Prediction of the main environmental and energy characteristics of diesel engines using an artificial neural network in a wide range of diesel and hvo fuel mixtures

Abstract

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 experimental findings showed a conspicuous decline in smoky emissions as the proportion of HVO rose, with HVO100 exhibiting the most notable drop. Similarly, it was shown that the stability and performance of BTE increased marginally with increasing concentrations of HVO. In order to forecast emissions and performance indicators under different operating scenarios, we used Artificial Neural Networks (ANNs). The ANNs demonstrated excellent prediction accuracy, exhibiting robust linear correlations between the expected and real values for all fuel types. This research emphasises the advantages of utilising HVO in diesel engines for both environmental impact and performance. It also emphasises the usefulness of ANNs in optimising engine settings to improve efficiency and minimise 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.