Predicting of the Fibrous Filters Efficiency for the Removal Particles from Gas Stream by Artificial Neural Network

Érica Regina Filletti¹, Juliana Maria da Silva², Valdemir Garcia Ferreira^3*

¹Department of Physical Chemistry, Institute of Chemistry, São Paulo State University-UNESP, Araraquara, Brazil.
²Institute of Science and Technology, Federal University of Alfenas, Poços de Caldas, Brazil.
³Department of Applied Mathematics and Statistics, Institute of Mathematics and Computer Sciences, University of São Paulo, São Carlos, Brazil.

DOI: 10.4236/aces.2015.53033                

Abstract

In this paper, artificial neural networks are used for predicting single fiber efficiency in the process of removing smaller particles from gas stream by fiber filters. For this, numerical simulations are obtained of a classic model of literature for fiber efficiency, which is numerically solved along with the convection diffusion equation in polar coordinates for particle concentration, with associated initial and boundary conditions. A sufficient number of examples from two numerical simulations are employed to construct a database, from which parameters of a novel neural model are adjusted. This model is constructed based on the back propagation algorithm in order to map two features, namely Peclet number and packing density, which are extracted from the numerical simulations into the corresponding single fiber efficiency. The results indicate that the developed neural model can be trained in a reasonable computational time and is capable of estimating single fiber efficiency from examples of the test set with a maximum error of 1.7%.

Keywords

Artificial Neural Network, Back Propagation Algorithm, Fiber Filters, Particle Capture

Conflicts of Interest

The authors declare no conflicts of interest.

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