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Prediction of NO x Emissions from a Transiently Operating Diesel Engine Using an Artificial Neural Network
Author(s) -
Krijnsen Henrike C.,
van Kooten Wijnand E. J.,
Calis Hans Peter A.,
Verbeek Ruud P.,
Bleek Cor M. van den
Publication year - 1999
Publication title -
chemical engineering and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.403
H-Index - 81
eISSN - 1521-4125
pISSN - 0930-7516
DOI - 10.1002/(sici)1521-4125(199907)22:7<601::aid-ceat601>3.0.co;2-t
Subject(s) - nox , diesel fuel , artificial neural network , automotive engineering , diesel engine , transient (computer programming) , engineering , environmental science , process engineering , computer science , chemistry , artificial intelligence , combustion , organic chemistry , operating system
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.