Towards a Novel Edge AI System for Particle Size Detection in Mineral Processing Plants

Flávio Cardoso, Flávio Cardoso, Mateus Silva, Natália Meira, Ricardo Oliveira, Ricardo Oliveira, Andrea Bianchi, Andrea Bianchi

2023

Abstract

Monitoring and controlling the particle size is essential to reducing the variability and optimizing energy efficiency in mineral process plants. The industry standard utilizes laboratory processes for particle size characterization; the problems that arise here are obtaining representative sample from the bulk and finding a rapid method of particle size assessment. We propose a machine vision concept based on Edge AI architecture and deep convolutional neural algorithms to enable a real-time analysis of particle size, as an alternative to offline laboratory process. The present paper is part of this proposed concept and aims exclusively to validate a deep convolutional neural network algorithm trained from synthetic datasets. The proposed model reached a mean Average Precision (mAP) of 0.96 and processing times of less than 1s. The results demonstrate the feasibility of deep convolutional neural networks for real-time particle size segmentation and establishes the first step towards a novel Edge AI system for particle size measurement in mineral processing plants.

Paper Citation

in Harvard Style

Cardoso F., Silva M., Meira N., Oliveira R. and Bianchi A. (2023). Towards a Novel Edge AI System for Particle Size Detection in Mineral Processing Plants. In Proceedings of the 25th International Conference on Enterprise Information Systems - Volume 1: ICEIS, ISBN 978-989-758-648-4, SciTePress, pages 312-323. DOI: 10.5220/0011748000003467

in Bibtex Style

@conference{iceis23,
author={Flávio Cardoso and Mateus Silva and Natália Meira and Ricardo Oliveira and Andrea Bianchi},
title={Towards a Novel Edge AI System for Particle Size Detection in Mineral Processing Plants},
booktitle={Proceedings of the 25th International Conference on Enterprise Information Systems - Volume 1: ICEIS,},
year={2023},
pages={312-323},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011748000003467},
isbn={978-989-758-648-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 25th International Conference on Enterprise Information Systems - Volume 1: ICEIS,
TI - Towards a Novel Edge AI System for Particle Size Detection in Mineral Processing Plants
SN - 978-989-758-648-4
AU - Cardoso F.
AU - Silva M.
AU - Meira N.
AU - Oliveira R.
AU - Bianchi A.
PY - 2023
SP - 312
EP - 323
DO - 10.5220/0011748000003467
PB - SciTePress