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Evaluation of an artificial neural network for NO X emission prediction from a transient diesel engine as a base for NO X control
Author(s) -
Krijnsen H. C.,
Van Kooten W. E. J.,
Calis H. P. A.,
Verbeek R. P.,
Van Den Bleek C. M.
Publication year - 2000
Publication title -
the canadian journal of chemical engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.404
H-Index - 67
eISSN - 1939-019X
pISSN - 0008-4034
DOI - 10.1002/cjce.5450780218
Subject(s) - artificial neural network , transient (computer programming) , diesel engine , diesel fuel , computation , automotive engineering , reduction (mathematics) , engineering , materials science , computer science , artificial intelligence , algorithm , mathematics , geometry , 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 predict accurately and fast the engine's NO x emission. In this article the application of a neural network is proposed. Measurements were performed on a transient diesel engine. The average absolute deviation between the measured NO x emission and the emission predicted by the neural network is 6.7%. 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.