Mass and Metallurgical Balance Forecast for a Zinc Processing Plant Using Artificial Neural Networks

The forecasting of ore concentrate and tailings mass and metallurgical recovery at a processing plant is not a simple task. It starts with data collection, which is expensive and laborious, and progresses to multivariate data analysis, which is used to identify the independent variables that should be used to build a prediction model. This is followed by the choice of a statistical technique that is able to deal with data particularity. After building a model, the differences between the flotation batch test and the true plant circuit need to be considered because it is difficult to build a laboratory test that exactly mimics the plant configuration. When the model and its up-scaling factor have been defined, the last step is to check the efficiency of the model in terms of forecasting the geometallurgical variables under study. Bearing in mind that such geometallurgical predictions help in mine planning, economic forecasting and environmental studies (tailings mass and metallurgical recoveries), this paper proposes a methodology that is able to predict six plant outputs simultaneously. These are metallurgical recovery of Zn from Zn concentrate, metallurgical recovery of Zn from Pb concentrate and metallurgical recoveries of Zn from tailings, Zn concentrate mass, Pb concentrate mass and tailings mass. A neural networks technique was used, and the predictions of the model with an up-scaling factor were reconciled with the plant responses, which showed consistent results.