Maximising overall value in plant design
Faculty of Computing, Health and Science
School of Computer and Information Science
Existing plant designs are often conservative and as a consequence 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, plant designs often require optimisation in relation to numerous variables, or objectives. To facilitate this task, a multi-objective evolutionary algorithm has been developed to optimise existing plants against multiple competing process drivers, as evaluated by simulation. 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. An extension of the evolutionary algorithm to employ wider flowsheet modifications is also discussed.