Particle-Scavenging prediction in sieve plate scrubber via dimension reduction in computational fluid dynamics

Abstract In this study, a computational fluid dynamics (CFD) model of a sieve plate scrubber was built to predict its particle-removal efficiency and predict the U-shaped curve of the particle-removal efficiency as particles became smaller. Due to the complexity of particle tracking, it takes considerable time to simulate the model by using a three-dimensional (3D) structure, which is not conducive to finding the appropriate setting of particle forces. Instead, we presented a dimension-reduction method to estimate the particle force setting by using a two-dimensional (2D) structure. The rationality of the dimension-reduction method was validated by the consistency in froth density for both 2D and 3D models at various air-inlet velocities. Furthermore, the result of the particle forces setting showed that besides the drag force, other forces, such as the lift force, pressure-gradient force, gravity force, and virtual mass force, should be employed in the CFD model to predict the particle-removal efficiency of the sieve plate scrubber. The prediction results of the 2D model remarkably matches the particle-removal efficiency results of experimental data from the literature for various gas velocities and particle sizes. In addition, the model predicts the U-shaped curve of the particle-removal efficiency for the particle-diameter range from 0.1 to 1.0 μm. Furthermore, a 3D model with the setting of the particle forces as in the 2D model was used to validate the consistency between the 2D and 3D models. The result showed that the particle-removal efficiency of the 3D model was considerably close to the prediction results of the 2D model.