Study of AR model based on EMD in flow regime identification of gas-liquid two-phase flow

An Autoregressive model (AR model) based on Empirical Mode Decomposition (EMD) was applied to identifying gas-liquid two-phase flow regime. At first, the dynamic differential pressure signals coming from Venturi tube were processed by normalization, EMD and denoise. Afterwards, the Intrinsic Mode Functions (IMF) contained flow regime information were selected to establish AR models. Through models analysis, the coefficients and the variances of residual errors were defined as eigenvector, templates of different flow regimes were built. Flow regime was identified by calculating the synthetic Mahalanobis distance of eigenvectors between template and pressure fluctuation signal. The experimental results of gas-liquid two-phase flow in horizontal pipes with 40 mm inner diameter manifest that this method can identify bubble flow, slug flow and plug flow with 93% accuracy rate. This method can be realized easily for flow regime identification of gas-liquid two-phase flow with few influence of environment.