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.

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http://dx.doi.org/10.1016/j.ijggc.2017.11.021 under the license http://creativecommons.org/licenses/by/3.0
https://academic.microsoft.com/#/detail/2771217775 under the license https://www.elsevier.com/tdm/userlicense/1.0/
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Published on 01/01/2017

Volume 2017, 2017
DOI: 10.1016/j.ijggc.2017.11.021
Licence: Other

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