The Identification of Two-Phase Flow Regimes Using HMM Model Based on ERT System and PCA Feature Extraction

Two-phase flow measurement plays an increasingly important role in the real-time, on-line control of industrial processes including fault detection and system malfunction. Many experimental and theoretical researches have done in the field of tomography image reconstruction. However, the reconstruction process cost quite long time so that there are number of challenges in the real applications. An alternative approach to monitor two-phase flow inside a pipe/vessel is to take advantage of identification of flow regimes. This paper proposes a new identification approach for common two phase flow using PCA feature extraction and Hidden Markov Model based on Electrical Tomography measurement. Simulation was carried out for typical flow regimes using the approach. The results show its feasibility, and the results indicate that this method is fast in speed and can identify these flow regimes correctly.