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Prediction of NOx emissions for a range of engine hardware configurations using artificial neural networks

Abstract:

The predictive ability of artificial neural networks where a large number of experimental data are available, has been studied extensively. Studies have shown that ANN models are capable of accurately predicting NOx emissions from engines under various operating conditions and different fuel types when trained well. One of the major advantages of an ANN model is its ability to relearn when new experimental data is available, thus continuously improving its accuracy. The present work explored ...

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Publication status:
Published
Peer review status:
Reviewed (other)

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Publisher copy:
10.1115/ICEF2020-2911

Authors


More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Sub department:
Engineering Science
Role:
Author
ORCID:
0000-0001-6360-9065
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
ORCID:
0000-0001-6656-2389
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
Publisher:
American Society of Mechanical Engineers
Journal:
Proceedings of the ICEF 2020 More from this journal
Article number:
V001T06A001
Publication date:
2020-12-14
Acceptance date:
2020-07-05
Event title:
ASME 2020 Internal Combustion Engine Fall Technical Conference
Event location:
Denver, CO, USA
Event website:
https://event.asme.org/ICEF
Event start date:
2020-11-01
Event end date:
2020-11-04
DOI:
ISBN:
978-0-7918-8403-4
Language:
English
Keywords:
Pubs id:
1117336
Local pid:
pubs:1117336
Deposit date:
2020-07-09

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