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 output: Contribution to journalArticlepeer-review

4 Citations (Scopus)
67 Downloads (Pure)


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 languageEnglish
Article number102014
JournalFlow Measurement and Instrumentation
Early online date29 Jul 2021
Publication statusPublished - Oct 2021

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  • 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


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