Prediction of NO_x Emissions from a Transiently Operating Diesel Engine Using and Artificial Neural Network

摘要

For an adequate control of the reductant flow i 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 i automotive NO_x control.