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A dynamic ensemble selection approach to developing softcomputing models for two-phase flow metering

Sun, Caiying, Yan, Yong, Zhang, Wenbiao, Wang, Lijuan (2018) A dynamic ensemble selection approach to developing softcomputing models for two-phase flow metering. Journal of Physics: Conference Series, 1065 . pp. 1-4. ISSN 1742-6588. E-ISSN 1742-6596. (doi:10.1088/1742-6596/1065/9/092022) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. (Contact us about this Publication)
Official URL
http://dx.doi.org/10.1088/1742-6596/1065/9/092022

Abstract

This paper presents a dynamic ensemble selection approach to developing least squares support vector regression (LSSVR) models for the flow metering of gas-liquid two-phase CO2. Ensemble models based on flow pattern recognition and dynamic ensemble selection (FPR-DES) are established for the measurement of total mass flowrate and gas volume fraction of two-phase CO2, respectively. The input variables of ensemble models are obtained from a Coriolis flowmeter and a differential-pressure transducer installed on a horizontal test section. Experimental tests were conducted with liquid CO2 mass flowrate ranging from 200 kg/h to 3100 kg/h and gas volume fraction between 3.1% and 88.4%. Performance comparisons between the proposed FPR-DES based ensemble model, bagging based ensemble model and the single LSSVR model are undertaken with experimental data under stratified, bubbly and mist flow conditions. Experimental results suggest that the FPR-DES based ensemble model outperforms the other two models with maximum errors of ±1% and ±10% for the total CO2 mass flowrate and gas volume fraction, respectively.

Item Type: Article
DOI/Identification number: 10.1088/1742-6596/1065/9/092022
Uncontrolled keywords: dynamic ensemble selection; ensemble models; softcomputing
Subjects: T Technology > TA Engineering (General). Civil engineering (General) > TA165 Engineering instruments, meters etc. Industrial instrumentation
Divisions: Faculties > Sciences > School of Engineering and Digital Arts > Instrumentation, Control and Embedded Systems
Depositing User: Lijuan Wang
Date Deposited: 25 Oct 2019 16:30 UTC
Last Modified: 06 Feb 2020 04:20 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/77847 (The current URI for this page, for reference purposes)
Wang, Lijuan: https://orcid.org/0000-0002-2517-2728
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