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

Optical Sensors and Sensing Congress 2022 (AIS, LACSEA, Sensors, ES) Pub Date : 1900-01-01 DOI:10.1364/ais.2022.am2f.5

D. Gagnon, Francis Vanier, J. Haddad, Elton Soares de Lima Filho, Antoine Hamel, C. Padioleau, Francis Boismenu, André Beauchesne, P. Bouchard, Tony Vaillancourt, M. Sabsabi, Alain Blouin, A. Plugatyr, A. Harhira

引用次数: 0

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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实时定量矿物分析(QMA)使用人工智能(AI)支持的LIBS传感器在采矿输送机上进行散装矿石分选

破碎和研磨岩石是能源密集型的。本文展示了一种前所未有的用于散装分选的原矿特征传感器的发展路径和性能，以减少废石的处理量。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Optical Sensors and Sensing Congress 2022 (AIS, LACSEA, Sensors, ES)

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