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The synthetic aperture radar (SAR) imaging technique for a frequency-modulated continuous wave (FMCW) has attracted wide attention in the field of burden surface profile measurement. However, the imaging data are virtually under-sampled due to the severely restricted scan time, which prevents the antenna being exposed to high temperatures and heavy dust in the blast furnace (BF) for an extended period. In traditional SAR imaging algorithm research, the insufficient accumulation of scattered energy in reconstructing the burden surface profile leads to lower imaging precision, and the harsh smelting increases the probability of distortion in shape detection. In this study, to address these challenges, a novel rotating SAR imaging algorithm based on the constructed mechanical swing radar system is proposed. This algorithm is inspired by the low-rank property of the sampled signal matrix and the sparsity of burden surface profile images. First, the sparse FMCW signal is modeled, and the position transform matrix, calculated according to the BF dimensions, is embedded into the dictionary matrix. Then, the low-rank and sparsity priors are considered and reformulated as split variables in order to establish a convex optimization problem. Lastly, the augmented Lagrange multiplier (ALM) is employed to solve this problem under double constraints, and the imaging results are obtained using the alternating direction method of multipliers (ADMM). The experimental results demonstrate that, in the subsequent shape detection, the root mean square error (RMSE) is 15.38% lower than the previous algorithm and 15.63% lower under low signal-to-noise (SNR) conditions. In both enclosed and harsh environments, the proposed algorithm is able to achieve higher imaging precision even under high noise. It will be further optimized for speed and reliability, with plans to extend its application to 3D measurements in the future.

A proper burden and porosity distribution of the bed in the upper shaft are important prerequisites for realizing a stable and efficient operation of the ironmaking blast furnace. The discrete element method was used to investigate the effects of the static friction coefficient between burden particles and shaft angle on the burden profile and porosity distribution in the bed formed by charging the burden with a bell-less charging equipment. The results indicate that a large static friction coefficient makes the particles stay closer to the impact point (i.e., where they fall) from the rotating chute. A large mixed region of the burden bed decreases the gas permeability, and an increase in the burden particle roughness will worsen this problem. The burden surface shape becomes flatter with an increase in the shaft angle. These findings explain the effect of particle properties and wall geometry on the inner structure of the burden bed.

The distribution of burden layers in an ironmaking blast furnace strongly influences the conditions in the upper part of the process. The bed permeability largely depends on the distribution of ore and coke in the lumpy zone, which affects the radial gas flow distribution in the shaft. Along with the continuous advancement of technology, more information about the internal conditions of the blast furnace can be obtained through advanced measurement equipment, including 2D profiles and 3D surface maps of the top burden surface. However, the change of layer structure along with the burden descent cannot be directly measured. A mathematical model predicting the burden distribution and the internal layer structure during the descending process is established in this paper. The accuracy of the burden distribution model is verified by a comparison with experimental results. A sensitivity study was undertaken to clarify the role of some factors on the arising layer distribution, including the descent-rate distribution, the initial burden surface profile, and the charging direction through the charging matrix. The findings can be used as a theoretical basis to guide plant operations for optimizing the charging.