Prediction of Responses for Simarouba Biodiesel based CRDI Engine using General Regression Neural Network

Abstract

The evaluation of performance and emission of Common Rail Direct Injection (CRDI) engine fuelled by various biodiesel at different operating conditions is time consuming and expensive. This can be overcome by using prediction techniques like GRNN. The GRNN model is developed using ‘newgrnn’ function in Matlab R2019b software to predict the performance and emission responses of CRDI engine for simarouba biodiesel. A total of 27 experimental dataset of each biodiesel is used for development of model. Out of 27 experimental dataset, 21 datasets are selected randomly for training the model. The remaining 6 datasets are utilized for testing the GRNN model. In this study, 20 different values of spread parameters within the range 0.05 to 1 with step increment of 0.05 are chosen. As a result, 20 simulations are performed and the best predicted results are chosen based on least mean error. The optimum spread parameter for simarouba, pongamia and composite biodiesel GRNN model was found to be 0.1, 0.1 and 0.05 respectively. The Root Mean Square Error (RMSE) values of different responses are found to be acceptable. The results indicated that GRNN model for the prediction of engine responses yields good correlation with experimental values and are acceptable for new predictions.