Predicting fibrous filter’s efficiency by two methods: Artificial Neural Network (ANN) and integration of Genetic Algorithm and Artificial Neural Network (GAINN)
Type de document
Études primaires
Année de publication
2018
Langue
Anglais
Titre de la revue
Aerosol Science and Engineering
Première page
197
Dernière page
205
Résumé
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. © 2018, Institute of Earth Environment, Chinese Academy Sciences.
Mots-clés
Filtre fibreux, Fibrous filter, Masque N95, N95 mask
Numéro de projet IRSST
n/a
Citation recommandée
Abdolghader, P., Haghighat, F. et Bahloul, A. (2018). Predicting fibrous filter’s efficiency by two methods: Artificial Neural Network (ANN) and integration of Genetic Algorithm and Artificial Neural Network (GAINN). Aerosol Science and Engineering, 2(4), 197-205. https://doi.org/10.1007/s41810-018-0036-2
