Non-intrusive classification of gas-liquid flow regimes in an S-shaped pipeline-riser using doppler ultrasonic sensor and deep neural networks

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dc.contributor.authorNnabuife, Somtochukwu Godfrey
dc.contributor.authorKuang, Boyu
dc.contributor.authorWhidborne, James F.
dc.contributor.authorRana, Zeeshan
dc.date.accessioned2020-07-29T15:13:25Z
dc.date.available2020-07-29T15:13:25Z
dc.date.freetoread2021-07-27
dc.date.issued2020-07-26
dc.description.abstractThe problem of predicting the regime of a two-phase flow is considered. An approach is proposed that classifies the flow regime using Deep Neural Networks (DNNs) operating on features extracted from Doppler ultrasonic signals of the flow using the Fast Fourier Transform (FFT) is proposed. The features extracted are categorised into one of the four flow regime classes: the annular, churn, slug, and bubbly flow regimes. The scheme was tested on signals from an experimental facility. To increase the number of samples without losing key classification information, this paper proposes a Twin-window Feature Extraction (TFE) technique. To further distinguish the performance of the proposed approach, the classifier was compared to four conventional machine learning classifiers: namely, the AdaBoost classifier, bagging classifier, extra trees classifier, and decision tree classifier. Using the TFE features, the DNNs classifier achieved a higher recognition accuracy of 99.01% and greater robustness for the overfitting challenge, thereby showing the superiority of the DNNs in flow regime classification when compared to the four conventional machine-learning classifiers, which had classification accuracies of 55.35%, 86.21%, 82.41%, and 80.03%, respectively. This approach demonstrates the application of DNNs for flow regime classification in chemical and petroleum engineering fields, using a clamp-on Doppler ultrasonic sensor. This appears to be the first known successful attempt to identify gas-liquid flow regimes in an S-shaped riser using Continuous Wave Doppler Ultrasound (CWDU) and DNNsen_UK
dc.identifier.citationNnabuife SG, Kuang B, Whidborne J, et al., (2021) Non-intrusive classification of gas-liquid flow regimes in an S-shaped pipeline-riser using doppler ultrasonic sensor and deep neural networks. Chemical Engineering Journal, Volume 403, January 2021, Article number 126401en_UK
dc.identifier.cris27608338
dc.identifier.issn1385-8947
dc.identifier.urihttps://doi.org/10.1016/j.cej.2020.126401
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/15609
dc.language.isoenen_UK
dc.publisherElsevieren_UK
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectTwin-window Feature Extraction (TFE)en_UK
dc.subjectMachine Learning (ML)en_UK
dc.subjectS-shaped riseren_UK
dc.subjectFast Fourier Transform (FFT)en_UK
dc.subjectDeep Neural Networks (DNNs)en_UK
dc.subjectDoppler ultrasounden_UK
dc.titleNon-intrusive classification of gas-liquid flow regimes in an S-shaped pipeline-riser using doppler ultrasonic sensor and deep neural networksen_UK
dc.typeArticleen_UK

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