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 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.

Current Trends and Applications of Machine Learning in Tribology—A Review

“…Therefore, optimal concentrations depending on the load case could be identified with rather little experimental effort. Humelnicu et al [121] applied an ANN to investigate the tribological behavior of vegetable oil-diesel fuel mixtures. The data were generated in pin-on-disk tests at constant conditions, whereas the concentration of rapeseed and sunflower oil was varied and the averaged COF values of five repetitions of each combination was used for further processing.…”

The Use of Artificial Intelligence in Tribology—A Perspective

“…Other researchers [26] also analyzed the use of ANNs to design lubricants with significantly lower COFs (Figure 4). They considered the optimization of mixtures of vegetable oil (sunflower and rapeseed oils) with diesel oil for use in diesel engines.…”

Practice of Simulation and Life Cycle Assessment in Tribology—A Review

“…There are several machine learning techniques that could be used, depending on the type of dataset that has been collected. If the dataset does not have any output data, the technique is called unsupervised machine learning, and the only way that this data can be dealt with is by clustering, where the data is grouped by finding the hidden pattern in the data [84]. On the other hand, if the input and responding output data are available, machine learning is referred to as supervised machine learning and can be used to train the algorithms to predict the response to any new unseen data.…”

“…Supervised machine learning can take the form of classification models that classify and separate input data into classes, categories or groups. The second type of supervised machine learning is that of regression [84]. This is used when the responding output is a real number, such as when predicting temperature or the lifetime of equipment or, as in this case, for predicting the friction coefficient or wear rate in tribology.…”