Geometallurgical Modeling at Olympic Dam Mine, South Australia

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• 作者：Jeff B. Boisvert (1)
  Mario E. Rossi (3)
  Kathy Ehrig (2)
  Clayton V. Deutsch (1)
  
• 关键词：Geostatistics ; Multivariate ; Mining ; Simulation ; Compositional
• 刊名：Mathematical Geosciences
• 出版年：2013
• 出版时间：November 2013
• 年：2013
• 卷：45
• 期：8
• 页码：901-925
• 全文大小：
• 作者单位：Jeff B. Boisvert (1)
  Mario E. Rossi (3)
  Kathy Ehrig (2)
  Clayton V. Deutsch (1)
  
  1. Centre for Computational Geostatistics, Department of Civil and Environmental Engineering, 3-133 Markin/CNRL Natural Resources Facility, University of Alberta, Edmonton, AB, Canada, T6G 2w2
  3. GeoSystems International, Inc., 2385 NW Executive Center Dr., Suite 100, Boca Raton, FL, 33431, USA
  2. Olympic Dam, BHP Billiton, 55 Grenfell Rd, Adelaide, 5001, SA, Australia
  
• ISSN：1874-8953

文摘

Modeling of geometallurgical variables is becoming increasingly important for improved management of mineral resources. Mineral processing circuits are complex and depend on the interaction of a large number of properties of the ore feed. At the Olympic Dam mine in South Australia, plant performance variables of interest include the recovery of Cu and U3O8, acid consumption, net recovery, drop weight index, and bond mill work index. There are an insufficient number of pilot plant trials (841) to consider direct three-dimensional spatial modeling for the entire deposit. The more extensively sampled head grades, mineral associations, grain sizes, and mineralogy variables are modeled and used to predict plant performance. A two-stage linear regression model of the available data is developed and provides a predictive model with correlations to the plant performance variables ranging from 0.65–0.90. There are a total of 204 variables that have sufficient sampling to be considered in this regression model. After developing the relationships between the 204 input variables and the six performance variables, the input variables are simulated with sequential Gaussian simulation and used to generate models of recovery of Cu and U3O8, acid consumption, net recovery, drop weight index, and bond mill work index. These final models are suitable for mine and plant optimization.