Conv-LSTM for Real Time Monitoring of the Mineral Grades in the Flotation Froth

Ahmed Bendaouia, Ahmed Bendaouia, El Abdelwahed, Sara Qassimi, Abdelmalek Boussetta, Intissar Benzakour, Oumkeltoum Amar, François Bourzeix, Achraf Soulala, Oussama Hasidi, Oussama Hasidi

2023

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 applications in other areas of the mining industry.

Paper Citation

in Harvard Style

Bendaouia A., Abdelwahed E., Qassimi S., Boussetta A., Benzakour I., Amar O., Bourzeix F., Soulala A. and Hasidi O. (2023). Conv-LSTM for Real Time Monitoring of the Mineral Grades in the Flotation Froth. In Proceedings of the 12th International Conference on Data Science, Technology and Applications - Volume 1: DATA; ISBN 978-989-758-664-4, SciTePress, pages 89-96. DOI: 10.5220/0012090100003541

in Bibtex Style

@conference{data23,
author={Ahmed Bendaouia and El Abdelwahed and Sara Qassimi and Abdelmalek Boussetta and Intissar Benzakour and Oumkeltoum Amar and François Bourzeix and Achraf Soulala and Oussama Hasidi},
title={Conv-LSTM for Real Time Monitoring of the Mineral Grades in the Flotation Froth},
booktitle={Proceedings of the 12th International Conference on Data Science, Technology and Applications - Volume 1: DATA},
year={2023},
pages={89-96},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012090100003541},
isbn={978-989-758-664-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 12th International Conference on Data Science, Technology and Applications - Volume 1: DATA
TI - Conv-LSTM for Real Time Monitoring of the Mineral Grades in the Flotation Froth
SN - 978-989-758-664-4
AU - Bendaouia A.
AU - Abdelwahed E.
AU - Qassimi S.
AU - Boussetta A.
AU - Benzakour I.
AU - Amar O.
AU - Bourzeix F.
AU - Soulala A.
AU - Hasidi O.
PY - 2023
SP - 89
EP - 96
DO - 10.5220/0012090100003541
PB - SciTePress