Artificial neural network modeling of performance, emission, and vibration of a CI engine using alumina nano-catalyst added to diesel-biodiesel blends

Abstract

In recent years, added nano-catalysts to fuels has improved their thermo-physical properties. In present study, the alumina as additive with concentrations of 30, 60, and 90 ppm were added to B5 and B10 blends for evaluation of the engine performance, emissions, and vibration levels. An ANN model based on standard back-propagation learning algorithm for the engine was developed. Multi-layer perception network (MLP) was used for a non-linear mapping between the input and target parameters. The input or independent parameters were fuel blend, engine speed, fuel density, fuel viscosity, LHV, intake manifold pressure, fuel consumption, exhaust gas temperature, oxygen contained in exhaust gases, oil temperature, relative humidity, and ambient air pressure. Whereas, the target parameters separately were engine power, torque, emissions of CO, CO2, UHC, NO, RMS and Kurtosis of engine’s vibration. The results for optimum ANN model showed, the training algorithm of back-propagation with 25-25 neurons in hidden layers (logsig-logsig) is able to predict different parameters of engine for different conditions. The corresponding R-values for training, validation and testing were 0.9999, 0.9994 and 0.9995, respectively. The performance and accuracy of the proposed ANN model was completely satisfactory.