Abstract The pelletizing process agglomerates ore fines previously considered as tailings and makes its application possible in the form of pellets. During the process, the pellets are subjected to various compressive forces and temperature changes, which cause ruptures in their surface. These ruptures (cracks) impair the performance of the pellets in the reduction furnaces and affect their strength. In addition to cracking, the formation of clusters inside the furnaces is also a problem for the reduction process because they compromise the flow of gases inside the furnaces. This bonding phenomenon occurs from the formation of iron bridges between pellets subjected to high temperatures. This problem can be minimized by coating the pellets with a mixture of magnesium oxides that inhibits bridging. The present study considered the importance of characterizing cracks and coating on the pellet surface and developed methodologies for the acquisition, processing and digital analysis of images acquired with a stereoscope. Adjustable sample holders were developed which made it possible to cover most of the surface area of the spherical object avoiding overlapping of analysis regions. The crack analysis routine used semantic segmentation and provided attributes such as mean thickness, area fraction and length. The coating analysis routine used color threshold segmentation and measured the fraction of the area occupied by the coating in the samples. The use of the sample holders was essential for the success of the acquisition procedure. The analysis routines were robust for different samples.

Abstract The discrimination between pores and cracks is an important step in the microstructural analysis of iron ore pellets. While the porosity is fundamental during the reduction process in blast furnaces, cracks are strongly detrimental to the mechanical strength. The usual image processing tools cannot automatically discriminate between these two types of features, especially in 3D images obtained, for instance, with x-ray microtomography (microCT). As pores and cracks have essentially the same x-ray absorbance, they cannot be discriminated by a simple intensity threshold. Given the complex shapes in 3D and the presence of many connections between pores and cracks, shape discrimination is not successful either. Thus, this article proposes the use of Deep Convolutional Neural Networks (DCNN) to discriminate between these 2 classes of discontinuities. The well-known U-NET architecture was employed. The network was trained by manually outlining representative objects of the 2 classes in a few layers of the 3D image. After optimization of the training parameters, the network was applied to the full image, successfully discriminating between pores and cracks. The trained network was then applied to the images of different pellets with good results. However, some residual errors are present. These characteristics are analyzed and possible solutions are proposed.

Abstract A correlative approach employing optical microscopy and scanning electron microscopy is proposed for the quantification of phases and pores in iron ore pellets. Combining mosaic images covering the full cross-sections of pellets, obtained with both techniques, it is possible to improve the discrimination of pores, quartz and silicates. First, the images must be carefully registered to show the exact same regions. The procedure for registration, discrimination and quantification was developed with Fiji open source software. A qualitative and quantitative analysis confirmed the advantages of the correlative method as compared to the individual techniques.

This work proposes a three-dimensional methodology to characterize porosity in iron ore pellets by X-ray Microtomography (microCT). An image analysis routine was developed to discriminate and quantify open/closed porosity. The results were compared to the traditional techniques of mercury intrusion porosimetry (MIP) and optical microscopy (OM). As expected, the porosity values obtained from microCT were much lower than those from MIP and OM, due to the lower spatial resolution of the proposed technique. However, the resolution can be optimized to detect the main peak of the pore size distribution, close to 10 µm. MicroCT was also able to discriminate between open and closed porosities, and revealed the volumetric spatial distribution of the pores, parameters that cannot be obtained from the other techniques. Thus, microCT may become a new standard for this analysis, eliminating the need for specimen preparation (as for OM) or the use of toxic materials (as in MIP).