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.
