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Dynamic measurement of gas volume fraction in a CO2 pipeline through capacitive sensing and data driven modelling

Shao, Ding, Yan, Yong, Zhang, Wenbiao, Sun, Shijie, Sun, Caiying, Xu, Lijun (2019) Dynamic measurement of gas volume fraction in a CO2 pipeline through capacitive sensing and data driven modelling. International Journal of Greenhouse Gas Control, 94 . p. 102950. ISSN 1750-5836. (doi:10.1016/j.ijggc.2019.102950)

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https://doi.org/10.1016/j.ijggc.2019.102950

Abstract

Gas volume fraction (GVF) measurement of gas-liquid two-phase CO2 flow is essential in the deployment of carbon capture and storage (CCS) technology. This paper presents a new method to measure the GVF of two-phase CO2 flow using a 12-electrode capacitive sensor. Three data driven models, based on back-propagation neural network (BPNN), radial basis function neural network (RBFNN) and least-squares support vector machine (LS-SVM), respectively, are established using the capacitance data. In the data pre-processing stage, copula functions are applied to select feature variables and generate training datasets for the data driven models. Experiments were conducted on a CO2 gas-liquid two-phase flow rig under steady-state flow conditions with the mass flowrate of liquid CO2 ranging from 200 kg/h to 3100 kg/h and the GVF from 0% to 84%. Due to the flexible operations of the power generation utility with CCS capabilities, dynamic experiments with rapid changes in the GVF were also carried out on the test rig to evaluate the real-time performance of the data driven models. Measurement results under steady-state flow conditions demonstrate that the RBFNN yields relative errors within ±7% and outperforms the other two models. The results under dynamic flow conditions illustrate that the RBFNN can follow the rapid changes in the GVF with an error within ±16%.

Item Type: Article
DOI/Identification number: 10.1016/j.ijggc.2019.102950
Uncontrolled keywords: Carbon capture and storage; Gas volume fraction; Two-phase CO2 flow; Data driven models; Copula functions
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: Yong Yan
Date Deposited: 08 Jan 2020 05:24 UTC
Last Modified: 09 Jan 2020 10:03 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/79507 (The current URI for this page, for reference purposes)
Yan, Yong: https://orcid.org/0000-0001-7135-5456
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