Application of fuzzy sets and neural network theory to the numerical pattern recognition of bubble flow and jet flow

Abstract

The injection of gas into a liquid is associated with two characteristic flow regimes: bubble flow and jet flow. In metallurgy, these flow regimes are of interest with respect to refractory lifetime and mass transfer. The flow regimes were investigated in the systems water‐air and ethanol‐air by monitoring the bottom vibrations of a cold model. The vibrations were analyzed by numerical pattern recognition. The nominal Mach number of 1.1 is the criterion for the transition from bubble flow to jet flow. A numerical pattern recognition system which is able to distinguish between the flow regime below and above the nominal Mach number of 1.1, just by analyzing the vibration signal, is presented. This task can be accomplished in spite of the use of different liquids, nozzle diameters and liquid heights. As a classificator, both a fuzzy k nearest neighbour algorithm and a neural network were employed successfully. Fuzzy logic was found to be especially useful for describing the different flow regimes.