This paper describes the details of a predictive model specifically designed for catalytic converters used for stationary industrial engines. This model uses inputs such as engine emission characteristics, fuel, exhaust system design and engine duty cycle.
The architecture of the model provides two main modes of computation: transient conditions and steady state conditions. The shared items and simplifying assumptions for deriving a steady state solution from a generalized transient model is discussed.
The applications and limitations of both modes of calculation are discussed. Correlations for reaction kinetics are based on pseudo-first order reaction rates with pore diffusion resistance and an Arrhenius expression for the reaction rate coefficient. Correlations for mass transfer are based on the boundary layer model. Details on the development of these correlations are provided. Information is also provided on the correlating of the physical properties of substrates to pressure drop and conversion efficiency.
The model is flexible for many catalytic converter types, such as three-way, oxidation and selective catalytic reduction. The model is also flexible for different cell densities, cell shapes and substrate types such as metal and ceramic.
The influence of fuel composition on engine out hydrocarbon emissions is briefly discussed with respect to natural gas fuelled engines. It is shown that it is necessary to take into account the fuel composition of natural gas when predicting conversion efficiency of hydrocarbons on catalytic converters used with lean-burn natural gas fuelled engines.
A method for quantifying deterioration rates for catalysts for long-term operation is also described. This method assumes that catalyst deterioration can be grouped into two mechanisms: the first involving deterioration of the catalyst activity in terms of changes to the reaction rate coefficient, and the second involving deterioration or masking of the substrate by applying a deterioration term to the mass transfer coefficient.
The validity of the model is shown by example case studies, where results show excellent correlation between the model and test cell and field data for a wide range of engines, catalyst types and operating conditions.