Sensor-Based Rock Hardness Characterization in a Gold Mine Using Hyperspectral Imaging and Portable X-Ray Fluorescence Technologies
Rock hardness significantly impacts comminution efficiency, one of mining’s most energy-intensive processes. Accurate, rapid, and non-invasive hardness characterization can enhance mine-to-mill optimization and energy management. This study investigates sensor-based technologies, hyperspectral imagi...
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MDPI AG
2025-06-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/12/2062 |
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| author | Saleh Ghadernejad Kamran Esmaeili Mariano P. Consens |
| author_facet | Saleh Ghadernejad Kamran Esmaeili Mariano P. Consens |
| author_sort | Saleh Ghadernejad |
| collection | DOAJ |
| description | Rock hardness significantly impacts comminution efficiency, one of mining’s most energy-intensive processes. Accurate, rapid, and non-invasive hardness characterization can enhance mine-to-mill optimization and energy management. This study investigates sensor-based technologies, hyperspectral imaging, and portable X-ray fluorescence (pXRF) integrated with machine learning (ML) algorithms for characterizing rock hardness in open-pit gold mining contexts. A total of 159 rock samples from two Canadian open-pit gold mines were analyzed through Leeb rebound hardness (LRH), short-wave infrared (SWIR) hyperspectral imaging, and a pXRF analyzer for chemical characterization. The most critical spectral features of SWIR images were extracted using a novel and automated feature extraction approach and further refined by applying a recursive feature elimination (RFE) algorithm to reduce the dimensionality of the spectral feature space. Three ML algorithms, including Random Forest Regressor (RFR), Adaptive Boosting (AdaBoost), and Multivariate Linear Regression (MLR), were applied to develop predictive hardness models considering three scenarios: using chemical features, using refined spectral features, and their combination. The findings underscore the potential of advanced sensor integration and analytics in remotely characterizing rock hardness, which could contribute to enhancing efficiency and sustainability in modern mining operations. |
| format | Article |
| id | doaj-art-75011ebee0a74aec8869cc3bb834b1a0 |
| institution | OA Journals |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-75011ebee0a74aec8869cc3bb834b1a02025-08-20T02:21:53ZengMDPI AGRemote Sensing2072-42922025-06-011712206210.3390/rs17122062Sensor-Based Rock Hardness Characterization in a Gold Mine Using Hyperspectral Imaging and Portable X-Ray Fluorescence TechnologiesSaleh Ghadernejad0Kamran Esmaeili1Mariano P. Consens2Department of Civil and Mineral Engineering, University of Toronto, Toronto, ON M5S 1A4, CanadaDepartment of Civil and Mineral Engineering, University of Toronto, Toronto, ON M5S 1A4, CanadaDepartment of Mechanical and Industrial Engineering, University of Toronto, Toronto, ON M5S 1A4, CanadaRock hardness significantly impacts comminution efficiency, one of mining’s most energy-intensive processes. Accurate, rapid, and non-invasive hardness characterization can enhance mine-to-mill optimization and energy management. This study investigates sensor-based technologies, hyperspectral imaging, and portable X-ray fluorescence (pXRF) integrated with machine learning (ML) algorithms for characterizing rock hardness in open-pit gold mining contexts. A total of 159 rock samples from two Canadian open-pit gold mines were analyzed through Leeb rebound hardness (LRH), short-wave infrared (SWIR) hyperspectral imaging, and a pXRF analyzer for chemical characterization. The most critical spectral features of SWIR images were extracted using a novel and automated feature extraction approach and further refined by applying a recursive feature elimination (RFE) algorithm to reduce the dimensionality of the spectral feature space. Three ML algorithms, including Random Forest Regressor (RFR), Adaptive Boosting (AdaBoost), and Multivariate Linear Regression (MLR), were applied to develop predictive hardness models considering three scenarios: using chemical features, using refined spectral features, and their combination. The findings underscore the potential of advanced sensor integration and analytics in remotely characterizing rock hardness, which could contribute to enhancing efficiency and sustainability in modern mining operations.https://www.mdpi.com/2072-4292/17/12/2062rock hardnesshyperspectral imagingportable X-ray fluorescencecomminution process |
| spellingShingle | Saleh Ghadernejad Kamran Esmaeili Mariano P. Consens Sensor-Based Rock Hardness Characterization in a Gold Mine Using Hyperspectral Imaging and Portable X-Ray Fluorescence Technologies Remote Sensing rock hardness hyperspectral imaging portable X-ray fluorescence comminution process |
| title | Sensor-Based Rock Hardness Characterization in a Gold Mine Using Hyperspectral Imaging and Portable X-Ray Fluorescence Technologies |
| title_full | Sensor-Based Rock Hardness Characterization in a Gold Mine Using Hyperspectral Imaging and Portable X-Ray Fluorescence Technologies |
| title_fullStr | Sensor-Based Rock Hardness Characterization in a Gold Mine Using Hyperspectral Imaging and Portable X-Ray Fluorescence Technologies |
| title_full_unstemmed | Sensor-Based Rock Hardness Characterization in a Gold Mine Using Hyperspectral Imaging and Portable X-Ray Fluorescence Technologies |
| title_short | Sensor-Based Rock Hardness Characterization in a Gold Mine Using Hyperspectral Imaging and Portable X-Ray Fluorescence Technologies |
| title_sort | sensor based rock hardness characterization in a gold mine using hyperspectral imaging and portable x ray fluorescence technologies |
| topic | rock hardness hyperspectral imaging portable X-ray fluorescence comminution process |
| url | https://www.mdpi.com/2072-4292/17/12/2062 |
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