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

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.