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Development of a Method to Measure Soft Particles from Diesel Type Fuels
Technical Paper
2020-01-0344
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
Renewable fuels have an important role to create sustainable energy systems. In this paper the focus is on biodiesel, which is produced from vegetable oils or animal fats. Today biodiesel is mostly used as a drop-in fuel, mixed into conventional diesel fuels to reduce their environmental impact. Low quality drop-in fuel can lead to deposits throughout the fuel systems of heavy duty vehicles. In a previous study fuel filters from the field were collected and analyzed with the objective to determine the main components responsible for fuel filter plugging. The identified compounds were constituents of soft particles. In the current study, the focus was on metal carboxylates since these have been found to be one of the components of the soft particles and associated with other engine malfunctions as well. Hence the measurement of metal carboxylates in the fuel is important for future studies regarding the fuel’s effect on engines. The first aim of this study was to create synthetic soft particles from biodiesel. Accelerated aging of fuels with different contaminations such as engine oil and calcium oxide were used to create the synthetic soft particles. The precipitates were collected and analyzed with different techniques such as FTIR and GC-MS, to identify the main components which were then compared with the results of the previous study. Following this, specific attention was given to calcium methyl azelate as it was shown to be found in field fuel filters. A method using GC-MS was developed to be able to estimate the amount of soft particles by measuring calcium methyl azelate. The specified method proved to be adequate for future studies to evaluate the filtration efficiency of different filter materials against soft particles.
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Csontos, B., Swarga, S., Bernemyr, H., Pach, M. et al., "Development of a Method to Measure Soft Particles from Diesel Type Fuels," SAE Technical Paper 2020-01-0344, 2020, https://doi.org/10.4271/2020-01-0344.Data Sets - Support Documents
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