Modeling and optimization of V

Artificial neural network NOx Response surface methodology SCR V(2)O(5)/TiO(2)

Journal

Journal of environmental management
ISSN: 1095-8630
Titre abrégé: J Environ Manage
Pays: England
ID NLM: 0401664

Informations de publication

Date de publication:
15 May 2019
Historique:
received: 07 04 2018
revised: 25 02 2019
accepted: 04 03 2019
pubmed: 12 3 2019
medline: 26 9 2019
entrez: 12 3 2019
Statut: ppublish

Résumé

In the present study, two statistical methods including the response surface method (RSM) and artificial neural network (ANN), were employed for modeling and optimization of selective catalytic reduction of NOx with NH

Identifiants

pubmed: 30856596
pii: S0301-4797(19)30308-1
doi: 10.1016/j.jenvman.2019.03.018
pii:
doi:

Substances chimiques

Ammonia 7664-41-7
Titanium D1JT611TNE

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

360-367

Informations de copyright

Copyright © 2019 Elsevier Ltd. All rights reserved.

Auteurs

Hamid Soleimanzadeh (H)

Department of Applied Chemistry, Faculty of Chemistry, University of Tabriz, Iran.

Aligholi Niaei (A)

Reactor & Catalyst Research Lab., Department of Chemical Engineering & Petroleum, University of Tabriz, Iran. Electronic address: aniaei@tabrizu.ac.ir.

Dariush Salari (D)

Department of Applied Chemistry, Faculty of Chemistry, University of Tabriz, Iran.

Ali Tarjomannejad (A)

Reactor & Catalyst Research Lab., Department of Chemical Engineering & Petroleum, University of Tabriz, Iran.

Simon Penner (S)

Institute of Physical Chemistry, University of Innsbruck, Innrain 52c, a-6020, Innsbruck, Austria.

Matthias Grünbacher (M)

Institute of Physical Chemistry, University of Innsbruck, Innrain 52c, a-6020, Innsbruck, Austria.

Seyed Ali Hosseini (SA)

Department of Chemistry, Faculty of Science, Urmia University, Urmia, Iran.

Seyed Mahdi Mousavi (SM)

Faculty of Chemistry, University of Kashan, Kashan, Iran.

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Classifications MeSH