Skip to main content

Advertisement

Log in

Prediction of Sooting Index of Fuel Compounds for Spark-Ignition Engine Applications Based on a Machine Learning Approach

  • Contributed by the 3rd International Discussion Meeting on Chemistry and Technology of Combustion Application (CTCA 2021)
  • Published:
Journal of Thermal Science Aims and scope Submit manuscript

Abstract

A joint consideration of potential combustion and emission performance in spark-ignition engines is essential for designing gasoline fuel replacements and additives, for which the knowledge of the fuels’ characteristic properties forms the backbone. The aim of this study is to predict sooting tendency of fuel molecules for spark-ignition engine applications in terms of their yield sooting indexes (YSI). In conjunction with our previously developed database for gasoline compounds, which includes the physical and chemical properties, such as octane numbers, laminar burning velocity, and heat of vaporization, for more than 600 species, the identification of fuel replacements and additives can thus be performed jointly with respect to both their potential thermal efficiency benefits and emission formation characteristics in spark-ignition engines. For this purpose, a quantitative structure-property relationship (QSPR) model is developed to predict the YSI of fuel species by using artificial neural network (ANN) techniques with 21 well-selected functional group descriptors as input features. The model is trained and cross-validated with the YSI database reported by Yale University. It is then applied to estimate the YSI values of fuels available in the database for gasoline compounds and to explore the sensitivity of fuel’s sooting tendency on molecular groups. In addition, the correlation of YSI values with other properties available in the gasoline fuel database is examined to gain insights into the dependence of these properties. Finally, a selection of potential gasoline blending components is carried out exemplarily, by taking the fuels’ potential benefits in thermal engine efficiency and their soot formation characteristics jointly into account in terms of efficiency merit function and YSI, respectively.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

References

  1. vom Lehn F., Cai L., Tripathi R., Broda R., Pitsch H., A property database of fuel compounds with emphasis on spark-ignition engine applications. Applications in Energy and Combustion Science, 2021, 5: 100018.

    Article  Google Scholar 

  2. vom Lehn F., Brosius B., Broda R., Cai L., Pitsch H., Using machine learning with target-specific feature sets for structure-property relationship modeling of octane numbers and octane sensitivity. Fuel, 2020, 281: 118772.

    Article  Google Scholar 

  3. Farrell J.T., Zigler B.T., Ratcliff M.A., et al., Co-optimization of fuels & engines: Efficiency merit function for spark-ignition engines; Revisions and Improvements Based on FY16-17 Research. US Department of Energy, 2018.

  4. Szybist J.P., Busch S., McCormick R.L., et al., What fuel properties enable higher thermal efficiency in spark-ignited engines? Progress in Energy and Combustion Science, 2021, 82: 100876.

    Article  Google Scholar 

  5. vom Lehn F., Cai L., Cáceres B.C., Pitsch H., Exploring the fuel structure dependence of laminar burning velocity: A machine learning based group contribution approach. Combustion and Flame, 2021, 232: 111525.

    Article  Google Scholar 

  6. Bond T.C., Doherty S.J., Fahey D.W., et al., Bounding the role of black carbon in the climate system: A scientific assessment. Journal of geophysical research: Atmospheres, 2013, 118(11): 5380–5552.

    Article  ADS  Google Scholar 

  7. Yield Sooting Index Database Volume 2: Sooting Tendencies of a Wide Range of Fuel Compounds on a United Scale. https://doi.org/10.7910/DVN/7HGFT8, 2021, (accessed on 13 August 2021).

  8. McEnally C.S., Pfefferle L.D., Sooting tendencies of oxygenated hydrocarbons in laboratory-scale flames. Environmental Science & Technology, 2011, 45(6): 2498–2503.

    Article  ADS  Google Scholar 

  9. McEnally C.S., Pfefferle L.D., Improved sooting tendency measurements for aromatic hydrocarbons and their implications for naphthalene formation pathways. Combustion and Flame, 2007, 148(4): 210–222.

    Article  Google Scholar 

  10. 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. Combustion and Flame, 2018, 190: 349–364.

    Article  Google Scholar 

  11. Pepiot-Desjardins P., Pitsch H., Malhotra R., et al., Structural group analysis for soot reduction tendency of oxygenated fuels. Combustion and Flame, 2008, 154(1–2): 191–205.

    Article  Google Scholar 

  12. Barrientos E.J., Lapuerta M., Boehman A.L., Group additivity in soot formation for the example of C-5 oxygenated hydrocarbon fuels. Combustion and Flame, 2013, 160(8): 1484–1498.

    Article  Google Scholar 

  13. St. John P.C., Kairys P., Das D.D., et al., A quantitative model for the prediction of sooting tendency from molecular structure. Energy & Fuels, 2017, 31(9): 9983–9990.

    Article  Google Scholar 

  14. Gao Z., Zou X., Huang Z., et al., Predicting sooting tendencies of oxygenated hydrocarbon fuels with machine learning algorithms. Fuel, 2019, 242: 438–446.

    Article  Google Scholar 

  15. Abdul Jameel A.G., Predicting sooting propensity of oxygenated fuels using artificial neural networks. Processes, 2021, 9(6): 1070.

    Article  Google Scholar 

  16. Kessler T., John P.C.S., Zhu J., et al., A comparison of computational models for predicting yield sooting index. Proceedings of the Combustion Institute, 2021, 38(1): 1385–1393.

    Article  Google Scholar 

  17. Cai G., Liu Z., Zhang L., et al., Systematic performance evaluation of gasoline molecules based on quantitative structure-property relationship models. Chemical Engineering Science, 2021, 229: 116077.

    Article  Google Scholar 

  18. Li R., Herreros J.M., Tsolakis A., et al., Machine learning-quantitative structure property relationship (ML-QSPR) method for fuel physicochemical properties prediction of multiple fuel types. Fuel, 2021, 304: 121437.

    Article  Google Scholar 

  19. Aikawa K., Jetter J.J., Impact of gasoline composition on particulate matter emissions from a direct-injection gasoline engine: Applicability of the particulate matter index. International Journal of Engine Research, 2014, 15(3): 298–306.

    Article  Google Scholar 

  20. Gulli A., Pal S., Deep learning with Keras. Packt Publishing Ltd, Birmingham, 2017.

    Google Scholar 

  21. Abadi M., Agarwal A., Barham P., et al., TensorFlow: large-scale machine learning on heterogeneous distributed systems. arXiv preprint arXiv:1603.04467, 2015.

  22. Joback K.G., Reid R.C., Estimation of pure-component properties from group-contributions. Chemical Engineering Communications, 1987, 57(1–6): 233–243.

    Article  Google Scholar 

  23. Landrum G., RDKit: A software suite for cheminformatics, computational chemistry, and predictive modeling. 2013.

  24. Weininger D., SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules. Journal of Chemical Information and Computer Sciences, 1988, 28(1): 31–36.

    Article  Google Scholar 

  25. Kohavi R., Sommerfield D., Feature subset selection using the wrapper method: overfitting and dynamic search space topology. Proceedings of the First International Conference on Knowledge Discovery and Data Mining, 1995, pp. 192–197.

  26. Kingma D.P., Ba J., Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014.

  27. Gevrey M., Dimopoulos I., Lek S., Review and comparison of methods to study the contribution of variables in artificial neural network models. Ecological Modelling, 2003, 160(3): 249–264.

    Article  Google Scholar 

  28. Goeb D., Davidovic M., Cai L., et al., Oxymethylene ether-n-dodecane blend spray combustion: Experimental study and large-eddy simulations. Proceedings of the Combustion Institute, 2021, 38(2): 3417–3425.

    Article  Google Scholar 

  29. Kerschgens B., Cai L., Pitsch H., et al., Di-n-buthylether, n-octanol, and n-octane as fuel candidates for diesel engine combustion. Combustion and Flame, 2016, 163: 66–78.

    Article  Google Scholar 

  30. Sileghem L., Vancoillie J., Demuynck J., et al., Alternative fuels for spark-ignition engines: mixing rules for the laminar burning velocity of gasoline-alcohol blends. Energy & Fuels, 2012, 26(8): 4721–4727.

    Article  Google Scholar 

  31. Kassai M., Aksu C., Shiraishi T., et al., Mechanism analysis on the effect of fuel properties on knocking performance at boosted conditions. SAE Technical Paper, 2019, 1: 35.

    Google Scholar 

Download references

Acknowledgements

This work was supported by the Fundamental Research Funds for the Central Universities. The work of Florian vom Lehn and Heinz Pitsch was performed as part of the Cluster of Excellence “The Fuel Science Center”, which is funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy — Cluster of Excellence 2186 “The Fuel Science Center” ID: 390919832.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Liming Cai.

Additional information

Electronic supplementary material

Supplementary material including Tables. S1–S2 and Figs. S1–S38 is uploaded along with the article.

Electronic supplementary material

11630_2023_1765_MOESM1_ESM.pdf

Prediction of sooting index of fuel compounds for spark-ignition engine applications based on a machine learning approach

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chen, Z., Vom Lehn, F., Pitsch, H. et al. Prediction of Sooting Index of Fuel Compounds for Spark-Ignition Engine Applications Based on a Machine Learning Approach. J. Therm. Sci. 32, 521–530 (2023). https://doi.org/10.1007/s11630-023-1765-3

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11630-023-1765-3

Keywords

Navigation