A neural network to correct mass flow errors caused by two-phase flow in a digital coriolis mass flowmeter

R.P. Liu, M.J Fuent, M.P Henry, M.D Duta

Research output: Contribution to journalArticle

71 Citations (Scopus)

Abstract

Coriolis mass flow meters provide accurate measurement of single-phase flows, typically to 0.2%. However gas–liquid two-phase flow regimes may cause severe operating difficulties as well as measurement errors in these flow meters. As part of the Sensor Validation (SEVA) research at Oxford University a new fully digital coriolis transmitter has been developed which can operate with highly aerated fluids. This paper describes how a neural network has been used to correct the mass flow measurement for two-phase flow effects, based entirely on internally observed parameters, keeping errors to within 2%. The correction strategy has been successfully implemented on-line in the coriolis transmitter. As required by the SEVA philosophy, the quality of the corrected measurement is indicated by the on-line uncertainty provided with each measurement value.
Original languageEnglish
Pages (from-to)53-63
Number of pages11
JournalFlow Measurement and Instrumentation
Volume12
Issue number1
Early online date24 Jan 2001
DOIs
Publication statusPublished - Mar 2001
Externally publishedYes

Keywords

  • Sensor validation
  • Neural networks
  • Coriolis mass
  • Flowmeter
  • Two phase flow

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