A journal of IEEE and CAA , publishes high-quality papers in English on original theoretical/experimental research and development in all areas of automation
Volume 11 Issue 4
Apr.  2024

IEEE/CAA Journal of Automatica Sinica

  • JCR Impact Factor: 11.8, Top 4% (SCI Q1)
    CiteScore: 17.6, Top 3% (Q1)
    Google Scholar h5-index: 77, TOP 5
Turn off MathJax
Article Contents
Z. Chen, X. Wang, W. Gui, J. Zhu, C. Yang, and  Z. Jiang,  “A novel sensing imaging equipment under extremely dim light for blast furnace burden surface: Starlight high-temperature industrial endoscope,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 4, pp. 893–906, Apr. 2024. doi: 10.1109/JAS.2023.123954
Citation: Z. Chen, X. Wang, W. Gui, J. Zhu, C. Yang, and  Z. Jiang,  “A novel sensing imaging equipment under extremely dim light for blast furnace burden surface: Starlight high-temperature industrial endoscope,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 4, pp. 893–906, Apr. 2024. doi: 10.1109/JAS.2023.123954

A Novel Sensing Imaging Equipment Under Extremely Dim Light for Blast Furnace Burden Surface: Starlight High-Temperature Industrial Endoscope

doi: 10.1109/JAS.2023.123954
Funds:  This work was supported by the National Natural Science Foundation of China (62273359), the General Project of Hunan Natural Science Foundation of China (2022JJ30748), and the National Major Scientific Research Equipment of China (61927803)
More Information
  • Blast furnace (BF) burden surface contains the most abundant, intuitive and credible smelting information and acquiring high-definition and high-brightness optical images of which is essential to realize precise material charging control, optimize gas flow distribution and improve ironmaking efficiency. It has been challengeable to obtain high-quality optical burden surface images under high-temperature, high-dust, and extremely-dim (less than 0.001 Lux) environment. Based on a novel endoscopic sensing detection idea, a reverse telephoto structure starlight imaging system with large field of view and large aperture is designed. Combined with a water-air dual cooling intelligent self-maintenance protection device and the imaging system, a starlight high-temperature industrial endoscope is developed to obtain clear optical burden surface images stably under the harsh environment. Based on an endoscope imaging area model, a material flow trajectory model and a gas-dust coupling distribution model, an optimal installation position and posture configuration method for the endoscope is proposed, which maximizes the effective imaging area and ensures large-area, safe and stable imaging of the device in a confined space. Industrial experiments and applications indicate that the proposed method obtains clear and reliable large-area optical burden surface images and reveals new BF conditions, providing key data support for green iron smelting.

     

  • loading
  • [1]
    Y. Zhang, P. Zhou, and G. Cui, “Multi-model based PSO method for burden distribution matrix optimization with expected burden distribution output behaviors,” IEEE/CAA J. Autom. Sinica, vol. 6, no. 6, pp. 1506–1512, 2019.
    [2]
    F. Jin, J. Zhao, C. Sheng, and W. Wang, “Causality diagram-based scheduling approach for blast furnace gas system,” IEEE/CAA J. Autom. Sinica, vol. 5, no. 2, pp. 587–594, 2018. doi: 10.1109/JAS.2017.7510715
    [3]
    Y. Huang, X. Lai, K. Zhang, J. An, L. Chen, and M. Wu, “Two-stage decision-making method for burden distribution based on recognition of conditions in blast furnace,” IEEE Tran. Industrial Electronics, vol. 68, no. 5, pp. 4199–4208, 2021. doi: 10.1109/TIE.2020.2982121
    [4]
    J. Li, C. Hua, Y. Yang, and X. Guan, “A novel MIMO T-S fuzzy modeling for prediction of blast furnace molten iron quality with missing outputs,” IEEE Trans. Fuzzy Systems, vol. 29, no. 6, pp. 1654–1666, 2021. doi: 10.1109/TFUZZ.2020.2983667
    [5]
    H. Zhou, H. Zhang, and C. Yang, “Hybrid-model-based intelligent optimization of ironmaking process,” IEEE Trans. Industrial Electronics, vol. 67, no. 3, pp. 2469–2479, 2020. doi: 10.1109/TIE.2019.2903770
    [6]
    L. Shen, Z. Chen, Z. Jiang, and W. Gui, “Soft sensor modeling of blast furnace wall temperature based on temporal-spatial dimensional finite-element extrapolation,” IEEE Trans. Instrumentation and Measurement, vol. 70, pp. 1–14, 2021. doi: 10.1109/TIM.2020.3010072
    [7]
    L. He, Z. Jiang, Y. Xie, Z. Chen, and W. Gui, “Velocity measurement of blast furnace molten iron based on mixed morphological features of boundary pixel sets,” IEEE Trans. Instrumentation and Measurement, vol. 70, pp. 1–12, 2021. doi: 10.1109/TIM.2021.3119149
    [8]
    D. Pan, Z. Jiang, Z. Chen, W. Gui, Y. Xie, and C. Yang, “Temperature measurement and compensation method of blast furnace molten iron based on infrared computer vision,” IEEE Trans. Instrumentation and Measurement, vol. 68, no. 10, pp. 3576–3588, 2019. doi: 10.1109/TIM.2018.2880061
    [9]
    K. Jiang, Z. Jiang, Y. Xie, D. Pan, and W. Gui, “Abnormality monitoring in the blast furnace ironmaking process based on stacked dynamic target-driven denoising autoencoders,” IEEE Trans. Industrial Informatics, vol. 18, no. 3, pp. 1854–1863, 2022. doi: 10.1109/TII.2021.3084911
    [10]
    Y. Li, S. Zhang, J. Zhang, Y. Yin, W. Xiao, and Z. Zhang, “Data-driven multiobjective optimization for burden surface in blast furnace with feedback compensation,” IEEE Trans. Industrial Informatics, vol. 16, no. 4, pp. 2233–2244, 2020. doi: 10.1109/TII.2019.2908989
    [11]
    J. Huang, Z. Chen Z. Jiang, and W. Gui, “3D topography measurement and completion method of blast furnace burden surface using high-temperature industrial endoscope,” IEEE Sensors J., vol. 20, no. 12, pp. 6478–6491, 2020. doi: 10.1109/JSEN.2020.2974253
    [12]
    Z. Chen, Z. Jiang, C. Yang, and W. Gui, “Detection of blast furnace stockline based on a spatial-temporal characteristic cooperative method,” IEEE Trans. Instrumentation and Measurement, vol. 70, pp. 1–13, 2020.
    [13]
    J. Zhu, W. Gui, Z. Chen, and Z. Jiang, “A novel non-contact and real-time blast furnace stockline detection method based on burden surface video streams,” IEEE Trans. Instrumentation and Measurement, vol. 72, pp. 1–13, 2023.
    [14]
    Q. Shi, J. Wu, Z. Ni, X. Lv, F. Ye, Q. H, and X. Chen, “A blast furnace burden surface deeplearning detection system based on radar spectrum restructured by entropy weight,” IEEE Sensors J., vol. 21, no. 6, pp. 7928–7939, 2021. doi: 10.1109/JSEN.2020.3045973
    [15]
    H. Wang, W. Li, T. Zhang, J. Li, and X. Chen, “Learning-based key points estimation method for burden surface profile detection in blast furnace,” IEEE Sensors J., vol. 22, no. 10, pp. 9589–9597, 2022. doi: 10.1109/JSEN.2022.3163373
    [16]
    J. Huang, Z. Jiang, W. Gui, Z. Yi, D. Pan, K. Zhou, and C. Xu, “Depth estimation from a single image of blast furnace burden surface based on edge defocus tracking,” IEEE Trans. Circuits and Systems for Video Technology, 2022.
    [17]
    X. Cheng and S. Cheng, “Infrared thermographic fault detection using machine vision with convolutional neural network for blast furnace chute,” IEEE Trans. Instrumentation and Measurement, vol. 71, pp. 1–9, 2022.
    [18]
    Z. Chen, Z. Jiang, W. Gui, and C. Yang, “A novel device for optical imaging of blast furnace burden surface: Parallel low-light-loss backlight high-temperature industrial endoscope,” IEEE Sensors J., vol. 16, no. 17, pp. 6703–6717, 2016. doi: 10.1109/JSEN.2016.2587729
    [19]
    Z. Yi, Z. Chen, Z. Jiang, and W. Gui, “A novel 3-D high-temperature industrial endoscope with large field depth and wide field,” IEEE Trans. Instrumentation and Measurement, vol. 69, no. 9, pp. 6530–6543, 2020. doi: 10.1109/TIM.2020.2970372
    [20]
    Z. Yi, Z. h. Jiang, J. Huang, X. Chen, and W. Gui, “Optimization method of the installation direction of industrial endoscopes for increasing the imaged burden surface area in blast furnaces,” IEEE Trans. Industrial Informatics, 2022.
    [21]
    C. Cao, S. Liao, Z. Liao, Y. Bai, B. Chen, and Z. Fan, “Design of off-axis reflective optical system with large field-of-view based on freeform surfaces,” Acta Optica Sinica, vol. 40, no. 8, pp. 37–45, 2020.
    [22]
    Y. Zhang, H. Shi, C. Wang, Y. Li, Z. Liu, S. Zhang, and H. Jiang, “Research on polarization aberration characteristics of off-axis freeform surface optical system,” Acta Optica Sinica, vol. 41, no. 18, pp. 190–200, 2021.
    [23]
    Z. Chen, Z. Jiang, C. Yang, W. Gui, and Y. Sun, “Dust distribution study at the blast furnace top based on k-Sε-u p model,” IEEE/CAA J. Autom. Sinica, vol. 8, no. 1, pp. 121–135, 2021. doi: 10.1109/JAS.2020.1003468
    [24]
    A. Mittal, R. Soundararajan, and A. C. Bovik, “Making a completely blind image quality analyzer”, IEEE Signal Processing Letters, vol. 22, no. 3, pp. 209−212, 2013.

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(14)  / Tables(8)

    Article Metrics

    Article views (81) PDF downloads(24) Cited by()

    Highlights

    • A starlight optical imaging system with large depth of field and wide field of view is designed
    • The intelligent self-maintenance protection device with water-air dual cooling system is developed
    • The optimum configuration of the installation position of the system is found
    • The system obtains clear and reliable large-area optical burden surface images
    • The system can reveal new BF conditions, providing key data support for green iron smelting

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return