Artificial neural networks based prediction of performance and exhaust emissions in direct injection engine using castor oil biodiesel-diesel blends

In this study, the performance and emission characteristics of a direct injection diesel engine using castor oil biodiesel (COB)-diesel blended fuels were investigated experimentally and then predicted by artificial neural networks. For this aim, castor oil was converted to its biodiesel via transesterification approach. Then, the effects of the biodiesel percentage in blend, engine load, and speed on brake power, brake specific fuel consumption (BSFC), nitrogen oxides (NOx), carbon dioxide (CO2), carbon monoxide (CO), and particle matter (PM) have been considered. Fuel blends with various percentages of biodiesel (0%, 5%, 10%, 15%, 20%, 25%, and 30%) at various engine speeds and loads were tested. The results indicated that blends of COB with diesel fuel provide admissible engine performance; on the other side, emissions decreased so much. Two types of neural networks, a group method of data handling (GMDH) and feed forward were used for modeling of the diesel engine to predict brake power, BSFC, and exhaust emissions such as CO, CO2, NOx, and PM values. The comparison results demonstrate the superiority of the feed forward neural network models over GMDH type models in terms of the statistical measures in the training and testing data but in the number of hidden neurons and model simplicity, GMDH models are preferred. (C) 2012 American Institute of Physics. [http://dx.doi.org/10.1063/1.4769200]