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Prediction of Blast Furnace Gas Generation Based on Bayesian Network

Author

Listed:
  • Zitao Wu

    (School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, China)

  • Dinghui Wu

    (School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, China)

Abstract

Due to the large fluctuation of blast furnace gas (BFG) generation and its complex production characteristics, it is difficult to accurately obtain its gas change rules. Therefore, this paper proposes a prediction method of BFG generation based on Bayesian network. First, the BFG generation data are divided according to the production rhythm of the hot blast stove, and the training event set is constructed for the two dimensions of interval generation and interval time. Then, the Bayesian network of generation and the Bayesian network of time corresponding to the two dimensions are built. Finally, the state of each prediction interval is inferred, and the results of the reasoning are mapped and combined to obtain the prediction results of the BFG generation interval combination. In the experiment part, the actual data of a large domestic iron and steel plant are used to carry out multi-group comparison experiments, and the results show that the proposed method can effectively improve the prediction accuracy.

Suggested Citation

  • Zitao Wu & Dinghui Wu, 2025. "Prediction of Blast Furnace Gas Generation Based on Bayesian Network," Energies, MDPI, vol. 18(5), pages 1-15, February.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:5:p:1182-:d:1601994
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    References listed on IDEAS

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    1. Sun, Wenqiang & Wang, Qiang & Zhou, Yue & Wu, Jianzhong, 2020. "Material and energy flows of the iron and steel industry: Status quo, challenges and perspectives," Applied Energy, Elsevier, vol. 268(C).
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