Real-Time Quantitative Mineral Analysis (QMA) using Artificial Intelligence (AI) Enabled LIBS Sensor for Bulk Ore Sorting on Mining Conveyors

Daniel Gagnon; Francis Vanier; Josette El Haddad; Elton Soares de Lima Filho; Antoine Hamel; Christian Padioleau; Francis Boismenu; André Beauchesne; Paul Bouchard; Tony Vaillancourt; Mohamad Sabsabi; Alain Blouin; Andriy Plugatyr; Aïssa Harhira

doi:10.1364/AIS.2022.AM2F.5

Optical Sensors and Sensing Congress 2022 (AIS, LACSEA, Sensors, ES)
Technical Digest Series (Optica Publishing Group, 2022),
paper AM2F.5
•https://doi.org/10.1364/AIS.2022.AM2F.5

Real-Time Quantitative Mineral Analysis (QMA) using Artificial Intelligence (AI) Enabled LIBS Sensor for Bulk Ore Sorting on Mining Conveyors

Daniel Gagnon, Francis Vanier, Josette El Haddad, Elton Soares de Lima Filho, Antoine Hamel, Christian Padioleau, Francis Boismenu, André Beauchesne, Paul Bouchard, Tony Vaillancourt, Mohamad Sabsabi, Alain Blouin, Andriy Plugatyr, and Aïssa Harhira

Applied Industrial Spectroscopy 2022

Vancouver, British Columbia Canada
11–15 July 2022
ISBN: 978-1-957171-10-4

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Energy and Extraction Industries (AM2F)

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D. Gagnon, F. Vanier, J. E. Haddad, E. S. de Lima Filho, A. Hamel, C. Padioleau, F. Boismenu, A. Beauchesne, P. Bouchard, T. Vaillancourt, M. Sabsabi, A. Blouin, A. Plugatyr, and A. Harhira, "Real-Time Quantitative Mineral Analysis (QMA) using Artificial Intelligence (AI) Enabled LIBS Sensor for Bulk Ore Sorting on Mining Conveyors," in Optical Sensors and Sensing Congress 2022 (AIS, LACSEA, Sensors, ES), Technical Digest Series (Optica Publishing Group, 2022), paper AM2F.5.

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Abstract

Crushing and grinding rocks are energy-intensive. This paper shown the development pathway and the performances of an unprecedented sensor dedicated to run-of-mine ore characterization for bulk sorting to reduce the amount of waste rocks processed.

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