Magnetic separation is a versatile technique widely used in the mining industry. Drum-type wet low-intensity magnetic separation (WLIMS) represents the backbone of the iron ore upgrading circuits since the mid 19^th century. However, it has been traditionally applied through guidelines that commonly disregard the ore properties and their interaction with the operating conditions to influence the final process selectivity. This work describes a three-stage methodology to achieve the comprehensive characterization and classification of an iron ore, seeking to recognize links between the ore properties and operating conditions, and their influence upon the process performance. This methodology integrates 1) laboratory testing, 2) particle-scale characterization of the ore and products from separation trials, and 3) data analysis to identify and categorize the particle attributes that control their behavior in a laboratory-scale magnetic separator. Dry sieving, Saturation Magnetization Analyzer (SATMAGAN) and Mineral Liberation Analysis (MLA) represent the basis to collect quantitative particle-level information for clustering the ore into classes of unique nature. The further determination of the volumetric magnetic susceptibility by particle class, together with the relative probability of particle capture, provides valuable insight on the ore magnetic behavior. The calculation of particle-classed partition coefficients resulted practical to assess the process selectivity in terms of particle attributes and operating conditions. The methodology proposes guidelines to comprehend the behavior of an ore from a particle-scale perspective. Moreover, the acquired data can be used for geometallurgical and process modeling, which represent promising forecasting tools to support decision-making in plants.

磁选是一种广泛应用于采矿业的通用技术。^{鼓式湿法低强度磁选 ( WLIMS} ) 是 19 世纪中期以来铁矿石提质回路的支柱世纪。然而，它传统上是通过通常忽略矿石性质及其与操作条件的相互作用来影响最终工艺选择性的指南来应用的。这项工作描述了一种实现铁矿石综合表征和分类的三阶段方法，旨在识别矿石性质和操作条件之间的联系，以及它们对工艺性能的影响。该方法集成了 1) 实验室测试，2) 矿石和分离试验产品的颗粒级表征，以及 3) 数据分析，以识别和分类控制其在实验室级磁选机中的行为的颗粒属性。干筛，饱和磁化分析仪 (SATMAGAN) 和矿物释放分析 (MLA) 代表了收集定量颗粒水平信息以将矿石分类为独特性质类别的基础。进一步确定颗粒类别的体积磁化率以及颗粒捕获的相对概率，为矿石磁性行为提供了有价值的见解。粒子分类分配系数的计算结果可用于根据粒子属性和操作条件评估过程选择性。该方法提出了从颗粒尺度的角度理解矿石行为的指南。此外，获取的数据可用于地质冶金和过程建模，这代表了支持工厂决策的有前景的预测工具。