a b s t r a c tThis bench reactor study investigates the impact of gaseous sulfur on the NO x reduction activity of Cuchabazite SCR (Cu-CHA) catalysts at SO 2 concentrations representative of marine diesel engine exhaust. After 2 h of 500 ppm SO 2 exposure at 250 and 400 • C in the simulated diesel exhaust gases, the NO x reduction activity of the sulfated Cu-CHA SCR catalysts is severely degraded at evaluation temperatures below 250 • C; however, above 250 • C the impact of sulfur exposure is minimal. EPMA shows that sulfur is located throughout the washcoat and along the entire length of the sulfated samples. Interestingly, BET measurements reveal that the sulfated samples have a 20% decrease in surface area. Furthermore, the sulfated samples show a decrease in NO x /nitrate absorption during NO exposure in a DRIFTS reactor which suggests that Cu sites in the catalyst are blocked by the presence of sulfur. SO 2 exposure also results in an increase in NH 3 storage capacity, possibly due to the formation of ammonium sulfate species in the sulfated samples. In all cases, lean thermal treatments as low as 500 • C reverse the effects of sulfur exposure and restore the NO x reduction activity of the Cu-CHA catalyst to that of the fresh condition.
The dynamics of a thermal pulse combustor model are examined. It is found that, as a parameter related to the fuel flow rate is varied, the combustor will undergo a transition from periodic pulsing to chaotic pulsing to a chaotic transient leading to flameout. Results from the numerical model are compared to those obtained from a laboratory-scale thermal pulse combustor. Finally the technique of maintenance (or anticontrol) of chaos is successfully applied to the model, with the result that the operation of the combustor can be continued well into the flameout regime. (c) 1997 American Institute of Physics.
We describe methods for automating the control and tracking of states within or near a chaotic attractor. The methods are applied in a simulation using a recently developed model of thermal pulse combustion as the dynamical system. The controlled state is automatically tracked while a parameter is slowly changed well beyond the usual flame-out point where the chaotic attractor ceases to exist because of boundary crisis. A learning strategy based on simple neural networks is applied to map-based proportional feedback control algorithms both with and without a recursive term. Adaptive recursive proportional feedback is found to track farther beyond the crisis (flame-out) boundary than does the adaptive non-recursive map-based control. We also found that a continuous-time feedback proportional to the derivative of a system variable will stabilize and track an unstable fixed point near the chaotic attractor. The positive results suggest that a pulse combustor, and other nonlinear systems, may be suitably controlled to reduce undesirable cyclic variability and extend their useful operating range.
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