Applicability of near-infrared spectroscopy for sensor based sorting of mill pebbles from the Los Bronces copper mine, Chile

Los Bronces is an open pit mine located on a large porphyry copper-molybdenum deposit near Santiago, Chile. Annual production is around 240 000 tonnes of copper and 2 700 tonnes of molybdenum. The extracted ore has a relatively low average copper grade of around 0.6%. This results in relatively high processing costs and creates the need to search for solutions to reduce the costs for ore processing at Los Bronces. Sorting by sensor technology may be a solution to decrease these processing costs. Sensor based sorting is a technique where singular particles are mechanically separated on certain physical properties after determining these properties by a sensor or detector. Sensor based sorting is of relatively low cost compared to other concentration methods. It is an evolving technology that is reaching the requirements for implementation in ore processing operations. However, sensor based sorting is completely dependant on an appropriate sensor that allows distinguishing between sub-economic and economic ore material. The applicability of a near-infrared (NIR) spectroscopic sensor was investigated on a set of 150 mill pebbles from the Los Bronces processing plant to asses the feasibility of sensor based sorting. The NIR spectroscopic sensor allows characterisation of the reflected electromagnetic radiation from solids along the NIR wavelength regions of the electromagnetic spectrum (350-2500 nm). Mainly due to the vibrational effects of certain molecule bonds in these solids, absorption bands of lower reflected radiation are present around characteristic wavelength locations. These absorption bands can therefore be diagnostic for certain mineral presences in a solid. Copper minerals do not cause any characteristic absorption bands in the NIR region. Various hydrothermal alteration minerals that are associated with the formation of porphyry copper systems on the other hand do. These minerals include muscovite, illite, chlorite, tourmaline and kaolinite. Assessing the applicability of NIR spectroscopy for sensor based sorting of the Los Bronces mill pebbles was therefore focused on finding a relation between the copper grade and these alteration minerals. From general geological models it is known that copper mineralisation can be associated with certain zones of hydrothermal alteration. However, in practice the alteration zones and copper mineralisation usually form very complex systems due to several periods of intrusion, brecciation and overprinting of the pre-existing hydrothermal alterations. The test work showed that no direct correlation between the copper grade and a NIR spectral characteristic was present. However, it did prove to be possible to classify the mill pebbles based on the NIR spectral response and mineralogy determined from petrography and x-ray diffraction. This resulted in several groups of pebbles that proved to be identifiable by NIR spectroscopy and contained different average copper grades. One group of low grade pebbles was particularly interesting. This group contains 30% of all pebble samples with an average copper grade of 0.29%. This group can be easily identified with NIR spectroscopy by a high depth ratio between the 1900 and 2200 nm absorption feature and presence of an absorption feature around 2350 nm. These spectral features are caused by a low crystallinity of mica minerals and presence of chlorite respectively. It was shown that it is technically feasible to make some discrimination on the copper grade of the Los Bronces mill pebbles by the response from a NIR spectroscopic sensor. However, because the discrimination possibilities are limited to only one group of pebbles, a detailed economical analysis still has to prove the economical feasibility of NIR spectroscopy as a sensor sorting application for the Los Bronces mill pebbles. A preliminary economical analysis already showed that the economical feasibility is mainly dependant on the copper price and the processing costs of the ore.Section Resource EngineeringCivil Engineering and Geoscience