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

  • Simon Huband
  • Lyndon While
  • David Tuppurainen
  • Philip Hingston
  • Luigi Barone
  • Ted Bearman
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4304)

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.

Keywords

Economic Optimisation Fourth Objective Milling Line Attainment Surface Case Study Problem 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Simon Huband
    • 1
  • Lyndon While
    • 2
  • David Tuppurainen
    • 3
  • Philip Hingston
    • 1
  • Luigi Barone
    • 2
  • Ted Bearman
    • 3
  1. 1.Edith Cowan UniversityMt Lawley
  2. 2.The University of Western AustraliaCrawley
  3. 3.Rio Tinto OTXPerth

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