PREDICTION OF THE MAIN ENVIRONMENTAL AND ENERGY CHARACTERISTICS OF DIESEL ENGINES USING AN ARTIFICIAL NEURAL NETWORK FOR PURE DIESEL FUEL, PURE HVO, AND A MIXTURE OF THESE FUELS IN THE RATIO 50/50 BY VOLUME

This research assesses the influence of different quantities of hydrotreated vegetable oil (HVO) in diesel fuel on the performance of the engine and the emissions it produces. The particular areas of interest are the level of smoke emitted and the brake thermal efficiency (BTE). A series of engine experiments were undertaken to quantify emissions and performance characteristics under various operating regimes using three distinct fuel blends: D100 (pure diesel), HVO50 (50% HVO mixture), and HVO100 (pure HVO). The results show a tendency to reduce soot emissions when the amount of HVO in the mixture reaches 50%, but in order to accurately determine the correlation between the amount of HVO and emissions, additional studies with various concentrations of HVO and diesel mixtures are necessary. Similarly, the results show a slight improvement in BTE stability with a 50% HVO blend, but more studies with different percentages of HVO diesel blends are needed to reliably determine changes in BTE. This indicates a trend of change in engine performance with increasing HVO concentration in diesel fuel. In order to forecast emissions and performance indicators under different operating scenarios, we used artificial neural networks ANNs, which demonstrated excellent prediction accuracy, exhibiting robust linear correlations between the expected and real values for all fuel types. This research emphasizes the advantages of utilizing HVO in diesel engines for both environmental impact and performance. It also emphasizes the usefulness of ANNs in optimizing engine settings to improve efficiency and minimize emissions. The results endorse the further use of HVO as a viable substitute for conventional diesel, leading to less ecological consequences and enhanced engine efficiency.