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Input variable selection for data-driven models of Coriolis flowmeters for two-phase flow measurement

Wang, Lijuan, Yan, Yong, Wang, Xue, Wang, Tao (2017) Input variable selection for data-driven models of Coriolis flowmeters for two-phase flow measurement. Measurement Science & Technology, 28 . Article Number 035305. ISSN 0957-0233. (doi:10.1088/1361-6501/aa57d6) (KAR id:59824)

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Input variable selection is an essential step in the development of data-driven models for environmental, biological and industrial applications. Through input variable selection to eliminate the irrelevant or redundant variables, a suitable subset of variables is identified as the input of a model. Meanwhile, through input variable selection the complexity of the model structure is simplified and the computational efficiency is improved. This paper describes the procedures of the input variable selection for the data-driven models for the measurement of liquid mass flowrate and gas volume fraction under two-phase flow conditions using Coriolis flowmeters. Three advanced input variable selection methods, including Partial Mutual Information (PMI), Genetic Algorithm - Artificial Neural Network (GA-ANN) and tree-based Iterative Input Selection (IIS) are applied in this study. Typical data-driven models incorporating Support Vector Machine (SVM) are established individually based on the input candidates resulting from the selection methods. The validity of the selection outcomes is assessed through an output performance comparison of the SVM based data-driven models and sensitivity analysis. The validation and analysis results suggest that the input variables selected from the PMI algorithm provide more effective information for the models to measure liquid mass flowrate while the IIS algorithm provides a fewer but more effective variables for the models to predict gas volume fraction.

Item Type: Article
DOI/Identification number: 10.1088/1361-6501/aa57d6
Uncontrolled keywords: Input variable selection; Data driven model; Coriolis flowmeter; Two-phase flow measurement; Artificial neural network; Support Vector Machine
Subjects: T Technology
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Engineering and Digital Arts
Depositing User: Tina Thompson
Date Deposited: 11 Jan 2017 09:01 UTC
Last Modified: 09 Dec 2022 07:13 UTC
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Wang, Lijuan:
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