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Various engine platforms employ Selective Catalytic Reduction (SCR) technology to reduce the tail pipe emissions of oxides of nitrogen (NOx) from diesel engines as part of an overall strategy to comply with the emission regulations in place in various countries. High levels of NOx conversion (greater than 98%) in SCR aftertreatment may provide operating margin to increase overall fuel efficiency. However, to realize the potential fuel efficiency gains, the SCR technology employed should achieve high NOx conversion with limited reductant slip over transient application cycles in addition to steady state operation. A new approach to SCR controls was developed and implemented. This approach does not rely on any maps to determine the amount of urea solution to be dosed, thus significantly reducing calibration and development time and effort when implementing the SCR technology on multiple engine platforms and applications.

A predictive numerical model was developed to determine the impact of phosphorus exposure on the performance of flow through aftertreatment components such as Diesel Oxidation Catalysts (DOC) or Selective Catalytic Reduction (SCR) catalysts. The model is able to successfully determine the distribution of the phosphorus over the catalyst as a function of the aging history (temperature, flow rates, oil consumption rate, phosphorus content of the oil) as well as the component properties (diameter, length, cell density, wall thickness). The model then incorporates this information regarding the distribution of phosphorus over the catalyst surface to determine the impact of the phosphorus exposure on the overall catalytic activity. The model results were successfully validated using accelerated bench aging tests for the oxidation of hydrocarbons over DOC's and NH₃ oxidation and NOx reduction over SCR catalysts.

An automated process was developed for the calibration of numerical aftertreatment models. The chemical kinetic mechanism examined in this case was part of a simplified SCR model. The process adopted for calibrating the SCR model was based on a micro-population multi-objective genetic algorithm. The algorithm developed was used to calibrate the SCR model using data derived from another, more detailed model to ensure that the evaluation focused only on the effectiveness of the calibration process and was not affected by issues of experimental inaccuracies or details of the model chemistry involved.