Classification of flow regimes using a neural network and a non-invasive ultrasonic sensor in an S-shaped pipeline-riser system

Simple item page
dc.contributor.authorNnabuife, Somtochukwu Godfrey
dc.contributor.authorKuang, Boyu
dc.contributor.authorRana, Zeeshan A.
dc.contributor.authorWhidborne, James F.
dc.date.accessioned2022-01-05T13:59:00Z
dc.date.available2022-01-05T13:59:00Z
dc.date.issued2021-11-24
dc.description.abstractA method for classifying flow regimes is proposed that employs a neural network with inputs of extracted features from Doppler ultrasonic signals of flows using either the Discrete Wavelet Transform (DWT) or the Power Spectral Density (PSD). The flow regimes are classified into four types: annular, churn, slug, and bubbly flow regimes. The neural network used in this work is a feedforward network with 20 hidden neurons. The network comprises four output neurons, each of which corresponds to the target vector's element number. 13 and 40 inputs are used for features extracted from PSD and DWT respectively. Experimental data were collected from an industrial-scale multiphase flow facility. Using the PSD features, the neural network classifier misclassified 3 out of 31 test datasets in the classification and gave 90.3% accuracy, while only one dataset was misclassified with the DWT features, yielding an accuracy of 95.8%, thus showing the superiority of the DWT in feature extraction of flow regime classification. The approach demonstrates the applicability of a neural network and DWT for flow regime classification in industrial applications using a clamp-on Doppler ultrasonic sensor. The scheme has significant advantages over other techniques as only a non-radioactive and non-intrusive sensor is used. To the best of our knowledge, this is the first known successful attempt for the classification of liquid-gas flow regimes in an S-shape riser system using an ultrasonic sensor, PSD-DWTs features, and a neural network.en_UK
dc.identifier.citationNnabuife SG, Kuang B, Rana ZA, Whidborne J. (2022) Classification of flow regimes using a neural network and a non-invasive ultrasonic sensor in an S-shaped pipeline-riser system, Chemical Engineering Journal Advances, Volume 9, March 2022, Article number 100215en_UK
dc.identifier.eissn2666-8211
dc.identifier.urihttps://doi.org/10.1016/j.ceja.2021.100215
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/17363
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.subjectUltrasonic sensoren_UK
dc.subjectArtificial Neural Network (ANN)en_UK
dc.subjectS-shaped riseren_UK
dc.subjectDiscrete Wavelet Transforms (DWTs)en_UK
dc.subjectPower Spectral Density (PSD)en_UK
dc.titleClassification of flow regimes using a neural network and a non-invasive ultrasonic sensor in an S-shaped pipeline-riser systemen_UK
dc.typeArticleen_UK

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Classification_of_flow_regimes-2021.pdf
Size:
11.99 MB
Format:
Adobe Portable Document Format
Description:
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.63 KB
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections

Staff publications (SATM)