In order to safe design and optimize performance of some industrial systems, it’s often needed to categorize two-phase flow into different regimes. In each flow regime, flow conditions have similar geometric and hydrodynamic characteristics. Traditionally, flow regime identification was carried out by flow visualization or instrumental indicators. In this research3 kind of neural networks have been used to predict system characteristic and flow regime, and results of them were compared: radial basis function neural networks, self organized and Multilayer perceptrons (supervised) neural networks. The data bank contains experimental pressure signalfor a wide range of operational conditions in which upward two phase air/water flows pass to through a vertical pipe of 5cm diameter under adiabatic condition. Two methods of signal processing were applied to these pressure signals, one is FFT (Fast Fourier Transform) analysis and the other is PDF (Probability Density Function) joint with wavelet denoising. In this work, from signals of 15 fast response pressure transducers, 2 have been selected to be used as feed of neural networks. The results show that obtained flow regimes are in good agreement with experimental data and observation.

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