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Catalytic Converter Modeling: Artificial Neural Networks for Perovskite Based Catalyst

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Title Catalytic Converter Modeling: Artificial Neural Networks for
Perovskite Based Catalyst
 
Creator Biniwale, Rajesh
Labhsetwar, Nitin
Kumar, R
Hasan, M Z
 
Subject Materials Science
 
Description Two-stroke vehicles including two and three wheelers
constitute about 62% of vehicles in India, and about 45-50%
in other developing countries in the region. These are the
major contributors to vehicular pollution. Catalytic
converters based on perovskite have been developed for 2-
stroke vehicles. Detailed characterization was carried out
during development of alumina washcoat and synthesis of
perovskite catalyst to establish the thermal stability of
alumina washcoat and phase formation of catalysts. A
number of prototypes based on alumina-supported perovskite
have been prepared and tested for mass conversion efficiency
with respect to CO, HC and NOx using the Indian Driving
Cycle (IDC). A catalytic converter model has been
developed using the MATLAB artificial neural network
toolbox for performance prediction. Experimental data
generated during the detailed characterization of catalytic
converters and its evaluation on engine dynamometer has
been used as training data. The model was used for
prediction of conversion efficiencies and mid-bed
temperature. Keep-one-out method was used for comparison
of predicted and experimental values. The algorithm
developed predicts the performance very well and will be
able to give prior information on the performance in view of
future emission standards.
 
Publisher SAE International
 
Date 2002
 
Type Article
PeerReviewed
 
Format application/pdf
 
Identifier http://neeri.csircentral.net/1105/1/SAE%202002-01-0061.pdf
Biniwale, Rajesh and Labhsetwar, Nitin and Kumar, R and Hasan, M Z (2002) Catalytic Converter Modeling: Artificial Neural Networks for Perovskite Based Catalyst. Society of Automotive Engineers. ISSN 0148-7191
 
Relation http://neeri.csircentral.net/1105/