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

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.