Abstract
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 NOx emissions from the engine's operating variables. Two algorithms for non-linear modelling are evaluated: neural networks (Solla et al., Adv. in Neural Information Processing Systems 12 (MIT Press, Five Cambridge Center, Cambridge, MA, 2000)) and the split & fit algorithm (Bakker et al., submitted for publication to NIPS). Measurements were carried out on a transient automotive diesel engine and a semi-stationary diesel engine. Both algorithms gave excellent predictions with a short computation time (0.03–0.13 ms). This makes them very promising tools in automotive catalytic NOx emission control.
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