ANN Prediction of Performance and Emissions of CI Engine Using Biogas Flow Variation

Compression ignition (CI) engines are popular in the transport sector because of their high compression ratio. However, recent years, it has become a major concern from an environmental point view emission and depleting fossil fuel. The advanced combustion concept been research topic CI engine. Low-temperature with alternate fuel helped reducing oxides nitrogen (NOx) soot Biogas is substitute energy especially deduced biomass its clean properties, as well being renewable source compared to non-renewable diesel resources. In experiments dual fuel, i.e., conventional (biogas) were carried out through them. present study, artificial neural network model was used estimate emissions check attributes performance. Different algorithms training functions train models. best algorithm Levenberge Marquardt function Tansig (Hyperbolic tangent sigmoid) Logsig (logarithmic sigmoid), which showed result regression coefficient (R > 0.98) Mean square error (MSE < 0.001). trained by evaluating MSE coefficient. Experimental results (ANN) prediction that experimental similar each other lie at same intervals. ANN predicting data earlier difficult experimentally perform using interpolation extrapolations. It observed there increase Brake Specific Energy Consumption (BSEC) decrease thermal efficiency (BTE) improved biogas flow rate reduced NOx chamber. Carbon monoxide (CO) hydrocarbon (HC) linearly rate, whereas smoke opacity decreases. could be concluded this study helps understanding effect (diesel-biogas) under different load conditions engine help ANN, protect environment.