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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. The flow regime parameter is one of the most important parameters in measurements. This paper proposes a new identification approach for common two phase flow regimes based on Electrical Tomography measurement. LDA feature extraction was employed to extract feature vectors. GRNN was used to train identify the flow regime models. 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. Keywords-electrical resistance tomography; flow regime identification; linear discriminant analysis; general regression neural network.

A Novel Identification Method of Two Phase Flow Based on LDA Feature Extraction and GRNN in ERT System