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Isocyanic acid hydrolysis and ammonia-SCR reaction over hydrothermally aged Cu-ZSM5
Technical Paper
2019-01-2234
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
For developing complicated after-treatment equipment for diesel-engine vehicles, such as urea-selective catalytic reduction (urea-SCR) systems, construction of a reaction model that can accurately predict ammonia (NH3) formation from urea is required. Hydrolysis of isocyanic acid (HNCO) is an important intermediate reaction in NH3 formation from urea. In our previous studies [1], a new rate constant for HNCO hydrolysis over fresh Cu-ZSM5 was derived using the measurements of the reaction rate of HNCO hydrolysis with high-purity HNCO formed from cyanuric acid. In this study, the reaction rates of the HNCO hydrolysis and NH3-SCR reactions were measured over a hydrothermally aged Cu-ZSM5 catalyst. A steady-state flow reactor equipped with a Fourier transform infrared spectrometer (FTIR) was employed to obtain the reaction rate of the HNCO hydrolysis and NH3-SCR reactions. Results showed that the reaction rate of HNCO hydrolysis over the hydrothermally aged catalyst was faster than that over the fresh catalyst, while the reaction rate of NOx reduction over the hydrothermally aged catalyst was slower than that over the fresh catalyst. Simulations performed using the obtained rate constant indicated that HNCO hydrolysis over the hydrothermally aged Cu-ZSM5 catalyst was not the rate-determining reaction in NOx reduction.
Authors
- Masahiro Matsuoka - Ibaraki University / Japan Automotive Research Institute
- Takaaki Kitamura - Japan Automotive Research Institute
- Akira Obuchi - National Institute of Advanced Industrial Science and Techno
- Jun Tsuchida - Ibaraki University
- Satoshi Sakaida - Ibaraki University
- Kotaro Tanaka - Ibaraki University
- Mitsuru Konno - Ibaraki University
Citation
Matsuoka, M., Kitamura, T., Obuchi, A., Tsuchida, J. et al., "Isocyanic acid hydrolysis and ammonia-SCR reaction over hydrothermally aged Cu-ZSM5," SAE Technical Paper 2019-01-2234, 2019, https://doi.org/10.4271/2019-01-2234.Data Sets - Support Documents
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