Signal processing for slug flow analysis via a voltage or instantaneous liquid holdup time-series

Slug flow is one of the most observed gas-liquid flow patterns in pipes. Owing to its high occurrence, the estimation of slug characteristics is essential for many engineering processes. The slug flow characterization is usually carried out by models and correlations previously calibrated with experimental data generated by the interpretation of voltage or instantaneous liquid holdup time-series. Historically, this interpretation required algorithms that depended on subjective parameters, which created high dispersion on the data. This paper proposes a new signal processing analysis, which does not require any subjective parameter. A statistical algorithm is used to calculate the film and slug cut threshold values, the disregard cut value to group slug pulses, and the disregard cut value to remove slug pulses, which are required to determine the slug characteristics. An experimental data set was used to validate the proposed methodology. The consistency check process followed two independent ways, both with good results. Based on the performance against the experimental data, the proposed algorithm is recommended for slug flow characterization.

Automated summary

Slug flow is a common gas-liquid flow pattern in pipes, and its characteristics play a crucial role in engineering processes. This paper presents a new signal processing analysis method that utilizes statistical algorithms to accurately determine slug flow characteristics, with validation from experimental data showing good results. The proposed algorithm is recommended for slug flow characterization based on its performance against the experimental data.