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A project has been undertaken to optimize the engine control software calibration of a modern heavy-duty diesel engine for operation with gas-to-liquids (GTL) diesel fuel, with the objective of developing an understanding of the scope for optimization with this fuel, which has different physical and combustion properties to that of conventional, crude-derived diesel. A data-driven, model-based calibration technique utilizing artificial neural networks was used to develop optimized transient and steady-state calibrations with both conventional diesel fuel, as well as neat GTL fuel. The engine control parameters that were optimized were injection timing, exhaust gas recirculation rate, rail pressure, and charge mass. The optimization aimed to minimize fuel consumption without deterioration in engine-out nitrogen oxide (NOx) and soot emissions. This paper reports on the calibration optimization methodology employed and the results achieved to date.

Comparative exhaust emission tests were performed with five diesel fuels, namely a Sasol Fischer-Tropsch diesel, a fuel meeting the CARB diesel fuel specification, a fuel meeting the US 2-D diesel fuel specification, and two blends of the Fischer-Tropsch diesel and the 2-D diesel. Hot-start and cold-start heavy-duty transient emission tests were performed using a 1999 model year DDC series 60 engine. Regulated exhaust emissions with the Fischer-Tropsch diesel were significantly lower than with the 2-D and CARB diesel fuels, in both the hot-start and cold-start tests. When compared with test results obtained previously with a 1991 engine, it was found that the reduction in NOX with the Fischer-Tropsch fuel was smaller in the 1999 engine, while the reduction in PM was greater.