Development of ANN model for prediction of performance and emission characteristics of hydrogen dual fueled diesel engine with Jatropha Methyl Ester biodiesel blends

•Experimental investigation carried out with hydrogen duel fueled diesel engine using JME biodiesel blends.•Experimental trail cases conducted were used for developing the ANN model.•Different ANN models were developed with various training algorithms and transfer functions.•Predictions of model were correlated with experimental outcomes.

The present study investigates the use of Artificial Neural Network modeling for prediction of performance and emission characteristics of a four stroke single cylinder diesel engine with Jatropha Methyl Ester biodiesel blends along with hydrogen in dual fuel mode. ANN model was developed to predict BTE, BSFC, CO, O2, CO2, NOx, HC and EGT based on initial experimental studies by varying load, blends of biodiesel and hydrogen flow rates. Seven training algorithms each with five combinations of trainings functions were investigated. Levenberg-Marquardt backpropagation training algorithm with logarithmic sigmoid and hyperbolic tangent sigmoid transfer function results in best model for prediction of performance and emissions characteristics. The overall regression coefficient, MSE and MAPE for the model developed are 0.99360, 0.0011 and 4.863001% respectively. It is found that the neural networks are good tools for simulation and prediction of dual fueled hydrogen jatropha biodiesel engine.