ABSTRACT
Geometallurgical variables are normally forecasted by regression models, which usually shows good results. When non-linear variables need to be predicted, as the metallurgical recovery, accurate and precise forecasts are not always achieved by this technique application. Once neural networks use non-linear activation functions, the predictions for non-linear variables tends to be very accurate. Aiming at comparing the forecasts achieved when regression and neural networks are applied, data from a Brazilian zinc mine was used, where tailings, zinc concentrate and lead concentrate mass recoveries are the variables which compose mass balance. The zinc metallurgical recovery was calculated for zinc and lead concentrates and tailings, aiming at knowing where and how much of the zinc content is going during mineral processing. The results obtained showed the forecast superiority achieved when neural networks were used, in addition to illustrate the possibility to generate a unique model to predict simultaneously six dependent variables.
