3-Dimension Burden Surface Imaging System with T-shaped MIMO Radar in the Blast Furnace

Wei, Jidong; Chen, Xianzhong; Wang, Zhengpeng; Kelly, James R.; Zhou, Peng

doi:10.2355/isijinternational.55.592

Cited by 10 publications

(6 citation statements)

References 19 publications

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“…Imaging radars can detect the whole surface shape and provide burden surface images with high precision and accuracy; hence, it is a trend of future BF radar development. [7][8][9] However, the complexity of these systems makes them sensitive to the harsh environment of high temperature, high pressure and heavy dust inside the BF. Hence, it is difficult for these systems to stably monitor the burden surface shape in the long term.…”

Section: Tracking the Burden Surface Radial Profile Of A Blast Furnacmentioning

confidence: 99%

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Tracking the Burden Surface Radial Profile of a Blast Furnace by a B-mode Mechanical Swing Radar System

et al. 2020

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Section: Tracking the Burden Surface Radial Profile Of A Blast Furnacmentioning

confidence: 99%

“…(6) yields the relationship between D and the index of the spectral lines of the peak denoted as j peak , as Eq. (9).…”

Section: Radar Ranging Principalmentioning

confidence: 99%

Tracking the Burden Surface Radial Profile of a Blast Furnace by a B-mode Mechanical Swing Radar System

et al. 2020

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“…Several pointwise methods have been employed to gauge the BF burden surface [5][6][7][8][9]. Direct detection methods for shape measurement of the BF burden surface mainly employ mechanical probes, radar probes, laser probes, and infrared imagers.…”

Section: Introductionmentioning

confidence: 99%

A Real-Time 3D Measurement System for the Blast Furnace Burden Surface Using High-Temperature Industrial Endoscope

Chen

Jiang

et al. 2020

Sensors

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Capturing the three-dimensional (3D) shape of the burden surface of a blast furnace (BF) in real-time with high accuracy is crucial for improving gas flow distribution, optimizing coke operation, and stabilizing BF operation. However, it is difficult to perform 3D shape measurement of the burden surface in real-time during the ironmaking process because of the high-temperature, high-dust, and lightless enclosed environment inside the BF. To solve this problem, a real-time 3D measurement system is developed in this study by combining an industrial endoscope with a virtual multi-head camera array 3D reconstruction method. First, images of the original burden surface are captured using a purpose-built industrial endoscope. Second, a novel micro-pixel luminance polarization method is proposed and applied to compensate for the heavy noise in the backlit images due to high dust levels and poor light in the enclosed environment. Third, to extract depth information, a multifeature-based depth key frame classifier is designed to filter out images with high levels of clarity and displacement. Finally, a 3D shape burden surface reconstruction method based on a virtual multi-head camera array is proposed for capturing the real-time 3D shape of the burden surface in an operational BF. The results of an industrial experiment illustrate that the proposed method can measure the 3D shape of the entire burden surface and provide reliable burden surface shape information for BF control.

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“…Therefore, researchers have adopted scaled experiments [4,6], computational fluid dynamics (CFD) simulations [7] and DEM simulations [8] for analysis. Industrial radar systems can detect the burden surface shape of an operating BF in real time [15,16]. Hence, the corresponding BSRD can be obtained.…”

Section: Introductionmentioning

confidence: 99%

Radar Detection-Based Modeling in a Blast Furnace: A Prediction Model of Burden Surface Descent Speed

Tian

Hou

Chen

2019

Metals

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The distribution of burden layers is a vital factor that affects the production of a blast furnace. Radars are advanced instruments that can provide the detection results of the burden surface shape inside a blast furnace in real time. To better estimate the burden layer thicknesses through improving the prediction accuracy of the burden descent during charging periods, an innovative data-driven model for predicting the distribution of the burden surface descent speed is proposed. The data adopted were from the detection results of an operating blast furnace, collected using a mechanical swing radar system. Under a kinematic continuum modeling mechanism, the proposed model adopts a linear combination of Gaussian radial basis functions to approximate the equivalent field of burden descent speed along the burden surface radius. A proof of the existence and uniqueness of the prediction solution is given to guarantee that the predicted radial profile of the burden surface can always be calculated numerically. Compared with the plain data-driven descriptive model, the proposed model has the ability to better characterize the variability in the radial distribution of burden descent speed. In addition, the proposed model provides prediction results of higher accuracy for both the future surface shape and descent speed distribution.

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3-Dimension Burden Surface Imaging System with T-shaped MIMO Radar in the Blast Furnace

Cited by 10 publications

References 19 publications

Tracking the Burden Surface Radial Profile of a Blast Furnace by a B-mode Mechanical Swing Radar System

Tracking the Burden Surface Radial Profile of a Blast Furnace by a B-mode Mechanical Swing Radar System

A Real-Time 3D Measurement System for the Blast Furnace Burden Surface Using High-Temperature Industrial Endoscope

Radar Detection-Based Modeling in a Blast Furnace: A Prediction Model of Burden Surface Descent Speed

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