Bi-level depth assessment of an abandoned tailings dam aiming its reprocessing for recovery of valuable metals. (15th March 2019)

• Record Type: Journal Article
• Authors: Figueiredo, Janine; Vila, M. Cristina; Góis, Joaquim; Biju, Bárbara Pavani; Futuro, Aurora; Martins, Diogo; Dinis, M. Lurdes; Fiúza, António
• Is Part Of: Minerals engineering. Volume 133(2019)
• Page Start: 1
• Page End: 9
• Publication Date: 2019-03-15
• Subjects: Tailings disposal -- Re-mining -- Tungsten -- Base metals -- Multivariate data analysis -- Geostatistics; Mines and mineral resources -- Periodicals; Ressources minérales -- Périodiques; Mines and mineral resources; Periodicals; Electronic journals; 622
• DOI: 10.1016/j.mineng.2018.12.016
• Languages: English
• ISSNs: 0892-6875

Abstract

Highlights: Valuable metals (W, Zn, Cu) in Cabeço do Pião tailings disposal were assessed. Multivariate Statistical Analysis proved to be a useful planning tool for tailings re-mining. Geostatistics corroborated the results of Principal Component Analysis. Abstract: The present work was developed in the scope of ERA-MIN/0007/2015 REMinE project, aiming recover of valuable metals through reprocessing of abandoned mine waste deposits. Cabeço do Pião site, the Portuguese case study in this project, is an old tailings disposal whose property was transferred from Panasqueira Mine to the Municipality of Fundão. The material deposited in the dam constitutes a risk to the environment, in particular to Zézere's river that flows close to the bottom of the tailings pile and is a relevant contributor for the supply of drinking water. The study area was sampled at two different depths according to a regular grid. About fifty samples were collected and analyzed by X-ray fluorescence. The multivariate analysis of the chemical and the particle size data, allowed to establish mineralogical associations, proving to be a useful planning tool in a future selective re-mining of the tailings pile. Information extracted from the results of the principal component analysis was corroborated by the results of the geostatistical techniques applied to the analytical data.