An Expert Neural Network to Control a Mineral Flotation Process

A multi-layer feed-forward neural network was trained (using the error back-propagation algorithm) to learn a subset of rules designed to stabilise the performances of the copper flotation section of a complex sulphide processing plant and then tested in order to evaluate its expert performances. Two types of neural networks were compared: the classical ones, fully connected between one layer and the adjacents (with different architectures), and entropy networks, partially connected and generated according to a methodology that simplifies its building up. The advantages and drawbacks of both were analysed and compared with classic (rule based) expert systems.