Artificial intelligence (AI) includes excellent tools for the control and supervision of industrial processes. Several thousand industrial applications have been reported worldwide. Recently, the designers of the AI systems have begun to hybridize the intelligent techniques, expert systems, fuzzy logic and neural networks, to enhance the capability of the AI systems. Expert systems have proved to be ideal candidates especially for the control of mineral processes. An expert system based on on-line classification of the ore type has been developed. Self-organizing maps (SOM) are used for pattern recognition of the type of feed. The system has been tested in two concentrators, the Outokumpu Finnmines Oy, Hitura Mine and Outokumpu Chrome Oy, Kemi Mine. The methodology for the development of the ore type based expert system is presented and the preliminary results obtained in the above plants are described.
[1]
Michael R. Anderberg,et al.
Cluster Analysis for Applications
,
1973
.
[2]
W. J. Whiten,et al.
A Data Based Expert System for Engineering Applications
,
1992
.
[3]
G. G. Stokes.
"J."
,
1890,
The New Yale Book of Quotations.
[4]
Sampsa Laine,et al.
On-line determination of ore type using cluster analysis and neural networks
,
1995
.
[5]
Teuvo Kohonen,et al.
The self-organizing map
,
1990,
Neurocomputing.
[6]
Mike James,et al.
Classification Algorithms
,
1986,
Encyclopedia of Machine Learning and Data Mining.