Combining Dynamic Logistics Simulation with Process Modeling to Support Capacity Expansion Studies: Experience at ArcelorMittal Mines Canada Pellet Plant

"Hatch and ArcelorMittal Mines Canada have combined their expertise to develop a dynamic model of the latter’s Iron Ore Pellet Plant in Quebec, Canada. This model was used to predict the benefits of different expansion projects, as well as asses the best implementation sequence.Two modelling tools were used: a discrete-event simulation software used to develop the dynamic model of the Pellet Plant and an in-house process simulator. The latter was based on operational data and known behavior of the process, and was calibrated with historical data gathered under different operating conditions.The results of the process simulator were used as input values for the dynamic model, which included all of the operation steps of the pelletizing process, from concentrate beneficiation to baking of the pellets. The model has been validated by assessing its capacity to represent adequately the operation of the plant during a 20-month operating period. It was able to reproduce the plant capacity within a precision of 1.2%.Simulated “what-if” scenarios allowed quantifying the production increase associated with the addition of new equipment such as ball mills or balling discs, implemented individually or in combination. Results revealed a potential 10% increase in the annual plant production by adding additional equipment in the two most limiting sectors of the plant, grinding and agglomeration. The simulation results made it possible to quantify the synergistic effect of two projects on plant throughput; the model showed that the combination of two projects resulted in a greater production increase than the sum of the same projects evaluated individually.Now that it has proven reliable, the simulation tool can readily be used by ArcelorMittal Mines Canada process engineers to perform simulations for equipment optimization, and to evaluate the impact of various operating parameters such as equipment availability and tonnage, product mix or concentrate quality. This work showed that a reliable model can be developed based on known parameters in combination with the proper integration of the process knowledge."