Conference item
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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Bibliographic Details
- Publisher:
- American Society of Mechanical Engineers Publisher's website
- Journal:
- Proceedings of the ICEF 2020 Journal website
- 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
Item Description
- Language:
- English
- Keywords:
- Pubs id:
-
1117336
- Local pid:
- pubs:1117336
- Deposit date:
- 2020-07-09
Terms of use
- Copyright holder:
- ASME
- Copyright date:
- 2020
- Rights statement:
- © 2020 by ASME
- Notes:
- This paper will be presented at the 2020 Internal Combustion Engine Division Fall Technical Conference, Denver, Colorado, USA, 1st - 4th November 2020. This is the accepted manuscript version of the article. The final version is available from American Society of Mechanical Engineers at: https://doi.org/10.1115/ICEF2020-2911
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