Combination of fuzzy identification algorithms applied to a column flotation process

The column flotation process is a very complex, nonlinear and multivariable system. Fuzzy modeling is a well-known modeling technique, which has been applied to complex and nonlinear processes. This paper proposes a fuzzy modeling identification technique, where the structure of the model is determined using a regularity criterion, and the rules are identified using fuzzy clustering optimized by a real-coded genetic algorithm. Real data is used for the design and validation of the column flotation fuzzy model. The results are compared to other well-known fuzzy modeling techniques. The validation results show that is possible to find a better model using the identification procedure proposed in this paper.