Flow rate measurement in air-water horizontal pipeline by neural networks

The Kohonen self-organising feature map (KSOFM) and the multi-layer backpropagation network (MBPN) were applied in a hybrid network model to measure the flow rate of individual phases in horizontal air-water flow. Feature sets derived from turbulent absolute and differential pressure signals obtained from a range of flow regimes were classified into clusters by the KSOFM according to flow regime. Samples belonging to each cluster were trained by the MBPN to measure the flow rate of individual phases. Two thirds of the samples were randomly selected to train the MBPN, the remainder was used for testing. Individual phase flow rates were identified with 10% accuracy.