DEVELOPMENT OF A BAGHOUSE FILTER CFD MODEL FOR EFFICIENT PARTICULATE REMOVAL IN AIR FILTRATION SYSTEMS

Abstract

Air pollution poses a serious challenge for our capital city, and one crucial line of defense is effective dust control. Employing computational fluid dynamics (CFD), we designed a dust collector aimed at efficiently tackling particles smaller than 10 micrometers, a critical factor in combating air pollution. OpenFOAM, an open-source CFD software, was instrumental in this design process. Notably, our dust collector is equipped with bag filters capable of filtering PM 2.5. The application of the k-ε turbulence model governed the flow through the baghouse in our CFD model, while the bag filter was treated as a porous medium following Darcy's law. To validate our approach, we conducted an airflow experiment through a bag filter installed in the baghouse, determining the coefficient of Darcy's equation and benchmarking against CFD results. Impressively, our baghouse model exhibited an average error of less than 6.46%. This CFD-guided modeling not only minimizes trial and error in design but also provides manufacturers with insights to optimize and innovate baghouses in the future.