An automated mineralogy derived criterion for clustering ore samples for mineral liberation studies

• Published in: Minerals engineering 2024-07, Vol.212, p.108714, Article 108714
• Main Authors: Ferreira, Rodrigo Fina, Lima, Rosa Malena Fernandes
• Format: Article
• Language: English
• Subjects: Cluster analysis; Liberation; Ore mineralogy
• ISSN: 0892-6875; 1872-9444
• DOI: 10.1016/j.mineng.2024.108714

[Display omitted] •A novel criterion for clustering ore samples for liberation studies is proposed.•It is easily obtained from the liberation spectrum for a top size of 1 mm.•The criterion has been experimentally validated for iron ore samples.•It is potentially applicable to other types of ore.•A clustering approach significantly reduces laboratory effort. Correctly determining the optimal grinding size is crucial for mineral processors, yet it poses several challenges. Still popular among practitioners, the empirical determination of the grinding target involves time-consuming and resource-intensive laboratory routines employed for generating quality, recovery and energy curves from which the target size is set. This approach becomes even more challenging for mines with diverse ore textures, requiring extensive testing on numerous samples to accurately capture the behaviour of the entire ore body. Consequently, a more efficient experimental method is highly desirable. A solution for reducing the laboratory effort is clustering samples according to the mineral liberation characteristics. This paper introduces a novel multivariate criterion for statistical clustering of ore samples for mineral liberation studies, derived from SEM-based automated mineralogy data. The criterion considers three variables: two coefficients (named A and k) derived from the exponential correlation equation between degree of liberation and the top size of the size fraction, and the overall degree of liberation, both easily obtained from the liberation spectrum for a top size of 1 mm. The effectiveness of the proposed criterion has been experimentally demonstrated. It was concluded that the clustering process correctly grouped samples with similar mineral liberation characteristics. The clustering approach can be used to significantly reduce the laboratory effort on liberation studies involving a great number of samples of different types of ore. Although developed using iron ore samples, the criterion's theoretical basis suggests its potential for adaptation to other types of ore.