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Emissions from diesel engines, particularly NOx and TPM emissions are harmful to the environment. Reduction of NOx emissions from diesel engines is of increasing concern. In 1998, a novel approach called Selective NOx Recirculation (SNR) was used to reduce NOx emissions in diesel engines. The SNR concept relies on two major parts, one to collect the NOx emissions from the exhaust by an adsorber, and another to decompose NOx using the in-cylinder combustion process by injecting the collected NOx emissions into the intake manifold at an elevated concentration. This paper deals with the destruction rates during the combustion process. A 1992 DDC series 60, 350 hp, 12.7 liter engine was connected to a 500 hp DC dynamometer. A full-scale dilution tunnel and analyzers capable of measuring continuous NOx, CO2, CO, HC, and PM in the exhaust were used.

Exhaust emissions from off-highway diesel engines are a significant contributor of both oxides of nitrogen (NOx) and particulate matter (PM) to air inventories. Yet, emissions research activities aimed solely at the off-highway arena have been minimal - largely overshadowed by the extensive efforts directed toward the on-highway sector. However, with current trends indicating that the performance of these off-highway vehicles will become increasingly more scrutinized by federal regulatory agencies, augmentation of current research efforts will be necessary. The global objective for this study was to collect vehicle activity information for diesel-powered off-highway vehicles while they were operated in the field. Engine speed and raw exhaust CO2 concentrations were recorded and then used to create engine dynamometer test cycles. The engine was exercised according to these cycles in the laboratory so that the mass emissions rates of exhaust gas pollutants could be measured.

When preparing inventory models, it is desirable to obtain representative distance-specific emissions factors that truthfully represent the vehicle activity on a particular road (facility) type. Unfortunately, emissions values are often measured using only one test schedule, which represents a single average speed and a specific type of activity. This paper investigated the accuracy of predicting the emissions for a test schedule based on measurements from a different test schedule for the case of a medium heavy-duty truck. First, the traditional Speed Correction Factor (SCF) approach was examined, followed by the use of a power-based model derived from continuous data, followed by an artificial neural network (ANN) approach. The SCF modeling used distance-averaged emissions and cycle-averaged vehicle speed to predict distance-averaged NOx. The power-based modeling was based on linear and polynomial correlations between continuous axle power and NOx.