Machine learning based predictive modelling of micro gas turbine engine fuelled with microalgae blends on using LSTM networks: An experimental approach

Abstract

Air transport plays an inevitable role in the transportation sector. In the modern world, the aviation contribution is very immense to establish worldwide developments. However, the emission released by the aviation industry is massively high. Due to the sudden increase in the air traffic the contribution of global CO2 and CO have increased in recent years. Hence the aviation sector seeks the replacement for fossil fuels. In this study, the micro gas turbine engine has been experimentally studied for different engine speeds and throttle position. The gas turbine was allowed to run in the different test fuels such as, Jet-A, A20 (20% microalgae 80% Jet-A) and A30 (30% microalgae 70% Jet-A) and the predicted results were compared. In addition to the typical experimental calibrations, machine learning has been applied to examine the differences in the both performance and emission characteristics of the biofuel blends with approximately 51 different fuel combinations using LSTM networks. Based on the predicted results, introduction of the biofuel affects the production of the static thrust. On the contrary, the emissions of the CO and CO2 were very low compared to Jet-A. With regard to the nitrogen of the oxides, no massive reduction has been witnessed despite running at different fuel conditions. Besides, the marginal decrease in the NOx was observed above 75000 rpm.