Maximising overall value in plant design

被引:25
作者
Huband, S.
Tuppurainen, D.
While, L. [1 ]
Barone, L.
Hingston, P.
Bearman, R.
机构
[1] Univ Western Australia, Sch Comp Sci & Software Engn, Nedlands, WA 6009, Australia
[2] Edith Cowan Univ, Sch Comp & Informat Sci, Mt Lawley, WA, Australia
[3] Rio Tinto OTX, Perth, WA, Australia
基金
澳大利亚研究理事会;
关键词
comminution; process optimisation; artificial intelligence;
D O I
10.1016/j.mineng.2006.07.007
中图分类号
TQ [化学工业];
学科分类号
0817 ;
摘要
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. (C) 2006 Elsevier Ltd. All rights reserved.
引用
收藏
页码:1470 / 1478
页数:9
相关论文
共 13 条
[1]  
[Anonymous], 1989, GENETIC ALGORITHM SE
[2]  
Bagchi Tapan P., 1999, Multiobjective Scheduling by Genetic Algorithms
[3]   THE DEVELOPMENT OF A COMMINUTION INDEX FOR ROCK AND THE USE OF AN EXPERT SYSTEM TO ASSIST THE ENGINEER IN PREDICTING CRUSHING REQUIREMENTS [J].
BEARMAN, RA ;
BARLEY, RW ;
HITCHCOCK, A .
MINERALS ENGINEERING, 1990, 3 (1-2) :117-127
[4]  
BROUSSAUD A, 1999, IND MINER LOND, V384, P101
[5]  
Darwin C., 2004, ORIGIN SPECIES
[6]   Design of graph-based evolutionary algorithms:: A case study for chemical process networks [J].
Emmerich, M ;
Grötzner, M ;
Schütz, M .
EVOLUTIONARY COMPUTATION, 2001, 9 (03) :329-354
[7]  
Huband S, 2005, IEEE C EVOL COMPUTAT, P1815
[8]  
Klockgether J., 1970, Proceedings of the 11th symposium on engineering aspects of magnetohydrodynamics, P141
[9]  
MCCAFFERY KM, 2001, 0169 SOC MIN MET EXP
[10]  
PRINCE RGH, 1997, P452 AMIRA