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.

中文翻译：

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用于矿物释放研究的矿石样品聚类的自动矿物学导出标准


正确确定最佳研磨尺寸对于矿物加工商来说至关重要，但它也带来了一些挑战。研磨目标的经验确定仍然受到从业者的欢迎，涉及耗时且资源密集的实验室例程，用于生成质量、回收率和能量曲线，并根据这些曲线设定目标尺寸。对于具有不同矿石质地的矿山来说，这种方法变得更具挑战性，需要对大量样品进行广泛测试，以准确捕获整个矿体的行为。因此，非常需要一种更有效的实验方法。减少实验室工作量的一个解决方案是根据矿物释放特征对样品进行聚类。本文介绍了一种新颖的多元标准，用于矿物释放研究的矿石样品统计聚类，该标准源自基于 SEM 的自动化矿物学数据。该标准考虑三个变量：两个系数（命名为 A 和 k），源自解放度与尺寸分数的顶部尺寸之间的指数相关方程，以及总体解放度，这两个系数都可以从顶部的解放谱轻松获得尺寸为 1 毫米。所提出标准的有效性已通过实验证明。结论是聚类过程正确地对具有相似矿物释放特征的样品进行了分组。聚类方法可用于显着减少涉及大量不同类型矿石样品的释放研究的实验室工作量。尽管该标准是使用铁矿石样品开发的，但其理论基础表明其具有适应其他类型矿石的潜力。