Wang, Lijuan, Yan, Yong, Wang, Xue, Wang, Tao, Duan, Quansheng, Zhang, Wenbiao (2017) Mass Flow Measurement of Gas-Liquid Two-Phase CO\(_2\) in CCS Transportation Pipelines using Coriolis Flowmeters. International Journal of Greenhouse Gas Control, 68 . pp. 269-275. ISSN 1750-5836. (doi:10.1016/j.ijggc.2017.11.021) (KAR id:64823)
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Official URL: https://dx.doi.org/10.1016/j.ijggc.2017.11.021 |
Abstract
Carbon Capture and Storage (CCS) is a promising technology that stops the release of CO\(_2\) from industrial processes such as electrical power generation. Accurate measurement of CO\(_2\) flows in a CCS system where CO\(_2\) flow is a gas, liquid, or gas-liquid two-phase mixture is essential for the fiscal purpose and potential leakage detection. This paper presents a novel method based on Coriolis mass flowmeters in conjunction with least squares support vector machine (LSSVM) models to measure gas-liquid two-phase CO\(_2\) flow under CCS conditions. The method uses a classifier to identify the flow pattern and individual LSSVM models for the metering of CO2 mass flowrate and prediction of gas volume fraction of CO\(_2\), respectively. Experimental work was undertaken on a multiphase CO\(_2\) flow test facility. Performance comparisons between the general LSSVM and flow pattern based LSSVM models are conducted. Results demonstrate that Coriolis mass flowmeters with the LSSVM model incorporating flow pattern identification algorithms perform significantly better than those using the general LSSVM model. The mass flowrate measurement of gas-liquid CO\(_2\) is found to yield errors less than ±2% on the horizontal pipeline and ±1.5% on the vertical pipeline, respectively, over flowrates from 250 kg/h to 3200 kg/h. The error in the estimation of CO\(_2\) gas volume fraction is within ±10% over the same range of flow rates.
Item Type: | Article |
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DOI/Identification number: | 10.1016/j.ijggc.2017.11.021 |
Uncontrolled keywords: | CCS; gas-liquid CO2 flow; flow measurement; Coriolis mass flowmeter; gas volume fraction; least squares support vector machine |
Subjects: | T Technology |
Divisions: |
Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Engineering and Digital Arts Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Mathematics, Statistics and Actuarial Science |
Depositing User: | Yong Yan |
Date Deposited: | 28 Nov 2017 11:59 UTC |
Last Modified: | 05 Nov 2024 11:01 UTC |
Resource URI: | https://kar.kent.ac.uk/id/eprint/64823 (The current URI for this page, for reference purposes) |
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