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NEURAL NETWORK MODELING OF THERMAL ENGINES POWERED BY ALTERNATIVE FUELS FOR THE PURPOSE OF REDUCING ATMOSPHERIC POLLUTION
Abstract
This research investigates the increasing use of alternative fuels in internal combustion engines, a phenomenon that has experienced significant growth in recent decades due to the heightened interest in reducing atmospheric pollution. Although the pollution level associated with alternative fuels is generally lower compared to fossil fuels, it is essential to emphasize that the pollutant impact of alcohol-based fuels depends on various factors, such as engine technology, mixture composition, fuel quality, and usage patterns. Thus, this study analyzes the influence of the alcohol proportion in gasoline on engine performance and, consequently, atmospheric pollution through an innovative optimization method. This method is based on the use of a neural modeling computer application, EasyNN, which generated a series of neural models with 1, 2, or 3 hidden layers. The data were obtained through tests performed on a four-stroke single-cylinder engine with a capacity of 582 cm3. Following the neural network modeling, it was concluded that the most advantageous combination is represented by an alternative fuel based on gasoline with a concentration of 6% methanol + 1.05% ethanol. In order to reduce the pollutant impact of vehicles, investigations in this field are ongoing, focusing on optimizing efficiency and reducing emissions associated with vehicles adopting alternative fuels.
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