Abstract
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.
Highlights
Burden layer distribution plays an important role in the internal state of a blast furnace (BF)because different layer distributions lead to different permeabilities, which in turn affect the temperature distribution and the height and shape of the cohesive zone [1,2]
To obtain the real-time BSRP, an mechanical swing radar (MSR) was installed on top of a BF [17]
Based on the modeling mechanism of kinematic flow, a model integrated with a data-driven technique for the prediction of the burden surface radius (BSRD) in the BF was proposed
Summary
Burden layer distribution plays an important role in the internal state of a blast furnace (BF). Compared with PFMs and VFMs, KMs are promising in terms of exhibiting high performance in modeling the movement of the solid burden in the bulk area of a BF (i.e., the throat and upper stack region of the BF) because they can reproduce experimental data quite accurately [14] and because they are structured to include only one model parameter. This type of model may be less affected by the possible inaccuracy of this parameter and more robust. The data used in the parameter identification and model verification stages were from the detection results of a real operating BF detected using a mechanical swing radar (MSR) system and are not from the results of other simulations or experiments [3,17]
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