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JACIII Vol.30 No.4 pp. 1033-1043
(2026)
doi: 10.20965/jaciii.2026.p1033

Research Paper:

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Intelligent Prediction of the Hearth Lining Temperature in Blast Furnaces Based on Spatiotemporal Feature Fusion

Qifu Chen*1,*2, Jiaxin Cheng*3,*4, Jianqi An*1,*3,*4,† ORCID Icon, and Jinhua She*5 ORCID Icon

*1School of Future Technology, China University of Geosciences
No.388 Lumo Road, Wuhan, Hubei 430074, China

*2Hunan Valin Lianyuan Iron and Steel Co., Ltd.
No.1005 Jinxing North Road, Louxing District, Loudi, Hunan 417009, China

*3School of Artificial Intelligence and Automation, China University of Geosciences
No.388 Lumo Road, Wuhan, Hubei 430074, China

*4Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems
No.388 Lumo Road, Wuhan, Hubei 430074, China

*5School of Engineering, Tokyo University of Technology
1404-1 Katakura machi, Hachioji, Tokyo 192-0982, Japan

†Corresponding author

Received:
December 3, 2025
Accepted:
January 27, 2026
Published:
July 20, 2026
Creative Commons License
Keywords:
blast furnace, hearth lining temperature, spatiotemporal feature fusion, dual-attention mechanism, temperature forecasting
Abstract

Accurate forecasting of the hearth lining temperature in blast furnaces is essential for operational safety and efficiency, however it remains challenging owing to the complex spatiotemporal coupling and time-lag effects among process variables. To address this issue, we present a new spatiotemporal feature modeling framework that integrates gated recurrent units (GRUs) with a dual-attention mechanism to capture multi-scale temporal dependencies and dynamically assess variable importance. A convolutional neural network module is incorporated to extract localized spatial features from the time-series data, thereby enhancing the representation of the underlying metallurgical mechanisms. The validation on real industrial data showed that the proposed model achieved a root mean square error of 0.0523 and a hit rate of 94.26%, outperforming conventional long short-term memory and GRU models. This approach offers a reliable solution for intelligent health monitoring and proactive maintenance in modern data-driven ironmaking operations under highly dynamic and uncertain conditions.

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Cite this article as:
Q. Chen, J. Cheng, J. An, and J. She, “Intelligent Prediction of the Hearth Lining Temperature in Blast Furnaces Based on Spatiotemporal Feature Fusion,” J. Adv. Comput. Intell. Intell. Inform., Vol.30 No.4, pp. 1033-1043, 2026.
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Last updated on Jul. 19, 2026