Neural network process vision systems for flotation process

Abstract

Froth flotation is a process whereby valuable minerals are separated from waste by exploiting natural differences or by chemically inducing differences in hydrophobicity. Flotation processes are difficult to model because of the stochastic nature of the froth structures and the ill‐defined chemorheology of these systems. In this paper a hierarchical configuration hybrid neural network has been used to interpret froth images in a copper flotation process. This hierarchical neural network uses two Pulse‐Coupled Neural Networks (PCNNs) as preprocessors that ‘convert’ the froth images into corresponding binary barcodes. Our technique demonstrates the effectiveness of the hybrid neural network for process vision, and hence, its potential for use for real time automated interpretation of froth images and for flotation process control in the mining industry. The system is simple, inexpensive and is very reliable.