A NOX Trap Study Using Fast Response Emission Analysers for Model Validation

Abstract

Lean burn after treatment systems using NO X traps for reducing emissions from diesel exhausts require periodic regeneration after each storage stage. Optimising these events is a challenging problem and a model capable of simulating these processes would be highly desirable. This study describes an experimental investigation, which has been designed for the purpose of validating a NO X trapping and regenerating model. A commercial computational fluid dynamics (CFD) package is used, to model NO X trapping and regeneration, using the porous medium approach. This approach has proved successful for three way catalysis modelling. To validate the model a one-dimensional NOx trap system has been tested on a turbocharged, EGR cooled, direct injection diesel engine controlled with an engine management system via DSPACE. Fast response emission analysers have been used to provide high resolution data across the after-treatment system for model validation. Measurements show CO is the primary reductant. After the trap NO and NO 2 spikes (NO X slippage) were observed both at the beginning and end of the regeneration period. The former is believed to be due to insufficient reductant. Whilst the model can qualitatively describe the main storage and regeneration phases it failed to predict NO X slippage.

Three-dimensional modelling of NOx and particulate traps using CFD: A porous medium approach

“…3(b) matches experimental data correctly. Improved tuning will be attempted when data are available from an ongoing experimental programme [18]. Fig.…”

Lean NOx trap study on a light-duty diesel engine using fast-response emission analysers

“…Several research groups have developed mathematical models to describe LNT performance [3,16,19,[21][22][23][24][25]. This research has demonstrated that there are multifarious factors that can influence NO x conversion efficiency, including exhaust gas composition and temperature, air-fuel ratio, the duration of the lean and rich cycles, and the composition of the BMO and noble metal type.…”

“…LNT behaviour has been studied in laboratory reactors using synthetic gases [2][3][4][5][6][7][8][9][10][11][12][13][14], on engine test beds [9,[15][16][17][18][19][20][21][22], and on vehicle drive cycle facilities [8,21,22]. Several research groups have developed mathematical models to describe LNT performance [3,16,19,[21][22][23][24][25]. This research has demonstrated that there are multifarious factors that can influence NO x conversion efficiency, including exhaust gas composition and temperature, air-fuel ratio, the duration of the lean and rich cycles, and the composition of the BMO and noble metal type.…”

Numerical simulation study on correlation between ion current signal and NO X emissions in controlled auto-ignition engine

“…Therefore, the NO X sensor for SCR should not only meet the requirements of high accuracy and fast response, but also have low costs. Most of the current on-vehicle NO X sensors are working for steady-state conditions with high costs, and the fast response NO X detection system which has complicated structure with high costs is normally employed only in the lab [10,11]. Although many researches are focused on the NO emissions prediction for model-based control, the prediction model requires necessary assumptions as well and the accuracy of the model mainly depends on both the algorithms and support parameters [12,13].…”