H.C. Krijnsen

Deactivation of zeolite catalysts used for NOx removal

Abstract The deactivation of Ce-zeolite deNO x catalysts has been studied. Catalysts were aged in exhaust gases of a diesel engine and a natural gas engine. The deactivation in real exhaust gas has been compared to deactivation in simulated exhaust gas. Ammonia and urea were used as reducing agent. Deactivated catalysts were characterized with amongst others XPS, XRF and DRIFT. Zeolite deNO x catalysts show different types of deactivation apart from hydrothermal dealumination. Deactivation may occur at low and at higher temperatures. Several depositions were found on the catalysts: coke, C x H y , S, Zn, P, N and Ca. Coke, C x H y and N containing species can be removed by heating under oxygen rich (deNO x ) conditions resulting in (partial) recovery of the catalyst activity. The type of lubricating oil used in the internal combustion engine might be an important factor in the deactivation of the deNO x catalyst.

Optimum NOx abatement in diesel exhaust using inferential feedforward reductant control

Abstract To adequately control the reductant flow for the selective catalytic reduction of NO x in diesel exhaust gas a tool is required that is capable of accurately and quickly predicting the engines fluctuating NO x emissions based on its time-dependent operating variables, and that is also capable of predicting the optimum reductant/NO x ratio for NO x 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 NO x predictions with a short computation time. Together with the small errors this makes the models very promising tools for on-line automotive NO x emission control. The optimum reductant/NO x ratio (to get the lowest combined NO x +reductant emission of the exhaust treating system) was best predicted by a neural network.

Prediction of NOx Emissions from a Transiently Operating Diesel Engine Using an Artificial Neural Network

For an adequate control of the reductant flow in selective catalytic reduction of NO x in diesel exhaust, a tool has to be available to accurately and quickly predict the engines 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 in automotive NO x control.

Optimum deNOx performance using inferential feedforward reductant flow control

To adequately control the reductant flow for the catalytic removal of NO x from diesel exhaust gases a tool is required that is capable of accurately and quickly predicting the engines NO x emissions based on its operating variables, and that is also capable of predicting the optimum ammonia/NO x ratio for NO x 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 NO x predictions with a short computation time (0.3 ms), making them very promising tools in real-time automotive NO x emission control. With regard to the estimation of the optimum NH 3 /NO x ratio, the neural network predicts the effect of NH 3 /NO x ratio on the final NO 2 emission very well.

Inferential NOx emission prediction in feedforward reductant control for SCR

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 engines 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.