Optimum NOx abatement in diesel exhaust using inferential feedforward reductant control

To adequately control the reductant flow for the selective catalytic reduction of NOx in diesel exhaust gas a tool is required that is capable of accurately and quickly predicting the engineʹs fluctuating NOx emissions based on its time-dependent operating variables, and that is also capable of predicting the optimum reductant/NOx ratio for NOx abatement. Measurements were carried out on a semi-stationary diesel engine. Four algorithms for non-linear modelling are evaluated. The models resulting from the algorithms gave very accurate NOx predictions with a short computation time. Together with the small errors this makes the models very promising tools for on-line automotive NOx emission control. The optimum reductant/NOx ratio (to get the lowest combined NOx+reductant emission of the exhaust treating system) was best predicted by a neural network.