Development of Data-Driven Models for the Prediction of Fuel Effects on Diesel Engine Performance and Emissions

A modelling tool has been developed for the prediction of fuel effects on the performance and exhaust emissions of a heavy-duty diesel engine. Recurrent neural network models with duty-cycle, engine control, and fuel property parameters as inputs were trained with transient test data from a 15-liter heavy-duty diesel engine equipped with a common-rail fuel injection system and a variable geometry turbocharger.The test fuels were formulated by blending market diesel fuels, refinery components, and biodiesel to provide variations in preselected fuel properties, namely, hydrogen-to-carbon (H/C) ratio, oxygen-to-carbon (O/C) ratio, derived cetane number (CN), viscosity, and mid-and end-point distillation parameters. Care was taken to ensure that the correlation between these fuel properties in the test fuel matrix was minimized to avoid confounding model input variables.The test engine was exercised over a wide variety of transient test cycles during which fuel rail pressure, injection timing, airflow, and recirculated exhaust gas flow were systematically varied. The resulting models could predict the transient engine torque and fuel consumption, and nitrogen oxide (NOx), soot, carbon monoxide (CO), total hydrocarbon (THC), and carbon dioxide (CO2) exhaust emissions with good accuracy, indicating that the limited number of fuel property parameters selected as model inputs was sufficient to capture the fuel-related effects.The modelling tool can also be used to estimate the relative contributions from changes in the individual fuel inputs to changes in exhaust emissions, and this is illustrated by means of an example blending study with crude-derived diesel fuel, biodiesel, and paraffinic gas-to-liquid (GTL) diesel fuel. This type of novel numerical analysis provides insights into fuel effects which are very difficult to achieve experimentally due to the high degree of intercorrelation between fuel properties that is usually present.