Mass and Metallurgical Balance Forecast for a Zinc Processing Plant Using Artificial Neural Networks
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Original Paper
Mass and Metallurgical Balance Forecast for a Zinc Processing Plant Using Artificial Neural Networks Fernanda Gontijo Fernandes Niquini
1,2
and Joa˜o Felipe Coimbra Leite Costa1
Received 19 February 2020; accepted 17 April 2020
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. KEY WORDS: Geometallurgy, Mass recovery, Metallurgical recovery, Neural networks, Machine learning.
INTRODUCTION Geometallurgical models that include environmental factors are not commonly applied in the mining industry, although the benefits of this approach are clear. As part of feasibility studies or in operating mines, geometallurgical tests are usually carried out aiming better understanding the behavior of the ore in the process, the chemical charac-
1
PPGE3M, Universidade Federal do Rio Grande do Sul, Av. Bento Gonc¸alves, 9500 - Setor 4 – Pre´dio 74 - Sala 211 - Campus do Vale, Porto Alegre, RS, Brazil. 2 To whom correspondence should be addressed; e-mail: [email protected]
teristics of the concentrate and mass recovery for the various saleable products. Tailings are rarely characterized, and mining companies invariably focus on final product characterization since this is the source of their income. Most mines also refrain from analyzing a variable, which has no immediate impact on mine planning and is not a priority. Environmental accidents involving mining companies, such as Brumadinho (in Minas Gerais, Brazil), Bento Rodrigues (Modena and Heller 2016) and Los Frailes (Achterberg et al. 1999) dam ruptures, have increased the level of concern from communities and governmental agen
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