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IEEE/CAA Journal of Automatica Sinica

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Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2024  > In Press, Accepted Manuscript
S. Lou, C. Yang, Z. Liu, S. Wang, H. Zhang, and P. Wu, “Release power of mechanism and data fusion: A hierarchical strategy for enhanced MIQ-related modeling and fault detection in BFIP,” IEEE/CAA J. Autom. Sinica, 2024. doi: 10.1109/JAS.2024.124821
Citation: S. Lou, C. Yang, Z. Liu, S. Wang, H. Zhang, and P. Wu, “Release power of mechanism and data fusion: A hierarchical strategy for enhanced MIQ-related modeling and fault detection in BFIP,” IEEE/CAA J. Autom. Sinica, 2024. doi: 10.1109/JAS.2024.124821
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Release Power of Mechanism and Data Fusion: A Hierarchical Strategy for Enhanced MIQ-Related Modeling and Fault Detection in BFIP

doi: 10.1109/JAS.2024.124821
Funds:  This work was supported in part by a grant from the National Natural Science Foundation of China (61933015, 61703371, 62273030), in part by a grant from the Central University Basic Research Fund of China under Grant K20200002 (for NGICS Platform, Zhejiang University), and in part by grants from the Social Development Project of Zhejiang Provincial Public Technology Research (LGF19F030004 and LGG21F030015).
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    Abstract
  • Data-driven techniques are reshaping blast furnace iron-making process (BFIP) modeling, but their “black-box” nature often obscures interpretability and accuracy. To overcome these limitations, our mechanism and data co-driven strategy (MDCDS) enhances model transparency and molten iron quality (MIQ) prediction. By zoning the furnace and applying mechanism-based features for material and thermal trends, coupled with a novel stationary broad feature learning system (StaBFLS), interference caused by nonstationary process characteristics are mitigated and the intrinsic information embedded in BFIP is mined. Subsequently, by integrating stationary feature representation with mechanism features, our temporal matching broad learning system (TMBLS) aligns process and quality variables using MIQ as the target. This integration allows us to establish process monitoring statistics using both mechanism and data-driven features, as well as detect modeling deviations. Validated against real-world BFIP data, our MDCDS model demonstrates consistent process alignment, robust feature extraction, and improved MIQ modeling—yielding better fault detection. Additionally, we offer detailed insights into the validation process, including parameter baselining and optimization. Details of the code are available online.1

     

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