Recursive Feature Elimination and Neural Networks Applied to the Forecast of Mass and Metallurgical Recoveries in A Brazilian Phosphate Mine

Geometallurgical models are commonly built by combining explanatory variables to obtain the response that requires prediction. This study presented a phosphate plant with three concentration steps: magnetic separation, desliming and flotation, where the yields and recoveries corresponding to each pr...

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Bibliographic Details

Published in:	Minerals (Basel) Vol. 13; no. 6; p. 748
Main Authors:	Fernanda Gontijo Fernandes Niquini, André Miranda Brito Branches, João Felipe Coimbra Leite Costa, Gabriel de Castro Moreira, Claudio Luiz Schneider, Florence Cristiane de Araújo, Luciano Nunes Capponi
Format:	Journal Article
Language:	English
Published:	MDPI AG 01-06-2023
Subjects:	feature selection geometallurgy neural networks phosphate recursive feature elimination
Online Access:	Get full text
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