Particle-based characterization and classification to evaluate the behavior of iron ores in drum-type wet low-intensity magnetic separation

[Display omitted] •Process selectivity in terms of particle properties and operating conditions.•Particle volume magnetic susceptibility as an insight on the ore magnetic nature.•The relative probability of capture allows categorizing the particle behavior.•Reducing the size of a particle directly affects its probability of collection.•Comprehending the process performance from a particle-scale perspective. 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 19th 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.