Forecasting mass and metallurgical balance at a gold processing plant using modern multivariate statistics

Fernanda Gontijo Fernandes Niquini,João Felipe Coimbra Leite Costa

doi:10.1590/0370-44672020730001

Fernanda Gontijo Fernandes Niquini, João Felipe Coimbra Leite Costa

Open Access

https://doi.org/10.1590/0370-44672020730001

Copy DOI

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.

Highlights

Geometallurgical models are frequently developed focussing on forecasting variables related to saleable products
The leaching concentrate mass recovery is shaded as the mass related to this part of the process is irrelevant compared to the others, and this mass recovery is rounded to zero
The results indicated that, in terms of mean absolute error (MAE) and root of the mean squared error (RMSE), the neural networks technique stood out when compared with the other techniques applied

Summary

Introduction

Geometallurgical models are frequently developed focussing on forecasting variables related to saleable products. Geometallurgy applied for tailings grades and mass are rarely explored, either for lack of investment to know variables which will not bring immediate financial return or for ignoring the fact that by knowing the quantity and the quality of tailings, financial benefit can be added. Knowing the tailings grade beforehand opens up the possibility of deciding where to send it: to the tailings dam, as usual, or to stockpile the tailings to make possible further use of it, if the observed grade is high enough to offset the process costs. By knowing the complete plant mass balance, including tailings mass, it would enable one to forecast when the tailings dam will reach its maximum storage capacity, making it possible to better plan its height or building a new one. A new solution would be to use dried tailings deposition in waste dumps, aiming at minimising the environmental impact

Methods

Findings

Discussion

Conclusion