Fast Coupled Monodirectional Cooling Image Analysis Cell for Accurate Cloud Point Detection and Cold Flow Optimization of Renewable Marine Diesel Blends

The marine sector is under increasing pressure to reduce greenhouse gas (GHG) emissions while maintaining fuel operability in cold climates. This study presents a new sensing technique to overcome the limitations of conventional cloud point (CP) measurements by combining fast monodirectional cooling (FMC) with digital image analysis, providing improved accuracy and reproducibility for marine biofuel blends. To support the freeze-tolerant integration of renewable fuels into fossil-based matrices, two blending strategies were tested with this newly developed cell. The first involves blending Arctic diesel and marine gas oil (MGO) with renewable components such as hydrotreated vegetable oil (HVO) and fatty acid methyl ester (FAME)-based biodiesel. While HVO effectively lowers CP and improves cold flow, biodiesel tends to increase CP due to early crystallization of saturated FAMEs. Notably, MGO-HVO blends exhibited a nonmonotonic CP trend, with some blends unexpectedly reducing the CP below that of either of the individual fuels, suggesting synergistic molecular interactions. The second strategy addresses the cold flow limitations of biodiesel by blending it with Jet A-1, a drop-in fuel known for its favorable low-temperature properties. However, even at high Jet A-1 concentrations, CP levels remained high due to persistent FAME crystallization. These findings highlight the importance of tailored blending strategies to address decarbonization goals with reliable cold weather performance, and provide practical guidance for formulating winter-grade marine fuels that meet both environmental and operational requirements.