4.7 Article

Numerical analysis of ammonia homogenization for selective catalytic reduction application

Journal

JOURNAL OF ENVIRONMENTAL MANAGEMENT
Volume 203, Issue -, Pages 1047-1061

Publisher

ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD
DOI: 10.1016/j.jenvman.2017.04.103

Keywords

Computational fluid dynamics; NOx reduction; Selective catalytic reduction; Spray preparation; Urea-water solution; Ammonia slip

Funding

  1. AVL List GmbH

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Selective catalytic reduction based on urea water solution as ammonia precursor is a promising method for the NOx abatement form exhaust gasses of mobile diesel engine units. It consists of injecting the urea water solution in the hot flue gas stream and reaction of its products with the NOx over the catalyst surface. During this process flue gas enthalpy is used for the urea-water droplet heating and for the evaporation of water content. After water evaporates, thermolysis of urea occurs, during which ammonia, a known NOx reductant, and isocyanic acid are generated. The uniformity of the ammonia before the catalyst as well as ammonia slip to the environment are important counteracting design requirements, optimization of which is crucial for development of efficient deNO(x) systems. The aim of this paper is to show capabilities of the developed mathematical framework implemented in the commercial CFD code AVL FIRE (R), to simulate physical processes of all relevant phenomena occurring during the SCR process including chemical reactions taking part in the catalyst. First, mathematical models for description of SCR process are presented and afterwards, models are used on the 3D geometry of a real SCR reactor in order to predict ammonia generation, NOx reduction and resulting ammonia slip. Influence of the injection direction and droplet sizes was also investigated on the same geometry. The performed study indicates importance of droplet sizes on the SCR process and shows that counterflow injection is beneficial, especially in terms of minimizing harmful ammonia slip to environment. (C) 2017 Elsevier Ltd. All rights reserved.

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