DEVELOPMENT OF A BAGHOUSE FILTER CFD MODEL FOR EFFICIENT PARTICULATE REMOVAL IN AIR FILTRATION SYSTEMS
Issued Date
2024-01-01
Resource Type
ISSN
21862982
Scopus ID
2-s2.0-85185334893
Journal Title
International Journal of GEOMATE
Volume
26
Issue
113
Start Page
82
End Page
89
Rights Holder(s)
SCOPUS
Bibliographic Citation
International Journal of GEOMATE Vol.26 No.113 (2024) , 82-89
Suggested Citation
Punyaponchai A., Priyadumkol J., Loksupapaiboon K., Thongkom S., Suvanjumrat C. DEVELOPMENT OF A BAGHOUSE FILTER CFD MODEL FOR EFFICIENT PARTICULATE REMOVAL IN AIR FILTRATION SYSTEMS. International Journal of GEOMATE Vol.26 No.113 (2024) , 82-89. 89. doi:10.21660/2024.113.g13288 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/97323
Title
DEVELOPMENT OF A BAGHOUSE FILTER CFD MODEL FOR EFFICIENT PARTICULATE REMOVAL IN AIR FILTRATION SYSTEMS
Author's Affiliation
Corresponding Author(s)
Other Contributor(s)
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.