Optimum NO\+v\x\-v\ abatement in diesel exhaust using inferential feedforward reductant control

To adequately control the reductant flow for the selective catalytic reduction of NO(subx) in diesel exhaust gas, a tool is required that is capable of accurately and quickly predicting the engine's fluctuating NO(subx) emissions based on its time-dependent operating variables, and that is also capable of predicting the optimum reductant/NO(subx) abatement. Measurements were carried out on a semi-stationary diesel engine. 4 algorithms for non-linear modelling were evaluated. The models gave accurate NO(subx) predictions and make very promising tools for on-line automotive NO(subx) emission control. The optimum reductant/NO(subx) ratio was best predicted by a neural network. (Original abstract - amended)