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Ore Type based Expert Systems in Mineral Processing Plants
Author(s) -
JämsäJounela SirkkaLiisa,
Laine Sampsa,
Ruokonen Eeva
Publication year - 1998
Publication title -
particle and particle systems characterization
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.877
H-Index - 56
eISSN - 1521-4117
pISSN - 0934-0866
DOI - 10.1002/(sici)1521-4117(199808)15:4<200::aid-ppsc200>3.0.co;2-3
Subject(s) - expert system , artificial neural network , computer science , fuzzy logic , mineral processing , process engineering , artificial intelligence , manufacturing engineering , engineering , materials science , metallurgy
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.