Fuel composition and quality sensing for diesel engines

Diesel;Fuel;Sensor;Biodiesel;Contamination;Degradation;Engine

Scheider, John C. ; Hansen ; Alan C.

关键词: Diesel;    Fuel;    Sensor;    Biodiesel;    Contamination;    Degradation;    Engine;   

Others  :  https://www.ideals.illinois.edu/bitstream/handle/2142/24305/Scheider_John.pdf?sequence=1&isAllowed=y

美国|英语

来源: The Illinois Digital Environment for Access to Learning and Scholarship

【 摘 要 】

ABSTRACTThe range of fuels that a diesel engine may be expected to burn is increasing including the use of biodiesels and alcohols produced from various plant or animal sources.Moreover, fuels can become unsuitable for application in diesel engines due to a variety of contamination, degradation, and cold flow issues.In order to optimize engine performance and meet EPA emissions standards over a wide array of fuels, information about the fuel composition must be known in real time.Furthermore, protecting the engine from potential fuel contaminants such as water, sulfur, glycerol, methanol, and urea requires a method of detecting these chemicals in the fuel system.This study evaluates a commercially available fluid properties sensor for use in diesel engines.The sensor provides four bulk outputs: temperature, density, dynamic viscosity, and dielectric constant, which can be used to monitor fuel properties.Extensive testing was carried out on the sensor to evaluate its effectiveness at detecting fuel type, blend, and quality over a temperature range of 0°-80°C.Fuel types tested include diesel #1, diesel #2, jet A, soy biodiesel, rapeseed biodiesel, false flax biodiesel, jatropha biodiesel, soy oil, rapeseed oil, false flax oil, and jatropha oil.Blends of diesel #1 and #2 with soy biodiesel were studied.Fuel properties under contamination and the sensor’s sensitivity to fuel degradation and cold flow situations were examined. A predictive model of fuel properties was developed based on fuel composition, contamination, and temperature range.Finally, an algorithm was developed to predict fuel type, blend, contamination condition, and degradation condition for unknown fuel samples.