A Multi-Scale Fusion Convolutional Network for Time-Series Silicon Prediction in Blast Furnaces

Qiancheng Hao (), Wenjing Liu, Wenze Gao and Xianpeng Wang
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Qiancheng Hao: School of Information Science and Engineering, Northeastern University, Shenyang 110819, China
Wenjing Liu: School of Metallurgy, Northeastern University, Shenyang 110819, China
Wenze Gao: School of Mechanical Engineering and Automation, Northeastern University, Shenyang 110819, China
Xianpeng Wang: Key Laboratory of Data Analytics and Optimization for Smart Industry, Northeastern University, Ministry of Education, Shenyang 110819, China

Mathematics, 2025, vol. 13, issue 8, 1-21

Abstract: In steel production, the blast furnace is a critical element. In this process, precisely controlling the temperature of the molten iron is indispensable for attaining efficient operations and high-grade products. This temperature is often indirectly reflected by the silicon content in the hot metal. However, due to the dynamic nature and inherent delays of the ironmaking process, real-time prediction of silicon content remains a significant challenge, and traditional methods often suffer from insufficient prediction accuracy. This study presents a novel Multi-Scale Fusion Convolutional Neural Network (MSF-CNN) to accurately predict the silicon content during the blast furnace smelting process, addressing the limitations of existing data-driven approaches. The proposed MSF-CNN model extracts temporal features at two distinct scales. The first scale utilizes a Convolutional Block Attention Module, which captures local temporal dependencies by focusing on the most relevant features across adjacent time steps. The second scale employs a Multi-Head Self-Attention mechanism to model long-term temporal dependencies, overcoming the inherent delay issues in the blast furnace process. By combining these two scales, the model effectively captures both short-term and long-term temporal dependencies, thereby enhancing prediction accuracy and real-time applicability. Validation using real blast furnace data demonstrates that MSF-CNN outperforms recurrent neural network models such as Long Short-Term Memory (LSTM) and the Gated Recurrent Unit (GRU). Compared with LSTM and the GRU, MSF-CNN reduces the Root Mean Square Error (RMSE) by approximately 22% and 21%, respectively, and improves the Hit Rate (HR) by over 3.5% and 4%, highlighting its superiority in capturing complex temporal dependencies. These results indicate that the MSF-CNN adapts better to the blast furnace’s dynamic variations and inherent delays, achieving significant improvements in prediction precision and robustness compared to state-of-the-art recurrent models.

Keywords: silicon content prediction; convolutional block attention module; self-attention mechanism; temporal dependencies (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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