Performance and Emission Characteristics of a 4 Stroke C.i. Engine Operated on Honge Methyl Ester Using Artificial Neural Network

In wake of the present energy environment crises it has become essential to identify renewable and alternative clean burning fuels. One of the significant routes to tackle the problem of increasing prices and the pollution problems of petroleum fuels is by the use of vegetable oil fuels known as biodiesels. In the present work biodiesel was prepared from Honge oil (Pongamia) and used as a fuel in C.I engine. Performance studies were conducted on a single cylinder fourstroke water-cooled compression ignition engine connected to an eddy current dynamometer. Experiments were conducted for different percentage of blends of Honge oil with diesel at various compression ratios. Experimental investigation on the Performance parameters and Exhaust emissions from the engine were done. Artificial Neural Networks (ANNs) were used to predict the Engine performance and emission characteristics of the engine. To train the network compression ratio, blend percentage, percentage load were used as the input variables where as engine performance parameters together with engine exhaust emissions were used as the output variables. Experimental results were used to train the ANN. Back-propagation algorithm was used to train the network. ANN results showed good correlation between the ANN predicted values and the desired values for various engine performance values and the exhaust emissions. The R values were very close to 1 and the mean relative error values were less than 9 percent.