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

ISSN Print: 2160-0392
ISSN Online: 2160-0406
www.scirp.org/journal/aces
E-mail: aces@scirp.org

"Predicting of the Fibrous Filters Efficiency for the Removal Particles from Gas Stream by Artificial Neural Network"
written by Érica Regina Filletti, Juliana Maria da Silva, Valdemir Garcia Ferreira,
published by Advances in Chemical Engineering and Science, Vol.5 No.3, 2015

has been cited by the following article(s):

[1]	An Improvement of a Mapping Method Based on Ant Colony Algorithm Applied to Smart Cities
	Applied Sciences, 2022

[2]	Transitando entre matéria condensada e informação quântica: fundamentos, metodologias e aplicações
	2022

[3]	Determination of stable structure of a cluster using convolutional neural network and particle swarm optimization
	2021

[4]	Seismicity analysis and machine learning models for short-term low magnitude seismic activity predictions in Cyprus
	2020

[5]	Simulation and estimation of the amount of dust in industry with perceptron multi-layer neural network model (Case study: Sabzevar cement factory)
	2020

[6]	Redes neurais artificiais e teoria do funcional da densidade: otimização de funcionais para modelagem de nanomateriais
	2019

[7]	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, 2018

[8]	Artificial neural networks for density-functional optimizations in fermionic systems
	2018

[1]	Determination of stable structure of a cluster using convolutional neural network and particle swarm optimization Theoretical Chemistry Accounts, 2021 DOI:10.1007/s00214-021-02726-z

[2]	Seismicity analysis and machine learning models for short-term low magnitude seismic activity predictions in Cyprus Soil Dynamics and Earthquake Engineering, 2020 DOI:10.1016/j.soildyn.2019.105932

[3]	Artificial neural networks for density-functional optimizations in fermionic systems Scientific Reports, 2019 DOI:10.1038/s41598-018-37999-1

[4]	Artificial neural networks for density-functional optimizations in fermionic systems Scientific Reports, 2019 DOI:10.1038/s41598-018-37999-1

[5]	Artificial neural networks for density-functional optimizations in fermionic systems Scientific Reports, 2019 DOI:10.1038/s41598-018-37999-1

[6]	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, 2018 DOI:10.1007/s41810-018-0036-2