10.6084/m9.figshare.5772489.v1
Jose Marques Braga Junior
João Felipe Coimbra Leite Costa
Applying chemometrics to predict metallurgical niobium recovery in weathered ore
2018
SciELO journals
chemometrics, mining industry
Response Surface
geometallurgy
multivariate statistics
2018-01-10 05:58:59
article
https://scielo.figshare.com/articles/dataset/Applying_chemometrics_to_predict_metallurgical_niobium_recovery_in_weathered_ore/5772489
<div><p>Abstract Niobium metallurgical recovery measures how much of the metal content in the ore is separated in the concentrate after the mineral processing stages. This information can be obtained through laboratory tests with ore samples obtained during drilling. Thereunto, representative ore samples are subjected to tests mimicking the ore concentration processing flow, but these experiments are time consuming and costly. The main objective of this study was to develop a more efficient way to obtain the metallurgical recovery information from ore samples. Based on the development of chemometrical studies, the chemical components currently analyzed in the ore with correlation to the metallurgical recovery were identified. These correlated variables were used to build a nonlinear multivariate regression model to explain the response variable, i.e. metallurgical recovery. The Principal Component Analysis was used in this work to define which chemical variables contribute most to explain the metallurgical recovery phenomenon. The Second order regression equation (Response Surface) was the most suitable methodology to explain the metallurgical niobium recovery and was created by the interaction of the five most important chemical variables. After the exclusion of outliers, the linear regression coefficient between the metallurgical recovery calculated and the metallurgical recovery analyzed was 82.59%. The use of the second order regression equation contributes to reduce the amount of experimental analysis to assess the geometallurgical niobium ore response, promoting the reduction of costs for metallurgical characterization of the ore samples. The methodology proposed proved to be efficient, maintaining an adequate precision in the forecasted response.</p></div>