A Hybrid Framework for Modeling Aftertreatment Systems: A Diesel Oxidation Catalyst Application

This paper presents a hybrid approach for developing a robust model of a diesel oxidation catalyst (DOC). Information from multiple sources including detailed thermal balances, laboratory performance data, phenomenological description of adsorption and desorption in catalyst pores, and experience based correlations are seamlessly integrated using optimization and statistical tools to create an easy-to-use, computationally inexpensive predictive model. Light-off, Light-out, and fuel quench data from a diesel pulsator and engine dynamometer are used for model calibration. The calibrated model predicts cumulative HC and CO tailpipe vehicle emissions as well as DOC NOx outlet composition (NO vs. NO₂).