钢铁钒钛2025，Vol.46Issue(5)：1-12,12.DOI:10.7513/j.issn.1004-7638.2025.05.001
基于深度学习的钒钛磁铁矿高炉铁水温度预测模型研究
Research on the prediction model of hot metal temperature in vanadium-titanium magnetite blast furnace based on deep learning
崔国栋 1朱焱麟 2马凯辉 3刘凌岭 3廖哲晗 2白晨光4
作者信息
- 1. 重庆大学材料科学与工程学院,重庆 400044||成都先进金属材料产业技术研究院股份有限公司,四川成都 610300
- 2. 成都先进金属材料产业技术研究院股份有限公司,四川成都 610300||钒钛资源利用国家重点实验室,四川成都 610301
- 3. 钒钛资源利用国家重点实验室,四川成都 610301||攀钢集团研究院有限公司,四川成都 610301
- 4. 重庆大学材料科学与工程学院,重庆 400044
- 折叠
摘要
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,ef-fectively 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.关键词
钒钛磁铁矿/铁水温度/预测模型/长短期记忆/注意力机制/深度学习Key words
vanadium-titanium magnetite/hot metal temperature/prediction model/long short-term memory/attention mechanism/deep learning分类
矿业与冶金引用本文复制引用
崔国栋,朱焱麟,马凯辉,刘凌岭,廖哲晗,白晨光..基于深度学习的钒钛磁铁矿高炉铁水温度预测模型研究[J].钢铁钒钛,2025,46(5):1-12,12.
