Electromagnetic Scattering of Blast Furnace Burden Surface With Stochastic Properties

Microwave radars have been utilized as detectors within blast furnaces to accomplish the measurement of the surface shape of the furnace burden. However, the additional physical information contained in the radar echoes has not yet been fully explored. The electromagnetic scattering model of the burden surface can serve as the theoretical foundation for the inversion of radar data. In this letter, first, the statistical values of the roughness parameters of the burden surface are obtained by measuring typical samples. Second, an equivalent dielectric constant model is proposed for the coexistence of coke, ore, water, and gas on the burden surface. Finally, the back-scattering coefficient of the burden surface was calculated by the integral equation method (IEM) based on the roughness parameters and geometric characteristics of the burden surface. The effects of charge size and volume fractions of ore, coke, and water on the electromagnetic scattering characteristics were analyzed. The experimental results indicate that for the blast furnace burden surface where coke, ore, and gas coexist, when the angle of incidence is 30° and the root mean square height is less than 0.03 m, the back-scattering is enhanced as the roughness of the burden surface increases. In comparison, for the surface of coke material, the moisture content has a greater impact on electromagnetic scattering than the incorporation of ore with the same volume fraction.

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Publication Details

Journal
IEEE Antennas and Wireless Propagation Letters
Published
2024-11-13
DOI
https://doi.org/10.1109/lawp.2024.3496903
Primary Topic
Electromagnetic Scattering and Analysis
Type
article
Field-Weighted Citation Impact
0.00

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article

Electromagnetic Scattering of Blast Furnace Burden Surface With Stochastic Properties

Jie Zhang, Xianzhong Chen, Qingwen Hou, Qing Yu
IEEE Antennas and Wireless Propagation Letters
Electromagnetic Scattering and Analysis
article

Electromagnetic Scattering of Blast Furnace Burden Surface With Stochastic Properties

Jie Zhang, Xianzhong Chen, Qingwen Hou, Qing Yu
article en

Abstract

Microwave radars have been utilized as detectors within blast furnaces to accomplish the measurement of the surface shape of the furnace burden. However, the additional physical information contained in the radar echoes has not yet been fully explored. The electromagnetic scattering model of the burden surface can serve as the theoretical foundation for the inversion of radar data. In this letter, first, the statistical values of the roughness parameters of the burden surface are obtained by measuring typical samples. Second, an equivalent dielectric constant model is proposed for the coexistence of coke, ore, water, and gas on the burden surface. Finally, the back-scattering coefficient of the burden surface was calculated by the integral equation method (IEM) based on the roughness parameters and geometric characteristics of the burden surface. The effects of charge size and volume fractions of ore, coke, and water on the electromagnetic scattering characteristics were analyzed. The experimental results indicate that for the blast furnace burden surface where coke, ore, and gas coexist, when the angle of incidence is 30° and the root mean square height is less than 0.03 m, the back-scattering is enhanced as the roughness of the burden surface increases. In comparison, for the surface of coke material, the moisture content has a greater impact on electromagnetic scattering than the incorporation of ore with the same volume fraction.

IEEE Antennas and Wireless Propagation LettersVol. 24(2)
University of Science and Technology Beijing (CN)
National Natural Science Foundation of China, Natural Science Foundation of Beijing Municipality
Openalex Percentile: Top 82%
Electromagnetic Scattering and Analysis
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Electromagnetic Scattering of Blast Furnace Burden Surface With Stochastic Properties — Jie Zhang, Xianzhong Chen, et al. · IEEE Antennas and Wireless Propagation Letters (2024) | TGRS Research Map | TGRS