Computational Fluid Dynamic Modelling of Optimal Water Level in Low-pressure Microbubbles Scrubbers

Abstract

Currently, there are many issues related to air pollution worldwide and many countries are tightening their emission regulations on fine dust-causing substances to address these problems. Thus, the removal of these substances is becoming increasingly important. Low-pressure microbubble (LPMB) scrubbers are hybrid scrubbers that combine the advantages of general scrubbers with those of microbubbles. LPMB scrubbers can be used to simultaneously remove particulate matter (PM), SOX, and NOX using microbubbles. Microbubbles are small bubbles with a diameter of 10-50 µm and play a key role in simultaneous removal of PM, SOX, and NOX. The performance of LPMB scrubbers depends on the amount of water inside them. Therefore, the initial water level is an extremely important operating condition in LPMB scrubbers. This study used computational fluid dynamics modelling to determine the optimal initial water level in LPMB scrubbers by conducting an experiment based on the initial water level. The results indicate that, with a pressure difference of 5,000 Pa, the LPMB scrubbers performed best (producing a flow rate of 16.58 m3/min) when the initial water level was the same height as the atomizer.