Evaluation of an artificial neural network for NO X  emission prediction from a transient diesel engine as a base for NO X  control | Zendy

Krijnsen H. C. | Zendy; Van Kooten W. E. J. | Zendy; Calis H. P. A. | Zendy; Verbeek R. P. | Zendy; Van Den Bleek C. M. | Zendy

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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.

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