Artificial neural network modeling of jatropha oil fueled diesel engine for emission predictions
| Autor: | T. Ganapathy, Krishnan Murugesan, Parkash Rakesh Gakkhar |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2009 |
| Předmět: |
Smoke
Artificial neural network Correlation coefficient Renewable Energy Sustainability and the Environment lcsh:Mechanical engineering and machinery artificial neural network model emissions Injector Diesel engine Automotive engineering jatropha oil law.invention injection timing learning algorithm law Linear regression Environmental science lcsh:TJ1-1570 NOx Test data |
| Zdroj: | Thermal Science, Vol 13, Iss 3, Pp 91-102 (2009) |
| ISSN: | 2334-7163 0354-9836 |
| Popis: | This paper deals with artificial neural network modeling of diesel engine fueled with jatropha oil to predict the unburned hydrocarbons, smoke, and NOx emissions. The experimental data from the literature have been used as the data base for the proposed neural network model development. For training the networks, the injection timing, injector opening pressure, plunger diameter, and engine load are used as the input layer. The outputs are hydrocarbons, smoke, and NOx emissions. The feed forward back propagation learning algorithms with two hidden layers are used in the networks. For each output a different network is developed with required topology. The artificial neural network models for hydrocarbons, smoke, and NOx emissions gave R2 values of 0.9976, 0.9976, and 0.9984 and mean percent errors of smaller than 2.7603, 4.9524, and 3.1136, respectively, for training data sets, while the R2 values of 0.9904, 0.9904, and 0.9942, and mean percent errors of smaller than 6.5557, 6.1072, and 4.4682, respectively, for testing data sets. The best linear fit of regression to the artificial neural network models of hydrocarbons, smoke, and NOx emissions gave the correlation coefficient values of 0.98, 0.995, and 0.997, respectively. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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