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Neural Network-Based Diesel Engine Emissions Prediction Using In-Cylinder Combustion Pressure
| Content Provider | Semantic Scholar |
|---|---|
| Author | Traver, Michael L. Atkinson, Richard C. Atkinson, Christopher M. |
| Copyright Year | 1999 |
| Abstract | This paper explores the feasibility of using in-cylinder pressure-based variables to predict gaseous exhaust emissions levels from a Navistar T444 direct injection diesel engine through the use of neural networks. The networks were trained using in-cylinder pressure derived variables generated at steady state conditions over a wide speed and load test matrix. The networks were then validated on previously "unseen" real-time data obtained from the Federal Test Procedure cycle through the use of a high speed digital signal processor data acquisition system. Once fully trained, the DSP-based system developed in this work allows the real-time prediction of NOX and CO2 emissions from this engine on a cycle-by-cycle basis without requiring emissions measurement. |
| Starting Page | 1166 |
| Ending Page | 1180 |
| Page Count | 15 |
| File Format | PDF HTM / HTML |
| DOI | 10.4271/1999-01-1532 |
| Volume Number | 108 |
| Alternate Webpage(s) | http://ww.atkinsonllc.com/Assets/downloads/PAPERS/1999-01-1532.pdf |
| Alternate Webpage(s) | https://doi.org/10.4271/1999-01-1532 |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
