Abstract Knowing the quantity and the quality of products and tailings generated by a beneficiation plant, even before ore processing, can make the mining operations more sustainable, more profitable, and safer. To forecast these values, it is necessary to submit samples to batch tests which mimic the processing workflow used on an industrial scale. Then, the results need to be analysed with the aim of finding a statistical model able to comprehend how Run of Mine (ROM) characteristics impact the performance at the beneficiation. After developing a model, it is possible to apply it to blocks where the ROM characteristics are known, but the metallurgical information is not, making it possible to estimate these. With this goal, a geometallurgical model was developed with a neural network technique using 37 samples collected at two Brazilian gold mines. The Au and S grades in ROM, and the mine from where the sample was collected, were used as input variables. The model was able to forecast the following variables with a Pearson correlation coefficient on the cross validation test set equal to the value in parenthesis: mass (0.55) and metallurgical (0.54) recovery in the gravimetric concentrate, mass (0.80) and metallurgical (0.12) recovery in the flotation tailings, mass (0.77) and metallurgical (0.11) recovery in the leaching tailings, mass recovery (0.84) of gas sent to the sulphuric acid plant, and metallurgical recovery (0.65) in the leaching concentrate. The results obtained with neural networks were superior to the ones obtained when three alternative techniques were tested.

Abstract The construction of block models with an estimation of grades in situ is a common practice throughout resource evaluation. However, this information is not enough to understand the behavior of the ore in the beneficiation process. Geometallurgy proposes the addition of the ore´s metallurgical behavior in the block model, making it more dependable and adhering when it comes to production capacity, which generates financial earnings and brings risks down. Mass recovery is an important metallurgical variable for economic and mine planning. This is often underused, due to the lack of data, making it hard to use in the planning process. In order to achieve better use of the data available, the multiple regression analysis technique was used so as to develop a statistic model that would relate the mass recovery with the in situ grades, allowing that deposit regions with no available metallurgical information have an estimation of this variable's values.