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This paper presents a method to achieve a low order system model of the urea-based SCR catalyst coated filter (SCR-in-DPF or SCRF or SDPF), while preserving a high degree of fidelity. Proper orthogonal decomposition (POD), also known as principal component analysis (PCA), or Karhunen-Loéve decomposition (KLD), is a statistical method which achieves model order reduction by extracting the dominant characteristic modes of the system and devises a low-dimensional approximation on that basis. The motivation for using the POD approach is that the low-order model directly derives from the high-fidelity model (or experimental data) thereby retains the physics of the system. POD, with Galerkin projection, is applied to the 1D + 1D SCR-in-DPF model using ammonia surface coverage and wall temperature as the dominant system states to achieve model order reduction.

The integration of selective catalytic reduction catalysts (SCR) into diesel particulate filters (DPF) as a way to treat nitrogen oxides (NOx) and particulate matter (PM) emission is an emerging technology in diesel exhaust aftertreatment. This is driven by ever-tightening limits on NOx and PM emission. In an integrated SCR-in-DPF (also known as SCRF®, SCR-on-DPF, SDPF, or SCR coated filter), the SCR catalyst is impregnated within the porous walls of the DPF. The compact, low weight/volume of the integrated unit provides improvement in the diesel engine cold start emission performance. Experimental investigations have shown comparable performance with standard SCR and DPF units for NOx conversion and PM control, respectively. The modelling of the integrated unit is complicated.