Artificial neural network application for multiphase flow patterns detection: A new approach

Artificial neural network application for multiphase flow patterns detection: A new approach Multiphase flow measurement is a very challenging issue in process industry. There are several techniques to estimate multiphase flow parameters. However, these techniques need correct identification of the flow patterns first. Artificial Intelligence is one of the promising technique for identifying the flow patterns. In this paper, we used an Artificial Neural Network (ANN) for flow pattern identification but with pre-processing stage using natural logarithmic normalization. This pre-processing stage helps to normalize large data range and to reduce overlapping between flow patterns. Thus, the validity of the model was extended by using dimensionless inputs to be implemented for horizontal pipes of various diameters, liquid densities and viscosities. The concept was validated by building and testing the model using experimental data as well as well-known multiphase flow models. An ANN model was built using three dimensionless parameters only, namely, Liquid Reynolds Number, Gas Reynolds Number and Pressure Drop Multiplier. The present model achieved more than 97% accuracy in classifying the flow patterns for wide range of flow conditions. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Petroleum Science and Engineering Elsevier

Artificial neural network application for multiphase flow patterns detection: A new approach

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Publisher
Elsevier
Copyright
Copyright © 2016 Elsevier B.V.
ISSN
0920-4105
eISSN
1873-4715
D.O.I.
10.1016/j.petrol.2016.06.029
Publisher site
See Article on Publisher Site

Abstract

Multiphase flow measurement is a very challenging issue in process industry. There are several techniques to estimate multiphase flow parameters. However, these techniques need correct identification of the flow patterns first. Artificial Intelligence is one of the promising technique for identifying the flow patterns. In this paper, we used an Artificial Neural Network (ANN) for flow pattern identification but with pre-processing stage using natural logarithmic normalization. This pre-processing stage helps to normalize large data range and to reduce overlapping between flow patterns. Thus, the validity of the model was extended by using dimensionless inputs to be implemented for horizontal pipes of various diameters, liquid densities and viscosities. The concept was validated by building and testing the model using experimental data as well as well-known multiphase flow models. An ANN model was built using three dimensionless parameters only, namely, Liquid Reynolds Number, Gas Reynolds Number and Pressure Drop Multiplier. The present model achieved more than 97% accuracy in classifying the flow patterns for wide range of flow conditions.

Journal

Journal of Petroleum Science and EngineeringElsevier

Published: Sep 1, 2016

References

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