Prediction of NOx Emissions from a Transiently Operating Diesel Engine Using an Artificial Neural Network
✍ Scribed by Henrike C. Krijnsen; Wijnand E. J. van Kooten; Hans Peter A. Calis; Ruud P. Verbeek; Cor M. van den Bleek
- Publisher
- John Wiley and Sons
- Year
- 1999
- Tongue
- English
- Weight
- 330 KB
- Volume
- 22
- Category
- Article
- ISSN
- 0930-7516
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✦ Synopsis
For an adequate control of the reductant flow in selective catalytic reduction of NO x in diesel exhaust, a tool has to be available to accurately and quickly predict the engine's NO x emission. For these purposes, elaborate computer models and expensive NO x analyzers are not feasible. The application of a neural network is proposed instead. Measurements were performed on a transient operating diesel engine. One part of the data was used to train the network for NO x emission prediction, the other part was used to test. The average absolute deviation between the predicted and measured NO x emission is 6.7 %. The reductant buffering capacity of the deNOx catalyst will diminish the effect of the deviation on the overall NO x removal efficiency. The high accuracy of the neural network predictions, combined with the short computation times (0.2 ms/data point), makes the neural network a very promising tool in automotive NO x control.