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

Abstract

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Harnessing the power of machine learning for carbon capture, utilisation, and storage (CCUS) – a state-of-the-art review

“…Experimental results suggest that errors of the measured GVF are mostly within ±10%. Shao et al 27 achieved the GVF measurement in a horizontal CO 2 pipeline based on a 12-electrode capacitive sensor and datadriven models, as shown in Figure 12. Three data-driven models, BPNN, RBFNN and LS-SVM, were established.…”

“…ML offers the potential to identify links between data/results that aren't readily identifiable, and it also provides alternative lower computing cost pathways. Within the field of CCUS, ML has begun to be utilised to evaluate new CO 2 sorbents and oxygen carrier materials 17 , simulate, control and operate capture processes [18][19][20][21][22][23] and simplify process economics, predict CO 2 solubilities in solvents and CO 2 capture capacities in adsorbents [24][25][26] , improve the accuracy of multiphase flowmeters used for CO 2 pipelines 27 , and predict leaks from CO 2 wells 28 ; each with the aim of advancing the field of CCUS in a cost and time effective manner. Meantime, it is also worth noting that ML is data-driven technology, and its performance usually depends on the size and quality of database.…”

A novel heterogeneous ensemble approach to variable selection for gas-liquid two-phase CO2 flow metering

“…CO2 flow in carbon capture and storage (CCS) systems is of complex nature (Wang et al, 2018;Shao et al, 2020) and it is thus challenging to measure its dynamic characteristics. To measure mass flowrate and gas volume fraction of multi-phase flow, data-driven modelling has been considered as an efficient and cost-effective way (Yan et al, 2018).…”