In this study, an electrical capacitance tomography (ECT) adopted with neural-network based multi-criterion optimization image reconstruction technique (NN-MOIRT) for real time and quasi-3D imaging of gas-liquid flow system is developed. The technique reconstruct the permittivity distribution (tomography image) from capacitance data obtained using 12-electrode twin-plane capacitance sensor. A combined series and parallel capacitance model is used convert the permittivity distribution into gas holdup distribution. Comparison of the overall gas holdups obtained by the ECT with those obtained from the pressure measurements shows a good agreement, validating the model proposed. The ECT is applied to study the hydrodynamic characteristics of the gas-liquid flow system including the bubbly flow structures, gas holdup profiles, and gas holdup variations along with the effect of the gas velocity.

This content is only available via PDF.
You do not currently have access to this content.