Browse Publications Technical Papers 2017-01-2386
2017-10-08

Kinetic Modeling Study of NOx Conversion Based on Physicochemical Characteristics of Hydrothermally Aged SCR/DPF Catalyst 2017-01-2386

Diesel engines have better fuel economy over comparable gasoline engines and are useful for the reduction of CO2 emissions. However, to meet stringent emission standards, the technology for reducing NOx and particulate matter (PM) in diesel engine exhaust needs to be improved. A conventional selective catalytic reduction (SCR) system consists of a diesel oxidation catalyst (DOC), diesel particulate filter (DPF), and urea-SCR catalyst. Recently, more stringent regulations have led to the development of SCR systems with a larger volume and increased the cost of such systems. In order to solve these problems, an SCR catalyst-coated DPF (SCR/DPF) is proposed. An SCR/DPF system has lower volume and cost compared to the conventional SCR system. The SCR/DPF catalyst has two functions: combustion of PM and reduction of NOx emissions. As PM is removed from the DPF at high temperatures (>650°C), the SCR/DPF system is exposed to higher temperatures as compared with those in the conventional SCR system. In this study, we investigated the NOx reduction performance and the properties of a hydrothermally aged SCR/DPF catalyst. Using these data, a model that can predict the NOx conversion of the hydrothermally aged SCR/DPF catalyst was constructed. A commercial copper-exchanged zeolite catalyst, Cu-ZSM-5, was used and aged in synthetic air with 10% water over the temperature range 650-750 °C. The effects of hydrothermal aging on the catalysts were investigated using a synthetic gas bench, and a detailed analysis of the structure of the hydrothermally aged catalyst was performed. Using the experimental data, we succeeded in constructing a hydrothermally aged SCR/DPF model for predicting the NOx conversion based on changes in the physicochemical characteristics of the catalysts with changes in the hydrothermal aging conditions. This work is the first step toward bridging the gap between a lab-simulated performance model and the global reactivity observed under real-world conditions.

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