A laboratory study to simulate ore sorting for a rare earth ore

Ore sorting is an emerging technology in mineral processing that can produce a significant impact on the total mining and processing economics. The technology is capable of preconcentrating an ore based on differences in the mineralogical characteristics, which can include color, brightness, transparency, size, shape, density or near-infrared response. The current practice of conducting an ore sorting test typically requires large amounts of materials to be sent to a commercial testing facility, which is expensive and time-consuming. Here, we present a novel method to simulate commercial ore sorting on a laboratory scale using a rare earth ore. Introduction The mining industry is highly energy intensive due to the various processes involved in extracting, transporting and processing minerals and ores. These energy-related costs can impact the profitability and sustainability of mining operations, as well as reserve estimates. One of the most energy-intensive stages in mining is comminution, which involves crushing, grinding and classifying to achieve the desired mineral liberation required by the downstream processes. The energy costs associated with comminution can be substantial and depend on factors such as the hardness of the ore and the desired liberation particle size. It is estimated that approximately 44 percent of the total energy consumption in a mining operation is from comminution operations [1,2]. Ore sorting is a relatively new technique introduced to the mining industry that has great potential to advance the way valuable minerals are extracted from ore deposits. It is a process that involves the separation of valuable minerals from gangue minerals based on their properties such as color, brightness, transparency, size, shape, density or near-infrared response. If these properties are homogeneous or the difference is below the detectable limit, the prospective ore sorting technology will likely not have any application. Otherwise, by selectively rejecting gangue materials early in the mining process, the amount of material to be processed downstream can be decreased, thereby improving the energy efficiency of comminution operations, reducing reagent and water consumptions, and decreasing capital costs in process development [3–7]. Materials and methods The rare earth ores used in this research work were provided by Mountain Pass Mine in California in the United States. The ores were precrushed to about 13 mm, which was the feed size for the sorting experiment. The density of approximately 100 individual rocks were measured using BELPYCNO L, a helium gas pycnometer manufactured by Microtrac MRB. The measured density was plotted to generate density histograms. Following the density measurement, the particles in each density bin were grouped and pulverized to generate powder samples (80 percent passing, 106 μm) for scanning electron microscopy (SEM) with energy-dispersive X-ray (EDX) analysis, X-ray diffraction (XRD) and X-ray fluorescence (XRF) analysis. The X-ray diffractograms were obtained using a Bruker D8 ADVANCE diffractometer. Mineral phase identification was carried out using DIFFRAC.EVA software by Bruker. SEM-EDX analysis was carried out on a JEOL JSM-7100 F field emission scanning electron microscope equipped with Oxford Instruments’ X-Max N Silicon Drift Detector. Back-scattered electron (BSE) imaging was carried out with an accelerating voltage of 15 kV and 10-mm working distance. Point and ID analysis as well as true elemental mapping was performed using Aztec software. Results Fig. 1 Abundance of various metal oxides in various density fractions. The concentration of metal oxides in various density bins after the sorting were obtained by XRF analysis. The results are shown in Fig. 1. It was observed that the major oxides in the rare earth ore in the feed included barium oxide (BaO), rare earth oxides (REOs), calcium oxide (CaO), silicon dioxide (SiO2) and magnesium oxide (MgO). The results suggest that the laboratory-scale sorting can concentrate the BaO and REO into the high-density fractions, the SiO2 into the low-density fraction, and the CaO and MgO into the low-medium density fractions. To understand the mineralogy of the sorted samples in each density fraction, XRD analysis was performed. For example, Fig. 2 shows the XRD of the sample sorted to the low-density (2.4 to 2.65), medium-density (3.18 to 3.43) and high-density (3.96 to 4.21) fractions. The analysis showed that the sorting can concentrate the light mineral of quartz into the light-density fractions, and the heavy minerals barite and bastnaesite into the higher-density fractions. For other minerals such as calcite and dolomite, the sorting resulted in their concentrations into the near-medium density fraction. These findings are in agreement with XRF analysis. Fig. 2 Abundance of various minerals in low, mediumand high-density fractions as determined by XRD. Fig. 3 Separation efficiency in the heavy fractionversus cut-off density with respect to REOs. Discussion The laboratory-scale sorting study offers important insights into how to run a potential ore sorting operation. The present sorting study mimics XRT; therefore, the cut-off density is a key operation parameter. The practical cut-off density for the sorting operation can certainly not be at the very high end as this will cause substantial loss of valuable minerals. To determine a reasonable operation range, we examined the separation efficiency between the valuable and gangue minerals, defined as the difference between the recoveries of valuable and gangue minerals into the concentrate, which is the heavy fraction in this case. For the current study, the separation efficiency between the REOs and individual gangue minerals at different cut-off densities is shown in Fig. 3. As observed, at a very low cut-off density at 2.79, the sorting can achieve very good separation between the REOs and SiO2 but that from the carbonate-associated oxides CaO and MgO was relatively low. If the cut-off density is higher at 3.05, the sorting would achieve a slightly lower separation between the REOs and SiO2 but an improved separation for CaO and MgO. Therefore, a reasonable and comprisable cut-off density for the rare earth ore with 13-mm feed size is recommended to be around 3.05 to 3.31. Conclusions The application of sensor-based ore particle sorting in the mineral’s processing industry has been gaining momentum due to the numerous benefits sorters offer, especially in energy and emission reductions. Preliminary vendor-based sorting inspection tests require large masses of samples and are time consuming. This short communication presents the development of a novel methodology to generate the data needed for industrial sorting performance testing on a laboratory scale. It also introduces methods to study the correlation between the grade and recovery and the cut-off density. It is believed that the mineral processing community can benefit from the method and analysis developed in this work. Selected references BCS Inc. (2007) Mining industry bandwidth study. In: U.S. Department of Energy, Industrial Technologies Program Report 1–43 Lessard J, Bakker JD, McHugh L (2014) Development of ore sorting and its impact on mineral processing economics. Miner Eng 65:88–97 Robben C, Condori P, Pinto A, Machaca R, Takala A (2020) X-ray transmission-based ore sorting at the San Rafel tin mine. Miner Eng 145:105870 Peukert D, Xu C, Doward P (2022) A review of sensor-based sorting in mineral processing: the potential benefits of sensor fusion. Minerals 12:1364 Calderon JL, Smith NM, Bazilian MD, Holley E (2024) Critical mineral demand estimates for low carbon technologies: what do they tell us and how can they evolve? Renew Sustain Energy Rev 189:113938 Schulz KJ, DeYoung JH Jr, Seal RR, Bradley DC (2017) Geol Surv Prof Paper 1802:797. Critical mineral resources of the United States—economic and environmental geology and prospects for future supply: U.S. Penaloza I, Tita A, Mc New E, Chu P (2023) Barite resources, production and recovery using froth flotation: a review. Miner Eng 203:10832 Wills BA, Finch J (2015) Wills’ Mineral Processing Technology, 8th ed. Elsevier, Amsterdam

A laboratory study to simulate ore sorting for a rare earth ore

Authored by: John Thella, Edward McNew, Mends Emmanuel, Christina Thella and Pengbo Chu

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