Prediction model of blast furnace molten iron temperature and molten iron silicon content based on improved arithmetic optimization twin support vector machine for regression
J. Min. Metall. Sect. B-Metall., 60 (3) (2024) 407-419. DOI:10.2298/JMMB240928033S
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Available online 14 decembar 2024
(Received 28 September 2024; Accepted 13 December 2024)
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The temperature and silicon content of molten blast furnace iron are directly related to its quality. Therefore, creating an effective prediction model for these parameters is cru-cial. To address these issues, an Improved Arithmetic Optimization Twin Support Vec-tor Machine for Regression (LAOA-TSVR) model was developed to predict the tem-perature and silicon content of molten blast furnace iron. First, SPSS was used to per-form a correlation analysis and identify the main influencing factors. Secondly, the model was compared with three common prediction models to verify its prediction per-formance: 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 for molten iron temperature and silicon content are within the error ranges of ±5% and ±0.5% at 92.12% and 92.53%, respectively, with a corresponding double-hit rate of 85.32%. The model effectively fulfills the production requirements of an iron mill and provides valu-able information for the production process in the blast furnace.
Keywords: Temperature of molten iron in blast furnace; Silicon content of molten iron; Quality of molten iron; Arithmetic optimization algorithm; Twinned support vector regression
Correspondence Address:
C.-Y. Shi,
School of Electrical and Automation Engineering, Liaoning Institute of Science and Technology, Benxi, China;
email: scy9090@126.com
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