How does orebody learning enable a mining operation to continuously improve their productivity?

Orebody learning refers to the process of acquiring knowledge about the orebody characteristics and behaviour through various data analysis and modeling techniques. This knowledge can be used to continuously improve the mining operation’s productivity in the following ways:

1. Better mine planning: Orebody learning can provide detailed information about the orebody’s characteristics, such as its crushability, grindability and recovery. This knowledge can be used to optimize mine planning, such as optimal blast design, the sequence of mining activities, and the design of mining equipment, which can improve efficiency and reduce operational costs.
2. More accurate resource estimation: By continuously learning about the orebody, mining operations can refine their resource estimation techniques, which can lead to more accurate predictions of the quantity and quality of the ore in the ground. This information can be used to optimise mining operations, such as the selection of the most suitable mining methods, and to ensure the best possible use of the available resources.
3. Improved ore processing: Orebody learning can help mining operations to better understand the characteristics of the ore, which can lead to improvements in ore processing techniques. For example, implicit characterisation of the ore’s mineralogy can help to optimize the flotation process, while knowledge of the ore’s hardness can help to optimize the crushing and grinding process.
4. Better environmental management: Orebody learning can also help mining operations to better understand the environmental impacts of their activities. By understanding the characteristics of the orebody, mining operations can design more effective environmental management plans that minimize the impact of their activities on the environment.

In summary, orebody learning enables mining operations to continuously improve their productivity by providing valuable knowledge that can be used to optimize mine planning, resource estimation, ore processing, and environmental management.