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

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@Article{Wang:2017:ieeeTIM,

• author = "Lijuan Wang and Jinyu Liu and Yong Yan and Xue Wang and Tao Wang",
• journal = "IEEE Transactions on Instrumentation and Measurement",
• title = "Gas-Liquid Two-Phase Flow Measurement Using Coriolis Flowmeters Incorporating Artificial Neural Network, Support Vector Machine, and Genetic Programming Algorithms",
• year = "2017",
• volume = "66",
• number = "5",
• pages = "852--868",
• month = may,
• keywords = "genetic algorithms, genetic programming, ANN, SVM",
• DOI = "doi:10.1109/TIM.2016.2634630",
• ISSN = "0018-9456",
• 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 into 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 to 14500 kg/h and a gas volume fraction between 0percent and 30percent. Artificial neural network (ANN), support vector machine (SVM), and genetic programming (GP) models are established through training with the experimental data. The performance of backpropagation-ANN (BP-ANN), radial basis function-ANN (RBF-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, 93.49percent of the experimental data yield a relative error less than +-1percent on the horizontal pipeline, while 96.17percent of the results are within +-1percent on the vertical installation. The SVM models predict the gas volume fraction with a relative error less than +-10percent for 93.10percent and 94.25percent of the test conditions on the horizontal and vertical installations, respectively.",
• notes = "Also known as \cite{7790803}",

}

Genetic Programming entries for Lijuan Wang Jinyu Liu Yong Yan Xue Wang Tao Wang

Citations