This paper was produced for the 2019 NAFEMS World Congress in Quebec Canada

Resource Abstract

Gas-liquid separators are used in many oil and gas industrial applications and separate the incoming liquid from gas. Droplet entrainment into a gas outlet of a separator can lead to serious consequences, for instance, damage to downstream rotating machinery. Quantitative prediction of separation performance of gas-liquid separators, which is important to study better design and avoid such troubles, requires understanding of the mist fraction in upstream piping and modelling of the separation efficiency of internals such as a mist eliminators. A prediction method of separation performance involving empirical correlations for droplet entrainment in inlet piping and performance of internals has been proposed. This paper focusses on validation of the prediction method by comparing the prediction with experimental data. A numerical simulation has also been performed to reproduce the phenomena especially in inlet piping and give better understanding in the mechanisms. The comparison between the results and the prediction showed that the mist fractions measured in the inlet piping agree well with the prediction and the droplet size distribution is also predicted with good accuracy. It is also found that the measured total separation efficiency of the scrubber showed good agreement with the prediction. However, the method provided conservative evaluation for the droplet size in the scrubber. In this regard, empirical correlations and assumptions utilized as basis of the method are discussed. The result of the numerical simulation reproducing detail behavior of droplet and bulk liquid (i.e. liquid film) in the inlet pipe bend is also discussed here to understand the effect of the bend which is not taken into account in the method. From the results and discussion mentioned above, it is concluded that the method has reasonable accuracy for assessment of separation performance, which is suitable to provide sufficiently insightful answers for better design decisions or trouble shooting.