Abstract
The temperature and silicon content of blast furnace molten iron are directly related to its quality. Therefore, establishing an effective prediction model for these parameters is crucial. To address these issues, an Improved Arithmetic Optimization Twin Support Vector Machine for Regression (LAOA-TSVR) model was developed to predict the temperature and silicon content of blast furnace molten iron. Initially, SPSS was used to perform a correlation analysis and identify the main influencing factors. Secondly, to verify the model's predictive performance, it was compared with three commonly used prediction models: Back Propagation Neural Network (BP), Support Vector Regression (SVR), and Twin Support Vector Machine for Regression (TSVR). Preliminary results indicate that the prediction accuracy of the LAOA-TSVR model is significantly higher than that of the other models. Finally, the model was applied to the actual production process of an iron mill for a total of 200 furnaces. The results show that the hit rates of molten iron temperature and silicon content within the error ranges of ±5% and ±0.5%, respectively, are 92.12% and 92.53%, with a corresponding double-hit rate of 85.32%. The model effectively meets the production requirements of an iron mill and provides valuable guidance for the blast furnace production process.
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