Artificial neural network approach for flow regime classification in gas–liquid–fiber flows based on frequency domain analysis of pressure signals

Abstract The feasibility of a transportable artificial neural network (ANN)-based technique for the classification of flow regimes in three phase gas/liquid/pulp fiber systems by using pressure signals as input was examined. Experimental data obtained in a vertical, circular column 1.8 m in height and 5.08 cm in diameter, with air/water/Kraft softwood paper pulp, were used. The pulp consistency (weight percent of dry pulp in the pulp–water mixture) was varied in the 0.0–1.5% range. Local pressure fluctuations were recorded at three different stations along the column using three independent but principally similar transducers. An ANN was designed, trained and tested for the classification of the flow regimes using as input some density characteristics of the power spectrum for one of the normalized pressure signals (from Sensor 1), and was shown to predict the flow regimes with good accuracy. A voting scheme was also examined in which the three sensors fed separately trained ANNs, and a correct flow regime would require a vote from at least two of the three ANNs. This scheme improved the agreement between the model predictions and the data. The ANN trained and tested for Sensor 1 predicted the flow regimes reasonably well when applied directly to the normalized pressure power spectrum density characteristics of the other two sensors, indicating a good deal of transportability. For further improvement of transportability, an ANN-based method was also developed, whereby the power spectrum density characteristics of other sensors were adjusted before they were used as input to the ANN that was based on Sensor 1 alone. The method was shown to improve the accuracy of the flow regime predictions. This method requires in practice, that the “replacement” sensor to which regime identification is transported will be, for some while, in simultaneous use with the sensor to be replaced; then the training of the “input-adjusting” ANN would be possible in industry practice. Such a situation is realistic for sensitive processes, where redundant sensors are implemented for fault tolerance.