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The major challenge of the post-processing of soot aggregates in transmission electron microscope (TEM) images is the detection of soot primary particles that have no clear boundaries, vary in size within the fractal aggregates, and often overlap with each other. In this study, we propose an automated detection code for primary particles implementing the Canny Edge Detection (CED) and Circular Hough Transform (CHT) on pre-processed TEM images for particle edge enhancement using unsharp filtering as well as image inversion and self-subtraction. The particle detection code is tested for soot TEM images obtained at various ambient and injection conditions, and from five different combustion facilities including three constant-volume combustion chambers and two diesel engines.

The focus has recently been directed towards the engine out soot from Diesel engines. Running an engine in PPC (Partially Premixed Combustion) mode has a proven tendency of reducing these emissions significantly. In addition to combustion strategy, several studies have suggested that using alcohol fuels aid in reducing soot emissions to ultra-low levels. This study analyzes and compares the characteristics of PM emissions from naphtha gasoline PPC, ethanol PPC, methanol PPC and methanol diffusion combustion in terms of soot mass concentration, number concentration and particle size distribution in a single cylinder Scania D13 engine, while varying the intake O2. Intake temperature and injection pressure sweeps were also conducted. The fuels emitting the highest mass concentration of particles (Micro Soot Sensor) were gasoline and methanol followed by ethanol. The two alcohols tested emitted nucleation mode particles only, whereas gasoline emitted accumulation mode particles as well.