A neural network for electromagnetic based ore sorting

Abstract

Purpose

This paper aims to presents a novel application of neural network (NN) pattern recognition to ore rock sorting using inductive electromagnetic (EM) sensors.

Design/methodology/approach

The impedance of a metallic rock can be measured with an inductive method based on Faraday’s law and eddy current theory. A virtual rock model is then created for the simulation of the EM measurements. An NN is trained to differentiate between waste and useful ore samples (containing high amount of minerals) based on the EM sensor signals produced by the rocks.

Findings

The NN solution showed high accuracy of rock classification and produced relatively robust results from signals with noise.

Originality/value

A pattern recognition NN was applied to classify low- and high-grade ore samples. It has the potential to determine the approximate amount of conductive materials inside ore rocks through multiple classes. This method can be used to improve the performance of EM-based ore sorting for mineral pre-concentration.

Citation

Li, M., Caushaj, A., Silva, R. and Lowther, D. (2018), "A neural network for electromagnetic based ore sorting", COMPEL - The international journal for computation and mathematics in electrical and electronic engineering, Vol. 37 No. 2, pp. 691-703. https://doi.org/10.1108/COMPEL-12-2016-0529