Economic optimisation of an ore processing plant with a constrained multi-objective evolutionary algorithm

Authors:
Simon Huband;Lyndon While;David Tuppurainen;Philip Hingston;Luigi Barone;Ted Bearman
Affiliations:
Edith Cowan University, Mt Lawley, Western Australia;The University of Western Australia, Crawley, Western Australia;Rio Tinto OTX, Perth, Western Australia;Edith Cowan University, Mt Lawley, Western Australia;The University of Western Australia, Crawley, Western Australia;Rio Tinto OTX, Perth, Western Australia
Venue:
AI'06 Proceedings of the 19th Australian joint conference on Artificial Intelligence: advances in Artificial Intelligence
Year:
2006

Citing 2
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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.