Prediction of Single Cylinder Diesel Engine Emission Characteristics Using Pongamia Oil Blends With Artificial Neural Network
Journal Title: International Journal for Research in Applied Science and Engineering Technology (IJRASET) - Year 2014, Vol 2, Issue 12
Abstract
In this study, an artificial neural network, an artificial intelligence technique, is developed to predict the exhaust emissions (CO, CO2, HC and NOx) of the four stroke, single cylinder, vertical, air cooled diesel engine. A single cylinder, four-stroke test engine was fuelled with diesel fuel with various percentages of pongamia oil (0, 5, 15 and 20%) to acquire the data’s for training and testing of the proposed ANN. To acquire data’s for training and testing of the proposed ANN, the test engine was operated at different speeds and loads. A standard back-propagation algorithm which is ANN model based for the engine was developed using some of experimental data’s for training and testing for the proposed ANN. The emission parameters of the single cylinder diesel engine were validated by comparing the proposed ANN’s prediction dataset with the experimental results. Results showed that the ANN gives the best accuracy for predicting the emission parameters for the single cylinder diesel engine with the minimum errors with the average range of 0.03998, 0.0002, 0.0002 and 0.0001 for CO, CO2, HC and NOx respectively.
Authors and Affiliations
R. Girimurugan, K. A. Rameshkumar
Keywords
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