Authors:
Ahmed Bendaouia
1
;
2
;
El Abdelwahed
1
;
Sara Qassimi
3
;
Abdelmalek Boussetta
4
;
Intissar Benzakour
4
;
Oumkeltoum Amar
2
;
François Bourzeix
2
;
Khalil Jabbahi
1
and
Oussama Hasidi
1
;
2
Affiliations:
1
Computer Systems Engineering Laboratory (LISI), Computer Science Department, Faculty of Sciences Semlalia, Cadi Ayyad University, Marrakech, Morocco
;
2
SEIA Departement, Moroccan Foundation for Advanced Science Innovation and Research (MAScIR), Rabat, Morocco
;
3
Computer and Systems Engineering Laboratory (L2IS), Computer Science Department, Faculty of Science and Technology, Cadi Ayyad University, Marrakech, Morocco
;
4
R&D and Engineering Center, Reminex, Managem Group, Marrakech, Morocco
Keyword(s):
Machine Vision, Deep Learning, Industry 4.0, Flotation Froth, Mining Industry, Monitoring.
Abstract:
The mining industry’s continuous pursuit of sustainable practices and enhanced operational efficiency has led to an increasing interest in leveraging innovative technologies for process monitoring and optimization. This study focuses on the implementation of Convolutional Neural Networks (CNN) for real-time monitoring of differential flotation circuits in the mining sector. Froth flotation, a widely used technique for mineral separation, necessitates precise control and monitoring to achieve maximum recovery of valuable minerals and separate them from gangue. The research delves into the significance of froth surface visual properties and their correlation with flotation froth quality. By capitalizing on CNN’s ability to identify valid, hidden, novel, potentially useful and meaningful information from image data, this study showcases how it surpasses traditional techniques for the flotation monitoring. The paper provides an in-depth exploration of the dataset collected from various s
tages of the Zinc flotation banks, labeled with elemental grade values of Zinc (Zn), Iron (Fe), Copper (Cu), and Lead (Pb). CNNs’ implementation in a regression problematic allows for real-time monitoring of mineral concentrate grades, enabling precise assessments of flotation performance. The successful application of CNNs in the Zinc flotation circuit opens up new possibilities for improved process control and optimization in mineral processing. By continuously monitoring froth characteristics, engineers and operators can make informed decisions, leading to enhanced mineral recovery and reduced waste.
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