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

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Abstract

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.

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Acknowledgements

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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Authors and Affiliations

  1. Department of Building, Civil and Environmental Engineering, Concordia University, Montreal, QC, Canada

    Pooya Abdolghader & Fariborz Haghighat

  2. Institut de recherche Robert-Sauvé en santé et en sécurité du travail (IRSST), Montreal, QC, Canada

    Ali Bahloul

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  1. Pooya Abdolghader
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  2. Fariborz Haghighat
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  3. Ali Bahloul
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Correspondence to Ali Bahloul.

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Abdolghader, P., Haghighat, F. & Bahloul, A. Predicting Fibrous Filter’s Efficiency by Two Methods: Artificial Neural Network (ANN) and Integration of Genetic Algorithm and Artificial Neural Network (GAINN). Aerosol Sci Eng 2, 197–205 (2018). https://doi.org/10.1007/s41810-018-0036-2

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  • DOI: https://doi.org/10.1007/s41810-018-0036-2

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