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

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%.