Authors:
Ahmed Bendaouia
1
;
2
;
El Abdelwahed
1
;
Sara Qassimi
3
;
Abdelmalek Boussetta
4
;
Intissar Benzakour
4
;
Oumkeltoum Amar
2
;
François Bourzeix
2
;
Achraf Soulala
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):
Deep Learning, Industry 4.0, Flotation Froth, Mining Industry, Monitoring.
Abstract:
Accurate monitoring of the mineral grades in the flotation froth is crucial for efficient minerals separation in the mining industry. In this study, we propose the use of ConvLSTM, a type of neural network that combines Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks, to create a model that can extract spatial and temporal patterns from flotation froth video data. Our model enables the analysis of both spatial and temporal patterns, making it useful for understanding the dynamic behavior of the froth surface in the flotation processes. Using ConvLSTM, we developed a more accurate and reliable model for monitoring and controlling the flotation froth quality. Our results demonstrate the effectiveness of our approach, with mean absolute error (MAE) of 2.59 in a lead, copper and zinc differential flotation site. Our findings suggest that artificial intelligence can be an effective tool for improving the flotation monitoring and control, with potential appl
ications in other areas of the mining industry.
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