Shape from Shading-Based Study of Silica Fusion Characterization Problems

Abstract

With the rapid development of AI (artificial intelligence) in recent years, AI has been widely used in the image processing of iron tailings. The main component of iron tailings is silica, which is the most difficult part of the iron tailing composition to fuse. Therefore, the melting behavior of iron tailings can be characterized by the melting behavior of silica. First of all, in the actual production process, the temperature of the high-temperature furnace exceeds 1500 °C, which leads to a short service life of the conventional testing equipment. A line array CCD (charge coupled device) camera system with amplification effect was used to acquire data on silica in a high-temperature melt pool in a non-contact manner. The corundum crucible position is fixed, which can be used to establish a two-dimensional coordinate system to reproduce the object’s motion pattern to solve the problem of the short service life of conventional inspection equipment. The color and wobble problems generated by the Rift Valley CCD system when taking pictures need to be corrected for accurate image processing. Secondly, the change in the center-of-mass position of silica during melting was studied using the temporal images of silica in a high-temperature melt pool, and the edge contour features were extracted from the silica images and filled inside the contours, from which the mass, area and perimeter were obtained. Finally, the volume change of silica in the time series image is calculated after eliminating the effect of background factors, and the 2D image is constructed into a 3D image. We built a SFS (shape from shading) model according to the lighting conditions, and the actual melting rate of silica is calculated accordingly. According to the model calculation results, it can be seen that with the change in time, silica surface area under heat gradually decreases, then the silica melting rate gradually decreases; among them, the faster melting rate is about 0.007 cubic millimeter/second, and the slower melting rate is about 0.0015 cubic millimeter/second. Data support was provided for the blast furnace slag direct fiber formation process. The introduction of the algorithmic model into the actual production process has certain advantages. Therefore, the SFS model established in this paper has some practical value and is worth promoting in related enterprises.