Research on the prediction model of hot metal temperature in vanadium-titanium magnetite blast furnace based on deep learning

CUI Guodong; ZHU Yanlin; MA Kaihui; LIU Lingling; LIAO Zhehan; BAI Chenguang

doi:10.7513/j.issn.1004-7638.2025.05.001

Volume 46 Issue 5

Oct. 2025

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CUI Guodong, ZHU Yanlin, MA Kaihui, LIU Lingling, LIAO Zhehan, BAI Chenguang. Research on the prediction model of hot metal temperature in vanadium-titanium magnetite blast furnace based on deep learning[J]. IRON STEEL VANADIUM TITANIUM, 2025, 46(5): 1-12. doi: 10.7513/j.issn.1004-7638.2025.05.001

Citation:

CUI Guodong, ZHU Yanlin, MA Kaihui, LIU Lingling, LIAO Zhehan, BAI Chenguang. Research on the prediction model of hot metal temperature in vanadium-titanium magnetite blast furnace based on deep learning[J]. IRON STEEL VANADIUM TITANIUM, 2025, 46(5): 1-12. doi: 10.7513/j.issn.1004-7638.2025.05.001

Citation:

CUI Guodong, ZHU Yanlin, MA Kaihui, LIU Lingling, LIAO Zhehan, BAI Chenguang. Research on the prediction model of hot metal temperature in vanadium-titanium magnetite blast furnace based on deep learning[J]. IRON STEEL VANADIUM TITANIUM, 2025, 46(5): 1-12. doi: 10.7513/j.issn.1004-7638.2025.05.001

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Research on the prediction model of hot metal temperature in vanadium-titanium magnetite blast furnace based on deep learning

doi: 10.7513/j.issn.1004-7638.2025.05.001

CUI Guodong^{1, 2
,},
ZHU Yanlin^{2, 3},
MA Kaihui^{3, 4},
LIU Lingling^{3, 4},
LIAO Zhehan^{2, 3},
BAI Chenguang^{1
,
,}

1.
College of Materials Science and Engineering, Chongqing University, Chongqing 400044, China
2.
Chengdu Advanced Metal Materials Industry Technology Research Institute Co., Ltd., Chengdu 610300, Sichuan, China
3.
State Key Laboratory of Vanadium and Titanium Resources Comprehensive Utilization, Chengdu 610031, Sichuan, China
4.
Pangang Group Research Institute Co., Ltd., Chengdu 610031, Sichuan, China

More Information

Received Date: 2025-03-20
Accepted Date: 2025-04-14
Rev Recd Date: 2025-03-31
Publish Date: 2025-10-30

Abstract

Abstract

Accurate and timely prediction of hot metal temperature (HMT) is crucial for ensuring stable operation and improving hot metal quality in vanadium-titanium magnetite blast furnaces. Leveraging long-term field data, an HMT prediction model was developed for blast furnaces by integrating domain knowledge with data-driven strategies and combining an attention mechanism with long short-term memory neural networks (LSTM). Firstly, a feature matrix of the vanadium-titanium magnetite blast furnace smelting process was constructed by integrating smelting experience, rules, and data analysis techniques. Dimensionality reduction techniques were applied to reduce the feature dimension to 28, effectively reducing the prediction complexity. Secondly, we constructed a multi-time-step prediction model based on the LSTM architecture, using historical operation data from different time windows as inputs. By introducing an attention mechanism from deep learning to capture the importance of input features, the model's prediction accuracy was further improved. The results show that the model achieved a hit rate of 92.5% within a ±5 ℃ error range, realizing high-precision online prediction of hot metal temperatures in vanadium-titanium magnetite blast furnaces. This model provides an important reference for condition judgment and operation evaluation of blast furnaces.
- vanadium-titanium magnetite,
- hot metal temperature,
- prediction model,
- long short-term memory,
- attention mechanism,
- deep learning

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References(34)

References

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