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 %.
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Coronavirus Research Articles published between Oct 11, 2021 to Oct 17, 2021
Oct 18, 2021
Articles Included: 3
Muhammad Ikbal and colleagues (2021) reported in ‘Visualisasi dan Analisa Data Penyebaran Covid-19 dengan Metode Klasifikasi Naïve Bayes’ that the cov...Read More
Climate change Research Articles published between Oct 11, 2021 to Oct 17, 2021
Oct 18, 2021
Articles Included: 5
Junjie Jia et al. (2021) reported in ‘Driving mechanisms of gross primary productivity geographical patterns for Qinghai–Tibet Plateau lake systems’ t...Read More