Heap Leach Pad Surface Moisture Monitoring Using Drone-Based Aerial Images and Convolutional Neural Networks: A Case Study at the El Gallo Mine, Mexico
An efficient metal recovery in heap leach operations relies on uniform distribution of leaching reagent solution over the heap leach pad surface. However, the current practices for heap leach pad (HLP) surface moisture monitoring often rely on manual inspection, which is labor-intensive, time-consum...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-04-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/13/8/1420 |
| _version_ | 1797538555038269440 |
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| author | Mingliang Tang Kamran Esmaeili |
| author_facet | Mingliang Tang Kamran Esmaeili |
| author_sort | Mingliang Tang |
| collection | DOAJ |
| description | An efficient metal recovery in heap leach operations relies on uniform distribution of leaching reagent solution over the heap leach pad surface. However, the current practices for heap leach pad (HLP) surface moisture monitoring often rely on manual inspection, which is labor-intensive, time-consuming, discontinuous, and intermittent. In order to complement the manual monitoring process and reduce the frequency of exposing technical manpower to the hazardous leaching reagent (e.g., dilute cyanide solution in gold leaching), this manuscript describes a case study of implementing an HLP surface moisture monitoring method based on drone-based aerial images and convolutional neural networks (CNNs). Field data collection was conducted on a gold HLP at the El Gallo mine, Mexico. A commercially available hexa-copter drone was equipped with one visible-light (RGB) camera and one thermal infrared sensor to acquire RGB and thermal images from the HLP surface. The collected data had high spatial and temporal resolutions. The high-quality aerial images were used to generate surface moisture maps of the HLP based on two CNN approaches. The generated maps provide direct visualization of the different moisture zones across the HLP surface, and such information can be used to detect potential operational issues related to distribution of reagent solution and to facilitate timely decision making in heap leach operations. |
| first_indexed | 2024-03-10T12:33:05Z |
| format | Article |
| id | doaj.art-aa83267c94174f6bbfde531d681bd2b8 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T12:33:05Z |
| publishDate | 2021-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-aa83267c94174f6bbfde531d681bd2b82023-11-21T14:31:26ZengMDPI AGRemote Sensing2072-42922021-04-01138142010.3390/rs13081420Heap Leach Pad Surface Moisture Monitoring Using Drone-Based Aerial Images and Convolutional Neural Networks: A Case Study at the El Gallo Mine, MexicoMingliang Tang0Kamran Esmaeili1Department of Civil & Mineral Engineering, University of Toronto, Toronto, ON M5S 1A4, CanadaDepartment of Civil & Mineral Engineering, University of Toronto, Toronto, ON M5S 1A4, CanadaAn efficient metal recovery in heap leach operations relies on uniform distribution of leaching reagent solution over the heap leach pad surface. However, the current practices for heap leach pad (HLP) surface moisture monitoring often rely on manual inspection, which is labor-intensive, time-consuming, discontinuous, and intermittent. In order to complement the manual monitoring process and reduce the frequency of exposing technical manpower to the hazardous leaching reagent (e.g., dilute cyanide solution in gold leaching), this manuscript describes a case study of implementing an HLP surface moisture monitoring method based on drone-based aerial images and convolutional neural networks (CNNs). Field data collection was conducted on a gold HLP at the El Gallo mine, Mexico. A commercially available hexa-copter drone was equipped with one visible-light (RGB) camera and one thermal infrared sensor to acquire RGB and thermal images from the HLP surface. The collected data had high spatial and temporal resolutions. The high-quality aerial images were used to generate surface moisture maps of the HLP based on two CNN approaches. The generated maps provide direct visualization of the different moisture zones across the HLP surface, and such information can be used to detect potential operational issues related to distribution of reagent solution and to facilitate timely decision making in heap leach operations.https://www.mdpi.com/2072-4292/13/8/1420heap leach pad monitoringconvolutional neural networksurface moisture mappingunmanned aerial vehicledronegold leaching |
| spellingShingle | Mingliang Tang Kamran Esmaeili Heap Leach Pad Surface Moisture Monitoring Using Drone-Based Aerial Images and Convolutional Neural Networks: A Case Study at the El Gallo Mine, Mexico Remote Sensing heap leach pad monitoring convolutional neural network surface moisture mapping unmanned aerial vehicle drone gold leaching |
| title | Heap Leach Pad Surface Moisture Monitoring Using Drone-Based Aerial Images and Convolutional Neural Networks: A Case Study at the El Gallo Mine, Mexico |
| title_full | Heap Leach Pad Surface Moisture Monitoring Using Drone-Based Aerial Images and Convolutional Neural Networks: A Case Study at the El Gallo Mine, Mexico |
| title_fullStr | Heap Leach Pad Surface Moisture Monitoring Using Drone-Based Aerial Images and Convolutional Neural Networks: A Case Study at the El Gallo Mine, Mexico |
| title_full_unstemmed | Heap Leach Pad Surface Moisture Monitoring Using Drone-Based Aerial Images and Convolutional Neural Networks: A Case Study at the El Gallo Mine, Mexico |
| title_short | Heap Leach Pad Surface Moisture Monitoring Using Drone-Based Aerial Images and Convolutional Neural Networks: A Case Study at the El Gallo Mine, Mexico |
| title_sort | heap leach pad surface moisture monitoring using drone based aerial images and convolutional neural networks a case study at the el gallo mine mexico |
| topic | heap leach pad monitoring convolutional neural network surface moisture mapping unmanned aerial vehicle drone gold leaching |
| url | https://www.mdpi.com/2072-4292/13/8/1420 |
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