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Optimum deNOxperformance using inferential feedforward reductant flow control

Authors
Identifiers
DOI: 10.1016/s1570-7946(00)80149-2
Disciplines
  • Computer Science

Abstract

To adequately control the reductant flow for the catalytic removal of NOx from diesel exhaust gases a tool is required that is capable of accurately and quickly predicting the engine's NOx emissions based on its operating variables, and that is also capable of predicting the optimum ammonia/NOx ratio for NOx removal. Two algorithms for non-linear modelling are evaluated: (1) neural networks and (2) the split & fit algorithm of-Bakker et al. [1,2]. Measurements were carried out on a semi-stationary diesel engine. Results of the split & fit algorithm and the neural network were compared to (3) the traditionally used engine map and (4) a linear fit. Both the neural network and the split & fit algorithm gave excellent NOx predictions with a short computation time (0.3 ms), making them very promising tools in real-time automotive NOx emission control. With regard to the estimation of the optimum NH3/NOx ratio, the neural network predicts the effect of NH3/NOx ratio on the final NO2 emission very well.

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