Predicting flotation efficiency using neural networks

This paper presents a model of flotation stage using a neural network to predict the efficiency and the effect of operational parameters on the efficiency of ink removing. Two methods are used to determine the kinetic parameters of the flotation process using particular experimental conditions: experimental data obtained at a laboratory level, and simulated data by means of a neural network. Simulated values obtained with a neural network correspond closely to the experimental results. Neural networks are long-range tools for studying processes when some knowledge of the phenomena that occur in the process is acquired in order to develop models based on the experimental results. The neural network model accurately reproduces all the effects of operation variables and can be used in a simulation of a deinking plant to determine the optimal operational conditions