Title

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

Document Type

Conference Proceeding

Faculty

Faculty of Computing, Health and Science

School

School of Computer and Information Science

RAS ID

4815

Comments

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.

Abstract

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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