Estimation of Speciation Data for Hydrocarbons using Data Science
KAUST DepartmentChemical Engineering Program
Clean Combustion Research Center
Combustion and Pyrolysis Chemistry (CPC) Group
Physical Science and Engineering (PSE) Division
KAUST Grant NumberOSR-2019-CRG7-4077
Permanent link to this recordhttp://hdl.handle.net/10754/672098
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AbstractStrict regulations on air pollution motivates clean combustion research for fossil fuels. To numerically mimic real gasoline fuel reactivity, surrogates are proposed to facilitate advanced engine design and predict emissions by chemical kinetic modelling. However, chemical kinetic models could not accurately predict non-regular emissions, e.g. aldehydes, ketones and unsaturated hydrocarbons, which are important air pollutants. In this work, we propose to use machine-learning algorithms to achieve better predictions. Combustion chemistry of fuels constituting of 10 neat fuels, 6 primary reference fuels (PRF) and 6 FGX surrogates were tested in a jet stirred reactor. Experimental data were collected in the same setup to maintain data uniformity and consistency under following conditions: residence time at 1.0 second, fuel concentration at 0.25%, equivalence ratio at 1.0, and temperature range from 750 to 1100K. Measured species profiles of methane, ethylene, propylene, hydrogen, carbon monoxide and carbon dioxide are used for machine-learning model development. The model considers both chemical effects and physical conditions. Chemical effects are described as different functional groups, viz. primary, secondary, tertiary, and quaternary carbons in molecular structures, and physical conditions as temperature. Both the Machine-learning models used in this study showed a good prediction accuracy with a test set regression score of 97.75 for support vector regression and 91.07 for random forest regression. This finding shows the great potential of machine learning application on combustion chemistry. By expanding the experimental database, machine-learning models can be further applied to many other hydrocarbons in future work.
CitationYalamanchi, K., chen, B., Sarankapani, R., & Sarathy, M. (2021). Estimation of Speciation Data for Hydrocarbons using Data Science. SAE Technical Paper Series. doi:10.4271/2021-24-0081
SponsorsThis work was supported by King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research under the award number OSR-2019-CRG7-4077, and the KAUST Clean Fuels Consortium (KCFC) and its member companies.
Conference/Event name15th International Conference on Engines & Vehicles