Prediction of performance and emission characteristics in a biodiesel engine using WCO ester: a comparative study of neural networks

In this study, the applicability of Artificial Neural Networks (ANNs) has been investigated for predicting the performance and emission characteristics of a diesel engine fuelled with Waste cooking oil (WCO). ANN modeling was done using multilayer perception (MLP) and radial basis functions (RBF). In the radial basis functions, centers were initialized by two different methods namely random selection method and using clustering algorithm. In the clustering method, center initialization was done using FCM (Fuzzy$$c$$cmeans) and CDWFCM (cluster dependent weighted fuzzy$$c$$cmeans) algorithms. The networks were trained using the experimental data, wherein load percentage, compression ratio, blend percentage, injection timing and injection pressure were taken as the input parameters and brake thermal efficiency, brake specific energy consumption, exhaust gas temperature and engine emissions were used as the output parameters. The investigation showed that ANN predicted results matched well with the experimental results over a wide range of operating conditions for both models. A comparison was made between ANN models and regression models. ANN performed better than the regression models. Similarly a comparison of MLP and RBF indicated that RBF with CDWFCM performed better than MLP networks with lower Mean Relative Error (MRE) and higher accuracy of prediction.