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Development and Applications of Intelligent Steelmaking System for Converter Based on Neural Network and Deep Learning
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Yin Zhang1, 4, 5, Bin Lu1, 4, 5, Hong Cui3, Lijun Wang2, Wangcai Diao1, 5, Bo Gao3, Qiang Wang3
Science & Technology of Baotou Steel | 2026, 52(2) : 10 - 14
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Science & Technology of Baotou Steel | 2026, 52(2): 10-14
• Production Practices and Management •
Development and Applications of Intelligent Steelmaking System for Converter Based on Neural Network and Deep Learning
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Yin Zhang1, 4, 5, Bin Lu1, 4, 5, Hong Cui3, Lijun Wang2, Wangcai Diao1, 5, Bo Gao3, Qiang Wang3
Affiliations
  • 1.Technical Center of Inner Mongolia Baotou Steel Union Co., Ltd., Baotou 014010, Inner Mongolia Autonomous Region, China
  • 2.Manufacturing Dept. of Inner Mongolia Baotou Steel Union Co., Ltd., Baotou 014010, Inner Mongolia Autonomous Region, China
  • 3.Steel-making Plant of Inner Mongolia Baotou Steel Union Co., Ltd., Baotou 014010, Inner Mongolia Autonomous Region, China
  • 4.Beijing Baotou Steel Technology Co., Ltd., Beijing 100083, China
  • 5.Inner Mongolia Key Laboratory of Rare Earth Steel Products Research & Development, Baotou 014010, Inner Mongolia Autonomous Region, China
Published: 2026-04-25
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The intelligent steelmaking system for converter integrating neural network and artificial intelligence deep learning technology is developed by taking the 1# converter of Baotou Steel as research object aiming at such problems as dependence of manual experiences, low intelligence as well as insufficient stability of production efficiency and quality of liquid steel for traditional converter steelmaking. The real-time judgment of converting state, accurate prediction of end point carbon and temperature as well as intelligent collaborative regulation and control of oxygen supply-oxygen lance position are realized by integrating such multi-source heterogeneous data as vision of flame at converter mouth, flue gas analysis and audio testing as well as establishing the dual-drive model of “mechanism-data” combining with such algorithms as the CNN, Bi-LSTM and reinforcement learning. The industrial tests showed that the dual hit rates of end point carbon and temperature for converter were increased to over 90%, smelting cycle of converter was shortened by 2 min as well as cost per ton of steel was reduced by CNY 1~2 Yuan with the system so that the transformation of converter operations from “experience driven” to “data driven” is effectively promoted.

converter steelmaking  /  neural network  /  deep learning  /  intelligent steelmaking  /  multi-source data fusion  /  endpoint prediction
Yin Zhang, Bin Lu, Hong Cui, Lijun Wang, Wangcai Diao, Bo Gao, Qiang Wang. Development and Applications of Intelligent Steelmaking System for Converter Based on Neural Network and Deep Learning[J]. Science & Technology of Baotou Steel, 2026 , 52 (2) : 10 -14 .
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  • Receive Date：2026-03-24
  • Online Date：2026-06-17
  • Published：2026-04-25
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  • Received：2026-03-24
Affiliations
    1.Technical Center of Inner Mongolia Baotou Steel Union Co., Ltd., Baotou 014010, Inner Mongolia Autonomous Region, China
    2.Manufacturing Dept. of Inner Mongolia Baotou Steel Union Co., Ltd., Baotou 014010, Inner Mongolia Autonomous Region, China
    3.Steel-making Plant of Inner Mongolia Baotou Steel Union Co., Ltd., Baotou 014010, Inner Mongolia Autonomous Region, China
    4.Beijing Baotou Steel Technology Co., Ltd., Beijing 100083, China
    5.Inner Mongolia Key Laboratory of Rare Earth Steel Products Research & Development, Baotou 014010, Inner Mongolia Autonomous Region, China
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表12种不同金属材料的力学参数
科
Family		 属数
Number of
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Number of
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Percentage of
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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