Gas-liquid Two-phase Flow Measurement Using Coriolis Flowmeters Incorporating Artificial Neural Network, Support Vector Machine and Genetic Programming Algorithms

Wang, Lijuan, Liu, Jinyu, Yan, Yong, Wang, Xue, Wang, Tao (2016) Gas-liquid Two-phase Flow Measurement Using Coriolis Flowmeters Incorporating Artificial Neural Network, Support Vector Machine and Genetic Programming Algorithms. IEEE Transactions on Instrumentation and Measurement, . ISSN 0018-9456. (doi:10.1109/TIM.2016.2634630)

Abstract

Coriolis flowmeters are well established for the mass flow measurement of single phase flow with high accuracy. In recent years attempts have been made to apply Coriolis flowmeters to measure two-phase flow. This paper presents data driven models that are incorporated in Coriolis flowmeters to measure both the liquid mass flowrate and the gas volume fraction of a two-phase flow mixture. Experimental work was conducted on a purpose-built two-phase flow test rig on both horizontal and vertical pipelines for a liquid mass flowrate ranging from 700 kg/h to 14500 kg/h and a gas volume fraction between 0 and 30%. Artificial Neural Network (ANN), Support Vector Machine (SVM) and Genetic Programming (GP) models are established through training with experimental data. The performance of BP-ANN (Back Propagation - ANN), RBF-ANN (Radial Basis Function - ANN), SVM and GP models is assessed and compared. Experimental results suggest that the SVM models are superior to the BP-ANN, RBF-ANN and GP models for two-phase flow measurement in terms of robustness and accuracy. For liquid mass flowrate measurement with the SVM models, 93.49% of the experimental data yield a relative error less than ±1% on the horizontal pipeline whilst 96.17% of the results are within ±1% on the vertical installation. The SVM models predict gas volume fraction with a relative error less than ±10% for 93.10% and 94.25% of the test conditions on horizontal and vertical installations, respectively.

Item Type:	Article
DOI/Identification number:	10.1109/TIM.2016.2634630
Subjects:	T Technology
Divisions:	Faculties > Sciences > School of Engineering and Digital Arts > Instrumentation, Control and Embedded Systems
Depositing User:	Tina Thompson
Date Deposited:	11 Nov 2016 14:47 UTC
Last Modified:	09 Jul 2019 11:51 UTC
Resource URI:	https://kar.kent.ac.uk/id/eprint/58588 (The current URI for this page, for reference purposes)