Mining companies are looking for technologies that can evaluate the characteristics of ore materials ( gold, copper, iron) or soft rock sedimentary materials (coal, carbonite shale), in real time as they are extracted, without impacting productivity.

A system is needed to calculate and relay information about material ore characteristics during the loading process, to allow for optimised planning of material movement. At the commencement of the mining process, an area to be mined is pre-analysed by a geologist in order to get an approximate understanding of the geological composition, strata, and mineral content. This is completed at a very high level (i.e. data sampling of the regions is done only on 50mx50m or 100m x100m grids meaning significant interpolation is required) to construct a geological model.

During the operations phase of the mining process this area is drilled and then blasted to break up the material and allow excavation machinery to excavate it and load it into haul trucks. The blasting techniques used are significant enough to cause the material to shift and expand from its original position. Therefore, the geological model which was initially created by the geologist is no longer accurate.

The essence of this task is to find a solution that is able to re-evaluate the composition of the material in front of the digging machine before or as the machine is digging it. This information can then be utilised to understand the properties of the material within a loaded truck so that it can be delivered to the correct destination. For example, if the truck is loaded with waste material is might go to a waste dump. A truck loaded with ore bearing material might go to a processing facility or a stock pile.

Outline of Challenge This challenge focuses on determining in real-time what type of material is being loading into haul trucks by digging units. As per the difficulties highlighted above, often a loaded truck is not sent to the correct location when material identification is based solely on the location of the material being dug being referenced to a geological model. Sometimes the operator can determine what is being loaded, but many materials cannot be differentiated visually, and machines also often work in wet, dusty, or muddy conditions. This challenge is to develop a platform which can sense what material is being loaded into the truck and then send that information to the truck management system so that the truck is sent to the right dumping location. The system will be applied at open pit mines with various types of mineral deposit including sedimentary deposits like coal. Focus Point A prototype of the platform and processes of ensure the truck can be sent to the correct locations. The system should be: " Robust or adaptable to different mineral types " Provide data with which mine geological plans can be updated with accurate material location information. " Be able to process information on or close to the operating machines as possible

A prototype system should consider components / functions including 1. Robust material sensing approach at some point through the loading process 2. Sensor suite and hardware platform 3. Capability for receiving and updating material models (resource volumetric, geometric, Geo-tech, Geo-met model) 4. Resource reassessment and revaluation based on the material models and data from sensing systems 5. Visualisation and feedback of output data for operators 6. Facilities for interaction and interfacing with truck management systems Potential Areas To Consider The operation involves material being dug from the mine working face, loading that material into a truck, and then transporting the material to either a dump (for waste) a stockpile (for ore), or a crusher (for ore) where it is dumped. Sensing needs to be done prior to the truck loading process being completed and before leaving the loading unit so that the truck operator can travel to the correct destination for the material The system may use exploratory drilling information as a guide to pre-determine what is approximately at the digging face. This may assist in sensing.