In
this paper, prediction of copper and molybdenum grades and their recoveries of
an industrial flotation plant are investigated using the Artificial Neural
Networks (ANN) model. Process modeling has done based on 92 datasets collected
at different operational conditions and feed characteristics. The prominent
parameters investigated in this network were pH, collector, frother and F-Oil
concentration, size percentage of feed passing 75 microns, moisture content in
feed, solid percentage, and grade of copper, molybdenum, and iron in feed. A
multilayer perceptron neural network, with 10:10:10:4 structure (two hidden
layers), was used to estimate metallurgical performance. To obtain the optimal
hidden layers and nodes in a layer, a trial and error procedure was done. In
training and testing phases, it achieved quite correlations of 0.98 and 0.93
for Copper grade, of 0.99 and 0.92 for Copper recovery, of 0.99 and 0.92 for
Molybdenum grade and of 0.99 and 0.94 for Molybdenum recovery prediction,
respectively. The proposed neural network model can be applied to determine the
most beneficial operational conditions for the expected Copper and Molybdenum
grades and their recovery in final concentration of the industrial copper
flotation process.
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