Neural networks in electrical capacitance tomography (ECT)-based interface detection

Abstract

Tomographic systems have been tested in separators for estimating the distribution of the three phases (including emulsion and foam) via interface detection in various laboratories. Experiments and simulations show that electrical capacitance tomography (ECT) is capable of detecting the interface accurately in spite of the high conductivity of the water present. Due to the high conductivity of the water/brine in the pipe separator, the sensitivity of the capacitance measurements is to a certain extent immune to variations in material properties. In a series of preliminary tests, capacitance tomography was used to estimate the interface in a pipe separator containing oil and water/brine. Results obtained from laboratory scale models are presented and discussed with some information on the uncertainties involved. Artificial neural networks exhibit enhanced ability to mask variations in unwanted/unimportant parameters in the separation process, thus reducing the complexities involved in the solution of the essentially underdetermined system of equations evolving out of different models developed for the system. Due to ample data being available from tomographic systems, a data driven soft sensor (virtual sensor) approach is also discussed with some considerations on processing times to address the potential of the ECT in real time measurement and control applications.