Pre-concentration consists of the preliminary discarding of a fraction of the mineral processing plant feed which contains little or none of the mineral of interest, reducing the mass to be processed in downstream operations (e.g. milling, concentration and dewatering), as well as the capital and operational costs. In this context, this study investigates the performance of density and sensor-based sorting separation methods in the removal of carbonate gangue of a zinc ore, in size fractions typical of crusher products, using sink and float tests with heavy liquids, jig stratification and laboratory scale ore sorting tests using an X-Ray Transmission (XRT) sensor. The best results were obtained through sink and float in heavy liquids, which indicated the possibility of discarding 30% of the feed mass, removing over 60% of the carbonates (CaO and MgO) and losing only 2% of the zinc. The ore sorting tests also presented positive results, with approximately 93% of metallurgical recovery in 70% of the mass for both size fractions tested. The jig stratification results were worse, since the zinc content discarded with this method was high. The results indicate significant reduction potential for Capex and Opex costs using pre-concentration strategy.

The frequent best practice for managing large low-grade run-of-mine (ROM) stockpiles is to average the entire stockpile to only one grade. Modern ore control and mineral processing procedures need better precision. Low-precision models hinder the ability to create a digital mine-to-mill model and optimize the holistic mining process. Prior to processing, poorly characterized stockpiles are often drilled and sampled, despite there being no geological reason for relationships between samples to exist. Stockpile management is also influenced by reserve accounting and lacks a common operational workflow. This paper provides a review of base and precious metal run-of-mine (ROM) pre-crusher stockpiles in the mining industry, and demonstrates how to build a spatial model of a large long-term stockpile using fleet management system (FMS) data and geostatistical code in Python and R Studio. We demonstrate a framework for modelling a stockpile believed to be readily workable for most modern mines through use of established geostatistical modelling techniques applied to the type of data generated in a FMS. In the method presented, each bench of the stockpile is modeled as its own geological domain. Size of dump loads is assumed to contain the same volume of material and grade values that match those of the grade data tracked in the FMS. Despite the limitations of these inputs, existing interpolation techniques can lead to increased understanding of the grade distribution within stockpiles. Using the framework demonstrated in this paper, engineers and stockpile managers will be able to leverage operational data into valuable insight for empowered decision making and smoother operations.

Dumping is one of the main unit operations of mining. Notwithstanding a long history of using large rear dump trucks in mining, little knowledge exists on the cascading behavior of the run-of-mine material during and after dumping. In order to better investigate this behavior, a method for generating high fidelity models (HFMs) of dump profiles was devised and investigated. This method involved using unmanned aerial vehicles with mounted cameras to generate photogrammetric models of dumps. Twenty-eight dump profiles were created from twenty-three drone flights. Their characteristics were presented and summarized. Four types of dump profiles were observed to exist. Factors that influence the determination of these profiles include the location of the truck relative to the dump crest, the movement of the underlying dump material during the dumping process and the differences in the dump profile prior to dumping. The HFMs created in this study could possibly be used for calibrating computer simulations of dumps to better match reality.