State estimation approach to dual-modal imaging of two-phase flow based on electromagnetic flow tomography and electrical tomography

Monitoring, control and design of industrial processes involving multiphase flows often call for analysis of data from multiple sensors which give information on different quantities of the flowing materials. An example of such case is the problem of monitoring the flow of oil-water mixture: the phase fractions of oil and water, their velocities and volumetric flow rates cannot be retrieved from measurements given by a single sensing/imaging modality. For this reason, multi-modal tomographic imaging systems have been developed. In multi-phase flows, the quantities retrieved from different tomographic instruments are often interconnected-for example, the evolutions of the phase fractions depend on their velocities and vice versa. However, the analysis of data from different tomographic modalities is usually done separately-without taking into account physics that link the quantities of interest. In this paper, we propose a novel approach to image reconstruction in dual-modal tomography of multiphase flows. The governing idea is to combine the two modalities via Bayesian state estimation, that is, we write models that approximate connections between different quantities involved in the process and use sequential measurements from both modalities to jointly estimate these temporally evolving quantities. As an example case, we consider a dual-modal system comprising the electromagnetic flow tomography (EMFT) and electrical tomography (ET). While the EMFT is sensitive to the velocity field but also depends on the phase fractions of fluids, ET measurements are directly linked to phase fractions only. We study the performance of state estimation in EMFT-ET tomography with a set of numerical simulations. The results demonstrate that it outperforms the conventional stationary reconstruction approach, and also provides means for uncertainty quantification in multiphase flow imaging.