Adaptive filtering techniques for velocity estimation of tomographic electric capacitance signals of two-phase gas/oil flows

Abstract

The development of adaptive real-time flow velocity estimation algorithms for two-phase flows can contribute to monitoring the pipelines of various complex processes, such as energy, chemical, petroleum and nuclear industries. Among the different non-invasive tomography techniques, electrical capacitance tomography (ECT) is gaining increasing attention for its potential use in real-time imaging and characterization of multiphase flow systems. The nature of ECT signals for two-phase flows can significantly degrade the velocity estimation process with cross-correlation approaches. We address the unique challenges of such signals and propose a preprocessing technique to improve the performance and robustness of the velocity estimation algorithm. Two adaptive filters are used to estimate the velocity of a two-phase type flow. A least mean square (LMS) and a fast block LMS (FBLMS) are used to model the time delay between the two signals captured by the twin sensor (ECT). Performance of the proposed technique is assessed by applying it to ECT data obtained from an experimental flow rig. The computed estimates are then compared with the calculated velocity from tracking motion of bubbles captured by a high speed camera monitoring the two phase flow in the pipe. Results show that the proposed technique provides consistent results across various flow patterns, and is advantageous compared to cross-correlation based techniques, specially for chaotic flow conditions. Furthermore, the proposed estimation algorithms can be applied to other electric based tomographic techniques.