Predicting mineral liberation during comminution remains a key factor in optimizing the processing of Critical Raw Materials (CRMs). This study presents a modeling system that couples a Roll crusher’s breakage Model B with Gaudin’s statistical liberation model to predict the liberation behavior of Cu, Zn and Pb sulfide ores. The main objective is to establish a predictive framework linking a roll crusher’s operating parameters to crushing modeling, enhancing our understanding of the relationship between comminution and liberation, with potential application to other types of materials.
The quartered Run-of-mine (ROM) material was characterized using Particle Size Distribution (PSD), X-ray diffraction (XRD), X-ray fluorescence (XRF), chemical analysis, and automated Mineral Liberation Analysis (MLA) to establish a quantitative mineralogical baseline. The ROM was split into two fractions: particles smaller and larger than 1250 μm. Liberation characteristics were evaluated for the undersize fraction, while the oversize material was subjected to comminution using a roll crusher. Comminution tests were conducted under controlled conditions, including compressive force, throughput, roll speed, and feed PSD. Models were calibrated with experimental data to simulate particle breakage using MATLAB® scripts, and MLA measurements of the crusher’s product were used to validate model predictions by quantifying the resulting mineral liberation.
The ROM material showed that 55% of chalcopyrite, 36% of sphalerite, and 40% of galena were liberated. Comminuted material showed a liberation degree of 42% of chalcopyrite, 33% of sphalerite, and 31% of galena. Through the model verification, the results prove the potential of the induced liberation by roll crusher, reducing the volume of material which requires further grinding in downstream comminution stages while maintaining comparable mineral liberation.
The proposed coupled modeling and validation framework could provide a foundation for predictive mineral processing techniques which target energy efficiency and improve the recovery of critical raw materials.
