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World wide, energy policy makers are increasingly keen to move away from petroleum based fuels to more diverse and renewable sources of energy for reasons of environmental protection, energy security and continued economic development. A well-known example is Gas-To-Liquid (GTL) diesel fuel which is derived from natural gas and thus provides a diversification away from crude oil, which is the feedstock for 97% of the world transport fuels. Towards the same goal, Fatty Acid Methyl Esters (FAME), from vegetable oils, recycled oils, or animal fat, are being used with increasing frequency as blends with diesel fuel, even at a concentration of 30% in some captive fleets (B30). In this paper, the impacts on engine pollutant emissions and performance are evaluated.

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.