Generating synthetic data for simulation modelling in iron ore

Abstract

Simulation modelling is a practice commonly used in the mining industry to evaluate alternative process designs. Such modelling is typically undertaken as an optimisation study to increase the efficiencies, productivity and product quality of operating mines. In this situation real short-term grade variability data of extracted ore are available from production records as input data into the simulation. There is also a need for simulation modelling to be performed before mines are approved for construction to clarify the grade variability characteristics that can be expected from the operating mine and assist in the optimisation of the process design. In this situation no historical short-term grade variability data are available. To achieve meaningful and reliable results from a simulation it is necessary to have input data for the ore to be extracted from the pits, representative of the short-term grade variability that would be expected for the operating mine. This paper describes an example of a path taken to generate realistic input data. A method described as composite cut-off criterion was used to distinguish ore from waste which gave substantially greater recovered tonnage at target grade compared to the conventional quadrant cut-off grade criteria. The only data available for the project was resource model data in the form of kriged block models, which are known to underestimate true ore grade variability. To achieve realistic results from the simulation it was essential to increase this variability while maintaining the accurate average grades. Areas of certain deposits that were modelled using both kriging and conditional simulation estimation techniques were quantitatively compared to establish the comparative variance and the kriged data was modified to match the conditionally simulated variance. A realistic mining model was generated via a process of discretisation and regularisation of the resource blocks. Quantitative assessment demonstrated that this method adequately compensated for ore dilution and that adjustment for ore loss was not required due to an ore skin surrounding the edge blocks. The conditioned data were then used to generate a schedule of daily mine extraction, considering grade variability, tonnage, equipment constraints and extensive blended-in-blended-out pre-crusher stockpiles to feed into the process design simulations. For the pre-crusher stockpiles a number of alternative allocation criteria were examined for ore being extracted from the pits to identify the best method in achieving reduced variability through the daily scheduling system. The study concluded that a single analyte separation criterion produced acceptable variability with minimal complexity. The blending efficiency of manually stacked and reclaimed pre-crusher stockpiles was studied to determine a realistic blending efficiency within the pile. A recommended method of building and reclaiming was determined to give maximum blending efficiency. Finally, the data were used as input into process design simulation models; simulation from crusher feed to ship loading demonstrated that control of shipment grade variability was achievable and that the conditioning of the data delivered realistic results.