Flow regime identification using fuzzy and neuro-fuzzy inference applied on differential pressure sensor

Intelligent identification of two-phase flow regime is indispensable for the safe design of industrial systems. This work presents the development and examination of intelligent prediction methods based on fuzzy logic designed to operate on a differential pressure sensor signal for two-phase flow regime identification. The effectiveness of intelligent paradigms for flow regime identification is established using experiments carried out in a 0.9 mm circular glass tube kept horizontally. Air and water are the two-phase fluids. Three flow regimes, namely bubbly, slug and annular flows are observed for various combinations of superficial fluid velocities. The pressure drop in the flow system is measured and recorded online using a differential pressure sensor connected to a data acquisition system. Signal features such as peak current, the difference in current and signal frequency are extracted after extensive analysis. Fuzzy rules are outlined and flow regime output is analyzed. The bottleneck observed in these techniques is qualitatively designated in terms of accuracy and required high human effort. A solution to this bottleneck is found out using adaptive neuro-fuzzy inference system (ANFIS). ANFIS applied on pressure sensor signal is found to provide an accurate characterization of flow regime.