Economic Optimisation of an Ore Processing Plant with a Constrained Multi-objective Evolutionary Algorithm

Document Type

Conference Proceeding


Faculty of Computing, Health and Science


School of Computer and Information Science




Huband, S., While, L., Tuppurainen, D., Hingston, P., Barone, L., & Bearman, T. (2006, December). Economic optimisation of an ore processing plant with a constrained multi-objective evolutionary algorithm. In Australasian Joint Conference on Artificial Intelligence (pp. 962-969). Springer, Berlin, Heidelberg.


Existing ore processing plant designs are often conservative and so the opportunity to achieve full value is lost. Even for well-designed plants, the usage and profitability of mineral processing circuits can change over time, due to a variety of factors from geological variation through processing characteristics to changing market forces. Consequently, existing plant designs often require optimisation in relation to numerous objectives. To facilitate this task, a multi-objective evolutionary algorithm has been developed to optimise existing plants, as evaluated by simulation, against multiple competing process drivers. A case study involving primary through to quaternary crushing is presented, in which the evolutionary algorithm explores a selection of flowsheet configurations, in addition to local machine setting optimisations. Results suggest that significant improvements can be achieved over the existing design, promising substantial financial benefits.

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