Data analysis

Exploratory data analyses of geochem, mineralogy and mapping data provide basis to define mineralization zonation and outline its signatures under cover. It also can direct to the most adequate sampling technique to address covered mineralization. Confirmation of geochemical zoning and erosion level will bring a good start to find the areas to target detailed work with geochemical mapping that we may perform specifically by sampling and elements.

Ore variability metallurgical study data analysis can provide optimization and precious indications for processing choice for various ore types.

Processing plant data gives behavior of various ores on the plant scale, interconnections between different process and metallurgical and economic performance.

Data analysis gives understanding of subtle metallurgical effects by gang minerals, processing insights, metallurgical factors and relations to consequent processes that become noticeable only after many years of experience with specific deposit. This knowledge brings ways for optimization and improving recovery and mining economics.
Methods

  • Quality control
  • Data sorting
  • Pearson correlations
  • Nonlinear correlations
  • Factor analysis
  • Cluster analysis
  • Multivariate correlations
  • Regression analysis

Objects


  • Geochemical and mineralogical data - targeting mineralization and initial data for process development
  • Processing plant operation parameters data - process and recovery optimization
  • Metallurgical data - process optimization

Objectives


  • Compositional rock classification
  • Exploration targeting
  • Geometallurgical ore classification
  • Recovery prediction model
  • Processing bottleneck solutions
  • Element deportment insights
  • Metallurgical solutions
  • Optimization of plant operation
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