An ANN Based Modelling, Forecasting, and Experimental Study of Emissions and Performance Parameters Running on Microalgae Biodiesel-Nanoparticles Blended Fuel

In this current analysis, biodiesel derived from third-generation microalgae spirulina is assessed as a replacement for diesel. Also, the influence of nano additives Al2O3 and MgO in microalgae spirulina amalgams on compression ignition engine attributes is studied. Additionally, using an Artificial Neural Network (ANN), an optimization model was designed to characterize the test variables. Test investigation revealed that, at peak load, the overall drop in BTE was observed to be 3.55, 3.18, and 4.16 respectively, for Al2O3, MgO doped blends, and biodiesel mixture (without additive), than SB0. On the other hand, the BSFC of Al2O3, MgO doped blends, and biodiesel mixture enhanced by 5.77, 5.31, and 7.49