SciELO - Scientific Electronic Library Online

 
vol.115 número6Q-coda estimation in the Kaapvaal CratonIntroduction to the production of clean steel índice de autoresíndice de materiabúsqueda de artículos
Home Pagelista alfabética de revistas  

Servicios Personalizados

Articulo

Indicadores

Links relacionados

  • En proceso de indezaciónCitado por Google
  • En proceso de indezaciónSimilares en Google

Compartir


Journal of the Southern African Institute of Mining and Metallurgy

versión On-line ISSN 2411-9717

Resumen

COMPAN, G.; PIZARRO, E.  y  VIDELA, A.. Geometallurgical model of a copper sulphide mine for long-term planning. J. S. Afr. Inst. Min. Metall. [online]. 2015, vol.115, n.6, pp. 549-556. ISSN 2411-9717.

One of the main problems related to mining investment decisions is the use of accurate prediction models. Metallurgical recovery is a major source of variability, and in this regard, the Chuquicamata processing plant recovery was modelled as a function of geomining-metallurgical data and ore characteristics obtained from a historical database. In particular, the data-set gathered contains information related to feed grades, ore hardness, particle size, mineralogy, pH, and flotation reagents. A systemic approach was applied to fit a multivariate regression model representing the copper recovery in the plant. The systemic approach consists of an initial projection of the characteristic grinding product size (P80), based upon energy consumption at the particle size reduction step, followed by a flotation recovery model. The model allows for an improvement in the investment decision process by predicting performance and risk. The final geometallurgical model uses eight operational variables and is a significant improvement over conventional prediction models. A validation was performed using a recent data-set, and this showed a high correlation coefficient with a low mean absolute error, which reveals that the geomet-allurgical model is able to predict, with acceptable accuracy, the actual copper recovery in the plant.

Palabras clave : geometallurgical modelling; multivariate regression; recovery prediction.

        · texto en Inglés     · Inglés ( pdf )