Simulation modelling of grade variability for iron ore mining, crushing, stockpiling and ship loading operations

Abstract

Cliffs Natural Resources Pty Ltd (CNR) operates iron ore mines in the Koolyanobbing region of Western Australia, ∼50 km north of the town of Southern Cross. Ore is trucked from three geographically isolated sources to the crusher at Koolyanobbing, where it is blended before and during crushing. Lump and fine products are produced and railed to Esperance for ship loading and export to Asian customers. The CNR is examining alternative processing paths, from mining to ship loading, with the aim of improving efficiency and reducing costs. Modifications to the system must be consistent with potential future expansions and maintain the low intershipment grade variability on which CNR prides itself and has built a strong relationship with its customers. In searching for the optimum process design, many options from mine face to ship loading must be evaluated and compared. Pilot plant studies are infeasible, while complex mineralogical interactions, competing goals and numerous possible system configurations limit the applicability of theoretical analysis. It was therefore concluded that simulation modelling would provide the confidence to take the next step into production trials. This paper describes techniques applied at CNR to simulate grade variability resulting from potential process design changes. The simulation models are easily run Excel based modules, with each module representing a different part of the process. The modules use extensive Visual Basic macros driven by Excel's user friendly interfaces. Presentation of the results is enhanced by Excel's excellent graphical capabilities. The simulation software stores and graphically presents time stamped data from a run, enabling detailed analysis of different process configurations. Final success of a simulation run is measured by intershipment variability (standard deviation and process capability) and in process ore tonnages. Meaningful results from the simulations require that the initial input data contain the same correlations present in the real production environment, between the mineral components, production linkages and across time. The data also have to allow simulation of potential changes to mining method and introduction of new pits into the blend. Mining data from the real operations under study are therefore used, with average grades and variability adjusted to match potential future development proposals. It is also necessary to filter out medium and long term variations from the production data, as this variability is best controlled through the conventional medium to long term mine planning process, not by the process design being studied. The filtering was carried out using a Fourier transform technique, which is described. For reasons of commercial confidentiality, detailed data, costs and quantitative conclusions are not reported in this paper.