Artificial neural network-based analysis of the tribological behavior of vegetable oil-diesel fuel mixtures

Abstract

The use of fuel mixtures of diesel and vegetable oils in diesel engines is a field of research due to the necessity of reducing pollution. Besides the properties required for the normal operation of diesel engines, other aspects that must be investigated are linked to the influence of these mixtures on piston ring-cylinder tribosystem behavior. Methods used for reducing the friction and wear on the engine cylinders, such as special surface machining, lubricant driving piston rings, etc., are well known. If the fuel mixture brings some improvement in this area, such as a reduction of the friction coeffcient value, this can be a way to reduce the power lost by friction into the engine cylinders. In this paper, a methodology is presented based on artificial neural networks for analyzing the complex relationship between vegetable oil percentages in fuel mixtures, with the goal of finding an optimal proportion of vegetable oil corresponding to a minimum value of the friction coeffcient. Regular methods were used for data acquisition, i.e., a pin-on-disk module mounted on a tribometer, and two types of vegetable oils were studied, namely sunflower and rapeseed oils. The obtained results show that for each type of vegetable oil there is an optimal proportion leading to the best tribological behavior.