Orebody learning is a technique used in mining operations to better understand the composition and characteristics of the ore being extracted. By collecting and analysing data on the orebody, mining companies can improve their ability to predict the properties and behaviour of the ore. This, in turn, can lead to more accurate predictions of the dig rate, which is the rate at which the ore can be excavated from the ground.
Increased accuracy in dig rate prediction can have a significant impact on load and haul productivity. When the dig rate is accurately predicted, mining companies can optimize their equipment and scheduling to maximize the amount of ore that can be excavated in a given amount of time. This can result in faster excavation times, reduced downtime, and lower costs.
For example, if the dig rate prediction is too low, the mining company may allocate too few trucks to the loading process, leading to delays and inefficiencies. Conversely, if the dig rate prediction is too high, the mining company may allocate too many trucks, resulting in unnecessary expense due to “over trucking”.
By using orebody learning to improve dig rate prediction, mining companies can achieve a better balance between resource allocation and excavation efficiency, ultimately increasing load and haul productivity.
