Crushing plant optimisation by means of a genetic evolutionary algorithm
Artikel i vetenskaplig tidskrift, 2005

Crushing plants are used both by aggregate producers and the mining industry. The overall crushed rock product production process can be improved by means of computer simulation and optimisation. To achieve optimal performance of a crushing plant, not only the design of the individual machines but also the cost of running them should be taken into account. In this paper a novel method for the modelling and optimisation of crushing plants is presented based on the structural modelling of crushing plants and parameter optimisation. Structural modelling is performed by utilizing mathematical models of the different production units, rock materials and economics of the crushing plant. A model that takes customer demands on the different products into consideration is also included. A statement of the problem of crushing plant optimisation is formulated and a genetic evolutionary algorithm has been included in the software in order to facilitate the optimisation process. The efficiency of the proposed algorithm is demonstrated with reference to a crushing plant containing a small number of machines. It is shown how the knowledge gained from the simulation and optimisation can be used to improve the overall production of crushed rock material. Depending on the optimisation criteria, the result obtained can include maximized profit, maximized desirable fractions, increased product quality, and increased machine efficiency. © 2004 Elsevier Ltd. All rights reserved.

Process optimisation


Artificial intelligence




Per Svedensten

Chalmers, Tillämpad mekanik

Magnus Evertsson

Chalmers, Tillämpad mekanik

Minerals Engineering

0892-6875 (ISSN)

Vol. 18 5 473-479



Övrig annan teknik



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