Support vector machine modelling applied to benchmark data set for two-phase Coriolis mass flow metering

Olga L. Ibryaeva; Denis K. Lebedev; Manus P. Henry

doi:10.1016/j.flowmeasinst.2021.102014

Support vector machine modelling applied to benchmark data set for two-phase Coriolis mass flow metering

Olga L. Ibryaeva, Denis K. Lebedev, Manus P. Henry

Research Centre for Fluid and Complex Systems

Research output: Contribution to journal › Article › peer-review

4 Citations (Scopus)

120 Downloads (Pure)

Abstract

An earlier paper introduced a dataset of Coriolis meter mass flow and density errors, induced by the effects of two-phase (gas/liquid) flow, as a benchmark for which various error correction strategies might be developed. That paper further presented a series of error correction models based on neural nets. The current paper presents an alternative analysis of the same data set, using a support vector machine (SVM) approach. The analysis demonstrates that, for the benchmark data set, more accurate models are generated than those developed using neural nets. More specifically, it is found that a linear SVM model provides better performance than non-linear SVM. This improved performance may arise from over-fitting by both non-linear SVM and neural nets on this relatively small data set.

Original language	English
Article number	102014
Number of pages	5
Journal	Flow Measurement and Instrumentation
Volume	81
Early online date	29 Jul 2021
DOIs	https://doi.org/10.1016/j.flowmeasinst.2021.102014
Publication status	Published - Oct 2021

Bibliographical note

© 2021, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/
Copyright © and Moral Rights are retained by the author(s) and/ or other copyright owners. A copy can be downloaded for personal non-commercial research or study, without prior permission or charge. This item cannot be reproduced or quoted extensively from without first obtaining permission in writing from the copyright holder(s). The content must not be changed in any way or sold commercially in any format or medium without the formal permission of the copyright holders.
This document is the author’s post-print version, incorporating any revisions agreed during the peer-review process. Some differences between the published version and this version may remain and you are advised to consult the published version if you wish to cite from it.

Keywords

Coriolis mass flow metering
Artificial neural network
Support vector machine (SVM)
Two-phase flow

ASJC Scopus subject areas

Instrumentation
Electrical and Electronic Engineering
Computer Science Applications
Modelling and Simulation

Access to Document

10.1016/j.flowmeasinst.2021.102014

Post-PrintAccepted author manuscript, 1.05 MBLicence: CC BY-NC-ND

Cite this

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abstract = "An earlier paper introduced a dataset of Coriolis meter mass flow and density errors, induced by the effects of two-phase (gas/liquid) flow, as a benchmark for which various error correction strategies might be developed. That paper further presented a series of error correction models based on neural nets. The current paper presents an alternative analysis of the same data set, using a support vector machine (SVM) approach. The analysis demonstrates that, for the benchmark data set, more accurate models are generated than those developed using neural nets. More specifically, it is found that a linear SVM model provides better performance than non-linear SVM. This improved performance may arise from over-fitting by both non-linear SVM and neural nets on this relatively small data set.",

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Support vector machine modelling applied to benchmark data set for two-phase Coriolis mass flow metering

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

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