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Reduction of Energy and Water consumption of mining Operations by fusion of sorting technologies LIBS and ME-XRT

Reference number
Coordinator Luleå tekniska universitet - Avdelningen för Geovetenskap och miljöteknik
Funding from Vinnova SEK 1 010 000
Project duration May 2018 - April 2021
Status Completed

Purpose and goal

This project, involving an international consortium of four partners, fulfilled the following main objectives: 1. Evaluation of improvement potential of ore sorting capabilities by sensor fusion of laser-induced breakdow spectroscopy (LIBS) and multi-energy X-ray transmission (ME-XRT). 2. Assessment of improvement potential and ideal particle size of rock selection strategies to optimize separation results at high treatment capacities. 3. Assessment of high-resolution, micro-scale scans of rock samples for (3D) geological investigation

Expected results and effects

- Save consumables such as water, energy and processing reagents by an increased separability of ore from gangue material. - Increase the efficiency of the separation process and reduce operating costs, as well as environmental and local societal impact - Reduce the uncertainty behind the separation of valuable material - Improve the quality of objective geological data acquisition for faster and more informed decision-making

Planned approach and implementation

Two different sensor technologies, LIBS and ME-XRT, were calibrated and tested with ore samples from various Cu porphyry mines in Chile. Machine learning algorithms such as deep neural networks were employed to test the capability to identify ore types and sort samples by cut-off grade. The detectability of geometallurgical parameters such as particle size distribution, material composition and ore texture were analyzed. In addition, the capabilities of the sensors to provide high-resolution, accurate imaging and data of specific rock samples were tested.

External links

The project description has been provided by the project members themselves and the text has not been looked at by our editors.

Last updated 21 May 2021

Reference number 2018-00601

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