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On-Board Fuel Identification using Artificial Neural Networks

Journal Article
2014-01-1345
ISSN: 1946-3936, e-ISSN: 1946-3944
Published April 01, 2014 by SAE International in United States
On-Board Fuel Identification using Artificial Neural Networks
Sector:
Citation: Mocanu, F., "On-Board Fuel Identification using Artificial Neural Networks," SAE Int. J. Engines 7(2):937-946, 2014, https://doi.org/10.4271/2014-01-1345.
Language: English

References

  1. Taglialatela , F. , Cesario , N. , Lavorgna , M. , Merola , S. et al. Use of Engine Crankshaft Speed for Determination of Cylinder Pressure Parameters SAE Technical Paper 2009-24-0108 2009 10.4271/2009-24-0108
  2. Guezennec , Y. and Gyan , P. A Novel Approach to Real- Time Estimation of the Individual Cylinder Combustion Pressure for S.I. Engine Control SAE Technical Paper 1999-01-0209 1999 10.4271/1999-01-0209
  3. Taraza , D. , Henein , N. , and Bryzik , W. Determination of the Gas-Pressure Torque of a Multicylinder Engine from Measurements of the Crankshaft's Speed Variation SAE Technical Paper 980164 1998 10.4271/980164
  4. Taraza , D. Quantifying Relationships Between the Crankshaft's Speed Variation and the Gas Pressure Torque SAE Technical Paper 2001-01-1007 2001 10.4271/2001-01-1007
  5. Taraza , D. Statistical Model and Simulation of Engine Torque and Speed Correlation SAE Technical Paper 2001-01-3686 2001 10.4271/2001-01-3686
  6. Taraza , D. Accuracy Limits of IMEP Determination from Crankshaft Speed Mesurements SAE Technical Paper 2002-01-0331 2002 10.4271/2002-01-0331
  7. Taraza Dinu , Henein Naeim A. , Gade Mangesh J. , Bryzik Walter Cylinder Pressure Reconstruction from Crankshaft Speed Measurements in a Four-Stroke Single Cylinder Diesel Engine ASME 2005 Internal Combustion Engine Division Spring Technical Conference (ICES2005) / 1023 387 395 10.1115/ICES2005-1023
  8. Ponti , F. , Ravaglioli , V. , Serra , G. , and Stola , F. Instantaneous Engine Speed Measurement and Processing for MFB50 Evaluation SAE Int. J. Engines 2 2 235 244 2009 10.4271/2009-01-2747
  9. Brand , D. , Onder , C. , and Guzzella , L. Estimation of the Instantaneous In-Cylinder Pressure for Control Purposes using Crankshaft Angular Velocity SAE Technical Paper 2005-01-0228 2005 10.4271/2005-01-0228
  10. Kallenberger , C. , Hamedović , H. , Raichle , F. , Breuninger , J. et al. Estimation of Cylinder-Wise Combustion Features from Engine Speed and Cylinder Pressure SAE Int. J. Engines 1 1 198 207 2008 10.4271/2008-01-0290
  11. Mocanu , F. and Taraza , D. Estimation of Main Combustion Parameters from the Measured Instantaneous Crankshaft Speed SAE Technical Paper 2013-01-0326 2013 10.4271/2013-01-0326
  12. Jayakumar Chandrasekharan , Nargunde Jagdish , Sinha Anubhav , Bryzik Walter et. al. Effect of Biodiesel, JP-8 and Ultra Low Sulfur Diesel Fuel on Autoignition, Combustion, Performance and Emissions in a Single Cylinder Diesel Engine Journal of Engineering for Gas Turbines and Power February 2012 134 022801 11 10.1115/1.4003971
  13. Nargunde , J. , Jayakumar , C. , Sinha , A. , Acharya , K. et al. Comparison between Combustion, Performance and Emission Characteristics of JP-8 and Ultra Low Sulfur Diesel Fuel in a Single Cylinder Diesel Engine SAE Technical Paper 2010-01-1123 2010 10.4271/2010-01-1123
  14. Jayakumar , C. , Zheng , Z. , Joshi , U. , Bryzik , W. et al. Effect of Intake Pressure and Temperature on the Auto- Ignition of Fuels with Different Cetane Number and Volatility SAE Technical Paper 2012-01-1317 2012 10.4271/2012-01-1317
  15. McCormick Bob National Renewable Energy Laboratory Effects of biodiesel on pollutant emissions March 16 2005 Golden, Colorado, U.S.A.
  16. Haykin Simon Neural Networks - A Comprehensive Foundation McMaster University Ontario, Canada 2007
  17. Mehrotra K , Mohan CK , Ranka S. Elements of artificial neural networks Cambridge, Massachusets, USA Bradford Book, MIT Press 1997
  18. Hornik K , Stinchcomb X , White X. Multilayer feedforward networks as universal approximators Neural Networks 1989
  19. Dayhoff JE New York Van Nostrand Reinhold An introduction to neural network architectures 1990
  20. Park J , Sandberg I. Approximation and radial-basis-function networks Neural Computation 1993 5 305 16
  21. Stone , M. Embedded Neural Networks in Real Time Controls SAE Technical Paper 941067 1994 10.4271/941067
  22. Fahlman S. An empirical study of learning speed in back-propagation networks Technical Rep. CMU-CS-88-162 Pittsburgh Carnegie-Mellon University 1988
  23. Cybenko G Approximation by superposition of sigmoidal functions in neural networks Math Control Signals Systems 1989 2 303 14
  24. Rogers , S. Adaptive Neural Network Control of Engine RPM SAE Technical Paper 2004-01-2680 2004 10.4271/2004-01-2680
  25. Brahma , I. , He , Y. , and Rutland , C. Improvement of Neural Network Accuracy for Engine Simulations SAE Technical Paper 2003-01-3227 2003 10.4271/2003-01-3227
  26. Mitsuhashi , K. , Tsuchiya , T. , Morishita , S. , Shiraishi , T. et al. Revolution Control for Diesel Engines by Neural Networks SAE Technical Paper 2004-01-1361 2004 10.4271/2004-01-1361
  27. Müller , R. and Schneider , B. Approximation and Control of the Engine Torque Using Neural Networks SAE Technical Paper 2000-01-0929 2000 10.4271/2000-01-0929
  28. Ayeb , M. , Lichtenthäler , D. , Winsel , T. , and Theuerkauf , H. SI Engine Modeling Using Neural Networks SAE Technical Paper 980790 1998 10.4271/980790
  29. Gnanam , G. , Burton , R. , Habibi , S. , and Sulatisky , M. Neural Network Control of a Bi-Fuel Engine SAE Technical Paper 2004-01-1360 2004 10.4271/2004-01-1360
  30. He , Y. and Rutland , C. Modeling of a Turbocharged DI Diesel Engine Using Artificial Neural Networks SAE Technical Paper 2002-01-2772 2002 10.4271/2002-01-2772
  31. Traver , M. , Atkinson , R. , and Atkinson , C. Neural Network-Based Diesel Engine Emissions Prediction Using In-Cylinder Combustion Pressure SAE Technical Paper 1999-01-1532 1999 10.4271/1999-01-1532
  32. Brahma , I. and Rutland , C. Optimization of Diesel Engine Operating Parameters Using Neural Networks SAE Technical Paper 2003-01-3228 2003 10.4271/2003-01-3228
  33. Longwic , R. Modelling the Combustion Process in the Diesel Engine with the Use of Neural Networks SAE Technical Paper 2008-01-2446 2008 10.4271/2008-01-2446
  34. Ouenou Gamo , S. , Ouladsine , M. , and Rachid , A. Diesel Engine Exhaust Emissions Modelling Using Artificial Neural Networks SAE Technical Paper 1999-01-1163 1999 10.4271/1999-01-1163
  35. Nareid , H. and Lightowler , N. Detection of Engine Misfire Events using an Artificial Neural Network SAE Technical Paper 2004-01-1363 2004 10.4271/2004-01-1363
  36. Jarrett , R. and Clark , N. Weighting of Parameters in Artificial Neural Network Prediction of Heavy-Duty Diesel Engine Emissions SAE Technical Paper 2002-01-2878 2002 10.4271/2002-01-2878
  37. Brusca , S. , Lanzafame , R. , and Messina , M. A Combustion Model for ICE by Means of Neural Network SAE Technical Paper 2005-01-2110 2005 10.4271/2005-01-2110
  38. Stone , M. Embedded Neural Networks in Real Time Controls SAE Technical Paper 941067 1994 10.4271/941067
  39. Wu , Z. and Lee , A. Misfire Detection Using a Dynamic Neural Network with Output Feedback SAE Technical Paper 980515 1998 10.4271/980515
  40. Syntroleum material safety data sheet: S-8 Synthetic jet fuel http://www.syntroleum.com/profiles/investor/fullpage.asp?f=1&BzID=2029&to=cp&Nav=0&LangID=1&s=0&ID=11912
  41. Southwest Research Institute Petroleum products research department test report June 7 2007 26
  42. Southwest Research Institute Petroleum products research department test report Nov 4 2011 27

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