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Gas-liquid Two-phase Flow Measurement Using Coriolis Flowmeters Incorporating Neural Networks

Wang, Lijuan, Liu, J.Y., Yan, Yong, Wang, X., Wang, T. (2016) Gas-liquid Two-phase Flow Measurement Using Coriolis Flowmeters Incorporating Neural Networks. In: Proceedings of IEEE International Instrumentation and Measurement Technology Conference (I2MTC 2016). . pp. 747-751. IEEE ISBN 978-1-4673-9221-1. E-ISBN 978-1-4673-9220-4. (doi:10.1109/I2MTC.2016.7520458)

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https://doi.org/10.1109/I2MTC.2016.7520458

Abstract

Coriolis flowmeters are commonly used to measure single phase flow. In recent years attempts are being made to apply Coriolis flowmeters to measure two-phase flows. This paper presents a neural network based approach that has been applied to Coriolis flowmeters to measure both the liquid flow rate and the gas void fraction of a two-phase flow. Experimental tests were conducted on a purpose-built two-phase flow test rig on both horizontal and vertical pipelines. The mass flow rate ranges from 700 kg/h to 14500 kg/h whilst the gas volume fraction is between 0 and 30%. A set of variables, including observed density, apparent mass flow, pressure of the fluid and signals to maintain flow tube oscillation, are considered as inputs to a neural network. Two neural networks are established through training with experimental data obtained from the flow rig on horizontal and vertical pipelines, respectively. The performance of both neural networks is assessed in comparison with the reference readings. Experimental results suggest that the relative errors of the corrected mass flow rate of liquid for the vertical and horizontal installations are no greater than ±1.5% and ±2.5%, respectively. The gas volume fraction is predicted with relative errors of less than ±10% and ±20%, respectively, for vertical and horizontal installations in most cases.

Item Type: Conference or workshop item (Paper)
DOI/Identification number: 10.1109/I2MTC.2016.7520458
Uncontrolled keywords: Keywords—two-phase flow; flow measurement; Coriolis mass flowmeter; gas volume fraction; neural network
Subjects: T Technology
Divisions: Faculties > Sciences > School of Engineering and Digital Arts > Instrumentation, Control and Embedded Systems
Depositing User: Tina Thompson
Date Deposited: 02 Jun 2016 13:02 UTC
Last Modified: 29 May 2019 17:26 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/55769 (The current URI for this page, for reference purposes)
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