Prediction modeling using artificial neural network (ANN) for the performance and emission characteristics of catalytic co-pyrolytic fuel blended with diesel in a CI engine.

Abstract

In the present research work, artificial neural network (ANN) is used to model the performance and emission parameters in a four-stroke, single-cylinder diesel engine combusting a blended fuel of diesel and catalytic co-pyrolysis oil produced from seeds of Pongamia pinnata, waste LDPE, and calcium oxide catalyst. The optimum yield of oil obtained was 92.5% at 500 °C temperature. Physical properties of the obtained oil, such as calorific value (44.85 MJ/kg) and density (797 kg/m3), level it by that of diesel while the flash point and fire point were found to be lower than that of pure diesel. An ANN model was then generated for the prediction of performance characteristics (BTE and BSFC) and emission characteristics (NOx and smoke) under varying loads, braking power, brake mean effective pressure, and torque as inputs using the Levenberg-Marquardt back-propagation training technique. The regression coefficients (R2) for BTE, BSFC, smoke, and NOx predictions were determined to be close to unity at 0.99859, 0.99814, 0.96129, and 0.92505, respectively (all values being close to unity). It has been discovered that ANN makes an effective simulation and prediction tool for blended fuels in CI engines. It is also suggested to predict the mechanical efficiency, volumetric efficiency, and CO, CO2, HC emissions using ANN in its future work.