Performance analysis of a turbocharged diesel engine using biodiesel with back propagation artificial neural network

This paper determines, using artificial neural network (ANN), performance of a turbocharged diesel engine using biodiesel produced from cotton and rapeseed oils through transesterification. To acquire data for training and testing of the proposed ANN, a three-cylinder, four-stroke test engine was fuelled with biodiesel-eurodiesel blended fuels with various percentages of biodiesel (B2, B5%), and operated at different engine speeds and loads. Backpropagation algorithms for the engine was developed using some of the experimental data for training. The performance of the ANN was validated by comparing the prediction dataset with the experimental results.It was observed that the ANN model can predict the engine performance quite well with correlation coefficient (R) 0.99, 0.98, 0.92 and 0.98 for the engine power, the engine torque, the specific fuel consumption (SFC) and exhaust gas temperature, respectively. The prediction MSE (Mean Square Error) error was between the desired outputs as measured values and the simulated values were obtained as 0.0004 by the model. © Sila Science.