Hybrid Prediction Model of Burn-Through Point Temperature with Color Temperature Information from Cross-Sectional Frame at Discharge End

Mengxin Zhao, Yinghua Fan, Jing Ge, Xinzhe Hao, Caili Wu, Xian Ma and Sheng Du ()
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Mengxin Zhao: School of Automation, China University of Geosciences, Wuhan 430074, China
Yinghua Fan: School of Automation, China University of Geosciences, Wuhan 430074, China
Jing Ge: School of Automation, China University of Geosciences, Wuhan 430074, China
Xinzhe Hao: School of Automation, China University of Geosciences, Wuhan 430074, China
Caili Wu: School of Future Technology, China University of Geosciences, Wuhan 430074, China
Xian Ma: School of Automation, China University of Geosciences, Wuhan 430074, China
Sheng Du: School of Automation, China University of Geosciences, Wuhan 430074, China

Energies, 2025, vol. 18, issue 14, 1-19

Abstract: Iron ore sintering is a critical process in steelmaking, where the produced sinter is the main raw material for blast furnace ironmaking. The quality and yield of sinter ore directly affect the cost and efficiency of iron and steel production. Accurately predicting the burn-through point (BTP) temperature is of paramount importance for controlling quality and yield. Traditional BTP temperature prediction only utilizes data from bellows, neglecting the information contained in sinter images. This study combines color temperature information extracted from the cross-sectional frame at the discharge end with bellows data. Due to the non-stationarity of the BTP temperature, a hybrid prediction model of the BTP temperature integrating bidirectional long short-term memory and extreme gradient boosting is presented. By combining the advantages of deep learning and tree ensemble learning, a hybrid prediction model of the BTP temperature is established using the color temperature information in the cross-sectional frame at the discharge end and time-series data. Experiments were conducted with the actual running data in an iron and steel enterprise and show that the proposed method has higher accuracy than existing methods, achieving an approximately 4.3% improvement in prediction accuracy. The proposed method can provide an effective reference for decision-making and for the optimization of operating parameters in the sintering process.

Keywords: BTP temperature; color temperature information; hybrid prediction model; sintering process (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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