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Data Sources & References

Property Data Map

  • Research Octane Number [1]-[6], [8]
  • Motor Octane Number [1]-[6], [8]
  • Cetane Number [1], [7], [9], [11]
  • Derived Cetane Number [9], [10], [12]-[18]
  • Yield Sooting Index [19]
  • Density [20]
  • Boiling Point [20]
  • Melting Point [20]
  • Upper Flammability Limit [21]-[23]
  • Lower Flammability Limit [23]-[25]
  • Viscosity [7], [20]
  • Enthalpy of Vaporization [5], [20]
  • Standard Enthalpy of Formation [20],[26],[30]-[33]
  • Vapor Pressure [20]
  • Lower Heating Value [23], [27], [28]
  • Surface Tension [20], [29]
  • Flash Point [20]

References

  1. Kubic W L. A group contribution method for estimating cetane and octane numbers[R]. Los Alamos National Laboratory (LANL), Los Alamos, NM (United States), 2016. https://doi.org/10.2172/1291241
  2. vom Lehn F, Brosius B, Broda R, et al. Using machine learning with target-specific feature sets for structure-property relationship modeling of octane numbers and octane sensitivity[J]. Fuel, 2020, 281: 118772. https://doi.org/10.1016/j.fuel.2020.118772
  3. Li R, Herreros J M, Tsolakis A, et al. Machine learning regression based group contribution method for cetane and octane numbers prediction of pure fuel compounds and mixtures[J]. Fuel, 2020, 280: 118589. https://doi.org/10.1016/j.fuel.2020.118589
  4. Abdul Jameel A G, Van Oudenhoven V, Emwas A H, et al. Predicting octane number using nuclear magnetic resonance spectroscopy and artificial neural networks[J]. Energy & fuels, 2018, 32(5): 6309-6329. https://doi.org/10.1021/acs.energyfuels.8b00556
  5. Nagaraja S S, Sarathy S M, Mohan B, et al. Machine learning-driven screening of fuel additives for increased spark-ignition engine efficiency[J]. Proceedings of the Combustion Institute, 2024, 40(1-4): 105658. https://doi.org/10.1016/j.proci.2024.105658
  6. McCormick R L, Fioroni G, Fouts L, et al. Selection criteria and screening of potential biomass-derived streams as fuel blendstocks for advanced spark-ignition engines[J]. SAE International Journal of Fuels and Lubricants, 2017, 10(2): 442-460. https://www.jstor.org/stable/26274125
  7. Li R, Herreros J M, Tsolakis A, et al. Integrated machine learning-quantitative structure property relationship (ML-QSPR) and chemical kinetics for high throughput fuel screening toward internal combustion engine[J]. Fuel, 2022, 307: 121908. https://doi.org/10.1016/j.fuel.2021.121908
  8. Al Ibrahim E, Farooq A. Octane prediction from infrared spectroscopic data[J]. Energy & Fuels, 2019, 34(1): 817-826. https://doi.org/10.1021/acs.energyfuels.9b02816
  9. Yanowitz J, Ratcliff M A, McCormick R L, et al. Compendium of experimental cetane numbers[R]. National Renewable Energy Lab.(NREL), Golden, CO (United States), 2017. https://doi.org/10.2172/1345058
  10. Dahmen M, Marquardt W. A novel group contribution method for the prediction of the derived cetane number of oxygenated hydrocarbons[J]. Energy & Fuels, 2015, 29(9): 5781-5801. https://doi.org/10.1021/acs.energyfuels.5b01032
  11. Kim Y, Cho J, Naser N, et al. Physics-informed graph neural networks for predicting cetane number with systematic data quality analysis[J]. Proceedings of the Combustion Institute, 2023, 39(4): 4969-4978. https://doi.org/10.1016/j.proci.2022.09.059
  12. Corporan E, Edwards J T, Stouffer S, et al. Impacts of fuel properties on combustor performance, operability and emissions characteristics[C]//55th AIAA aerospace sciences meeting. 2017: 0380. https://doi.org/10.2514/6.2017-0380
  13. Won S H, Dooley S, Veloo P S, et al. The combustion properties of 2, 6, 10-trimethyl dodecane and a chemical functional group analysis[J]. Combustion and Flame, 2014, 161(3): 826-834. https://doi.org/10.1016/j.combustflame.2013.08.010
  14. Carpenter D, Nates S, Dryer F L, et al. Evaluating ignition propensity of high cycloparaffinic content alternative jet fuel by a chemical functional group approach[J]. Combustion and Flame, 2021, 223: 243-253. https://doi.org/10.1016/j.combustflame.2020.09.024
  15. Guan C, Zhai J, Han D. Cetane number prediction for hydrocarbons from molecular structural descriptors based on active subspace methodology[J]. Fuel, 2019, 249: 1-7. https://doi.org/10.1016/j.fuel.2019.03.092
  16. Tekawade A, Oehlschlaeger M A. Spray ignition experiments for alkylbenzenes and alkylbenzene/n-alkane blends[J]. Fuel, 2017, 195: 49-58. https://doi.org/10.1016/j.fuel.2017.01.047
  17. Abdul Jameel A G, Naser N, Emwas A H, et al. Predicting fuel ignition quality using 1H NMR spectroscopy and multiple linear regression[J]. Energy & Fuels, 2016, 30(11): 9819-9835. https://doi.org/10.1021/acs.energyfuels.6b01690
  18. ASTM I. Standard test method for determination of ignition delay and derived cetane number (DCN) of diesel fuel oils by combustion in a constant volume chamber[J]. D6890-10a, 2010. https://www.astm.org/standards/d6890
  19. Das D D, John P C S, McEnally C S, et al. Measuring and predicting sooting tendencies of oxygenates, alkanes, alkenes, cycloalkanes, and aromatics on a unified scale[J]. Combustion and flame, 2018, 190: 349-364. https://doi.org/10.1016/j.combustflame.2017.12.005
  20. Haynes W M. CRC handbook of chemistry and physics[M]. CRC press, 2016. https://doi.org/10.1201/9781315380476
  21. Gharagheizi F. Prediction of upper flammability limit percent of pure compounds from their molecular structures[J]. Journal of Hazardous Materials, 2009, 167(1-3): 507-510. https://doi.org/10.1016/j.jhazmat.2009.01.002
  22. Gharagheizi F. Chemical structure-based model for estimation of the upper flammability limit of pure compounds[J]. Energy & fuels, 2010, 24(7): 3867-3871. https://doi.org/10.1021/ef100207x
  23. 801 Database. https://www.aiche.org/dippr
  24. Gharagheizi F. A new group contribution-based model for estimation of lower flammability limit of pure compounds[J]. Journal of hazardous materials, 2009, 170(2-3): 595-604. https://doi.org/10.1016/j.jhazmat.2009.05.023
  25. Chen C C, Lai C P, Guo Y C. A novel model for predicting lower flammability limits using Quantitative Structure Activity Relationship approach[J]. Journal of Loss Prevention in the Process Industries, 2017, 49: 240-247. https://doi.org/10.1016/j.jlp.2017.07.007
  26. Yalamanchi K K, Van Oudenhoven V C O, Tutino F, et al. Machine learning to predict standard enthalpy of formation of hydrocarbons[J]. The Journal of Physical Chemistry A, 2019, 123(38): 8305-8313. https://doi.org/10.1021/acs.jpca.9b04771
  27. Albahri T A. Accurate prediction of the standard net heat of combustion from molecular structure[J]. Journal of Loss Prevention in the Process Industries, 2014, 32: 377-386. https://doi.org/10.1016/j.jlp.2014.10.005
  28. Pan Y, Jiang J C, Wang R, et al. Predicting the net heat of combustion of organic compounds from molecular structures based on ant colony optimization[J]. Journal of Loss Prevention in the Process Industries, 2011, 24(1): 85-89. https://doi.org/10.1016/j.jlp.2010.11.001
  29. Albahri T A, Alashwak D A. Modeling of pure compounds surface tension using QSPR[J]. Fluid Phase Equilibria, 2013, 355: 87-91. https://doi.org/10.1016/j.fluid.2013.06.052
  30. Huang W, Zheng D, Chen X, et al. Standard thermodynamic properties for the energy grade evaluation of fossil fuels and renewable fuels[J]. Renewable Energy, 2020, 147: 2160-2170. https://doi.org/10.1016/j.renene.2019.09.127
  31. Haseli Y. Interrelations between standard entropy, formation enthalpy and boiling temperature of hydrocarbons[J]. Fuel, 2020, 280: 118541. https://doi.org/10.1016/j.fuel.2020.118541
  32. Paulechka E, Kazakov A. Efficient DLPNO–CCSD (T)-based estimation of formation enthalpies for C-, H-, O-, and N-containing closed-shell compounds validated against critically evaluated experimental data[J]. The Journal of Physical Chemistry A, 2017, 121(22): 4379-4387. https://doi.org/10.1021/acs.jpca.7b03195
  33. Roganov G N, Pisarev P N, Emel'yanenko V N, et al. Measurement and prediction of thermochemical properties. Improved Benson-type increments for the estimation of enthalpies of vaporization and standard enthalpies of formation of aliphatic alcohols[J]. Journal of Chemical & Engineering Data, 2005, 50(4): 1114-1124. https://doi.org/10.1021/je049561m