Artificial neural network modeling of jatropha oil fueled diesel engine for emission predictions

Thermal Science

Volumn 13, Issue 3, 2009, Pages 91-102

  Ganapathy, Thirunavukkarasu ^a   Gakkhar, Rakesh Parkash ^a   Murugesan, Krishnan ^a  

^a INDIAN INSTITUTE OF TECHNOLOGY ROORKEE   (India)

Author keywords

Artificial neural network model; Emissions; Injection timing; Jatropha oil; Learning algorithm

Indexed keywords

DIESEL ENGINES; HYDROCARBONS; LEARNING ALGORITHMS; NEURAL NETWORKS; NITROGEN OXIDES; TIMING CIRCUITS;

ARTIFICIAL NEURAL NETWORK MODELING; DATA BASE; EMISSION; EMISSIONS PREDICTION; INJECTION TIMING; JATROPHA OIL; MEAN PERCENT ERROR; NEURAL NETWORK MODEL; NO X EMISSION; UNBURNED HYDROCARBONS;

SMOKE;

EID: 77749308611     PISSN: 03549836     EISSN: None     Source Type: Journal    
DOI: 10.2298/TSCI0903091G     Document Type: Article

References (16)

1. Forson, F. K., Oduro, E. K., Hammaond Donkoh, E. Performance of Jatropha-Oil Blends in a Diesel Engine, Renewable Energy, 29 (2004), 7, pp. 1135-1145
2. Joshi, H. C., Biodiesel from Jatropha an Alternative Fuel for the Future, Invention Intelligence, Scientific Research Magazine, National Research Development Corporation New Delhi, Sep.-Oct. 2003, pp. 205-216
3. Senthil Kumar, M., Ramesh, A., Nagalingam, B., An Experimental Comparison of Methods to Use Methanol and Jatropha-Oil in a Compression Ignition Engine, Biomass and Bioenergy, 25 (2003), 3, pp. 309-318
4. Pramanik, K. Properties and use of Jatropha Curcas Oil and Diesel Blends In Compression Ignition Engine, Renewable Energy, 28 (2003), 2, pp. 239-248
5. Narayana Reddy, J., Ramesh, A., Parametric Studies for Improving the Performance of a Jatropha-Oil Fueled Compression Ignition Engine, Renewable Energy, 31 (2006), 12, pp. 1994-2016
6. Yamane, K., Ueta, A., Shimamoto, Y., Influence of Physical and Chemical Properties of Bio-Diesel Fuels on Injection, Combustion and Exhaust Emission Characteristics in a Direct Injection Compression Ignition Engine, Int J Engine Res, 2 (2001), 4, pp. 249-261
7. Celikten, I., An Experimental Investigation of the Effect of Injection Pressure on the Engine Performance and Exhaust Emission in Indirect Injection Diesel Engines, Applied Thermal Engineering, 23 (2003), 16, pp. 2051-2060
8. Icingur, Y., Altiparmak, D., Effect of Cetane Number and Injection Pressure on a DI Diesel Engine Performance and Emissions, Energy Convers Manage, 44 (2003), 3, pp. 389-97
9. Arcaklioglu, E., Celikten, I., A Diesel Engine's Performance and Exhaust Emissions, Applied Energy, 80 (2005), 1,pp. 11-22
10. Canakci, M., Erdil, A., Arcaklioglu, E., Performance and Exhaust Emissions of a Biodiesel Engine, Applied Energy, 83 (2005), 6, pp. 594-605
11. Cenk Sayin, H. et al., Performance and Exhaust Emissions of a Gasoline Engine Using Artificial Neural Network, Applied Thermal Engineering, 27 (2007), 1, pp. 46-54
12. Go lcu, M.,etal., Artificial Neural Network Based Modeling of Variable Valve-Timing in a Spark-Ignition Engine, Applied Energy, 81 (2005), 2, pp. 187-197
13. Celik, V., Arcaklioglu, E., Performance Maps of a Diesel Engine, Applied Energy, 81 (2005), 3, pp. 247-259
14. Deng Yuanwang, et al., An Analysis for Effect of Cetane Number on Exhaust Emissions from Engine with Neural Network, Fuel, 81 (2002), 15, pp. 1963-1970
15. Korres, D. M., et al., A Neural Network Approach to the Prediction of Diesel Fuel Lubricity, Fuel, 81 (2002), 10, pp. 1243-1250
16. Haykin, S., Neural Networks: a Comprehensive Foundation, MacMillan Publishing Company, New York, USA, 1994