Artificial neural networks used for the prediction of the Diesel Engine performance and pollution of waste cooking oil biodiesel

In this research work, a comprehensive combustion analysis has been conducted to evaluate the performance of a low speed diesel engine (M8/1 Lister) using biodiesel fuel. Waste vegetable cooking oil as an alternative fuel. Biodiesel obtained from waste vegetable cooking oil (WCO) as an alternative fuel. The properties of biodiesel produced from WCO was measured based on ASTM standards. In order to compare brake power, torques, brake specific fuel consumption (BSFC) and concentration of the UHC and CO emissions of the engine, it has been tested under same load of Dynamometer(5 levels) and biodiesel fuel blends (levels)) at constant engine speed(750 rpm). The results were found to be very comparable. An artificial neural network (ANN) was developed based on the collected data of this work. Multi layer perceptron network (MLP) was used for nonlinear mapping between the input and the output parameters. Different activation functions and several rules were used to assess the percentage error between the desired and the predicted values. The results showed that the training algorithm of Back Propagation was sufficient in predicting the engine torque, brake power, specific fuel consumption and exhaust gas components for different engine loads and different fuel blends ratios.