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),2026,47(4):66-73.[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:
47
Number of periods:
2026 Issue 4
Page number:
66-73
Column:
Public date:
2026-07-10
- Title:
- Oxygen Supply Prediction Method for Converter Steelmaking Based on Knowledge and Data Fusion Driven
- Keywords:
- fusion driven; collaborative modeling; dual-branch; converter steelmaking; oxygen supply prediction
- CLC:
- TP301. 6TF713
- 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 production patterns. Finally, actual production data from a domestic steel plant was used for the experiment. The experimental results showed that compared with mainstream methods such as GBRBM‑DBN, HyGPR, Stacking, and BOA‑LGBM, the MAE and RMSE of oxygen supply with SPHC steel grade decreased by a maximum of 7.59% and 6.80% respectively, and the accuracy (relative error ±5%) reached 85.29%. With 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:
2026-09-21
