Artificial Neural Network-Based Analysis of the Tribological Behavior of Vegetable Oil–Diesel Fuel Mixtures

Humelnicu Humelnicu,Amortila Amortila,Ciortan Ciortan

doi:10.3390/lubricants7040032

Humelnicu Humelnicu, Amortila Amortila + Show 1 more

Open Access

https://doi.org/10.3390/lubricants7040032

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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 coefficient 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 coefficient. 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.

Highlights

The continuously growing number of internal combustion engines has led to a dangerous increase of gaseous emissions into the atmosphere
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 coefficient
Given some experimental measurements and an artificial neural network (ANN) that is trained for the modeling of friction coefficient evolution, several investigations can be performed to predict friction coefficient values for coefficient evolution, several investigations can be performed to predict friction coefficient values for different oil–diesel mixtures or to establish the optimal vegetable oil content of such mixtures

Summary

Introduction

The continuously growing number of internal combustion engines has led to a dangerous increase of gaseous emissions into the atmosphere. The greenhouse effect has become more and more powerful, leading to many undesired effects, such as global temperature rise, ozone layer degradation, air pollution, etc. Europe has targeted a unilateral reduction in greenhouse gas emissions of 20%. Internal combustion engines are an important source of pollutant emissions (around 20%) [2]. Romania has taken the decision to increase its consumption of biofuels. These are biofuel mixtures with at least 8% ethanol for gasoline and 6.5% biodiesel for diesel, starting since

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