M.W.N. Buxton

Cut-off Grade Based Sublevel Stope Mine Optimisation

Research in the field of cut-off grade optimisation has shown a relationship between cut-off grade, project life and Net Present Value. Lane’s theory demonstrates that cut-off grades can be optimised in order to maximise project profitability. Although the theory forms the basis for many open pit mining projects, application of the theory in underground mining remains limited to-date. The main reason for this is the complex interaction between all processes in underground mine planning which makes it difficult to apply Lane’s mathematical optimisation approach. Recently a new Stope Optimiser product was released. The AMS Stope Optimiser automates the design of underground stopes at user defined cut-off grades and allows for rapid evaluation of mine designs at different cut-off grades. Using this software, an optimisation approach was developed and validated on an underground gold deposit in northern Sweden. Potential project NPV increased by approximately 30% when using this new approach. Spatial grade uncertainty in mineral resources was identified to be a major risk in underground stope design. The optimisation approach was further extended to account for grade risk using estimated and stochastic simulated resource models. The resulting optimisation process accounts for grade risk early in the design process and reduces the risk of a stope not meeting the cut-off grade with subsequent financial loss.

Ore–Waste Discrimination in Epithermal Deposits Using Near-Infrared to Short-Wavelength Infrared (NIR-SWIR) Hyperspectral Imagery

Near-infrared (NIR) and short-wavelength infrared (SWIR) hyperspectral imagery can be used to detect certain alteration minerals. At epithermal deposits, the formation of alteration minerals is, in theory, related to the mineralisation of gold and silver. In order to provide foundations for developing sensor-based sorting applications at a mine that exploits such a deposit, it was investigated if NIR-SWIR hyperspectral imagery can be used to distinguish between ore and waste particles by characterising the alteration mineralogy. Maps were produced from the NIR-SWIR hyperspectral images of 827 drill core samples that show mineral occurrences, mineral absorption feature intensities and characteristics of the iron oxide mineralogy. Partial least squares discriminant analysis (PLS-DA) was applied to the information contained in these maps to investigate if this information can be used to discriminate between ore and waste. The results showed that NIR-SWIR hyperspectral imagery could be used to segment a population of waste samples by detecting occurrences of pyrophyllite, dickite and/or illite. This result can be explained by the fact that these minerals are commonly deposited further away from the ore-bearing epithermal veins, while the absence of SWIR-active minerals or detected occurrences of alunite are more closely associated with these structures. The ability to identify waste with NIR-SWIR spectral sensors means there is potential that sensor-based sorting can be used to remove this waste from mineral processing operations. Additional research is still required to assess the economic feasibility of such a sensor-based sorting application.

Infrared detection of ore variability that influences the environmental risks during perlite mining and processing

In the mining of perlite deposits, controlling the generation of fine particles and the concentration of metals is of outstanding importance to meet the environmental and market requirements. Particle size and chemical purity are conventionally manipulated during the processing of the ore to achieve high product specifications. However, the current practices do not consider a proactive approach that focuses in the in-pit characterisation of the ore that would minimise the environmental impact and optimise the mining process since its early stages. This paper presents a method for the in-pit detection of the perlite ore variability that is related to the generation of fine particles and the elevated concentration of metals. Particle size and chemical purity showed to be dependent on the mineralogical variations of the ore, specifically opal and montmorillonite. Using a portable infrared spectrometer, an index that establishes the relative proportions of these minerals in the perlite ore was created. Such index provided insight into the correlation between mineralogy, fine particles and concentration of metals. Consequently, the index could be used not only for mineralogical determination but also as a predictor of the presence of the main impurities in the perlite ore. These results can be implemented in perlite mining to reduce the generation of waste and can influence the production of high-quality perlite products.

Applicability of near-infrared hyperspectral imagery (NIR-HI) for sensor based sorting of an epithermal Au-Ag ore

In the presented study test work was performed with near-infrared hyperspectral imagery (NIR-HI) on 36 ore samples from a South-American epithermal Au-Ag mine. The aim of the test work was to investigate if NIR-HI provides information about the alteration mineralogy of samples that can be used to predict their economic value. Mineral distribution maps were produced from the hyperspectral images by using correlation coefficients between the image pixels and a set of reference spectra. These maps showed that detection of mineralogy with NIR-HI can be used to distinguish; i) ore particles with low Au and Ag grades, ii) ore particles with high carbon contents, iii) ore particles with high sulphur contents.