Reconstruction and classification of 3D burden surfaces based on two model drived data fusion

Blast furnace (BF) burden surface modeling is the basis for automating precise charging operations of BFs, and it can also be used to predict gas flow distributions based on a burden profile. In this paper, first, a mechanism model is established according to the charging operation, and it is convenient for predicting the burden profile after the charging operation. Then, the Gaussian process regression (GPR) algorithm is used to fuse the charging mechanism model and the radar detection data to better reconstruct the burden profile. Finally, the traditional shape of a burden surface is researched based on the point cloud data of a phased array radar, and 4 classes of burden surfaces are defined and reconstructed. The reconstructed burden surface is classified by expert-defined features and deep features extracted by convolutional neural networks (CNNs).