Mineralogy and texture of the Storforshei iron formation, and their effect on grindability

Abstract

Investigating how ore mineralogy and texture affect the recovery from the processing plant is important for any mining operation. The results will assist in production planning and optimising the utilisation of a deposit. Easily available validated tests are desirable and useful.The Storforshei iron formation (IF) consists of several iron oxide deposits with mineralogical and textural differences. Although the Fe grades of the ores are similar, mineralogical and textural characteristics of the deposits affect the individual recoveries from the magnetic separation. For this paper three of the ore deposits were sampled, and important mineralogical and textural properties were investigated and tested. The investigations included geological mapping and optical microscopy, and the test work involved surface hardness measurements by Schmidt hammer and Equotip, and autogenous milling tests (i.e., grindability). The aim of the study was to investigate whether ore mineralogy and textures can be correlated to surface hardness measurements, and whether these three parameters can be used to evaluate grindability. The ores were classified into six ore types based on mineralogy and textures. The results show that the ore mineralogy and texture influence the surface hardness. Fine-grained ore types with irregular-to-no visible grain boundaries have higher surface hardness than coarser-grained ore types with straight grain boundaries. Furthermore, surface hardness measurements and grindability evaluations (using throughput (kg/h) and specific energy consumption (kWh/tonne)) of samples from three of the iron oxide deposits indicate that grindability decreases with increasing surface hardness. The relationship found between the parameters ore mineralogy, texture, surface hardness, and grindability suggests that geological mapping and surface hardness measurements can be used to evaluate grindability, and thus assess ore processing performance.

A neural network approach for spatial variation assessment – A nepheline syenite case study

“…Unfortunately, often the relation between datasets cannot be explained only by modal mineralogy and bulk chemistry. There are multiple influencing factors such as mineral textures (Lund et al, 2015;Tøgersen et al, 2018) mineral associations and the degree of liberation (Hunt et al, 2011;Johnson et al, 2007;Minz et al, 2013). These properties were not measured in this study, though they should be considered in future geometallurgical investigations due to the impact they might have in characterizing the material and its processability (Lamberg, 2011;Williams, 2013).…”

“…However, mineral processing performance indicators can be explained with mineralogical data, unfortunately it often presents additivity issues (Dunham and Vann, 2007;Lishchuk et al, 2019). Examples of mineralogical data explaining processing performance are mineralogical textures and associations (Johnson et al, 2007;Koch et al, 2019;Lund et al, 2015;Mwanga et al, 2015;Pérez-Barnuevo et al, 2018;Tøgersen et al, 2018). Usually, the relation between mineralogical data and mineral processing performance is non-linear relationship, thus a non-linear estimation technique, like an artificial neural network framework, could favourably be used.…”

The interrelation of petrographical propeties of rocks and their crushing and grindig features : A literature review

“…The relationships between petrographical and mechanical properties of rocks have been investigated in several studies [2,3,21,22,23,24]. The rock mechanics and crushability tests are frequently used in the simulation and modelling of the processes occurring in the comminution equipment, and they can be used in the studies of the relationship between rock properties and grindability [25].…”

Performance Improvements during Mineral Processing Using Material Fingerprints Derived from Machine Learning—A Conceptual Framework

“…It reveals the lines between the chemical system and the mineralogical system by partially selecting the important features of each dataset. Several case studies demonstrated the value of mineralogical and textural information through data fusion, for optimisation of process performances such as comminution [6][7][8]. Based on this success, new ML techniques could reveal the links between the data.…”