R as an environment for data mining of process mineralogy data: A case study of an industrial rougher flotation bank

Abstract Through a series of in-house routines of R, an open-source programming language for statistical computing, statistical analysis is applied to automated process mineralogy data to describe the performance of an industrial scheelite rougher flotation bank. These routines allow (1) freeing the user from the limitations of the menu-driven built-in processing and spreadsheet-based analyses routines; in particular when processing data from several streams, and (2) a more flexible manipulation of the data at any level of aggregation. In an illustration case study, it was determined that ideally floating scheelite particles are coarser than 40 µm and are more than 40% liberated. Most of the scheelite lost to the rougher tailings stream is either ultrafine or coarse with little surface liberation and associated with silicates. More importantly, the presence of a depressant does not permit the selective flotation of scheelite from other semi-soluble salt-type minerals such as calcite. This is linked to particle size, as there appears to be some overgrinding before the rougher flotation. While the impact of the depressant requires more observation, less fine grinding could already potentially improve the concentrate grade and decrease operational costs.