The catalytic generation of ammonia from a liquid urea solution is a critical process determining the performance of selective catalytic reduction (SCR) systems. Solid deposits on the catalyst surface from the decomposition of urea have to be avoided, as this leads to reduced system performance or even failure. At present, reactor design is often empirical, which poses a risk for costly iterations due to insufficient system performance. The presented research project proposed a performance prediction and modeling approach for SCR hydrolysis reactors generating ammonia from urea. Different configurations of hydrolysis reactors were investigated experimentally. Ammonia concentration measurements provided information about parameters influencing the decomposition of urea and the system performance. The evaporation of urea between injection and interaction with the catalyst was identified as the critical process driving the susceptibility to deposit formation. The spray of urea solution was characterized in terms of velocity distribution by means of particle-image velocimetry. Results were compared with theoretical predictions and calculation options for processes in the reactor were determined. Numerical simulation was used as an additional design and optimization tool of the proposed model. The modeling approach is presented by a step-by-step method, which takes into account design constraints and operating conditions for hydrolysis reactors.
Modeling Approach for a Hydrolysis Reactor for the Ammonia Production in Maritime Selective Catalytic Reduction Applications
Contributed by the IC Engine Division of ASME for publication in the JOURNAL OF ENGINEERING FOR GAS TURBINES AND POWER. Manuscript received February 20, 2018; final manuscript received February 27, 2018; published online May 24, 2018. Editor: David Wisler.
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Johe, K., and Sattelmayer, T. (May 24, 2018). "Modeling Approach for a Hydrolysis Reactor for the Ammonia Production in Maritime Selective Catalytic Reduction Applications." ASME. J. Eng. Gas Turbines Power. September 2018; 140(9): 092802. https://doi.org/10.1115/1.4039762
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