Simulation Modeling of an Iron Ore Operation to Enable Informed Planning

An iron ore mining company, operating in Western Australia, trucks ore from three geographically isolated sources to a crusher, where it is blended before and during crushing. The company prides itself on its key performance indicator: the relatively low inter-shipment grade variability of its products. The company has been considering alternative production process designs for all stages from mining to ship loading to improve efficiency and reduce costs. Currently product grade variability is well controlled and it is important not to jeopardize the company’s reputation for grade reliability. However, the company needs to identify potential efficiencies, reduced costs, and alternative product control systems for potential future expansions. Many process design options from mine face to ship loading must be compared and evaluated, with emphasis on maintaining or improving on current grade variability in final shipments. Pilot studies are infeasible, while complex interactions, competing goals, and numerous system configurations make theoretical analysis unmanageable. Real system trials require strong prior probability of success, particularly where product variability to customers is concerned. Simulation studies provide a suitable method to evaluate and compare process design alternatives. This paper describes successful simulation modeling of grade variability. The work provides users with easily run Excel models, tailor-made for specific process designs, using Visual Basic macros and graphical output, to explore the implications of decision parameter choice, under a range of possible operating conditions and ore variability. The models have been designed in modular form for the user to interconnect, simulating different configurations of the mine to ship system, to help make informed decisions as to potential system modification. Construction of input mine grade data is a major first step. Meaningful results require input data maintain the correlations, present in the real production environment, between the mineral components, production linkages, and across time. Mining data from real operations are therefore used, with the means and variability adjusted to match potential development proposals. The studies simulate only short-term variability. Long-term variability should not be controlled by the process design but rather by longer-term ore extraction plans and ultimately resource availability. It is therefore necessary to filter out medium and long-term variations from the production data before use in the simulation model. A Fourier transform technique to do this is described.