ANN Modeling For Forecasting of VCR Engine Performance And Emission Parameters Fuelled With Green Diesel Extracted From Waste Biomass Resources
Abstract In this research work, the experimental tests were conducted on a single-cylinder, constant speed, variable compression ratio (VCR) engine fuelled with green diesel extracted from waste trichosanthes cucumerina seeds. The engine test blends are prepared with different trichosanthes cucumerina biodiesel (TCB) proportions of 30%, 50% and 70% in diesel fuel, and their thermo-physical properties were assessed as per the ASTM standards. At full load condition, the TCB30 blend operated at the CR 18:1 gives better engine performance and reduced emission levels of HC by 13.51%, CO by 10.82% and smoke opacity by 16.87%, equated with neat diesel fuel. With the support of experimental results, the performance (BTE, BSFC and EGT) and emission parameters (HC, CO, NOx, smoke opacity and CO2) are predicted using multiple regression artificial neural network (ANN) model. This trained ANN model results in an average correlation coefficient (R2) value is 0.9967, which is closer to 1. It indicates that the proposed ANN model can generate the exact correlation between input factors and output responses.