Modeling of Three-Way Catalyst Dynamics for a Compressed Natural Gas Engine during Lean–Rich Transitions

2019 
The main objective of the present research activity was to investigate the effect of very fast composition transitions of the engine exhaust typical in real-world driving operating conditions, as fuel cutoff phases or engine misfire, on the aftertreatment devices, which are generally very sensitive to these changes. This phenomenon is particularly evident when dealing with engines powered by natural gas, which requires the use of a three-way catalyst (TWC). Indeed, some deviations from the stoichiometric lambda value can interfere with the catalytic converter efficiency. In this work, a numerical “quasi-steady” model was developed to simulate the chemical and transport phenomena of a specific TWC for a compressed natural gas (CNG) heavy-duty engine. A dedicated experimental campaign was performed in order to evaluate the catalyst response to a defined λ variation pattern of the engine exhaust stream, thus providing the data necessary for the numerical model validation. Tests were carried out to reproduce oxygen storage phenomena that make catalyst behavior different from the classic steady-state operating conditions. A surface reaction kinetic mechanism concerning CH4, CO, H2, oxidation and NO reduction has been appropriately calibrated at different λ values with a step-by-step procedure, both in steady-state conditions of the engine work plan and during transient conditions, through cyclical and consecutive transitions of variable frequency between rich and lean phases. The activity also includes a proper calibration of the reactions involving cerium inside the catalyst in order to reproduce oxygen storage and release dynamics. Sensitivity analysis and continuous control of the reaction rate allowed evaluating the impact of each of them on the exhaust composition in several operating conditions. The proposed model predicts tailpipe conversion/formation of the main chemical species, starting from experimental engine-out data, and provides a useful tool to evaluate the catalyst’s performance.
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