Machine learning data fusion for Ore Tracking:
Towards real-time implementation of geometallurgical models
This project aims to track the ore from the mine to the processing plant and create predictive models that connects the ore properties in the block model to the actual plant performance. This will be done by integrating 3D geological data with existing mining and operating data using machine learning algorithms. The predictive models generated can be the seed for the digital twin geometallurgical models for the processing plant, which is needed towards real-time mine planning and optimization.
The project is funded by Boliden Mineral AB.
Researchers from Luleå University of Technology
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