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.

Pre-crusher stockpile modelling to minimise grade variability

“…The following alternative allocation criteria were included in the investigation: 1) Principal components: the calculation of principal components of each floorstock grade 6 takes into account all analytes in maximizing the separation of grades. Theoretically this method would provide the best compromise of maximum spread of all analytes over all stockpiles.…”

Modelling Large Heaped Fill Stockpiles Using FMS Data

“…Within mining, pre-crusher stockpiling is often used for its operational simplicity, but it typically lowers the confidence of the ore grade and reduces certainty in feed quality [15]. Pre-crusher stockpiling takes on five forms which are illustrated in Figure 1 [15][16][17]:…”

“…For example, iron ore is often shipped directly from the mine to the customer. Since the tonnages involved are immense, extensive work has already been carried out to model the way that iron ore should be stockpiled for re-handle [16,17,24,25]. Likewise, due to hazards around coal (loss of energy through oxidation, spontaneous combustion, etc.)…”

“…Much work has been carried out to determine when base and precious metal mines should stockpile (cut-off grades, net present value (NPV) calculations, rehandle costs, milling costs, production contracts, mine planning, etc.) [1,[11][12][13][14][15][16][17]. The discipline of managing stockpiles throughout mining operations to maximize the profit gained from their eventual processing is still emerging [18].…”

Role of pre-crusher stockpiling for grade control in iron ore mining

“…For example, because of the high cross-correlation between analytes in iron ores, selecting iron, silica or phosphorus alone can be sufficient. Alternatively, a linear composite such as the first principal component can be used (Everett et al, 2010). The first principal component is the linear composite which best explains the variability of all the analytes, maximising the eigenvalue, defined as the sum of the squared correlations (or loadings) with the analyte grades.…”