Mineralogical Characterization of Tailings by using Hyperspectral Techniques and its Application in Predicting Rheological Properties

Advanced mineral characterization techniques play a crucial role in the mining industry, particularly in predicting and controlling the rheological behavior of suspensions such as mineral pulps, concentrates, and tailings.For the latter, the ability to accurately assess mineral composition, especially the presence of phyllosilicates, is essential for understanding the challenges associated with tailings transport and disposal, which pose significant long-term risks.Previous studies have shown that solid content by volume, particle size, and mineralogy are the main factors influencing tailings rheology.This study aims to quantify the effects of these properties, with a specific focus on the influence of some phyllosilicates, on the viscosity () and yield stress () of tailings.Techniques such as hyperspectral, XRF, XRD, and laser diffraction particle size analysis were assessed as tools for supporting predictive control of tailings mineralogy and rheology.A total of 108 tailings mixtures were adjusted by blending Chilean copper mine tailings with kaolinite and montmorillonite, varying factors such as solids content by volume (), particle size (d 80 ), phyllosilicate content in the solid phase (wt% phy ), and the type of phyllosilicate (phy.type) to generate flow curves.Two linear regression models were applied to predict viscosity and yield stress, with the logarithmic model yielding the best results (R=0.98 for Log() and 0.96 for Log()).These models were validated using phyllosilicate abundance data from reflectance spectroscopy measurements on briquettes with the same composition as the experimental samples.While kaolinite content was underestimated and montmorillonite overestimated, leading to an overestimation of and at higher values, the findings confirm that the key variables effectively estimate viscosity and yield stress.This supports the potential for implementing predictive control systems using advanced mineral characterization techniques.