Acoustic-emission data assisted process monitoring

Gas-liquid two-phase flows are widely used in the,chemical industry. Accurate measurements of flow parameters; such as flow regimes, are the key of operating efficiency. Due to;the interface complexity of a two-phase flow it is very difficult to monitor and distinguish flow regimes on-line and real. time., In-this paper we propose a cost-effective and computation-efficient acoustic emission (AE) detection system; combined, with artificial neural network technology to recognize four major patterns in an air-water vertical two-phase flow column. Several crucial AE parameters are explored and validated, and we found that the density of acoustic emission events and ring-down counts are two excellent indicators for the flow pattern recognition problems. Instead of the, traditional Fair map, a hit-count map is developed arid a multilayer Perceptron neural network is designed as' a decision maker to describe an approximate transmission stage of a given two-phase flow system. (C) 2002 ISA-The Instrumentation, Systems, and Automation Society.