Supervision of control valves in flotation circuits based on artificial neural network

Abstract

Flotation circuits play an important role in extracting valuable minerals from the ore. To control this process, the level is used to manipulate either the concentrate or the tailings grade. One of the key elements in controlling the level of a flotation cell is the control valve. The timely detection of any problem in these valves could mean big operational savings. This paper compares two Artificial Neural Network architectures for detecting clogging in control valves. The first one is based on the traditional autoassociative feedforward architecture with a bottleneck layer and the other one is based on discrete principal curves. We show that clogging can can be promptly detected by both methods; however, the second alternative can carry out the detection more efficiently than the first one. © Springer-Verlag Berlin Heidelberg 2005.