Identification of flow regimes using conductivity probe signals and neural networks for counter-current gas-liquid two-phase flow

▸ Experimental investigation on vertical gas-liquid counter-current two-phase flow. ▸ Complex nonlinear relationships between the developed flow regimes and input parameters. ▸ Automatic classification and prediction of flow regimes based on conductive probe voltage signals. ▸ Parallel probe is found to be the most effective one. ▸ GA-tuned radial basis function neural network with entropy-based clustering is found to be the most effective approach.