GRSG Conference 2022: Orbit to Outcrop
Poster Title: Assessing the composition of porphyry copper tailings using visible light and long wave infrared reflectance spectroscopy
Author: Michael Denk
Tailings are fine-grained residuals from ore processing. Though often considered mine waste, such materials can contain relevant quantities of economically important raw materials. Thus, tailings storage facilities (TSF) are of increasing interest in times of high raw material demands, volatile markets and the aspiration to use existing secondary deposits. Since TSF can be difficult to access and analysing such materials is time consuming and cost-intensive, new approaches are required to aid the exploration of tailings and to reduce analytical workloads.
In this study, we compared the capability of visible light to shortwave infrared with mid- to longwave infrared spectroscopy for quantifying copper and molybdenum contents and for assessing the mineralogical composition of porphyry copper tailings. Since the presence of Cu is linked to specific particle sizes of the tailing material, we further aimed at analysing the sand, silt and clay contents for assessing the raw material potential of tailings.
The samples for this research were collected at the tailings dam of the porphyry copper mine in Erdenet, Mongolia. The TSF covers an area of approximately 7.5 km² and shows highly variable micro-morphology, wetness conditions and texture in situ, causing hetereogenous physico-chemical parameters of the deposited tailings. 170 samples were analyzed for their elementary composition using X-ray fluorescence and ICP-OES. Grain size fractions were determined with a HELOS/KR laser diffraction.
The mineralogical composition was determined using an JEOL-SEM 6300 coupled with Gen 6 Bruker Flash 6010 EDX-detector and the Bruker Esprit Feature tool. Spectral laboratory measurements of air dried and homogenized samples were conducted using a Spectral Evolution SR-3500 spectrometer (VNIR/SWIR, 0.35-2.5 µm) and an Agilent 4300 Handheld FTIR for covering the mid and longwave infrared (MWIR/LWIR, 2.5-15 µm). The spectra were corrected for unwanted scattering effects using Multiplicative Scatter Correction (MSC).
Statistical models were calibrated using Partial Least Squares Regression (PLSR) and validated using a 20-segment random cross-validation set-up. Continuum removed spectra were parametrized to determine absorption depths and wavelength positions of absorption minima. Afterwards, these parameters were correlated with the mineral abundances.
We found comparable solid results for predicting Cu based on VNIR/SWIR as well as on MWIR/LWIR data (R²val = 0.75 and 0.77, respectively). For Mo, the results were moderate for both wavelength ranges (R²val = 0.47 and 0.51). The grain size fractions clay, silt and sand could be predicted with high accuracies (R²val = 0.84-0.94), whereas the MWIR/LWIR data provided superior models compared to VNIR/SWIR models. Furthermore, we will present results of the mineralogical analysis and our findings on semi-quantitative relations between mineral abundances and absorption feature parameters of the tailing spectra.
Our study shows that robust predictions of the copper content and grain size fractions in porphyry copper tailings can be achieved using VNIR/SWIR as well as MWIR/LWIR spectra. The spectra can be further utilized to extract valuable qualitative and semi-quantitative information on the mineral composition of such material. These results indicate that reflectance spectroscopy is a complementary tool for assessing the raw material potential of porphyry copper tailings. This can aid exploring secondary deposits and support the recovery of valuable materials.