Performance prediction of PM 2.5 removal of real fibrous filters with a novel model considering rebound effect

Cai, Rong-Rong; Zhang, Li-Zhi; Yan, Yuying

Abstract

Fibrous filters have been proved to be one of the most cost-effective way of particulate matters (specifically PM 2.5) purification. However, due to the complex structure of real fibrous filters, it is difficult to accurately predict the performance of PM2.5 removal. In this study, a new 3D filtration modeling approach is proposed to predict the removal efficiencies of particles by real fibrous filters, by taking the particle rebound effect into consideration. A real filter is considered and its SEM image-based 3D structure is established for modeling. Then based on the simulation result, the filtration efficiency and pressure drop are calculated. The obtained values are compared and validated by experimental data and empirical correlations, and the results are proven to be in good agreement with each other. At last, influences of various parameters including the face velocity, particle size and the particle rebound effect on the filtration performance of fibrous filters are investigated. The results provide useful guidelines for the optimization and enhancement of PM2.5 removal by fibrous filter.

Citation

Cai, R.-R., Zhang, L.-Z., & Yan, Y. (2017). Performance prediction of PM 2.5 removal of real fibrous filters with a novel model considering rebound effect. Applied Thermal Engineering, 111, https://doi.org/10.1016/j.applthermaleng.2016.07.162

• Journal Article Type: Article
• Acceptance Date: Jul 23, 2016
• Online Publication Date: Jul 25, 2016
• Publication Date: Jan 25, 2017
• Deposit Date: Mar 3, 2017
• Publicly Available Date: Mar 3, 2017
• Journal: Applied Thermal Engineering
• Print ISSN: 1359-4311
• Electronic ISSN: 1873-5606
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 111
• DOI: https://doi.org/10.1016/j.applthermaleng.2016.07.162
• Keywords: PM2.5; Filtration performance; Fibrous filter; Particle rebound; Micro-macro modeling; Material property
• Public URL: https://nottingham-repository.worktribe.com/output/838968
• Publisher URL: http://www.sciencedirect.com/science/article/pii/S1359431116313035
• Contract Date: Mar 3, 2017