Your browser doesn't support javascript.
loading
Theoretical Framework of a Polydisperse Cell Filtration Model.
Wang, Yujun; Gong, Jian; Su, Changsheng; Ou, Qisheng; Lyu, Qiang; Pui, David; Cunningham, Michael J.
Afiliação
  • Wang Y; Cummins Incorporated, 1900 McKinley Avenue, MC 50010, Columbus, Indiana 47201, United States.
  • Gong J; Cummins Incorporated, 1900 McKinley Avenue, MC 50010, Columbus, Indiana 47201, United States.
  • Su C; Cummins Incorporated, 1900 McKinley Avenue, MC 50010, Columbus, Indiana 47201, United States.
  • Ou Q; Particle Technology Laboratory, Mechanical Engineering, University of Minnesota, 111 Church Street Southeast, Minneapolis, Minnesota 55455, United States.
  • Lyu Q; Particle Technology Laboratory, Mechanical Engineering, University of Minnesota, 111 Church Street Southeast, Minneapolis, Minnesota 55455, United States.
  • Pui D; State Key Laboratory of Multiphase Flow in Power Engineering, School of Energy and Power Engineering, Xi'an Jiaotong University, Xi'an 710049, China.
  • Cunningham MJ; Particle Technology Laboratory, Mechanical Engineering, University of Minnesota, 111 Church Street Southeast, Minneapolis, Minnesota 55455, United States.
Environ Sci Technol ; 54(18): 11230-11236, 2020 09 15.
Article em En | MEDLINE | ID: mdl-32786575
ABSTRACT
Filtration via a porous medium is a ubiquitous process where high-fidelity physical models are needed. The classical cell model oversimplifies the filtration medium and results in biased and inaccurate predictions of the filter performance. This paper presents the discrete framework of a polydisperse cell model that can incorporate any measured pore size distribution. A new equation connecting the polydisperse cell efficiencies and the medium efficiency is derived from first principles. For ceramic filters, the discrete model demonstrates a generic prediction capability of the filtration efficiency with a root-mean-squared difference of 5.4%, while the counterpart of the classical cell model is 26.4%. In addition, the discrete model eliminates the biased predictions of the classical cell model on sub-100 nm particles.
Assuntos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Filtração / Modelos Teóricos Tipo de estudo: Prognostic_studies Idioma: En Revista: Environ Sci Technol Ano de publicação: 2020 Tipo de documento: Article País de afiliação: Estados Unidos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Filtração / Modelos Teóricos Tipo de estudo: Prognostic_studies Idioma: En Revista: Environ Sci Technol Ano de publicação: 2020 Tipo de documento: Article País de afiliação: Estados Unidos