Ore base modelling
Using innovative technology to model the ore base drives efficiency in mine exploration. New technologies, often enabled by AI, allow for more accurate reserve estimation and much clearer assessment of geological faults. These two functions of ore base modelling play an important role in de-risking the exploration process, and in making it more productive and profitable.
Digital twin for mine planning
A 'digital twin' is a digital replica of a planned physical asset, enabling its performance to be analysed before it is even built. This means that the considerable capital expenditure involved can be carefully optimised to maximise ROI over the longer term. Using a digital twin also enables engineers to model and plan the most cost-effective and productive technology solutions. Incorporating geological, geomechanical and hydrogeological modelling into a single 'source of truth' for engineering both open-pits and underground mines.
Increase in mine productivity
Artificial Neural Networks for fault prediction
Artificial Neural Networks (ANNs) assess normal, affected and fault zones to predict the risk of floods. This is particularly important for safety and production in mines, where even small faults can weaken the rock resistance and create an escape route for water. ANNs replicate the pattern-finding abilities of the human mind and process data for key factors like vein depth and width, accumulated gas quantities, input changes and degree of fragmentation. These factors are then integrated into a 99% accurate model that can predict a risk of flooding in small geological faults.
Our team has decades of collective experience, spanning all areas of industrial operations.
Dr Nick Mayhew
Chief Commercial Officer
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