Oxygen Supply Prediction Method for Converter Steelmaking Based on Knowledge and Data Fusion Driven
[1]LIU Jing,JIANG Wenjie,FENG Hailing,et al.Oxygen Supply Prediction Method for Converter Steelmaking Based on Knowledge and Data Fusion Driven[J].Journal of Zhengzhou University (Engineering Science),2027,48(XX):1-8.[doi:10.13705/j.issn.1671-6833.2026.04.013]
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Journal of Zhengzhou University (Engineering Science)[ISSN
1671-6833/CN
41-1339/T] Volume:
48
Number of periods:
2027 XX
Page number:
1-8
Column:
Public date:
2027-12-10
- Title:
- Oxygen Supply Prediction Method for Converter Steelmaking Based on Knowledge and Data Fusion Driven
- Keywords:
- fusion driven; dual-branch; knowledge fusion; converter steelmaking; oxygen supply prediction
- CLC:
- TP301.6；TF713
- Abstract:
- Aiming at the problem of the disconnection between domain knowledge and data-driven models in traditional oxygen supply prediction methods in converter steelmaking process, a knowledge and data fusion driven oxygen supply prediction method for converter steelmaking was proposed. A three-level knowledge fusion module was constructed, embedding metallurgical mechanisms into deep learning models. Secondly, a dual-branch architecture was designed to collaboratively mine process characteristics and cross-furnace temporal patterns. Finally, actual production data from a steel plant was used for the experiment. The experiment results showed that compared with mainstream methods such as GBRBM-DBN, HyGPR, Stacking, and BOA-LGBM, the MAE and RMSE of oxygen supply under SPHC steel grade decreased by a maximum of 7.59% and 6.80%, respectively, and the accuracy (relative error ±5%) reached 85.29%. Under the HRB400E steel grade, the MAE and RMSE decreased by a maximum of 15.24% and 15.13%, respectively, with an accuracy (relative error ±5%) of 87.91%, verifying the oxygen supply prediction ability of the proposed method.
- References:
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Last Update:
2026-04-08
