Abstract
Building first-principle computer models of mineral processing equipment is difficult because of the complex three-phase, chemically reacting flows generally occurring. Thus, it is desirable to develop techniques for building empirical computer models that accurately reflect the processes taking place in the equipment. Researchers at the University of Alabama have developed a technique in which linguistic models can be developed exclusively from engineering data for mineral processing equipment. This technique combines traditional fuzzy, rule-based systems and genetic algorithms. The efficacy of such an approach is demonstrated with the development of linguistic models for both a grinding circuit and a hydrocyclone separator.
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