Modelling and optimization of Mn/activate carbon nanocatalysts for NO reduction: Comparison of RSM and ANN techniques

Environmental Technology (United Kingdom)

Volumn 34, Issue 11, 2013, Pages 1377-1384

  Mousavi, Seyed Mahdi ^a   Niaei, Aligholi ^a   Salari, Dariush ^a   Panahi, Parvaneh Nakhostin ^a   Samandari, Masoud ^a  

^a UNIVERSITY OF TABRIZ   (Iran)

Author keywords

activated carbon; artificial neural network; response surface methodology; SCR

Indexed keywords

ACTIVATED CARBON; CALCINATION; CATALYSTS; NEURAL NETWORKS; OPTIMIZATION; SURFACE PROPERTIES; THYRISTORS; TOPOLOGY;

CALCINATION TEMPERATURE; CATALYST EFFICIENCY; CATALYST PREPARATION; CENTRAL COMPOSITE DESIGNS; FEED-FORWARD BACK PROPAGATION NETWORKS; LEVENBERG-MARQUARDT TRAINING ALGORITHM; NONLINEAR BEHAVIOURS; RESPONSE SURFACE METHODOLOGY;

LOADING;

ACTIVATED CARBON; MANGANESE; METAL; NITROGEN OXIDE;

ACTIVATED CARBON; ALGORITHM; ARTIFICIAL NEURAL NETWORK; CATALYST; COMPARATIVE STUDY; MANGANESE; NANOTECHNOLOGY; NITROGEN OXIDES; OPTIMIZATION; REDUCTION;

ALGORITHM; ANALYSIS; ARTICLE; ARTIFICIAL NEURAL NETWORK; BIOTRANSFORMATION; NANOCATALYST; PARETO GRAPHIC ANALYSIS; PREDICTIVE VALUE; REDUCTION; RESPONSE SURFACE METHOD; TEMPERATURE;

EID: 84879693669     PISSN: 09593330     EISSN: 1479487X     Source Type: Journal    
DOI: 10.1080/09593330.2012.750381     Document Type: Article

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