Development of a strategy and interpretation of the NIR spectra for application in automated sorting

Abstract

The potential of Near Infrared (NIR) spectroscopy for application in automated sorting was investigated on a sample of iron oxide copper-gold ore. The ore contains a substantial amount of carbonate material which results in excessive acid consumption in the leaching circuit during copper extraction, thereby increasing the processing cost. To separate this unwanted gangue material (carbonate) from the valuable metal (copper), a strategy for classification of ore according to copper and carbonate content was developed. The chemical and mineralogical composition of the ore was determined using XRF, respectively XRD. This data was correlated with NIR spectra measured on the surface of ore particles. NIR spectra showed distinct characteristic absorption features for carbonate rich particles that distinguish these from copper bearing particles, which are fairly featureless at longer NIR wavelengths (range 2000nm -2405nm). Combined interpretation of spectral features and chemical and mineralogical data indicates that NIR-based sorting has potential forthis type of ore.

A Review of Sensor-Based Sorting in Mineral Processing: The Potential Benefits of Sensor Fusion

“…Numerous studies of sorting using optical/infrared imaging were identified within the review [19,22,24,[28][29][30][31][32][33][34][35]. It was found that optical and infrared imaging were suitable for sorting a wide range of ores including coal, copper, gold, lead, marble, mineral sands, rare earth elements, tin and zinc.…”

Motion-based visual inspection of optically indiscernible defects on the example of hazelnuts

“…In many cases, sensor-based sorting systems can be employed for the inspection and physical cleaning of products by means of removing low-quality and foreign, potentially harmful, entities from a given product stream. The main fields of application of sensor-based sorting are recycling, for instance the preparation of glass [9] by removing materials harmful to the melting process such as stones and ceramic glass [10], mining, mainly to remove unwanted gangue from ore, e.g., copper-gold ore [11], as well as agricultural products and foodstuff. Regarding the latter, examples of applications are diverse, including the removal of fungusinfected wheat kernels [12], low-quality rice grains [13], and sunflower seeds [14], or quality insurance in bulgur The turquoise objects represent particles to be accepted, typically the product, and the red ones those to be removed, for instance foreign particles.…”

Optimizing multi-spectral ore sorting incorporating wavelength selection utilizing neighborhood component analysis for effective arsenic mineral detection

“…Ghosh 39 introduced an infrared thermography-based method for sorting alumina-rich iron ores, while Kern 40 suggested utilizing short-wavelength infrared and dual-energy X-ray transmission sensors for the Hammerlein Sn–In–Zn deposit. Phiri 41 investigated the potential of near-infrared sensors for separating carbonate-rich gangue from copper-bearing particles in a copper–gold ore sample. Tusa 32 advanced the field by evaluating hyperspectral short-wave infrared sensors, combined with machine learning methods, for pre-sorting complex ores.…”