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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.

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.