Browse Topic: Cetane
This study explores the feasibility of using a sustainable lignin-based fuel, consisting of 44 % lignin, 50 % ethanol, and 6 % water, in conventional compression ignition (CI) marine engines. Through experimental evaluations on a modified small-bore CI engine, we identified the primary challenges associated with lignin-based fuel, including engine startup and shutdown issues due to solvent evaporation and lignin solidification inside the fuel system, and deposit formation on cylinder walls leading to piston ring seizure. To address these issues, we developed a fuel switching system transitioning from lignin-based fuel to cleaning fuel with 85 vol% of acetone, 10 vol% of water and 5 vol% of ignition improving additive, effectively preventing system clogs. Additionally, optimizing injection parameters, adopting a constant pressure delivery valve, and fine-tuning injection timing mitigated lignin deposit formation related to incomplete combustion or spray tip penetration to the cylinder
Cetane number (CN) is an important fuel property in designing high-performance fuels in recently diversifying compression ignition engines. We introduce graph neural networks (GNNs) that predict CNs of multicomponent surrogate mixtures when only 2D structures and mole fractions of molecules are given. It considers the influences of mixing multiple components and their chemical structures on CN, reproducing the non-linear blending behavior observed for certain mixtures. We trained the GNNs using the CNs of 1,143 mixtures, and reliable accuracy was achieved with mean absolute errors of 3.4-3.8 from the cross-validation. Lastly, we analyzed the chemical structural effects on non-linear blending behavior.
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