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

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@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

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