, 89.17%, and 97.50%, respectively, falling within the error range of ±1%, ±2%, and ±3% for Mn and ±1%, ±3%, and ±5% for Si. The results demonstrate that the t-SNE-WOA-LSTM model outperforms the backpropagation (BP), LSTM, and WOA-LSTM models in terms of prediction accuracy. The model was applied to actual production in a Chinese plant. The actual performance of the industrial application is within a ±3% error range, with an accuracy of 100%. Furthermore, the elemental yield predicted by the model and then added the ferroalloys resulted in a reduction in the elemental content of the product by 0.017%. The model enables accurate prediction of alloying element yields and was effectively applied in industrial production.