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Ore fines undergo agglomeration processes, such as pelletising or sintering, before their use in reduction furnaces. The material produced in one of the stages of sintering is the focus of this work. These fines first undergo micro-agglomeration, which is critical, as many of the properties of the sinter depend on the structure of the pre-heat treatment cold agglomerate. There are three typical structures: quasiparticles, micropellets, and non-agglomerated particles. This paper proposes an automatic image analysis routine to eliminate the human operator subjectivity, while also providing measures that cannot be obtained manually. The routine identifies the different granulometry particles and classifies them in the three classes. It then calculates the class area fraction, circularity, and thickness of the quasiparticles’ adherent layer. It also quantifies the mineral phases present in the quasiparticle cores. The routine runs upon mosaic images containing thousands of particles, allowing prediction of the future characteristics of the sinter.

An image analysis system for automatic characterisation of iron ore sintering quasiparticles