Study on two-phase flow regime visualization and identification using 3D electrical capacitance tomography and fuzzy-logic classification

• Gas–liquid flow pattern identification using fuzzy evaluation of 3D ECT images.
• Nonlinear electrical 3D capacitance tomography reconstruction algorithms are used.
• A set of fuzzy-based features is calculated for flow regime classification.
• Fuzzy logic inference can classify flow patterns with a very high accuracy.

From variety of industry-oriented imaging solutions the electrical capacitance tomography applied to the two-phase gas–liquid mixtures visualization and the phase distribution calculation is getting popular especially when flow key parameters are required. Industry demands particularly include efficient non-invasive automatic phase fraction calculation and flow structure identification in the vertical and horizontal pipelines. This can be solved by using non-deterministic fuzzy-logic based techniques for analysis of volumetric images. This paper presents a preliminary study on automated two-phase gas–liquid flow pattern identification based on a fuzzy evaluation of series of reconstructed 3D ECT volumetric images. The set of volume data is obtained by using nonlinear electrical capacitance tomography reconstruction algorithms. Finally a set of fuzzy-based features is calculated for flow substructure classification. As a result of this analysis obtained features will be used to classify given volumetric image into one of known flow regime structures.