Development of Artificial Intelligence Algorithms For Improved Correlation of Sensor Responses to Ore Grade

The benefit of ore sorting is rejecting waste material prior to downstream processing. This results in reducing material handling costs and environmental liability, lowering energy consumption, and feeding more consistent and higher ore grades to the concentrator. Sorting allows for a lower cost bulk mining method resulting in lowering the cut-off grade and increasing the resource size. Despite the potential benefits, sorting is not widely applied due to barriers in the current technology. These relate to limited ability of sensors to discriminate between barren rock and valuable rock, and the low throughput capacity of available industrial machines. This project aims improve the use of sensors by developing an advanced AI algorithm applied in sensors to increase the throughput capacity of the sorter. In addition, a pilot scale sorter would be set up based on the advanced algorithm to validate the performance.

Faculty Supervisor:

Bern Klein

Student:

Xu Yang

Partner:

Karamount Mineral Exploration

Discipline:

Engineering - other

Sector:

Mining and quarrying

University:

University of British Columbia