Predicting Removal Efficiency of Formaldehyde from Synthetic Contaminated Air in Biotrickling Filter Using Artificial Neural Network Modeling-Reference-Cited by-同舟云学术

Predicting Removal Efficiency of Formaldehyde from Synthetic Contaminated Air in Biotrickling Filter Using Artificial Neural Network Modeling

Published:2019-09 Issue:9 Volume:145 Page:04019056
ISSN:0733-9372
Container-title:Journal of Environmental Engineering
language:en
Short-container-title:J. Environ. Eng.

Author:

Delnavaz Mohammad¹^ORCID,Farahbakhsh Javad²,Talaiekhozani Amirreza³,Mehdinezhad Nouri Komeil²

Affiliation:

1. Associate Professor, Faculty of Engineering, Dept. of Civil Engineering, Kharazmi Univ., No. 43, South Mofatteh Ave., Tehran 15719-14911, I.R. Iran (corresponding author). ORCID: .

2. M.Sc. Student, Environmental Engineering, Faculty of Engineering, Dept. of Civil Engineering, Kharazmi Univ., No. 43, South Mofatteh Ave., Tehran 15719-14911, I.R. Iran.

3. Assistant Professor, Dept. of Chemical Engineering, Jami Institute of Technology, Foulad Shahr, Ayatollah Taleghani Blvd., Isfahan 84919-63395, I.R. Iran.

Publisher

American Society of Civil Engineers (ASCE)

Subject

General Environmental Science,Civil and Structural Engineering,Environmental Chemistry,Environmental Engineering

Link

http://ascelibrary.org/doi/pdf/10.1061/%28ASCE%29EE.1943-7870.0001566

Reference59 articles.

1. Biofiltration of trimethylamine in biotrickling filter inoculated with Aminobacter aminovorans

2. Green engineering: Environmentally conscious design of chemical processes and products

3. Modeling of Fixed-Bed Column System of Hg(II) Ions on Ostrich Bone Ash/nZVI Composite by Artiﬁcial Neural Network

4. Indoor formaldehyde removal over CMK-3

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