Aerosol Science and Engineering

, Volume 2, Issue 4, pp 197–205 | Cite as

Predicting Fibrous Filter’s Efficiency by Two Methods: Artificial Neural Network (ANN) and Integration of Genetic Algorithm and Artificial Neural Network (GAINN)

  • Pooya Abdolghader
  • Fariborz Haghighat
  • Ali BahloulEmail author
Original Paper


In this study, we used both methods of ANN and GAINN for predicting the fibrous filter’s efficiency. In this regard, we collected the experimental penetration data for particles in the range of 10.7–191.1 nm. Experimental data were collected with different constant flow rates and from one type of N95 filtering facepiece respirator. A satisfactory number of data from experimental setup were exploited to build up a database. These methods are according to the back-propagation algorithm to map two components, namely, particle diameter and constant air flow rates into the corresponding penetration. The developed ANN and GAINN methods were capable of predicting precise values of penetration from experimental data. Also by comparing the results of these two methods, it is understandable that ANN method can predict the penetration data from examples of the experimental setup more efficiently than GAINN within an acceptable computational time.


Nanoparticles Filtration Artificial neural networks Genetic algorithm HVAC filters 



The authors would like to express their gratitude to the Concordia University for funding this work, and Ms. Farinaz Haghighat for her valuable comments and suggestions.


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Copyright information

© Institute of Earth Environment, Chinese Academy Sciences 2018

Authors and Affiliations

  1. 1.Department of Building, Civil and Environmental EngineeringConcordia UniversityMontrealCanada
  2. 2.Institut de recherche Robert-Sauvé en santé et en sécurité du travail (IRSST)MontrealCanada

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