Advancing Flotation Process Optimization Through Real-Time Machine Vision Monitoring: A Convolutional Neural Network Approach

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

2023

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 stages 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.

Paper Citation

in Harvard Style

Bendaouia A., Abdelwahed E., Qassimi S., Boussetta A., Benzakour I., Amar O., Bourzeix F., Jabbahi K. and Hasidi O. (2023). Advancing Flotation Process Optimization Through Real-Time Machine Vision Monitoring: A Convolutional Neural Network Approach. In Proceedings of the 15th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR; ISBN 978-989-758-671-2, SciTePress, pages 429-436. DOI: 10.5220/0012237300003598

in Bibtex Style

@conference{kdir23,
author={Ahmed Bendaouia and El Abdelwahed and Sara Qassimi and Abdelmalek Boussetta and Intissar Benzakour and Oumkeltoum Amar and François Bourzeix and Khalil Jabbahi and Oussama Hasidi},
title={Advancing Flotation Process Optimization Through Real-Time Machine Vision Monitoring: A Convolutional Neural Network Approach},
booktitle={Proceedings of the 15th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR},
year={2023},
pages={429-436},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012237300003598},
isbn={978-989-758-671-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR
TI - Advancing Flotation Process Optimization Through Real-Time Machine Vision Monitoring: A Convolutional Neural Network Approach
SN - 978-989-758-671-2
AU - Bendaouia A.
AU - Abdelwahed E.
AU - Qassimi S.
AU - Boussetta A.
AU - Benzakour I.
AU - Amar O.
AU - Bourzeix F.
AU - Jabbahi K.
AU - Hasidi O.
PY - 2023
SP - 429
EP - 436
DO - 10.5220/0012237300003598
PB - SciTePress