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

Acronym REWO-SORT (Reference Number: ERA-MIN-2017_89)
Duration 01/05/2018 - 30/04/2021
Project Topic In recent years, mining faces multiple challenges across Europe and worldwide. Depletion of the high grade deposits are leading to lower concentration material at the beginning of the process, increasing the amount of energy and water needed to extract the valuable material. Different strategies have recently been evaluated, but so far none of these has fully solved the energy restrictions the mining industry is currently facing. Powerful multimodal sorting techniques can increase the concentration as soon as possible in the process and contribute to solve the mentioned problems. In this project we propose to classify the mineral particles on a conveyor belt by combining LIBS and ME-XRT by using deep learning technology. The combination of laser-induced breakdown spectroscopy (LIBS) and multi energy X-ray transmission (ME-XRT) is very promising, as it combines complementary features of the scanned material: LIBS can provide elemental analysis but is limited the surface and ME-XRT offers volumetric data but is limited in terms of elemental accuracy as it provides integral elemental information. By combining both technologies surface measurement data can be extrapolated to the entire sample and therefore create representative data per specimen. The usage of deep neural networks furthermore enables capabilities like self-adopting to new or different kinds of host rocks. Constant monitoring of the exact mineral composition of the output of a pit will provide the data to allow on-line and in-situ measurement of geological, mineralogical, rock mechanical and metallurgical properties of the pit. Ore samples with different textures and valuable material will be selected from the small, medium and big mining companies in Chile, containing both copper oxide and copper sulphides. The samples will be selected, characterized and send from Chile to Germany for tests using ME-XRT, LIBS and the combined techniques to evaluate the performance of these methods.
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Network ERA-MIN 2
Call ERA-MIN Joint Call 2017

Project partner

Number Name Role Country
1 Fraunhofer Gesellschaft Coordinator Germany
2 University of Chile Partner Chile
3 Luleå University of Technology Partner Sweden
4 SECOPTA analytics GmbH Partner Germany